4217670-12077704000020000Chapter 1 INTRODUCTION Background of the Study East Asian countries continued to lead the world in mathematics achievement

4217670-12077704000020000Chapter 1
INTRODUCTION
Background of the Study
East Asian countries continued to lead the world in mathematics achievement. Singapore, Korea, and Hong Kong, followed by Chinese Taipei and Japan, were the top-performing countries at the fourth grade. Similarly, at the eighth grade, Korea, Singapore, and Chinese Taipei outperformed among other countries, followed by Hong Kong and Japan, (ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “ISBN” : “9781889938639”, “abstract” : “Mullis, I.V.S., Martin, M.O., Foy, P., & Arora, A. (2012). Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.”, “author” : { “dropping-particle” : “”, “family” : “Mullis, I.V.S., Martin, M.O., Foy, P., & Arora”, “given” : “a.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issued” : { “date-parts” : “2012” }, “number-of-pages” : “193-197”, “title” : “Results in Mathematics”, “type” : “book”, “volume” : “43” }, “uris” : “http://www.mendeley.com/documents/?uuid=e5eddf0f-4bcb-4ef6-a1ca-f612626d70c1” } , “mendeley” : { “formattedCitation” : “(Mullis, I.V.S., Martin, M.O., Foy, P., & Arora, 2012)”, “plainTextFormattedCitation” : “(Mullis, I.V.S., Martin, M.O., Foy, P., & Arora, 2012)”, “previouslyFormattedCitation” : “(Mullis, I.V.S., Martin, M.O., Foy, P., & Arora, 2012)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }Mullis et al., 2012).

On the other hand, the Philippines was one of the poor achievers in Math and Science internationally. According to the international study in the 2003 Math Achievement Test by the Trends in International Mathematics and Science students, the country belonged to the 41st out of the 43 countries and ranked 36th in Mathematics out of 38 countries in 2010, ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “author” : { “dropping-particle” : “”, “family” : “Dulay”, “given” : “”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issued” : { “date-parts” : “2015” }, “title” : “53b82a2d87425f87297e5499778ff01971d86f04 @ www.manilatimes.net”, “type” : “article” }, “uris” : “http://www.mendeley.com/documents/?uuid=36b1f98e-bf86-435e-93bd-fa152873cb98” } , “mendeley” : { “formattedCitation” : “(Dulay, 2015)”, “plainTextFormattedCitation” : “(Dulay, 2015)”, “previouslyFormattedCitation” : “(Dulay, 2015)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }Dulay (2015). Also, the Organization for Economic Cooperation and Development (OECD) Program for International Student Assessment for the subjects Science, Math, and Reading, Philippines failed to be within the first forty (40) countries, (ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1007/s10648-012-9200-4”, “ISBN” : “1040-726X”, “ISSN” : “1040726X”, “PMID” : “12658535”, “abstract” : “Stereotype threat is known as a situational predicament that prevents members of negatively stereotyped groups to perform up to their full ability. This review shows that the detrimental influence of stereotype threat goes beyond test taking: It impairs stereotyped students to build abilities in the first place. Guided by current theory on stereotype threat processes and boundary conditions, this review integrates findings on test taking, disidentification, and learning. A new three-stage account of stereotype threat is proposed that includes stereotype threat effects on both ability and performance. Implications for future research and practice are discussed. In many countries around the world, some ethnic minorities (e.g., African Americans in the USA) and students with an immigration background from specific regions (e.g., individuals with a Turkish background in central Europe) underachieve in educational settings (OECD 2009). Women are often underrepresented in science, technology, engineering, and mathe-matics (STEM). These achievement gaps have been a matter of great concern among social scientists, policy makers, and the general public. Given the projected shortage of an educated workforce in the near future, eliminating factors responsible for the achievement gap can be a key for future economic growth (World Economic Forum 2011). One of the factors that have been discussed as a cause of the achievement gap is stereotype threat, an extra pressure experienced by members of a negatively stereotyped group (Steele and Aronson 1995; Steele et al. 2002; Inzlicht and Schmader 2012). Stereotype threat is knownu2014first and foremostu2014as a factor that inhibits stereotyped individuals to perform up to their full ability. Second, stereotype threat has been linked to disidentification from the stereotyped domain. A main emphasis of the present review is on a hitherto neglected influence: Stereotype threat and learning.”, “author” : { “dropping-particle” : “”, “family” : “Appel”, “given” : “Markus”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Kronberger”, “given” : “Nicole”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Educational Psychology Review”, “id” : “ITEM-1”, “issue” : “4”, “issued” : { “date-parts” : “2012” }, “page” : “609-635”, “title” : “Stereotypes and the Achievement Gap: Stereotype Threat Prior to Test Taking”, “type” : “article-journal”, “volume” : “24” }, “uris” : “http://www.mendeley.com/documents/?uuid=76825906-7b2d-4c6b-8aeb-99d7bfef145f”, “http://www.mendeley.com/documents/?uuid=42faa70c-2d2e-4851-9604-015c5455f000” } , “mendeley” : { “formattedCitation” : “(Appel & Kronberger, 2012)”, “manualFormatting” : “Appel & Kronberger (2012)”, “plainTextFormattedCitation” : “(Appel & Kronberger, 2012)”, “previouslyFormattedCitation” : “(Appel & Kronberger, 2012)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }Appel & Kronberger, 2012).

Additionally, ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.11591/ijere.v2i1.1803”, “ISSN” : “2252-8822”, “abstract” : “The deteriorating performance of Filipino students in the national and international mathematics tests for the last decade has become a major challenge to Philippine education. The Department of Education attributed this problem to studentsu2019 poor reading comprehension. Previous studies showed varied findings on the association between variables in reading and mathematics. The present study utilized the six elements of reading comprehension skills to determine their relationship to studentsu2019 performance in mathematics. A total of 666 students belonging to the randomly selected first year classes from 18 public and private high schools were taken as sample. A correlation research design was used and a competency-based achievement tests in reading comprehension and mathematics were the research instruments. Students in private schools performed better in reading comprehension skills and mathematics than their counterparts. While reading comprehension skills were insignificantly correlated to private school studentsu2019 mathematics performance, the case is different in public schools wherein three skills namely understanding vocabulary in context, getting main idea, and making inference surfaced to have connection with mathematics. The overall studentsu2019 reading comprehension skills were not significantly correlated to mathematics performance. Hence, the poor mathematics performance could be explained by other factors not related to reading comprehension skills.”, “author” : { “dropping-particle” : “”, “family” : “Imam”, “given” : “Oa”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Mastura”, “given” : “Ma”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Jamil”, “given” : “Hajri”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “u2026 Journal of Evaluation and Research in u2026”, “id” : “ITEM-1”, “issue” : “1”, “issued” : { “date-parts” : “2012” }, “page” : “1-8”, “title” : “Correlation between Reading Comprehension Skills and Students’ Performance in Mathematics”, “type” : “article-journal”, “volume” : “2” }, “uris” : “http://www.mendeley.com/documents/?uuid=0f62510e-6708-4e81-b638-9df4eee8a97c”, “http://www.mendeley.com/documents/?uuid=e98c00a1-776e-4b3f-9dce-1963cd7f3e97” } , “mendeley” : { “formattedCitation” : “(Imam, Mastura, ; Jamil, 2012)”, “manualFormatting” : “Imam et al., (2012)”, “plainTextFormattedCitation” : “(Imam, Mastura, ; Jamil, 2012)”, “previouslyFormattedCitation” : “(Imam, Mastura, ; Jamil, 2012)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }Imam et al. (2012) stated that the declining performance of Filipino students in the national and international mathematics tests for the last decade had become a main challenge to Philippine Education. The Department of
Education associated this problem to students’ poor reading comprehension. It was found that students in private schools performed better in reading comprehension skills and mathematics than their counterparts. The overall students’ reading comprehension skills were not significantly correlated to mathematics performance. Thus, poor mathematics performance could be associated with other factors but not reading comprehension skills.

The demand for excellence in education in the Philippines is strengthened by the authority of Basic Education Act of 2001 (R.A. 9155). The act provided the main goal of basic education which is to produce an advanced Filipino learner by developing their knowledge and competencies. These include literacy, numeracy, critical analysis, and necessary values to become caring, independent, productive, nationalistic, and accountable citizen, (ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “author” : { “dropping-particle” : “”, “family” : “Department of Education”, “given” : “”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issued” : { “date-parts” : “2002” }, “page” : “1-28”, “title” : “The 2002 Basic Education Curriculum”, “type” : “article” }, “uris” : “http://www.mendeley.com/documents/?uuid=75e00c3f-1378-42b1-85de-4b5d09be8d4d” } , “mendeley” : { “formattedCitation” : “(Department of Education, 2002)”, “manualFormatting” : “Department of Education (2002)”, “plainTextFormattedCitation” : “(Department of Education, 2002)”, “previouslyFormattedCitation” : “(Department of Education, 2002)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }Department of Education, 2002).

According to ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “author” : { “dropping-particle” : “”, “family” : “Corpuz”, “given” : “”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Salandanan”, “given” : “”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issued” : { “date-parts” : “2007” }, “title” : “Principles_of_Learning_Horne_and_Pine_1990_in_Corpuz_and_Salandanan_2007 @ www.academia.edu”, “type” : “article” }, “uris” : “http://www.mendeley.com/documents/?uuid=58531be9-b708-46dc-baca-e37329d1aeb5”, “http://www.mendeley.com/documents/?uuid=26f71725-cab4-4ac0-ac60-2e5078cc8898” } , “mendeley” : { “formattedCitation” : “(Corpuz ; Salandanan, 2007)”, “manualFormatting” : “Corpuz ; Salandanan (2007)”, “plainTextFormattedCitation” : “(Corpuz ; Salandanan, 2007)”, “previouslyFormattedCitation” : “(Corpuz ; Salandanan, 2007)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }Corpuz & Salandanan (2007), it is necessary to find out if the learning objectives were attained after the teaching-learning process. In the curriculum, these refer to the student learning outcomes (SLO). Student learning outcomes are the results or products of the students in the learning process. Performance is an element of a curriculum that should be given importance. The curriculum is considered to be effective if the learners’ performances are higher than the objectives set. On the other hand, if the learners’ performances are low then it means that the curriculum is not successful. Thus, an effective curriculum is one that results high or outstanding performance.

ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1016/J.SBSPRO.2011.03.236”, “abstract” : “The following study examines gender differences existing in various cognitive motivational variables (locus of control, academic self-concept and use of learning strategies) and in performance attained in school subjects of Literature and Mathematics. For this purpose, a sample of 363 students was selected from the high school students in the first, second and third academic years. For achieving to the purpose used of locus of control questionnaire, self-concept questionnaire and LASSI. Results show the existence of gender difference in variables under consideration, with girls showing internal locus of control, using attitude, motivation, time management, anxiety, and self-testing strategies more extensively, and getting better marks in Literature. With boys using concentration, information processing and selecting main ideas strategies more, and getting better marks in mathematics. Gender differences were not found in external locus of control, in academic self-concept, and in study aids and test strategies. Results suggest that differences exist in the cognitive-motivational functioning of boys and girls in the academic environment, with the girls have a more adaptive approach to learning tasks. However, the influence of contextual variables that may differently affect boysu2019 and girlsu2019 motivation was not taken into account. Thus future research should address the influence of such factors.”, “author” : { “dropping-particle” : “”, “family” : “Ghazvini”, “given” : “Sayid Dabbagh”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Khajehpour”, “given” : “Milad”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Procedia – Social and Behavioral Sciences”, “id” : “ITEM-1”, “issued” : { “date-parts” : “2011”, “1”, “1” }, “page” : “1040-1045”, “publisher” : “Elsevier”, “title” : “Gender differences in factors affecting academic performance of high school students”, “type” : “article-journal”, “volume” : “15” }, “uris” : “http://www.mendeley.com/documents/?uuid=69acdf36-a704-340b-a100-9b17faeb28f1” } , “mendeley” : { “formattedCitation” : “(Ghazvini ; Khajehpour, 2011)”, “plainTextFormattedCitation” : “(Ghazvini ; Khajehpour, 2011)”, “previouslyFormattedCitation” : “(Ghazvini ; Khajehpour, 2011)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }Ghazvini & Khajehpour, (2011) stated that gender differences exist in the academic performance of boys and girls. It revealed that the girls have a more adaptive approach in learning new task than the boys.

The correlation study of Ali, S., et al. (2013) on the relationship between performance and age showed that the latter plays a significant role in increasing a student’s academic performance. The results of the correlation study of Ali, S., et al. (2013) showed that performance and age have negative correlation. Coleman et al., (1996) and White’s (1982) studies showed that as students become older, the correlation between age and performance remains constant over time
The study of Selcuk R. Sirin, 2005 showed a slight decrease in the average correlation of socioeconomic status–achievement since the initial review of White’s (1982) meta-analysis was published. Also, Ma, Li-Chen; Wooster, Robert A (1979) studied the effect of civil status on the academic performance of college students. It was revealed that married students attained higher grades than unmarried students. Also, married students without children attain higher grades than those with children.

According to Akungu (2014), learning material resources have a significant effect on students’ academic achievement since it aid the learning of abstracts and ideas and discourage route learning. Additionally, Adeogun (2001) discovered a very strong positive significant relationship between learning materials and academic performance. According to him, schools with more materials performed better than schools with less learning materials.

The study of Quintillan-Bugas (2010) found out that it was through understanding the factors that affect one’s performance that the teaching-learning process was more effective and fruitful. It was found out that high performing students were introverts and thinking types, while low performing students were extroverts and feeling types. It implied that there was variation in understanding and processing of concepts and principles across learning styles, interests, and motivations.

Learning Style. Singh & Singh (2014) defined learning style as the learner’s ability to recognize and process information in a given learning situation. The knowledge of the teacher on learning styles helps in lesson planning and choosing teaching strategies that is suited to an individual’s learning style. They found that the learning style of pre-service teachers were significantly different with respect to their gender, academic discipline, and habitat. The majority of pre-service teachers favored diverging learning style followed by the simulator learning style and that they least favored the accommodative and convergent learning styles.
According to Stewart & Felicetti (1992), learning styles are concerned with how the learners learn rather than what they learn. Also, learning style is an educational condition under which a student is more likely to learn. Dunn, R., et al., (2010) indicated that utilizing a teaching strategy suited to student’s learning-style preferences is beneficial to their academic achievement. They stated that accommodating students’ learning styles can achieve 75% of a standard deviation higher than students whose learning styles preferences weren’t accommodated.
Wehrwein, A., Lujan, H., and DiCarlo, S. (2006) stated that students have different learning style preferences. It include visual (V; learning from graphs, charts, and flow diagrams), auditory (A; learning from speech), read-write (R; learning from reading and writing), and kinesthetic (K; learning from touch, hearing, smell, taste, and sight). Awang, H., et al (2017) studied the relationship between learning styles and academic achievement of polytechnic students base on VARK learning styles. The result showed that there is no difference between academic achievements across learning style. It was also emphasized that each learning style has its degree of strengths and weaknesses.

According to Vaishnav & Chirayu (2013), kinesthetic learning style was more predominant than visual and auditory learning styles among secondary high school students. A positive high correlation between kinesthetic learning style and academic performance of the students was also found. However, Galasinki (2000) said that speech is one of the most common means of communication in today’s modern world. And speech uses the ear receptor which is under auditory in the VAK learning style. While the study of Abdullah (2012) showed that visual learners performed best in their academic performances.
Nzesei, M. M. (2015) studied the correlation between learning styles and academic achievement among secondary school students in Kenya. It was revealed that there is no significant difference among high and low academic achievement. It also showed that there exist a strong and positive relationship between learning styles and academic achievements.

Lucas & Corpuz (2007) affirmed that thinking/learning style is an important factor that assists student diversity. Students think and learn in different ways. Every student had different learning preferences, specifically in the learners’ way of processing information. Some would learn better when they do it with their hands rather than just merely listening while others may prefer to watch a video of the lesson. These preferences are based on their thinking/learning styles. Thinking/learning style simply means a tendency of the learner to behave in a certain manner. One’s style is most often described as an aspect of personality that can influence a person’s attitudes, values, and social interaction.

Study Habits. Losare (2009) defined study habits as the learner’s way of managing his/her time in a way that he/she can study and review regularly. Additionally, Crede & Kuncel, (2008) said that study habits are consistent patterns of behavior which are well-planned and purposeful on the part of learners towards learning academic disciplines. Study habit was found from numerous studies to improve student’s academic performance more than any other non-intellectual variable.

The study of Belen (2008) on study habits, attitudes, and academic performance revealed that the respondents of his study have very low or poor study habits and attitudes, average verbal intelligence, below average non-verbal, average intelligence, and average personality. However, they still managed to have good academic performance despite all these factors. Also, Cerna & Pavliushchenko (2015) believed that study habit is an important determinant of academic performance. Their study revealed that study habit has a negative and positive effect to a student’s academic performance.
Lawrence A. S., A. (2014) investigated the study habits and academic achievement of higher secondary school students with reference to the background variables. The results showed that there was no significant difference between study habits and academic achievement of higher secondary school students.
Guray (2017) studied the influence of peers in the study habits of BEEd student. Results revealed that there were significant differences in the level of influence of peers in the students’ study habits. The results of the study showed that the respondents who attained good grades were provided with positive influences.
Also, Estrella (2015) stated that there are many cognitive and non-cognitive factors that explain the academic performances of college students. The ability to form one’s identity and self-awareness and the pattern of behavior possessed by a student in the attainment of learning are important vehicles in the educative process. His study revealed that there no significant relationship between the levels of study habits and academic performance.

Emotional Intelligence. Grieve (2013) defined emotional intelligence as an ability to recognize, control and manage emotion in self and in others. A good teacher must have strong emotional intelligence. Understanding what makes a student’s emotional impulse can be helpful for effective learning. However, trying to train teachers to have more emotional intelligence might be impossible. Instead, helping future teachers to develop sets of emotional competencies such as flexibility, confidence, and effective adoption during their training might be more fruitful.
Salovey & Mayer (1990) disclosed a framework for emotional intelligence, a set of skills assumed to contribute to the exact assessment and expression of emotion in oneself and in others, the effective management of emotion in self and others, the use of feelings to inspire, prepare, and accomplish something in life.

ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1016/S0160-2896(96)90011-2”, “abstract” : “This article is concerned with individual differences in the ability to connect thoughts to emotions. People who are good at connecting thoughts to feelings may better u201chearu201d the emotional implications of their own thoughts, as well as understand the feelings of others from what they say. We had 321 participants read the writings of a target group of people and guess what those targets had felt. Several criteria were used to evaluate the participants’ emotional recognition abilities, including agreement with the group consensus and agreement with the target. Participants who agreed more highly with the group consensus and with the target also scored higher than the other participants on scales of empathy and self-reported SAT scores, and lower on emotional defensiveness. Such results are interpreted to mean that some forms of emotional problem solving require emotional openness as well as general intelligence.”, “author” : { “dropping-particle” : “”, “family” : “D. Mayer”, “given” : “John”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Geher”, “given” : “Glenn”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Intelligence”, “id” : “ITEM-1”, “issued” : { “date-parts” : “1996”, “3” }, “number-of-pages” : “89-113”, “title” : “Emotional Intelligence and the identification of emotion”, “type” : “book”, “volume” : “22” }, “uris” : “http://www.mendeley.com/documents/?uuid=14c863dd-1953-4fa2-b6ae-60fca7a9d52b”, “http://www.mendeley.com/documents/?uuid=944f81ab-6c82-4eb4-a72e-418cced16c72” } , “mendeley” : { “formattedCitation” : “(D. Mayer & Geher, 1996)”, “manualFormatting” : “D. Mayer & Geher (1996)”, “plainTextFormattedCitation” : “(D. Mayer & Geher, 1996)”, “previouslyFormattedCitation” : “(D. Mayer & Geher, 1996)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }Mayer & Geher (1996) said that people who can relate their thoughts to feelings may be good with the emotional implications of their own thoughts and understanding feelings of others. Emotional intelligence (EI; one’s ability to recognize, combine, and manage emotions) can influence assessments of stressful and succeeding task performance, (Schneider, Lyons, ; Williams, 2005).

Additionally, Goleman (1995) stated that IQ affects only 20% of one’s work and professional success, but emotional intelligence affects the remaining 80%. It means that the factor that can determine one’s success don’t depend much on his IQ, but his emotional ability instead.
The study of ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1016/j.sbspro.2013.07.095”, “ISBN” : “1877-0428”, “ISSN” : “18770428”, “abstract” : “This study investigates the influence of Emotional Intelligence on academic achievement among students of Education Faculty, Universiti Teknologi Mara (UiTM). The data of this research were obtained through the use of a questionnaire which elicits information on the studentsu2019 Emotion al Intelligence level as well as their academic performance. The results of the study reveal that the respondents have high level of Emotional Intelligence. Two domains (Self-Emotion Appraisal and Understanding of Emotion) of the Emotional Intelligence investigated are found to be significantly and positively associated with the respondentsu2019 academic achievement. The findings of the study hold import antimplications on the value of Emotional Intelligence and their relationships to studentsu2019 academic perform ance especially among pre-service teachers.”, “author” : { “dropping-particle” : “”, “family” : “Mohzan”, “given” : “Maizatul Akmal Mohd”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Hassan”, “given” : “Norhaslinda”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Halil”, “given” : “Norhafizah Abd”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Procedia – Social and Behavioral Sciences”, “id” : “ITEM-1”, “issue” : “InCULT 2012”, “issued” : { “date-parts” : “2013” }, “page” : “303-312”, “title” : “The Influence of Emotional Intelligence on Academic Achievement”, “type” : “article-journal”, “volume” : “90” }, “uris” : “http://www.mendeley.com/documents/?uuid=41517679-19cc-4c3b-8789-14c88f511cb1” } , “mendeley” : { “formattedCitation” : “(Mohzan, Hassan, & Halil, 2013)”, “plainTextFormattedCitation” : “(Mohzan, Hassan, & Halil, 2013)”, “previouslyFormattedCitation” : “(Mohzan, Hassan, & Halil, 2013)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }Mohzan et al. (2013) on the influence of emotional intelligence on academic achievement among students of teacher education revealed that the respondents have a high level of emotional intelligence. Self-emotion appraisal and understanding of emotion are found to be significantly and positively related to the respondents’ academic achievement. The study implied the value of emotional intelligence and their relationships to students’ academic performance, particularly among pre-service teachers.

Corcoran et al. (2012) also argued that emotional intelligence (EI) is an important Teacher’s skill. Yet, there is a need for more data on whether student teachers’ levels of emotional intelligence affect their teaching performance. Moreover, gender and prior academic attainment were also seen as possible contributors to teaching performance. This increases the demand to study one’s understanding of emotions and teaching.
Márquez, P., ; Palomera, R., ; Brackett, M. (2006) examined the relationship between EI and important social and academic performance of high school students. The results supported the validity of EI and provided positive indicators on the importance of EI in student’s academic performance and social development.
According to ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1111/j.1467-9280.2005.01641.x”, “ISBN” : “1467-9280 0956-7976”, “ISSN” : “0956-7976”, “PMID” : “16313657”, “abstract” : “u2014In a longitudinal study of 140 eighth-grade students, self-discipline measured by self-report, parent report, teacher report, and monetary choice question- naires in the fall predicted final grades, school attendance, standardized achievement-test scores, and selection into a competitive high school program the following spring. In a replication with 164 eighth graders, a behavioral delay-of- gratification task, a questionnaire on study habits, and a group-administered IQ test were added. Self-discipline measured in the fall accounted for more than twice as much variance as IQ in final grades, high school selection, school attendance, hours spent doing homework, hours spent watching television (inversely), and the time of day students began their homework. The effect of self-disci- pline on final grades held even when controlling for first- marking-period grades, achievement-test scores, and measured IQ. These findings suggest a major reason for students falling short of their intellectual potential: their failure to exercise self-discipline. What”, “author” : { “dropping-particle” : “”, “family” : “Duckworth”, “given” : “Angela L.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Seligman”, “given” : “Martin E P”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Psychological Science”, “id” : “ITEM-1”, “issue” : “12”, “issued” : { “date-parts” : “2012” }, “page” : “939-944”, “title” : “Self-Disciplice outdoes IQ in predicting academic performance of adolescents”, “type” : “article-journal”, “volume” : “16” }, “uris” : “http://www.mendeley.com/documents/?uuid=1d627c74-ae96-4814-ae85-007540f18869”, “http://www.mendeley.com/documents/?uuid=71a48f4b-5e05-4895-a1c9-bf6b0848f2fe” } , “mendeley” : { “formattedCitation” : “(Duckworth & Seligman, 2012)”, “manualFormatting” : “Duckworth & Seligman (2012)”, “plainTextFormattedCitation” : “(Duckworth & Seligman, 2012)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }Duckworth & Seligmann (2012), it is certain that intellectual strengths and non-intellectual strengths affect a student’s academic performance. Generally, the related researches mentioned in the study revealed that there are lots of factors that contribute to learners’ performances. These factors include age, sex, civil status, family monthly income, source of review materials, study habits, learning styles, and emotional intelligences. The above scenario encouraged the researchers to conduct a study on the correlates in the performance of pre-service teachers in the Professional Enhancement 1.

Theoretical Framework
ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.2190/CS.11.2.d”, “ISBN” : “1521025115210251”, “ISSN” : “1521-0251”, “abstract” : “Student retention and performance in higher education are important issues for educators, students, and the nation facing critical professional labor shortages. Expectancy and goal setting theories were used to predict academic performance and college student retention. Students’ academic expectancy motivation at the start of the college significantly predicted cumulative GPA at the end of their first year. Compared to students who did not return, students that returned for their sophomore year reported greater peer competition with respect to academic goals, perceived good grades to be more attractive, and reported more effort to get good grades. Students’ SAT scores and high school grade point average were significantly related to both cumulative GPA and retention after the first year. Study implications are discussed with an emphasis on the motivational set of college applicants, in conjunction with more traditional criteria (e.g., high school GPA) that together may increase student performance and retention. (Contains 5 tables.)”, “author” : { “dropping-particle” : “”, “family” : “Friedman”, “given” : “Barry A.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Mandel”, “given” : “Rhonda G.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Journal of College Student Retention: Research, Theory & Practice”, “id” : “ITEM-1”, “issue” : “2”, “issued” : { “date-parts” : “2009” }, “page” : “227-246”, “title” : “The Prediction of College Student Academic Performance and Retention: Application of Expectancy and Goal Setting Theories”, “type” : “article-journal”, “volume” : “11” }, “uris” : “http://www.mendeley.com/documents/?uuid=aa3edce3-ace1-45c5-83dd-c2ddcd5cc19b”, “http://www.mendeley.com/documents/?uuid=7ed2f348-6d54-4def-9613-9c97c2c92fef” } , “mendeley” : { “formattedCitation” : “(Friedman & Mandel, 2009)”, “manualFormatting” : “Friedman & Mandel (2009)”, “plainTextFormattedCitation” : “(Friedman & Mandel, 2009)”, “previouslyFormattedCitation” : “(Friedman & Mandel, 2009)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }Friedman & Mandel (2009) said that the retentions and performances of college students in higher education (pre-service teachers) are important concern in educational institution. This study on the correlates in the performance of pre-service teachers in the Professional Enhancement 1 adapted the theory of Gardner’s Multiple Intelligence, Dunn ; Dunn’s VAK Learning Style Theory, Theory of Goleman’s Emotional Intelligence, and Piaget’s Theory of Cognitive Development.

Gardner’s (1993) Theory of Multiple Intelligence presented a theoretical foundation for understanding the students’ different abilities and talents. Gardner defined intelligence as the ability or set of abilities that allow a person to solve problems that is relevant in our daily life. He believed that each learner is different from each other. A learner can perform well in one domain area, but not in another. Hence, we are all born intelligent, but in different degrees of strength.

The VAK (Visual, Auditory & Kinesthetic) learning style, also known as VAKT (Visual, Auditory, Kinesthetic, & Tactile) uses the three main sensory receivers to determine the dominant learning style. These sensory receivers are the channels by which human expression can take place and is a combination of perception and memory, (Dunn & Dunn, 2003).

According to Klitmøller (2015), Dunn and Dunn learning style model is a commonly used model, claiming effectiveness based on findings from experimental research. A central aspect of the model concerns the use of visual, auditory, kinesthetic, and tactual in learning. Although with respect to perceptual preferences, the Dunn and Dunn model is not recommended for use in educational practices until a number of issues regarding the model and the model literature have been resolved.

Additionally, Silver et al. (2000) said that students don’t learn in the same way. The teacher’s knowledge of different learning styles serves as a tool to understand differences and help students develop. Recognizing student’s learning style can help students attain better outcomes in their academic performance and improve their attitudes toward learning, (Green, 1999).

The concept of Goleman’s (1995) theory of emotional intelligence gave emphasis to an individual’s self-awareness to recognize one’s feelings and manage one’s emotions. A person with a high emotional intelligence is also capable of understanding the feelings of others, thus, he/she is good at handling relationships of all kinds. He proved that emotional intelligence is more superior to intelligence quotient. If a person is intellectually intelligent, it does not necessarily follow that they were emotionally intelligent.
In addition, Cherry (2018) stated that like Gardner’s Theory of Multiple Intelligence, intelligence quotient is not a full and precise representation of a person’s ability. A person with good memory, or good problem-solving abilities, does not necessarily mean that he is capable of dealing with emotions or of motivating themselves and of others.
Piaget’s (1936) cognitive theory stressed the construction and development of thought processes. It emphasized that thoughts and expectations deeply affect an individual’s attitudes, beliefs, values, perceptions, and actions. Also, Sincero (2018) defined cognitive psychology as the study of how human processes information, handles problems or develops one’s behavior and characteristics.

Conceptual Framework
Numerous studies have been done that emphasized the cognitive factors as predictors of academic success. However, there has been an increasing interest on the non-cognitive factors recently. A number of researchers have studied the effect of non-cognitive variables such as study habits (Crede and Kuncel, 2008; Belen, 2008; and Guray, 2017), learning styles (Stewart and Felicetti, 1992; and Singh & Singh, 2014), and emotional intelligences (Estrella, 2015; and Corcoran et al. 2012) on academic achievement. Some concluded that the combination of the different factors could explain students’ academic performance while others claimed these factors to have strong relationships with the academic performance of students.

The construction of conceptual paradigm was based from the theoretical framework of the study on the correlates in the performance of pre-service teachers in the Professional Enhancement 1. This study made use of the Input and Output model which presents the relationship between independent and dependent variables. The respondents’ profile in terms of their personal attributes which include sex, age, civil status, family monthly income, source of review materials, study habits practices, learning styles, and emotional intelligences were the independent variables. On the other hand, the dependent variables were the levels of performance of the respondents in the Professional Enhancement 1. Please see Figure 1 on the next page.

Independent Variables Dependent Variables
Respondents’ Profile
sex;
age;
civil status;
family monthly income;
source of review materials;
study habits;
learning styles; and
emotional intelligences
Levels of Performance
of the
Respondents
in the
Professional Enhancement 1
Figure 1:Conceptual Paradigm showing the Independent and Dependent
variables applied in the study
Statement of the Problem
This study aimed to determine the Correlates in the Performance of Pre-Service Teachers of the College of Teacher Education at Urdaneta City University in the Professional Enhancement 1 during the academic year 2017–2018.

Specifically, it sought to answer the following questions:
1. What is the personal profile of the pre-service teachers in terms of the following attributes?
a. sex;
b. age;
c. civil status;
d. family monthly income;
e. source of review materials;
f. study habits;
g. learning styles; and
h. emotional intelligences?
2. What is the respondents’ level of performance in the Professional Enhancement 1?
3. Is there a significant difference between the respondents’ level of performance across their profile attributes?
4. Is there a significant relationship between the respondents’ level of performance and their profile attributes?
Null Hypothesis
Based on the above-mentioned problems, the researchers formulated the null hypothesis of the study and tested at 0.05, level of significance:
1. There is no significant difference in the respondents’ level of performance across their profile attributes, and
2. There is no significant relationship between the respondents’ level of performance and their profile attributes.

Scope and Delimitation of the Study
This study mainly focused on the correlates in the performance of 218 Pre-Service Teachers in the Professional Enhancement 1. It covered the Pre-Service Teachers enrolled in Professional Enhancement 2 and Professional Enhancement 1 at Urdaneta City University during the Second Semester of Academic Year 2017-2018. The profile of the respondents was categorized based on their attributes which include personal profiles, study habits, learning style, and emotional intelligences. The profile variables was determined using a questionnaire checklist prepared by the researchers and to be validated by five experts who are the faculty of the Psychology Department, the Dean of the College of Social Work, the Assistant Dean of the Graduate School, the Coordinator of the Internship, and the Senior Guidance Counselor at Urdaneta City University. The researchers also used the documentary analysis of the respondents in the Professional Enhancement 1 to measure their level of performance.
Significance of the Study
This study aimed to determine the correlates in the performance of the pre-service teachers in the Professional Enhancement 1. The result of this study can be significant to the following:
Administration. The result of the study can enlighten them about the factors that contribute to the low level of a student’s academic performance. It can also encourage them to develop more effective programs that can help or improve the level of students’ academic performance.

Instructors. The result of this study could serve as their basis of improving their instruction and teaching strategies. It enables them to understand the nature of students’ capabilities (strengths and weaknesses) in learning.

Students. This study will enable the students to understand and evaluate the factors that affect their academic performance, providing them the necessary information to improve and have a positive attitude towards learning.

Parents. The result of the study will help them to have awareness on the factors that affect their child’s learning performance. This study may also encourage them to give full support and guidance to their child/children.

Future Researchers. The result of this study will give additional information that can help future studies on the factors that affect a learner’s performance.

Definition of Terms
The following terms were defined lexically and operationally for a better understanding of the study.

Correlate. According to Oxford Dictionary, it is the relationship between two or more quantities (variables). In this study, these are the factors that affect the level of performance of the respondents in the Professional Enhancement 1 such as study habits practices, learning style, and emotional intelligence.
Performance. It was defined as the observable behavior of a person in a particular situation usually experimental situation (Simpson ; Weiner, 1989). In this study, it refers to the scores earned by the respondents in the different subjects, namely, Science, English, Mathematics, Filipino, and Social Science in Professional Enhancement 1.

Pre-Service Teachers. They refer to those students who participated in Pre-Service training or education, “a course or program of study which Student Teachers complete before they begin teaching”, Richards ; Schmidt (1985). In this study, Pre-Service Teachers are the students enrolled in Professional Enhancement 2 and Enhancement 1 during the Second Semester of the Academic Year 2017-2018.

Professional Enhancement 1. In the course description of Urdaneta City University, Professional Enhancement 1 is a course to enhance the performance of the students in the licensure exam being given by PRC particularly for the would be teachers. It also helps the students to acquire in-depth understanding of the varied General Education such as English, Math, Science, Filipino, and Social Science Subjects.
Study Habit. According to Losare (2009), it simply means how a learner manages his/her time in such a way that he/she can study and review regularly. In this study, it refers to the respondents’ preferred place to study, how much to study, and way of studying.

Learning Style. According to Lucas & Corpuz (2014), it refers to the way an individual processes information. In this study, it describes a person’s typical mode of thinking, remembering, or problem-solving. The learning styles in this study are:
Visual Learning Style. According to the University of Pennsylvania (2009), visual learning style is suited to learners who prefer to learn through written text, demonstrations, videos, and other visual aids. In this study, it refers to the learning style for the learners who prefer to write, visualize, and use imagination.

Auditory Learning Style. According to Colorado State University, auditory learners are those who learn better through hearing or saying the word aloud. In this study, it is a way of doing or learning something with the help of the sense of hearing.

Kinesthetic Learning Style. According to Lucas ; Corpuz (2014), kinesthetic learners do things from a hands-on approach, they prefer learning by doing. In this study, it is a way of doing or learning something with the sense of touch.

Emotional Intelligence. According to Schneider, Lyons, ; Williams (2005), it is the ability to recognize, combine, identify and manage emotions. In this study, it refers to the respondents’ way of managing one’s emotion and of others, in terms of:
Emotional Awareness. According to Radwan (2017), emotional awareness is being aware of one’s own emotion in such a way that one knows why he/she feels good or bad towards someone/something. In this study, it is respondent’s awareness of his/her own feeling and emotion.

Emotional Management. According to Bradberry (2014), emotional management refers to the ability of managing one’s awareness and actions. In this study, it is respondent’s self-control and composure during stressful times.

Social Emotional Awareness. According to Airth (2018), it is the ability to properly respond to the things that are happening in our environment or in the society and being able to understand the emotions of the people around you. In this study, it is respondent’s understanding of the way other people feel.

Relationship Management. According to Investopedia, it is the strategy of a person to be engaged with the people with whom he/she interact. In this study, it is respondent’s relationship or engagement with other people.

Chapter 2
627253033972500
METHODOLOGY
This chapter presents the methods of research to be used in making this study. It includes the research design, respondents of the study and sampling scheme, data gathering instrument, procedure, validation, ethical considerations, and tools for data analysis.

Research Design
The correlation study approach was utilized in this study. According to McLeod (2008), correlation means association or relationship between two or more things. Specifically, it is a measure of the extent to which two variables are related. Furthermore, it was stated that correlation method is a quantitative method of research in which the relationship between two or more variables from the same group of participants will be determined.

It aimed to explain the degree or strength of relationships of the variables being studied. Moreover, the researchers identify the types whether positive or negative (Guevara and Lambinicio, 2011). Thus, it is suited to this study, since it can determine the correlates in the performance of pre-service teachers in the Professional Enhancement 1.

Respondents of the Study and Sampling Scheme
The respondents of this study were the pre-service teachers of the College of Teacher Education enrolled in the subject Professional Enhancement 2 and Professional Enhancement 1 at Urdaneta City University during the Second Semester of Academic Year 2017-2018. The number of the respondents was determined by using the Sloven’s Formula. Proportionate Simple Random Sampling is the utilized sampling scheme in identifying the subjects, as shown in the table below
Table 1
Distribution of the Respondents according
to their Classification
n = 218
Indicators N n %
Generalist 49 23 11
Early Childhood 28 14 6
English 32 15 7
Filipino 62 29 13
General Science 37 18 8
Mathematics 31 15 7
Physical Education, Health, & Music (PEHM) 47 22 10
Social Science 26 13 6
Special Education 20 10 5
Professional Enhancement 1 126 59 27
Total 458 218 100
It can be seen from the table that the majority of the respondents are from the enrollees of Professional Enhancement 1 with a frequency of 59 or 27 percent of the sample size. However, the least are the 10 respondents from Special Education (5 percent). Further, those who enrolled in the Professional Enhancement 2 are the 29 or 13 percent with Filipino as their Major of Specialization.

Data Gathering Instrument
The main data gathering instrument of the study used by the researchers were questionnaire-checklist and documentary analysis of the respondents’ scores in the Professional Enhancement 1. The documentary analysis of the respondents was used to determine the level of performance of the respondents in the Professional Enhancement 1.

Moreover, the researchers used questionnaire-checklist to determine the respondents’ profile, namely personal profile, study habits practices, learning styles and emotional intelligences. The questionnaire-checklist was adapted from Mohapel (2012), Martin & Osborne (1989) and O’Brien (1985). Minor revisions on the questionnaire-checklist were made. After the revision, it was then validated by (five experts) the faculty of the Psychology Department, the Dean of the College of Social Work, the Assistant Dean of the Graduate School, the Coordinator of the Internship, and the Senior Guidance Counselor of Urdaneta City University.

Procedure
The researchers asked permission to conduct the study from all concerned entities, namely, the Dean of College of Teacher Education, the faculty-in-charge of the Professional Enhancement 1, and the SMART Reviewers. After asking permission, the researchers administered the questionnaire-checklist personally to determine the respondents’ personal profile, study habits, learning styles, and emotional intelligences.
Validation
The questionnaire-checklist was validated by five experts. The experts were from the faculty of the Psychology Department, the Dean of the College of Social Work, the Assistant Dean of the Graduate School, the Coordinator of the Internship, and the Senior Guidance Counselor of the Urdaneta City University.

Ethical Considerations
Ethical issues that came up during the study include variations in the instrument administration and confidentiality issues. The researchers secured permission from the Dean of the College of Teacher Education to conduct the said study.
The researchers also sought permission from the faculty-in-charge of Professional Enhancement 1 and the OIC of Registrar in giving the necessary data for the study. To secure the identity of the respondents, they were asked to give their student ID numbers.

Tools for Data Analysis
To answer the stated problems in Chapter 1, the data was tallied, classified, and analyzed with the use of appropriate tools to come up with a valid and reliable interpretation of data.
For problem number 1 regarding the personal profile of the pre-service teachers in terms of the following attributes; sex, age, civil status, family monthly income, and source of review materials, percentage was used:

where:%= percentage equivalent of each category
f = number of respondents that fall in each category
n = total number of respondents
Further, the respondents’ profile in terms of study habit practices, learning styles, and emotional intelligence, and the level of performance in the Professional Enhancement 1 were analyzed using the weighted mean formula as:

where:WM = computed average of each category
f = number of respondents that fall in each category
X = point value classification
n = total number of respondents
To determine the respondents’ extent of practices according to study habits, learning styles, and emotional intelligence, a five-point Likert scale was used as shown below.

Study Habits
Point Value Mean Range Descriptive Equivalent Interpretation
5 4.50-5.00 Always The respondents continuously practice the study habits in terms of place, how much, and how to study.

4 3.50-4.49 Often The respondents regularly practice the study habits in terms of place, how much, and how to study.

3 2.50-3.49 Sometimes The respondents occasionally practice the study habits in terms of place, how much, and how to study.

2 1.50-2.49 Seldom The respondents rarely practice the study habits in terms of place, how much, and how to study.

1 1.00-1.49 Never The respondents do not practice the study habits in terms of place, how much, and how to study.

Learning Styles
Point Value Mean Range Descriptive Equivalent Interpretation
5 4.50-5.00 Always The respondents continuously practice the visual, auditory, and kinesthetic learning style
4 3.50-4.49 Often The respondents regularly practice the visual, auditory, and kinesthetic learning style
3 2.50-3.49 Sometimes The respondents occasionally practice the visual, auditory, and kinesthetic learning style
2 1.50-2.49 Seldom The respondents rarely practice the visual, auditory, and kinesthetic learning style
1 1.00-1.49 Never The respondents do not practice the visual, auditory, and kinesthetic learning style
Emotional Intelligence
Point Value Mean Range Descriptive Equivalent Interpretation
5 4.50-5.00 Always The respondents have very high emotional intelligence in terms of emotional awareness, emotional management, social emotional awareness, and relationship management.

4 3.50-4.49 Often The respondents have high emotional intelligence in terms of emotional awareness, emotional management, social emotional awareness, and relationship management.

3 2.50-3.49 Sometimes The respondents have average emotional intelligence in terms of emotional awareness, emotional management, social emotional awareness, and relationship management.

2 1.50-2.49 Seldom The respondents have low emotional intelligence in terms of emotional awareness, emotional management, social emotional awareness, and relationship management.

1 1.00-1.49 Never The respondents have very low emotional intelligence in terms of emotional awareness, emotional management, social emotional awareness, and relationship management.

The respondents’ levels of performance in the Professional Enhancement
1 was interpreted using a three-point Likert scale as shown below.

Point
Value Subject
Score
Range Total Score
Range Descriptive
Equivalent Interpretation
3 30-40 150-200 High Students have performed above the expected level of competencies in the different fields of English, Math, Science, Filipino and Social Science.

2 20-29 100-149 Average Students have performed within the expected level of competencies in the different fields of English, Math, Science, Filipino and Social Science.

1 0-19 0-99 Low Students have performed below the expected level of competencies in the different fields of English, Math, Science, Filipino and Social Science.

Further, to answer problems number 3 and 4, the researchers used open program software. The correlates were from the respondents’ level of performance and their profile attributes.

5221605-91059000 Chapter 3
RESULTS AND DISCUSSION
This chapter deals with the presentation, analysis, and interpretation of data about the specific problems in Chapter 1. The data discussed in this chapter include four parts of the study. These are: pre-service teachers’ profile in terms of sex, age, civil status, family monthly income, source of review materials, study habits, learning styles, and emotional intelligences; performances in the Professional Enhancement 1; the differences between the respondents’ level of performances in terms of their profile attributes; and the relationship between the respondents’ level of performances and their profile attributes.

Profile of the Respondents
Table 2 on the next page shows the distribution of the respondents according to classification in terms of profile as to sex, age, civil status, family monthly income, and source of review materials with the corresponding frequency (f) count and percentage (%) equivalent of each attribute.

It can be gleaned from the table and Figure 2 on the next page that in terms of sex, most of the respondents are female as shown by the frequency of 172 or 79 percent, while least in number are the males with 46 or 21 percent.
Table 2
Distribution of the Respondents
in terms of Profile
n=218
Indicators f %
Sex Male 46 21
Female 172 79
Age 18 years old and below 4 2
19 years old 36 16
20 years old 96 44
21 years old 41 19
22 years old and above 41 19
Civil Status Single 208 95
Married 8 4
Separated 2 1
Widow/er 0 0
Family Monthly Income Php 10,000 and Below 128 59
Php 10,001 – Php 20,000 44 20
Php 20,001 – Php 30,000 34 16
Php 30,001 and Above 12 5
Source of Review Material Internet 162 74
Old Reviewers 96 44
New Reviewers 149 68
Handout 200 92

Figure 2.Distribution of the Respondents in terms of Sex

Figure 3.Distribution of the Respondents in terms of Age
Similarly, It can be seen from the same table on the previous page and Figure 3 above that in terms of age, most of the respondents are 20 years old as shown by the frequency of 96 or 44 percent and 41 or 19% on both 21 and 22 years old and above while the least are the 4 or 2 percent under 18 years old.

Also, Table 2 and Figure 4 below show that in terms of civil status, most of the respondents are single as shown by the frequency of 208 or 95 percent while the least in number are 2 or 1 percent who is separated.
Figure 4.Distribution of the Respondents in terms of Civil Status

Figure 5.Distribution of the Respondents in terms
of Family Monthly Income
Further, Table 2 and Figure 5 above show that in terms of family monthly income, most of the respondents’ have a family monthly income of Php10,000.00 and below as shown by the frequency of 128 or 59 percent correspondingly. However, the least in number has a family monthly income of Php 30,001.00 and above as shown by the frequency of 12 or 5 percent.
Finally, the same table and Figure 6 on the next page present that 92 percent of the respondents use handouts as their source of review material while the least is the 96 or 44 percent on old reviewers.

Figure 6.Distribution of the Respondents in terms of Source of
Review Materials
Thus, Table 2 indicates that the majority of the respondents are female, 20 years old of age, single with family monthly income of Php 10,000.00 and below and use handout as source of review material which imply that the Pre-Service Teachers are dominated by those of the feminine gender, of proper age according to their level of schooling, no marital obligations, with lowest family monthly income and use handouts as review material.

Respondents’ Practices on Study Habits, Learning
Styles and Emotional Intelligences
Table 3 on the succeeding pages show the respondents practices on the study habits in terms where to study, when and how much to study and how to study with the corresponding weighted mean (WM) and descriptive equivalent (DE) of each category.

The table and Figure 7 on the succeeding pages elucidate that the respondents answered “Often” on the study habits in terms of ‘Where to Study’ as evidenced by the average weighted mean of 4.29 in which the highest among the categories is on “I study where there is good lighting” (with WM=4.50, DE of Always) while the lowest is on “I study in an area free of unnecessary materials” (with WM=4.01, DE of Often).
It indicates that the respondents continuously practice to study in a place where there is good lighting. It implies that they prefer studying in a room or place with good lighting.

Table 3
Respondents’ Practices on Study Habits
in terms of Where to Study
n = 218
Indicators WM DE
1.) I study where there is good lighting. 4.50 A
2.) I study in a room where there is good ventilation. 4.45 O
3.) I study in an area free of unnecessary materials. 4.01 O
4.) I study in a quiet area. 4.35 O
5.) I study facing a wall or a corner to minimize distracting sights. 4.16 O
Average Weighted Mean 4.29 O
Legend: Mean Range Descriptive Equivalent
4.50 – 5.00 Always
3.50 – 4.49 Often
2.50 – 3.49 Sometimes
1.50 – 2.49 Seldom
1.00 – 1.49 Never

Figure 7.Distribution of the Respondents’ Practices on Study Habits
in terms of Where to Study
Table 3 (continuation)
Respondents’ Practices on Study Habits in
terms of When and How Much to Study
n = 218
Indicators WM DE
1.) I study during my active hours. 4.22 O
2.) I review my notes during my vacant time. 3.92 O
3.) I set goals, according to the difficulty of the topic. 4.05 O
4.) I see to it that I take a break every after an hour. 4.06 O
5.) I study the “tough” subjects when I am most aware. 4.17 O
Average Weighted Mean 4.08 O
Legend: Mean Range Descriptive Equivalent
4.50 – 5.00 Always
3.50 – 4.49 Often
2.50 – 3.49 Sometimes
1.50 – 2.49 Seldom
1.00 – 1.49 Never

Figure 8.Distribution of the Respondents’ Practices on Study Habits
in terms of When and How Much to Study
The continuation of Table 3 and Figure 8 on the shown above explained that the respondents answered “Often” on the study habits in terms of ‘When and How much to Study’ as evidenced by the average weighted mean of 4.08 in which the highest among the categories is on “I study during my active hours” (with WM = 4.22, DE of Often) while the lowest is on “I review my notes during my vacant time” (with WM = 3.92, DE of Often). It indicates that the respondents regularly practice to study during their active hours. This implies that studying during active hours was the most preferred time and method of reviewing by the respondents.

The continuation of Table 3 and Figure 9 on the succeeding page illustrate that the respondents answered “Often” on the study habits in terms of ‘How to Study’ as evidenced by the average weighted mean of 4.28. In which the highest among the categories is on “I try to underline, take notes, or identify material that will help me answer the questions that I previously listed” (WM = 4.42, DE of Often). However, the lowest is on “I try to think of and list additional questions that I should be able to answer from reading a learning material” and “I try to think of and list additional questions that I should be able to answer from reading a learning material” (WM = 4.13, DE of Often). It indicates that the respondents regularly practice the study habits in terms of how to study specifically, on trying to underline, take notes, or identify material that will help answer the questions previously listed and trying to relate in real life situations when learning a principle or definition. It implies that the respondents generally preferred trying to underline, take notes, or identify material that will help answer the questions previously listed as the best method to review.

Table 3 (continuation)
Respondents’ Practices on Study
Habits in terms of How to Study
n = 218
Indicators WM DE
1.) I analyze further the given topic. 4.33 O
2.) I try to relate in real life situations when learning a principle or definition. 4.40 O
3.) I try to think of and list additional questions that I should be able to answer from reading a learning material. 4.13 O
4.) I try to underline, take notes, or identify material that will help me answer the questions that I previously listed. 4.42 O
5.) I review the material and continue to go over my recitation to the questions and my notes. 4.13 O
Average Weighted Mean 4.28 O
Legend: Mean Range Descriptive Equivalent
4.50 – 5.00 Always
3.50 – 4.49 Often
2.50 – 3.49 Sometimes
1.50 – 2.49 Seldom
1.00 – 1.49 Never

Figure 9.Distribution of the Respondents’ Practices on Study Habits
in terms of How to Study
The continuation of Table 3 and Figure 10 shown below present that the respondents answered “Often” on the study habits as evidenced by the total overall average weighted mean of 4.22. In which the highest among the categories is on “Where to Study” (WM = 4.29, DE of Often). Meanwhile, the lowest is on “When/How Much to Study” (WM = 4.08, DE of Often).
Table 3 (continuation)
Respondents’ Practices on Study Habits
n = 218
Indicators WM DE
Where to Study 4.29 A
When/How Much to Study 4.08 A
How to Study 4.28 A
Average Weighted Mean 4.22 A
Legend: Mean Range Descriptive Equivalent
4.50 – 5.00 Always
3.50 – 4.49 Often
2.50 – 3.49 Sometimes
1.50 – 2.49 Seldom
1.00 – 1.49 Never

Figure 10.Distribution of the Respondents’ Practices on Study Habits
It indicates that the repondents regularly practice the study habits in terms of where to study. This implies that most of the respondents preferred to have a good place to study in terms of their study habits.

According to Crede and Kuncel (2018), study habits are consistent behavior which is well planned and purposeful on the parts of learners towards learning academic disciplines. The study of Belen (2008) showed that the respondents have good academic performance despite having very low or poor study habits.
Table 4 and Figures 11–14 on the succeeding pages show the respondents practice of the learning styles in terms of Visual, Auditory, and Kinaesthetic with the corresponding weighted mean (WM) and descriptive equivalent (DE) of each category.

The table and Figure 11 on the next page show that the respondents answered “Often” on the “Learning Styles” in terms of “Visual” (AWM = 4.19). In which the highest among the category is on “I remember something better if I write it down” (WM=4.46). While the least is on “I can “see” the textbook page and where the answer is located if I am taking a test” (WM=3.80).
It indicates that the respondents regularly practice to write something in order to remember it better. It implies that they prefer writing something in order to remember it easier.

Table 4
Respondents’ Practices on Visual Learning Style
n = 218
Indicators WM DE
1.) I remember something better if I write it down. 4.46 O
2.) I can “see” the textbook page and where the answer is located if I am taking a test. 3.80 O
3.) I tend to visualize what someone is saying while listening. 4.23 O
4.) I use visual aids to help me retain material for the tests. 4.33 O
5.) I find difficulty to understand what a person is saying when there are lots of mess and movement in the area. 4.11 O
Average Weighted Mean 4.19 O
Legend: Mean Range Descriptive Equivalent
4.50 – 5.00 Always
3.50 – 4.49 Often
2.50 – 3.49 Sometimes
1.50 – 2.49 Seldom
1.00 – 1.49 Never

Figure 11.Distribution of the Respondents’ Practices on Visual
Learning Style
The continuatuin of Table 4 and Figure 12 on the next page elucidate that the respondents answered “Often” on the learning styles in terms of ‘Auditory’ as evidenced by the total average weighted mean of 4.03. In which the highest among the categories is on “I get work done better in a quiet place.” (WM=4.52, DE of Always). However, the least is on “I remember things that I hear, rather than things that I see or read.” (WM=3.75, DE of Often). It indicates that the respondents regularly practice to get work done in a quiet place. This implies that they work better in a room or place where there are unnecessary noises.

Table 4 (continuation)
Respondents’ Practices on Auditory Learning Style
n = 218
Indicators WM DE
1.) I get work done better in a quiet place. 4.52 A
2.) I spell it out loud and hear the words if it sounds right if I am unsure. 4.13 O
3.) I understand how to do something if someone tells me, rather than having to read the same thing to myself. 3.94 O
4.) I remember things that I hear, rather than things that I see or read. 3.75 O
5.) I prefer to learn new information from a lecture or textbook through hearing it rather than reading it, if given a choice. 3.83 O
Average Weighted Mean 4.03 O
Legend: Mean Range Descriptive Equivalent
4.50 – 5.00 Always
3.50 – 4.49 Often
2.50 – 3.49 Sometimes
1.50 – 2.49 Seldom
1.00 – 1.49 Never

Figure 12.Distribution of the Respondents’ Practices
on Auditory Learning Style
The continuatuin of Table 4 and Figure 13 below elucidate that the respondents answered “Often” on the learning styles in terms of ‘Kinesthetic’ as evidenced by the total average weighted mean of 4.10.
Table 4 (continuation)
Respondents’ Practices on Kinesthetic Learning Style
n = 218
Indicators WM DE
1.) I learn best when I am shown how to do something, and I have the opportunity to do it. 4.40 O
2.) I tend to solve problems through a more trial-and-error approach, rather than from a step-by-step method. 3.98 O
3.) I follow directions easily if I see someone else do it first. 3.98 O
4.) I think more when I have the freedom to move around. 4.16 O
5.) I remember the items best if I move around and use my fingers to name them. 4.00 O
Average Weighted Mean 4.10 O
Legend: Mean Range Descriptive Equivalent
4.50 – 5.00 Always
3.50 – 4.49 Often
2.50 – 3.49 Sometimes
1.50 – 2.49 Seldom
1.00 – 1.49 Never

Figure 13.Distribution of the Respondents’ Practices
on Kinesthetic Learning Style
Further, highest among the categories is on “I learn best when I am shown how to do something, and I have the opportunity to do it” (WM=4.40, DE of Often). Meanwhile, the least is on “I tend to solve problems through a more trial-and-error approach, rather than from a step-by-step method and I follow directions easily if I see someone else do it first” (WM=3.98, DE of Often). It indicates that the respondents regularly practice learning best if shown how to do something. This implies that they tend to learn best if shown how to do something and was given the opportunity to do it.

The table and Figure 13 on the succeeding page elucidate that the respondents answered “Often” on their learning styles as evidenced by the average weighted mean of 4.11. In which the highest among the categories is on “Visual learning style” (with WM=4.19, DE of Often). However, the least is on “Auditory learning style” (with WM=4.03, DE of Often). This indicates that the respondents regularly practice the visual learning style. It implies that among the three, visual is their dominant learning style.
Lucas ; Corpuz (2007) affirmed that thinking/learning style is an important factor that assists student diversity. Students think and learn in different ways. Every student has different learning preferences, specifically in the learners’ way of processing information. Some would learn better when they do it with their hands rather than just merely listening while others may prefer to watch a video of the lesson.
Table 4 (continuation)
Respondents’ Practices on Learning Styles
n = 218
Indicators AWM DE
Visual Learning Style 4.19 O
Auditory Learning Style 4.03 O
Kinesthetic Learning Style 4.10 O
Average Weighted Mean 4.11 O
Legend: Mean Range Descriptive Equivalent
4.50 – 5.00 Always
3.50 – 4.49 Often
2.50 – 3.49 Sometimes
1.50 – 2.49 Seldom
1.00 – 1.49 Never

Figure 14.Distribution of the Respondents’ Practices
on Learning Styles
According to Williams (2009) most of all information processed by our brain is developed from the things we see. It suggested that visual communication is the main support system that plays a significant role for human success and development.

Table 5 and the figures on the succeeding pages present the respondents’ level of emotional intelligence in terms of emotional awareness; emotional management, social awareness and relationship management with the corresponding weighted mean (WM) and descriptive equivalent (DE) of each statement.

The table below and Figure 15 on the next page shows that the respondents answered “Often” on the “Emotional Intelligence” in terms of “Emotional Awareness” (WM = 4.07). In which the highest among the categories is on “I am aware of what is happening to me even when I am upset” (WM = 4.17, DE of Often). However, the least is on “I am easily affected by external factors” (MW 3.97, DE of Often). These indicate that the respondents have high emotional awareness. It implies that they are often aware of what is happening around them even stressful times and is able to stand apart from my thoughts and feelings and examine them.

Table 5
Respondents’ Practices on Emotional Intelligence
in terms of Emotional Awareness
n = 218
Indicators WM DE
1.) I understand my feelings at any given moment. 4.08 O
2.) I find it easy to put words to my feelings. 3.98 O
3.) I am easily affected by external factors. 3.97 O
4.) I am aware of what is happening to me even when I am upset. 4.17 O
5.) I am able to stand apart from my thoughts and feelings and examine them. 4.15 O
Average Weighted Mean 4.07 O
Legend: Mean Range Descriptive Equivalent
4.50 – 5.00 Always
3.50 – 4.49 Often
2.50 – 3.49 Sometimes
1.50 – 2.49 Seldom
1.00 – 1.49 Never

Figure 15.Distribution of the Respondents’ Practices on Emotional
Intelligence in terms of Emotional Awareness
The table and Figure 16 on the next page shows that the respondents answered “Often” on the emotional management as evidenced by the total average weighted mean of 4.19. In which the highest among the categories is on “I accept responsibility for my actions” (WM=4.50, DE of Always). Meanwhile, the least is on “I maintain my composure, even during stressful times” (WM=3.07, DE of Often).
This indicates that the respondents also have high emotional management. It implies that they always accept responsibility of their own actions and find it easy to make goals and stick with them.

Table 5 (Continuation)
Respondents’ Practices on Emotional Intelligence
in terms of Emotional Management
n = 218
Indicators WM DE
1.) I accept responsibility for my actions. 4.50 A
2.) I find it easy to make goals and stick with them. 4.21 O
3.) I can accept critical comments from others without becoming angry. 4.11 O
4.) I maintain my composure, even during stressful times. 4.05 O
5.) I control my urges to overindulge in things that could damage my well-being. 4.07 O
AWM 4.19 O
Legend: Mean Range Descriptive Equivalent
4.50 – 5.00 Always
3.50 – 4.49 Often
2.50 – 3.49 Sometimes
1.50 – 2.49 Seldom
1.00 – 1.49 Never

Figure 16.Distribution of the Respondents’ Practices on Emotional
Intelligence in terms of Emotional Management

Further, the continuation of Table 5 Figure 17 on the next page show that the respondents answered “Often” on the social emotional awareness as evidenced by the total average weighted mean of 4.25. In which the highest among the categories is on “I usually know when to speak and when to be silent” (WM=4.25, DE of Often). While the least is on “I can easily tell if people around me are becoming annoyed” (WM=4.06, DE of Often).
Table 5 (Continuation)
Respondents’ Practices on Emotional Intelligence
in terms of Social Emotional Awareness
n = 218
Indicators WM DE
1.) I consider the impact of my decisions on other people. 4.28 O
2.) I can easily tell if people around me are becoming annoyed. 4.06 O
3.) I am generally able to understand the way other people feel. 4.18 O
4.) I usually know when to speak and when to be silent. 4.36 O
5.) I am genuinely bothered to see other people suffer. 4.33 O
Average Weighted Mean 4.25 O
Legend: Mean Range Descriptive Equivalent
4.50 – 5.00 Always
3.50 – 4.49 Often
2.50 – 3.49 Sometimes
1.50 – 2.49 Seldom
1.00 – 1.49 Never

Figure 17.Distribution of the Respondents’ Practices on Emotional
Intelligence in terms of Social Emotional Awareness
This indicates that the respondents have high social emotional awareness. It implies that they are cautious about when to speak and when to be silent and genuinely bothered to see other people suffer.

The table below and Figure 18 on the next page shows that the respondents answered “Often” on relationship management as evidenced by the total average weighted mean of 4.27. In which the highest among the categories is on “I like helping people” (WM=4.59, DE of Always).
However, the least is on “I find it easy to share my deepest feelings with others” (WM=4.00, DE of Often). It indicates that the respondents have high relationship management. It implies that they like helping other people and can easily make friends.

Table 5 (Continuation)
Respondents’ Practices on Emotional Intelligence
in terms of Relationship Management
n = 218
Indicators WM DE
1.) I like helping people. 4.59 A
2.) I find it easy to share my deepest feelings with others. 4.00 O
3.) I am good at motivating others. 4.24 O
4.) I can easily make friends. 4.30 O
5.) I am able to talk to someone down if they are very upset. 4.20 O
Average Weighted Mean 4.27 O
Legend: Mean Range Descriptive Equivalent
4.50 – 5.00 Always
3.50 – 4.49 Often
2.50 – 3.49 Sometimes
1.50 – 2.49 Seldom
1.00 – 1.49 Never

Figure 18.Distribution of the Respondents’ Practices on Emotional
Intelligence in terms of Relationship Management
The table below shows that the respondents mostly answered “Often” on emotional intelligence as evidenced by the total average weighted mean of 4.19.
Table 5 (Continuation)

Respondents’ Practices on Emotional Intelligence
n = 218
Indicators WM DE
Emotional Awareness 4.07 O
Emotional Management 4.19 O
Social Emotional Awareness 4.25 O
Relationship Management 4.27 O
Average Weighted Mean 4.19 O
Legend: Mean Range Descriptive Equivalent
4.50 – 5.00 Always
3.50 – 4.49 Often
2.50 – 3.49 Sometimes
1.50 – 2.49 Seldom
1.00 – 1.49 Never

Figure 19.Distribution of the Respondents’ Practices
on Emotional Intelligence
Further, highest among the categories is on “Relationship Management” (WM=4.27, DE of Often) while the lowest is on “Emotional Awareness” (TWM=4.07, DE of Often). Figure 19 indicates that the respondents have high emotional intelligence in terms of relationship management as supported by their social emotional awareness. It implies that their social emotional awareness affects or influences their relationship management.
According to Salovey and Mayer (1990) disclosed a framework for emotional intelligence, a set of skills assumed to contribute to the exact assessment and expression of emotion in oneself and in others, the effective management of emotion in self and others, the use of feelings to inspire, prepare, and accomplish something in life
However, it seems that the respondents aren’t aware of their own emotion but is aware of the feelings of the people they are with. Contradicting to what Mayer and Geher (1996) have said, that people who can relate their thoughts are people who can understand the feelings of others. This means that the respondents’ ability to interact with other people is not affected by the self-emotional awareness.
Respondents’ Level of Performances
in Professional Enhancement 1
Table 6 below shows the respondents’ level of performances in Professional Enhancement 1 which are the chief concern of the study with the corresponding frequency (f) count and percentage (%) equivalent of each attribute.

Table 6
Distribution of the Raw Scores in Professional Enhancement 1 of the Respondents
Indicators English Mathematics Science Filipino Social
Science Total
f % f % f % f % f % f %
Low 42 19 71 32 79 37 75 34 68 31 61 28
Average 129 59 106 49 125 57 104 48 106 49 144 66
High 47 22 41 19 14 6 39 18 44 20 13 6
Legend: Mean Range Descriptive Equivalent
30 – 40 150-200 High
20 – 29 100-149 Average
0 – 19 0-99 Low

Figure 20.Bar Graph of the Distribution of the Respondents’ Raw
Scores in the Subject Area
It can be gleaned from table 6 on the previous page, that according to English subject most are the 129 or 59 percent of the respondents are on the “Average”, while the least are the 42 or 19 percent “Low”. This indicates that the majority of the respondents have performed within the expected level of competencies in the said subject.
In the Math subject, Table 6 reveals that the majority of the respondents are average performing (106 or 49 percent) while there are only 41 or 18.8 percent in the high level of performance. This also indicates that the majority of the respondents have performed within the expected level of competencies in the Math subject
Also in the Science subject, the highest is 124 or 56.9 percent in the average level of performance while the lowest is 14 or 6.4 percent in the high level of performance. This indicates that the majority of the respondents have performed within the expected level of competencies in the Science subject.
Further, in the Filipino subject, the highest is 104 or 47.7 percent in the average level of performance while the lowest is 39 or 18 percent in the high level of performance. This indicates that the majority of the respondents have performed within the expected level of competencies in the Filipino Subject.
Lastly, in the Social Science subject, the highest is 106 or 48.6 percent in the average level of performance while the lowest is 44 or 20.2 percent in the high level of performance. This indicates that the majority of the respondents have performed within the expected level of competencies in the Social Science subject.

Furthermore, in terms of their total scores in Professional Enhancement 1, Majority of the respondents are under the average level of performance with a frequency count of 144 or 61 percent. While the least in number is the high level of performance with a frequency of 13 or 6 percent of the respondents. This indicates that the majority of the respondents have performed within the expected level of competencies while there were only 6 percent who performed above the expected level of competencies in Professional Enhancement 1.

Statistical Measures of the Respondents’ Performances
Table 7 shows the tabulation of the statistical measures of the respondents’ performances in Professional Enhancement 1.

It can be gleaned from the table shown below and Figure 21 on the next page that there were 200 items all in all, 40 items in every subject areas (English, Math, Science, Filipino, and Social Science). The respondents obtained 172 as the highest which is above the expected level of competencies and 59 as the lowest which is below the expected level of competencies.

Since the median is greater than mean, we obtained a negative skewness. It indicates that the respondents’ score are negatively skewed. It means that most of the respondents obtained high scores and showed good performance in Professional Enhancement 1.
Table 7
Statistical Measures of the Respondents’ Performances
in Professional Enhancement 1
n = 218

Indicators Total
Highest Possible Score 200
Lowest Possible Score 0
Highest Score Obtained 172
Lowest Score Obtained 59
Mean 114.27
Median 114.50
Standard Deviation 25.95
Skewness -0.48
Kurtosis 0.66
It was also evidenced by the kurtosis of 0.66 which is leptokurtic curve which means that most of the respondents’ scores are close or homogenous.

Figure 21.Histogram of the Distribution of the Respondents’ Scores in
Raw Professional Enhancement 1
According to ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “author” : { “dropping-particle” : “”, “family” : “Corpuz”, “given” : “”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Salandanan”, “given” : “”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issued” : { “date-parts” : “2007” }, “title” : “Principles_of_Learning_Horne_and_Pine_1990_in_Corpuz_and_Salandanan_2007 @ www.academia.edu”, “type” : “article” }, “uris” : “http://www.mendeley.com/documents/?uuid=58531be9-b708-46dc-baca-e37329d1aeb5”, “http://www.mendeley.com/documents/?uuid=26f71725-cab4-4ac0-ac60-2e5078cc8898” } , “mendeley” : { “formattedCitation” : “(Corpuz & Salandanan, 2007)”, “manualFormatting” : “Corpuz & Salandanan (2007)”, “plainTextFormattedCitation” : “(Corpuz & Salandanan, 2007)”, “previouslyFormattedCitation” : “(Corpuz & Salandanan, 2007)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }Corpuz & Salandanan (2007), it is necessary to find out if the learning objectives were attained after the teaching-learning process. In the curriculum, these refer to the student learning outcomes (SLO). Student learning outcomes are the results or products of the students in the learning process. Performance is an element of a curriculum that should be given importance. The curriculum is considered to be effective if the learners’ performances are higher than the objectives set. On the other hand, if the learners’ performances are low then it means that the curriculum is not successful. Thus, an effective curriculum is one that results high or outstanding performance.

Differences between the Pre-Service
Teachers’ Level of Performances
Tables 8–9 present the pre-service teachers’ level of performances in terms of their profile attributes with the computed significance and critical values with the corresponding significance indicators.

Table 8
Difference between the Pre-Service Teachers’
Level of Performances in terms of Sex

Indicator n Mean Mean Diff var t df p – value Sig.

Male versus 46 116.91 2.75 620.126 0.162 216 0.44 NS
Female 172 114.16 607.786 Legend :S – Significant
NS – Not Significant
Table 8 indicates that there is no significant difference between the respondents’ performances in Professional Enhancement 1 across their sex since the p–value shown is greater than the 0.05 level of significance. The condition arrived at accepting the null hypothesis of the study, which is stated as there is no significant difference between the respondents’ level of performances in Professional Enhancement 1 across their profile. These indicate that the respondents’ performances in Professional Enhancement 1 did not significantly differ across their sex. It implies that the classifications of the Pre-Service Teachers according to their biological classsification did not in any way influence their performances in Professional Enhancement 1.

This contrasted the study of ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1016/J.SBSPRO.2011.03.236”, “abstract” : “The following study examines gender differences existing in various cognitive motivational variables (locus of control, academic self-concept and use of learning strategies) and in performance attained in school subjects of Literature and Mathematics. For this purpose, a sample of 363 students was selected from the high school students in the first, second and third academic years. For achieving to the purpose used of locus of control questionnaire, self-concept questionnaire and LASSI. Results show the existence of gender difference in variables under consideration, with girls showing internal locus of control, using attitude, motivation, time management, anxiety, and self-testing strategies more extensively, and getting better marks in Literature. With boys using concentration, information processing and selecting main ideas strategies more, and getting better marks in mathematics. Gender differences were not found in external locus of control, in academic self-concept, and in study aids and test strategies. Results suggest that differences exist in the cognitive-motivational functioning of boys and girls in the academic environment, with the girls have a more adaptive approach to learning tasks. However, the influence of contextual variables that may differently affect boysu2019 and girlsu2019 motivation was not taken into account. Thus future research should address the influence of such factors.”, “author” : { “dropping-particle” : “”, “family” : “Ghazvini”, “given” : “Sayid Dabbagh”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Khajehpour”, “given” : “Milad”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Procedia – Social and Behavioral Sciences”, “id” : “ITEM-1”, “issued” : { “date-parts” : “2011”, “1”, “1” }, “page” : “1040-1045”, “publisher” : “Elsevier”, “title” : “Gender differences in factors affecting academic performance of high school students”, “type” : “article-journal”, “volume” : “15” }, “uris” : “http://www.mendeley.com/documents/?uuid=69acdf36-a704-340b-a100-9b17faeb28f1” } , “mendeley” : { “formattedCitation” : “(Ghazvini & Khajehpour, 2011)”, “plainTextFormattedCitation” : “(Ghazvini & Khajehpour, 2011)”, “previouslyFormattedCitation” : “(Ghazvini & Khajehpour, 2011)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }Ghazvini & Khajehpour (2011) which stated that gender differences exist in the academic performance of boys and girls and revealed that girls have more adaptive approach in learning new task than boys.

Moreover, Table 9 on the next page show that there are significant differences between the respondents’ performances in Professional Enhancement 1 across their profile since the computed significance values are lesser than the critical value of 0.05 as to: Source of Review Material (significance value = 0.00), Study Habits (significance value = 0.00) and Emotional Intelligence (significance value = 0.00) thereby rejecting the null hypothesis of the study which is stated as there is no significant difference between the respondents’ level of performances in Professional Enhancement 1 across their profile.
These indicate that the respondents’ performances in Professional Enhancement 1 have significantly differed according to their source of review material, study habits and emotional intelligence.
These imply that the materials utilized by the pre–service teachers together with their practices in studying and emotional intelligences have greatly influenced their ratings in the first course of professional enhancement.

Further, the table shows that there are no significant differences between the respondents’ performances in Professional Enhancement 1 across their profile since the computed significance values are lesser than the critical value of 0.05 as to: Age (significance value = 0.22), Civil Status (significance value = 0.22), Family Monthly Income (significance value = 0.25) and Learning Styles (significance value = 0.28) thus accepting the null hypothesis of the study.

Table 9
Difference between the Pre-Service Teachers’ Level
of Performances in terms of Profile Attributes

Profile Sum of
Squares df Mean
Square F Sig. Remark
Age Between Groups 3534.12 4 883.53 1.46 0.22 NS
Within Groups 128636.68 213 603.93 Total 132170.79 217 Civil Status Between Groups 1850.31 2 925.15 1.53 0.22 NS
Within Groups 130320.49 215 606.14 Total 132170.79 217 Family Monthly Income Between Groups 2511.26 3 837.09 1.382 0.25 NS
Within Groups 129659.53 214 605.89 Total 132170.79 217 Source of Review Material Between Groups 9511.89 4 2377.97 4.13 0.00 S
Within Groups 122658.90 213 575.86 Total 132170.79 217 Study Habits Between Groups 32410.27 29 1117.60 2.106 0.00 S
Within Groups 99760.52 188 530.64 Total 132170.79 217 Learning Styles Between Groups 21304.98 31 687.26 1.15 0.28 NS
Within Groups 110865.81 186 596.05 Total 132170.79 217 Emotional Intelligence Between Groups 39575.63 37 1069.61 2.079 0.00 S
Within Groups 92595.16 180 514.42 Total 132170.79 217 Legend :S – Significant
NS – Not Significant
The study of Ma and Wooster (1979) on the effect of civil status on the academic performance of college students revealed that married students attained higher grades than their counterparts and those without children attain higher grades than those with children. Moreover, Sirin (2005) found a slight decrease in the average correlation of socioeconomic status–achievement since the initial review of White’s (1982) meta-analysis was published. Also, Akungu (2014) stressed that learning material resources have a significant effect on students’ academic achievement since it aid the learning of abstracts and ideas and discourage rote learning. Coleman et al., (1966) and White (1982) also believed that students who are older than their classmates tend to perform less and continue to fell the older they get. Márquez, Palomera Martín ; Brackett (2006) supported the validity of Emotional Intelligence (EI) and provided positive indicators on the importance of EI in student’s academic performance and social development.
In line with the study of Nordin et al. (2011) which concluded that age does not affect academic performance. Also, Lawrence’s (2014) investigation showed the influence of study habits and academic achievement of higher secondary school students with respect to their background variables. It was showed that there was no significant difference between study habits and academic achievement of higher secondary school students. Further, Nzesei (2015) revealed that there is no big difference on the learning style preference and academic achievement levels of the students. Awang et al. (2017) stated that there is no difference between students’ academic achievements across learning styles and revealed that each learning style has its own degrees of strengths and weaknesses.

Relationships between the Pre-Service Teachers’
Level of Performances

The table on the next page shows the relationships between the respondents’ performances in Professional Enhancement 1 and their profile attributes with the computed Pearson correlation, significance values and corresponding significance indicators.

Moreover, Table 10 on the next page shows that there are significant relationships between the respondents’ performances in Professional Enhancement 1 across their profile since the computed significance values are lesser than the critical value of 0.05 as to: Source of Review Material (significance value = 0.00), Where to Study (significance value = 0.00), How to Study (significance value = 0.00), Auditory Learning Style (significance value = 0.00), and Relationship management (significance value = 0.00) thereby rejecting the null hypothesis of the study which is stated as there is no significant relationship between the respondents’ level of performances in Professional Enhancement 1 and their profile.
Further, the table shows that there are no significant relationship between the respondents’ performances in Professional Enhancement 1 across their profile since the computed significance values are lesser than the critical value of 0.05
Table 10

Relationship between the Pre-Service Teachers’ Level
of Performances in terms of Profile Attributes

Profile Pearson Correlation Performance Remark
Sex Pearson Correlation -0.046 NS
Sig. (2-tailed) 0.248 Age Pearson Correlation -0.081 NS
Sig. (2-tailed) 0.118 Civil Status Pearson Correlation 0.065 NS
Sig. (2-tailed) 0.170 Family Monthly Income Pearson Correlation 0.047 NS
Sig. (2-tailed) 0.245 Source of Review Material Pearson Correlation 0.207 S
Sig. (2-tailed) 0.001 Where to Study Pearson Correlation 0.245 S
Sig. (2-tailed) 0.000 When and How much to Study Pearson Correlation 0.035 NS
Sig. (2-tailed) 0.302 How to Study Pearson Correlation 0.168 S
Sig. (2-tailed) 0.007 Visual Learning Style Pearson Correlation 0.070 NS
Sig. (2-tailed) 0.152 Auditory Learning Style Pearson Correlation -0.144 S
Sig. (2-tailed) 0.017 Kinesthetic Learning Style Pearson Correlation -0.010 NS
Sig. (2-tailed) 0.440 Emotional Awareness Pearson Correlation -0.063 NS
Sig. (2-tailed) 0.176 Emotional Management Pearson Correlation -0.034 NS
Sig. (2-tailed) 0.307 Social Emotional Awareness Pearson Correlation 0.066 NS
Sig. (2-tailed) 0.166 Relationship Management Pearson Correlation -0.209 S
Sig. (2-tailed) 0.001 Legend : S- Significant
NS- Not Significant
as to: Sex (significance value = 0.248), Age (significance value = 0.118), Civil Status (significance value = 0.170), Family Monthly Income (significance value = 0.245), When and How Much to Study (significance value = 0.302), Visual (significance value = 0.152) and Kinesthetic Learning Styles (significance value = 0.440), Emotional Awareness (significance value = 0.176), Emotional Management (significance value = 0.307) and Social Emotional Awareness (significance value = 0.166), thus accepting the null hypothesis of the study.

Selcuk R. Sirin, 2005 found a slight decrease in the average correlation of socioeconomic status–achievement since the initial review of White’s (1982) meta-analysis was published. Also, Ma, Li-Chen; Wooster, Robert A (1979) found out that there exist an association between married students and unmarried students in their academic performance.
Moreover, Adeogun (2001) discovered a very strong positive significant relationship between learning materials and academic performance. According to him, schools with more materials performed better than schools with less learning materials. Cerna ; Pavliushchenko (2015) also believed that study habit is an important determinant of academic performance. Their study revealed that study habit has a negative and positive effect to a student’s academic performance.
The study of Abdullah (2012) revealed that visual learners perfrormed best in their academic performances. Galasinki (2000) said that speech is one of the most common means of communication in today’s modern world. And speech uses the ear receptor which is under auditory in the VAK learning style. In contrast with the findings of Vaishnav ; Chirayu (2013), it was revealed that kinesthetic learning style was more predominant than visual and auditory learning styles among secondary high school students. A positive high correlation between kinesthetic learning style and academic performance of the students was also found.

In contrast with this study, the study of ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1016/j.sbspro.2013.07.095”, “ISBN” : “1877-0428”, “ISSN” : “18770428”, “abstract” : “This study investigates the influence of Emotional Intelligence on academic achievement among students of Education Faculty, Universiti Teknologi Mara (UiTM). The data of this research were obtained through the use of a questionnaire which elicits information on the studentsu2019 Emotion al Intelligence level as well as their academic performance. The results of the study reveal that the respondents have high level of Emotional Intelligence. Two domains (Self-Emotion Appraisal and Understanding of Emotion) of the Emotional Intelligence investigated are found to be significantly and positively associated with the respondentsu2019 academic achievement. The findings of the study hold import antimplications on the value of Emotional Intelligence and their relationships to studentsu2019 academic perform ance especially among pre-service teachers.”, “author” : { “dropping-particle” : “”, “family” : “Mohzan”, “given” : “Maizatul Akmal Mohd”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Hassan”, “given” : “Norhaslinda”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Halil”, “given” : “Norhafizah Abd”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Procedia – Social and Behavioral Sciences”, “id” : “ITEM-1”, “issue” : “InCULT 2012”, “issued” : { “date-parts” : “2013” }, “page” : “303-312”, “title” : “The Influence of Emotional Intelligence on Academic Achievement”, “type” : “article-journal”, “volume” : “90” }, “uris” : “http://www.mendeley.com/documents/?uuid=41517679-19cc-4c3b-8789-14c88f511cb1” } , “mendeley” : { “formattedCitation” : “(Mohzan, Hassan, & Halil, 2013)”, “plainTextFormattedCitation” : “(Mohzan, Hassan, & Halil, 2013)”, “previouslyFormattedCitation” : “(Mohzan, Hassan, & Halil, 2013)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }Mohzan et al. (2013) on the influence of emotional intelligence on academic achievement among students of teacher education revealed that the respondents have a high level of emotional intelligence. Self-emotion appraisal and understanding of emotion are found to be significantly and positively related to the respondents’ academic achievement. The study implied the value of emotional intelligence and their relationships to students’ academic performance, particularly among pre-service teachers.

However, the results of the correlation study of Ali, S., et al. (2013) showed that performance and age have negative correlation. Coleman et al., (1966) and White’s (1982) studies showed that as students become older, the correlation between age and performance remains constant over time. Belen (2008) said that his respondents have very low or poor study habits. Still, they managed to have good academic performance despite all these factors.

4217670-12077704000020000Chapter 4
SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS
This chapter presents the summary, salient findings, conclusions drawn from the findings, and recommendations based on the study.

SUMMARY
This portion presents the restatement of the problem, salient findings, conclusions drawn and recommendations on the study regarding the determination of the correlates in the performance of pre-service teachers of the College of Teacher Education at Urdaneta City University in the subject Professional Enhancement 1 during the academic year 2017–18.

Specifically, it sought to answer the following questions: What is the personal profile of the pre-service teachers in terms of the following attributes as sex; age; civil status; family monthly income; source of review materials; study habits; learning styles; and emotional quotient?; What is the respondents’ level of performance in the Professional Enhancement 1?; Is there a significant difference between the respondents’ level of performance in terms of their profile attributes?; Is there a significant relationship between the respondents’ level of performance and their profile attributes?;and What is the best predictor of the respondents’ performance in the Professional Enhancement 1?
The correlation study approach was utilized in this study. The respondents were the pre-service teachers of the College of Teacher Education enrolled in the subject Professional Enhancement 1 and 2 at Urdaneta City University during the second semester of academic year 2017–2018.
The number of the respondents was determined using the Sloven’s formula and proportionate simple random sampling was the utilized sampling scheme in identifying the subjects. The main data gathering instrument of the study was a questionnaire–checklist and the documentary analysis of the respondents’ scores in the subject, Professional Enhancement 1.

Problem number 1 regarding the personal profile of the pre-service teachers in terms of the following attributes; sex, age, civil status, family monthly income, and source of review materials, the percentage formula was used. The respondents’ profile in terms of study habits, learning style, and emotional intelligence and the level of performance in the Professional Enhancement 1 was analyzed using the weighted mean formula with the aid of a five and three points Likert scale, respectively. For problems 3 and 4, open source program was used.
FINDINGS
Based on the analyzed data, the researchers arrived at the following salient findings as to:
1. Majority of the respondents are female, 20 years old of age, single, with family monthly income of Php 10,000.00 and below and use handout as source of review material;
2. The respondents:
A. regularly practice the study habits, specifically on where to study. In terms of where to study, the respondents continuously practice to study where there is good lighting; in terms of when and how much to study, the respondents regularly practice studying during active hours; and in terms of where to study, the respondents regularly practice to underline, take notes, or identify material that will help answer the questions previously listed;
b. regularly practice the (VAK) Visual, Auditory and Kinesthetic learning style, specifically on visual learning style.; in terms of visual learning style, the respondents regularly remember something if they write it down; in terms of auditory learning style, the respondents continuously work better in a quiet place; and in terms of kinesthetic learning style, the respondents regularly learn best when shown how to do something, and given the opportunity to do it;
c. have high emotional intelligence specifically, on relationship management. In terms of emotional awareness, the respondents are often aware of what is happening even when stressful times; in terms of emotional management, the respondents always accept responsibility for one’s action; in terms of social emotional awareness, the respondents often know when to speak and when to be silent; and in terms of relationship management, the respondents always help the people they are around with.

3. Majority of the respondents have performed within the expected level of competencies in the Professional Enhancement 1, specifically in the order of the subject, English followed by General Science, then Mathematics and Social Studies, and the lowest Filipino.

4. The respondents’ performances in the Professional Enhancement 1 significantly differed across their source of review material, study habits, and emotional intelligence.

5. There are significant relationships between the respondents’ performances in Professional Enhancement 1 and their source of review material, practices on where to study and how to study, auditory learning style and relationship management.

CONCLUSIONS
Based on the salient findings, the researchers have drawn the following conclusions as to:
1. The respondents belong to the feminine gender, proper stage of maturity according their level of schooling, no marital obligations with lowest level of family monthly income and utilized teachers’ and enhancement facilitators’ prepared handout as review material;
2. The respondents prefer studying in a conducive place. Specifically, with lighting fixtures conducive to reading and free from unnecessary materials. However, they seem to have a problem with their time to study;
3. The respondents mostly prefer visual learning style, specifically writing something as to remember it easier; However, they are not easily distracted with the unnecessary noises around;
4. The respondents have high emotional intelligence specifically on relationship management and social emotional awareness. However, they seem to be low on their emotional awareness;
5. The respondents were dominated by those who performed within the expected level of competencies in Professional Enhancement 1 specifically, in the subject, English;
6. The respondents’ performances in Professional Enhancement 1 were greatly correlated by the location and method of study area, and emotional intelligences.

RECOMMENDATIONS
Based on the conclusions drawn, the researchers propose the following recommendations as to:
1. Inclusion of other personal attributes such as methods of classroom management, hobbies of students, and the like;
2. Encourage students to scan and study notes during vacant time;
3. Inspire students to list additional questions and should be able to answer from reading a learning material;
4. Encourage students in trying to think of and list additional questions that can be answered from reading a learning material and reviewing the material and continue to go over recitation to the questions and my notes;
5. Embolden students to see the textbook page and where the answer is located during the taking of a test;
6. Reassure students that remembering things that were heard and preferring to learn new information from a lecture or textbook through hearing it rather than reading it, if given a choice;
7. Directing students to follow directions easily and tend to solve problems through a more trial-and-error approach, rather than from a step-by-step method;
8. Assure students to be easily affected by external factors and to find it easy to put words to personal matters;
9. Guarantee students to maintain composure during stressful times;
10. Encourage students to easily tell if people around are becoming annoyed;
11. Encourage students to find it easy to share deepest feelings with others;

REFERENCES
Abdullah, A. A. (2012). Effect of students’ learning styles on classroom perfor-
mance in performance in problem-based learning. Medical Teacher 34:sup1, pages S14-S19.

Airth, M. (2018). Social awareness. Retrieved from https://study.com/academy/le
sson /social-awareness-definition-example-theories.html.
Adeogun, A. A. (2001). The principal and the financial management of public secondary schools in Osun State. Journal of Educational System and Development. 5(1), pp.1 – 10Akungu, J. A. (2014). Influence of teaching and learning resources on students’ performance in Kenya certificate of secondary education in Free Day Secondary Education, 1–105.

Akungu, J. A. (2014). Influence of Teaching And Learning Resources On Students’ Performance In Kenya Certificate Of Secondary Education In Free Day Secondary Education, 1–105.

Ali, S. , Haider, Z. , Munir, F. , Khan, H. , & Ahmed, A. (2013). Factors contribu-ting to the Students Academic Performance: A Case Study of Islamia University Sub-Campus. American Journal of Educational Research, 1(8), 283-289.

Appel, M., & Kronberger, N. (2012). Stereotypes and the achievement gap: Ste-
reotype threat prior to test taking. Educational Psychology Review, 24(4), 609-635. Retrieved from https://doi.org/10.1007/s10648-012-9200-4.

Awang, H., Abd Samad, N., Mohd Faiz, N. S., Roddin, R., & Kankia, J. D. (2017).
Relationship between the learning styles preferences and academic achievement. IOP Conference Series: Materials Science and Engineering, 226(1), 1–6. Retrieved from https://doi.org/10.1088/1757-899X/226/1/0 12193.

Belen, R. (2008). The study habits and attitudes, academic aptitude, personality
profile and academic performance of the students of TIPQC. TIP Research Journal Quezon City, 5(1). Retrieved from http://ejournals.ph/form /cite.php?id=9237.

Bradberry, T. (2014). Emotional intelligence – EQ. Retrieved from https://www.fo
rbes./sites/travisbradberry/2014/01/09/emotional-intelligence/#7869eada1 ac0.

Cerna, M. A., & Pavliushchenko, K. (2015). Influence of study habits on academic performance of international college students in Shanghai. Higher Education Studies, 5(4), 42.

Cherry, K. (2018). Gardner’s theory of multiple intelligences. Retrieved from https
//www.verywellmind.com/gardners-theory-of-multiple-intelligences-2795161.

Coleman, J. et al. (1996). Equality of educational opportunity. Washington D.C:
US government printing office.

Colorado State University (2008). Retrieved from https://secure.casa.colostate.ed
u/applications/learningstyles/auditory.aspx.

Corcoran, R., ; Tormey, R (2012). How emotionally intelligent are pre-service
teachers?Teaching and Teacher Education 28(5), 750-759, 2012 Retrieved from https://infoscience.epfl.ch/record/176004/files/How%20emotionally y%20intelligent%20are%20preservice%20teachers%20(postprint%20version).pdf.

Corpuz, B., ; Salandanan, G. (2007).Principles of learning. Horne and Pine (199
in Corpuz and Salandanan (2007). Retrieved from http://www.acad cademia.edu/27576833/Principles_of_Learning_Horne_and_Pine_1990_in_Corpuz_and_Salandanan_2007.

English living Oxford dictionaries. (2018). Correlate. Retrieved from https://en.ox forddictionaries.com/definition/correlate.

Crede, M.,; Kuncel, N. (2008). Study habits, skills, and attitudes: The third pillar
supporting collegiate academic performance. Perspectives on Psychological Science Vol. 3(6), 425-453. Retrieved from https://doi. org/10.1111 /j.1745-6924.2-6924.2008.00089.x.

Department of Education. (2002).The 2002 basic education curriculum. Retrieved
from http://www.deped.gov.ph/sites/default/files/order/2002/Dos200204 3.pdf.

Duckworth, A. L., ; Seligman,M. E. (2012). Self-disciplice outdoes IQ in predict-
ing academic performance of adolescents. Psychological Science, 16(12), 939-944. Retrieved from https://doi.org/10.1111/j.1467-9280.2005.0 1641.x.

Dulay, B. (2015). Math as a way out of poverty. Retrieved from http://www.ma
nilatime s.net/math-as-a-way-out-of-poverty/175451/.

Dunn, R. (2003). The Dunn and Dunn learning style model: Theoretical come-
stone, research and practical applications. In Arstrong, S. ; Graft, M. (Ed), bringing theory and practice, proceedings of the 8th annual European learning styles infromatio. Network Conference Hull: University of Hull.

Rita Dunn, Shirley A. Griggs, Jeffery Olson, Mark Beasley ; Bernard S. Gorman
(2010). A meta-analytic validation of the Dunn and Dunn model of learning-style preferences, The Journal of Educational Research,88:6, 353-362, DOI: 10.1080/00220671.1995.9941181.

Estrella, E. (2015). Relationship of levels of self-esteem, study habit, and acade-
mic performance of college students. IAMURE International Journal of Social Sciences,13(1). Retrieved from http://ejournals.ph/form/cite.php?id =2391.

Friedman, B., ; Mandel, R. (2009). The prediction of college student academic
performance and retention: Application of expectancy and goal setting theories. Journal of College Student Retention: Research, Theory ; Practice, 11(2), 227–246. Retrieved from https://doi.org/10.2190/ CS.11.2.d.

Galasinski, D. (2000). The language of deception. A discourse analytic study. Thousand Oaks: Sage.

Gardner, H. (1993).Multiple intelligences: The theory in practice. New York, N.Y.:
Basic Books.

Gardner, H. (1983).Frames of mind:The theory of multiple intelligences.New
York, N.Y.: Basic Books.

ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1016/J.SBSPRO.2011.03.236”, “abstract” : “The following study examines gender differences existing in various cognitive motivational variables (locus of control, academic self-concept and use of learning strategies) and in performance attained in school subjects of Literature and Mathematics. For this purpose, a sample of 363 students was selected from the high school students in the first, second and third academic years. For achieving to the purpose used of locus of control questionnaire, self-concept questionnaire and LASSI. Results show the existence of gender difference in variables under consideration, with girls showing internal locus of control, using attitude, motivation, time management, anxiety, and self-testing strategies more extensively, and getting better marks in Literature. With boys using concentration, information processing and selecting main ideas strategies more, and getting better marks in mathematics. Gender differences were not found in external locus of control, in academic self-concept, and in study aids and test strategies. Results suggest that differences exist in the cognitive-motivational functioning of boys and girls in the academic environment, with the girls have a more adaptive approach to learning tasks. However, the influence of contextual variables that may differently affect boysu2019 and girlsu2019 motivation was not taken into account. Thus future research should address the influence of such factors.”, “author” : { “dropping-particle” : “”, “family” : “Ghazvini”, “given” : “Sayid Dabbagh”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Khajehpour”, “given” : “Milad”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Procedia – Social and Behavioral Sciences”, “id” : “ITEM-1”, “issued” : { “date-parts” : “2011”, “1”, “1” }, “page” : “1040-1045”, “publisher” : “Elsevier”, “title” : “Gender differences in factors affecting academic performance of high school students”, “type” : “article-journal”, “volume” : “15” }, “uris” : “http://www.mendeley.com/documents/?uuid=69acdf36-a704-340b-a100-9b17faeb28f1” } , “mendeley” : { “formattedCitation” : “(Ghazvini & Khajehpour, 2011)”, “plainTextFormattedCitation” : “(Ghazvini & Khajehpour, 2011)”, “previouslyFormattedCitation” : “(Ghazvini & Khajehpour, 2011)” }, “properties” : { “noteIndex” : 0 }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }Ghazvini ; Ghazvini, S. D., ; Khajehpour, M. (2011). Gender differences in factors affecting academic performance of high school students. Procedia – Social and Behavioral Sciences, 15, 1040–1045. Retrieved from https://doi.org/10.1016/J.SBSPRO.2011.03.236.

Goleman, D. (1995). Emotional intelligence. New York, USA:Bantam Books.

Green, F. (1999).Brain and learning research: Implications for meeting the needs
of diverse learners. Education,119(4), 682-688.

Grieve, R., ; Mahar, D.(2010).  The emotional manipulation, psychopathy nexus:
Relationships with emotional intelligence, alexithymia and ethical.Cross Ref.

Guevara, N., ; Lambinicio, J. (2011). Research writing made easy. Urdaneta,
Pangasinan. Uniwide Printing Press.

Guray, N. D. et al., (2017). Influence of peers in the study habits among bachelor
in elementary education students. QSU-CTE Journal of Education Practices and Standards, 2(1). Retrieved from http://ejournals.ph/form/cite.php?id= 11823.

Imam, O, Mastura, M., ; Jamil, H. (2012). Correlation between reading comprehension skills and students’ performance in mathematics. Journal of Evaluation and Research in2(1), 1–8. Retrieved from https://doi.org/10.11 591/ijere.v2i1.1803.

Investopedia. (2018). Relationship management. Retrieved from https://www. investopedia .com/terms/r/relationship-management.asp.

Klitmøller, J. (2015).  Review of the methods and findings in the Dunn and Dunn
learning styles model research on perceptual preferences, Nordic Psycholo
gy, 67:1, 2-26, DOI: 10.1080/19012276.2014.997783.

LAWRENCE A. S., A. (2014). Relationship Between Study Habits and Academic
Achievement of Higher Secondary School Students. The Indian Journal of Medical Research (Vol. IV). Retrieved from https://doi.org/10.15373 /2249555X/June2014/43.

Losare, R. (2009). Study habit definition. Retrieved from https://www.termpaperwarehouse.com/essay-on/Study-Habits/38869.

Lucas, M. R., & Corpuz, B. (2007). Facilitating learning: A metacognitive process.
776 Quezon City, Metro Manila: Lorimar Publishing, Inc. pg. 75-79.

Lucas, M. R., & Corpuz, B. (2014). Facilitating learning: A metacognitive process.

776 Quezon City, Metro Manila: Lorimar Publishing, Inc. pg. 62.

Ma, L-C., & Wooster, R. A. (1979). Marital status and academic performance in
college. College Student Journal, v13 n2 p106-11.

Márquez, P. G., Palomera Martín, R., & Brackett, M. A. (2006). Relating emotional intelligence to social competence and academic achievement in high school students, Psichothema, 18, 118-123.

Martin, G., & Osborne, J. G.(1989). A questionnaire to review your study habits.
Englewood Cliffs, N J: Prentice Hall. Retrieved from https://umanitoba.ca /faculties/arts/departments/psych_services/media/A_Questionnaire_to_Re view_Your_Study_Habits.pdf.

Mayer, J., & Geher, G. (1996). Emotional intelligence and the identification of
emotion. Intelligence (Vol. 22). Retrieved from https://doi.org/10.1016 /S0160-2896(96)90 011-2.

McLeod, S. (2008). Correlation. Retrieved from https://www.simplypsychology.org/correlation.html.

Mohapel, P. (2012). The quick emotional intelligence self-assessment. San Diego
City College Mesa Program. Retrieved from http://www.dreanthonya llen.com/newsite/wpcontent/uploads/2012/08/emotional-intelligence-self-assessment.pdf.

Mohzan, M., Hassan,N., & Halil, N. (2013).The influence of emotional intelligence
on academic achievement. Procedia-Social and Behavioral Sciences, 90(InCULT 2012), 303–312. Retrieved from https://doi.org/10.1016/j.sbs pro.20 13.07.095.

Mullis, I.V. S, Martin, M. O., Foy, P., & Arora, A. (2012). Results in Mathematics
(Vol.43). Retrieved from https://timssadpirlsandpirls.bc.edu/timss2011/do wnloads/T11_IR_Mathematics_FullBook.pdf.

Norden, AD. Et al., (2011). Survival among patients with primary central nervous
system lymphoma, 1973-2004. J Neurooncol 101:487-481.

Nzesei, M. M. (2015). a Correlation Study Between Learning Styles and Academic Achievement Among Secondary School Students in Kenya, 104.

O’Brien, (1985). The modality (learning channel preference) questionnaire. Ret-
rieved from http://repository.urosario.edu.co/bitstream/handle/10336/133 17/Learning%20Style%20Questionnaire.pdf?sequence=2;isAllowed=y.

Piaget, J. (1936). Origins of intelligence in the child. London, U.K. : Routledge ;
Kegan Paul.

Quintillan-Bugas, R. (2010). Factors affecting math performance. LEAPS: Mirriam
College Faculty Research Journal,33(1). Retrieved from http://ejournals. ph/form/cite. php?id=3499.

Radwan, F. (2017). Emotional awareness definition. 2 know myself. Retrieved
from https://www.2knowmyself.com/Emotional_awareness_definition.

Richards, J., ; Schmidt, R. (1985). Dictionary of language teaching and applied
linguistics. London: Pearson Education.

Salovey, P. ; Mayer, J. (1990). Emotional intelligence. Imagination, cognition
and personality, 9(3), 185–211. Retrieved from https://doi.org/10.2190/ DUGG-P24 E-52WK-6CDG.

Schneider, T., Lyons, J., ; Williams, M. (2005). Emotional intelligence and auto-
nomic self-perception: Emotional abilities are related to visceral acuity. Personality and Individual Differences, 39(5), 853-861. doi: Retrieved from http://dx.doi.o g/10.1016/j.paid.2005.02.025.

Sirin, S. R.(2005). Socioeconomic status and academic achievement: A meta-ana
lytic review of research. Review of Educational Research. Vol 75, Issue 3, pp. 417 – 453. Retrieved from  https://doi.org/10.3102/0034654307500 3417.

Silver, H. F., Strong, R. W., ; Perini, M. J. (2000). So each may learn:Integrating
learning styles and multiple intelligences. Alexandria, West Virginia USA: Association for Supervision and Curriculum Development.

Simpson J. A. ; Weiner E. S. C (1989).  The Oxford English Dictionary. Oxford,
U.K.: Clarendon Press. Oxford University Press.

Sincero, S. (2018). Cognitive learning theory. Retrieved from https://explorable. com/cognitive-learning-theory.

Singh, D. ; Singh, D. (2014). Learning styles and the related variables: A study
of pre-service teachers. Asia Pacific Journal of Multidisciplinary Research, 2(3). Retrieved from http://ejournals.ph/form/cite.php?id=5781.

Stewart, K. L., ; Felicetti, L. A. (1992). Learning styles of marketing ma-
jors. Educational Research Quarterly, 15(2), 15-23.

University of Pennsylvania.(2009).Visual learners convert words to pictures in the
the brain brand vice versa, says psychology study. Sciencedaily. Retrieved 2018 from www.sciencedaily.com/releases/2009/03/090325091834.htm.

Vaishnav, R. S., ; Chirayu, K. C. (2013). Learning style and academic achievement of secondary school students. Voice of Research, 1(4), 1-4.

Wehrwein, E. A., Lujan, H. L., ; DiCarlo, S. E. (2007). Gender differences in lear-
ning style preferences among undergraduate physiology students. American Journal of Physiology – Advances in Physiology Education, 31(2), 153-157. DOI: 10.1152/advan.00060.2006.

White, K. (1982). The relation between socio-economic status and academic a-
chievement. Psychological Bulleting. 91:461-481.

Williams, R. (2009). Visual learning style theory. Retrieved from aweorego.org/re
research_theory.html.