The Impact of Academic Motivation on the Comprehensive Abilities Development of Malaysian Secondary School Students

LEE SHI EN

2023.1.EDU02.0008

Outline

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Literature Review

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Research Problem & Objectives

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Research Questions

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Significant of the study

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Limitation of the study

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Presentation title

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Outline

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Research Methods

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Sampling Method & Size

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Instruments used to collect data

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Data Collection method

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Data Analyse

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Research Problem�

Academic Performance

• Research Aim: Explore the link between academic motivation and performance in secondary school students.
• Key Areas: Analyze subject interests, examine perceptions of subject importance, and investigate expectations for academic success.
• Supporting Studies: Refer to relevant research by Smith & Brown (2021) and Johnson & Davis (2022), along with recent findings in Journal of Educational Psychology by Anderson & White (2020) emphasizing the role of intrinsic motivation in predicting academic achievement.

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Self-Directed Learning Skills

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• Research Focus: Explore how academic motivation shapes secondary school students' self-directed learning.
• Key Aspects: Analyze time management effectiveness, study plan development, and deep-level learning pursuit.
• Supporting Studies: Cite research by Johnson & Davis (2022), Thompson & Davis (2023), and Educational Psychology Review studies by Martinez & Johnson (2021) highlighting motivation's role in fostering self-directed learning.

Problem-Solving and Innovation Skills

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• Research Focus: Examine the connection between academic motivation and problem-solving/innovation skills in secondary school students.
• Key Elements: Investigate motivation for complex problem-solving, appreciation for innovative thinking, and proactive approaches to challenges.
• Supporting Studies: Cite research by Williams & Garcia (2020), Thompson & Davis (2023), and findings in Contemporary Educational Psychology (Anderson & White, 2020) indicating a positive relationship between academic motivation and problem-solving skills.

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Objective of the Study�

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Research Questions

Relationship between Academic Performance and Comprehensive Abilities

• Is there a correlation between academic performance and the development of comprehensive abilities among secondary school students?

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Role of Self-Directed Learning Skills

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• Do self-directed learning skills positively influence the comprehensive abilities of Malaysian secondary school students?

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Contribution of Problem-Solving and Innovation Skills​

• What role do problem-solving and innovation skills play in the development of comprehensive abilities among secondary school students?

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Significance of the study

Research Aim:

Explore the impact of learning motivation on Malaysian secondary school students' comprehensive abilities.

Focus Areas

Analyze academic performance, self-directed learning, problem-solving, and innovation skills.

Practical Recommendations

• Provide insights for the Malaysian Ministry of Education.
• Offer practical recommendations for holistic student development.

Research Contribution

Address gaps by exploring the influence of learning motivation on self-directed learning and problem-solving in the Malaysian context.

Cultural Specificity

Offer a culturally specific perspective for a more comprehensive understanding.

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Limitation of the Study

Literature Review

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Academic Performance

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1. Academic Motivation Overview:

Encompasses constructs like ability self-concepts, task values, goals, and achievement motives (Murphy, 2000).

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1. Impact on Academic Performance:

Seli et al. (2015) and Ng et al. (2020) emphasize the crucial role of academic motivation in shaping overall academic success.

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2. Personal Agency's Role:

Anderson et al. (2019) highlight the importance of personal agency, including self-efficacy and perceived control, in mitigating the decline in student engagement during the middle-to-high school transition.

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3. Teacher-Student Relationships:

Mucaj et al. (2020) explore the impact of teacher-student relationships on academic performance, considering internal factors like temperament, personality, ability, and psychological effects.

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Academic Performance

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4. Mediation of Learning Experiences:

Ning et al.'s (2012) research emphasizes the mediation of learning experiences through motivation and self-regulation in academic success.

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5. Challenges Faced by Gifted Students:

Almukhambetova et al. (2020) shed light on the underachievement of gifted students and their adjustment to the demands of the university environment.

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6. Psychological Health during Adolescence:

Martínez et al. (2021) analyze the relationship between burnout, self-efficacy, and future outlooks during adolescence.

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7. Affective Factors in English Reading:

Liu et al.'s (2021) study underscores the motivational function of affective factors in learning, particularly in English reading.

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Academic Performance

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8. Computational Thinking and Learning Perception:

Ye et al.'s (2022) research emphasizes the critical role of learning perception in computational thinking.

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9. Motivation in Mathematics:

Examining the influence of motivation on mathematics, as studied by Hossein-Mohand et al. (2023), sheds light on the intricate relationship between motivation and the perception of mathematics among secondary school students.

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Conclusion:

The literature review provides profound insights into the intricate relationship between academic motivation and performance, suggesting promising directions for future studies.

Self-Directed Learning Skills

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1. Moodle-Based E-Learning:

Angriani et al. (2019) found it enhances intrinsic motivation for self-directed learning, ensuring theoretical validity.

2. Indonesia's Learning Engagement Ranking:

Fachmi et al. (2019) explored the impact of self-efficacy, self-concept, and social support on school engagement, citing Indonesia's 2015 PISA ranking.

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3. Mathematics Learning Outcomes:

Meiliati et al. (2019) studied the influence of learning motivation, self-efficacy, and self-regulated learning on eleventh-grade students' mathematics outcomes.

4. Cooperative Learning Impact:

Situmorang (2020) examined the impact of the TGT model on PAK learning motivation in eleventh-grade students, assuming a positive effect.

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5. Self-Determination Theory for Gifted Students:

Almukhambetova et al. (2020) applied self-determination theory to understand internal and external motivational forces influencing gifted students' adaptation and achievement.

6. Correlation with Self-Directed Learning:

Coros et al. (2021) found a significant relationship between self-directed learning, learning self-efficacy, and academic motivation.

7. Factors Influencing Student Achievements:

Rahmawati et al. (2021) investigated the influence of interest, achievement motivation, learning style, and independent learning on student achievements.

8. Reading Comprehension Impact:

Saputra (2022) revealed the impact of interactive strategy technique and conventional teaching technique on eighth-grade students' reading comprehension.

Self-Directed Learning Skills

Self-Directed Learning Skills

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9. Teacher Communication Skills and Learning Outcomes:

Saputra (2022) explored the influence of teacher communication skills and students' learning motivation on Economics subject learning outcomes for eleventh-grade students.

10. Key Themes and Conclusions:

Moodle-based e-learning positively impacts intrinsic motivation; self-determination theory challenges traditional definitions for gifted students.

11. Relationships Among Motivation, Self-Directed Learning, Technology, and Teacher-Student Interactions:

Insights into the positive relationship between self-directed learning, learning self-efficacy, academic motivation, and teacher-student relationships in fostering students' holistic development.

12. Future Research Emphasis:

Identify research gaps for a comprehensive understanding of complex relationships and foster students' holistic development.

Problem-Solving and Innovation Skills

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Problem-Solving and Innovation Skills

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Conclusion:

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1. Academic Motivation & Performance:

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• Correlation observed; positive motivation linked to improved grades and subject performance.

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2. Academic Motivation & Self-Directed Learning:

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• Positive motivation influences active engagement and fosters self-directed learning skills.

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3. Academic Motivation & Problem-Solving/Innovation:

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• Correlation with proactive problem-solving and innovative thinking.

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Conclusion:

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• Academic motivation crucial for holistic student development.
• Inspiring motivation enhances performance, self-directed learning, and problem-solving.

Research Method

• Quantitative Approach

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1. Utilizes structured surveys, particularly questionnaires, for data collection.
2. Aims to gather numerical data on key variables related to academic motivation.
3. Enables measurement of specific factors influencing students' motivation.
4. Survey designed for quantification of responses, facilitating statistical analysis.
5. Focus on identifying patterns, correlations, and trends in the data.
6. Provides a quantitative overview of the relationship between academic motivation and comprehensive abilities.

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• Qualitative Approach

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1. Utilizes in-depth interviews to capture nuanced and subjective aspects.
2. Provides participants an opportunity to express perspectives in their own words.
3. Complements quantitative findings for a deeper understanding of factors.
4. Focuses on academic motivation and comprehensive abilities development.
5. Dual-method strategy for a holistic understanding of the research question.

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Sampling Techniques:

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• Probability Sampling: Involves random sample selection.
• Nonprobability Sampling: Convenient approach for researchers.

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Chosen Sampling Approach:

• Nonprobability Sampling: Selected for convenience in gathering data from secondary schools in Malaysia.

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Common Nonprobability Sampling Techniques:

• Convenience Sampling: Recommended for its convenience, reducing time and costs.

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Rationale for Selection:

• Objective: Investigate elements influencing Academic Motivation's impact on Comprehensive Abilities Development.
• Appropriateness: Convenience sampling aligns with the research objective.

Sample Size:

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• High school students from various schools in Malacca (Form 1 to Form 5).
• Schools include SMK Tinggi Melaka, SMK Infant Jesus Convent, SMK Tinggi ST David, SMK Yok Bin, SMK ST Francis, Malacca Girls's High School, SMK Canossa Convent, SMK Bukit Baru, SMK Tun Tuah, SMK Tun Tijah, SMJK Katholik, SMK Gajah Berang, SMK Munshi Abdullah, SMK(P) Methodist, SMK (M) Methodist, Pay Fong Middle School Malacca, and SMK Durian Daun.
• Represents the high school student body in Malacca.
• Sample Size:
• Approximately 400 respondents.
• Chosen based on the recommendation of the Check Market Calculator.
• Population of Malacca in 2019: 579,000.
• Minimum suggested sample size: 384 respondents.
• Selected sample size: 400 respondents, exceeding the suggested minimum.

Sample Size:

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2. Rationale for Sample Size:

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• Ensures sufficient data for study reliability and validity.
• Covers a diverse group of students across different schools and grades.
• Aims for comprehensive and representative research results.
• Provides insights into the influence of academic motivation on overall student development.

Research Instrument and Measurement

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1. Questionnaire Design:
1. Formalized set of specific questions.
2. Simple design for easy understanding by secondary school students.

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• Questionnaire Structure:
• Divided into three sections: A, B, and C.
• Section A: Demographic profile (gender, Form level, school).
• Section B: Independent variables (academic performance, self-directed learning, problem-solving, innovation).
• Section C: Dependent variable (Impact of Academic Motivation on Comprehensive Abilities).

Research Instrument and Measurement

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3. Measurement Scales:
1. Nominal Scale:
1. Used for demographic profiling.
2. Assigns numbers as labels to define and classify objects.
2. Interval Scale:
• Utilized for dependent and independent variables.
• Indicates differences between objects.
3. Likert Scale:
• Measures values, attitudes, and opinions.
• Options: "Strongly Agree," "Agree," "Neutral," "Disagree," "Strongly Disagree."

These instruments aim to comprehensively explore the Impact of Academic Motivation on the Comprehensive Abilities Development of Malaysian Secondary School Students, covering aspects such as academic performance, self-directed learning skills, problem-solving, and innovation abilities.

Sample of Questionaire

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Data Collection Method

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1. Secondary Data:
1. Gathered from academic journals and online articles.
2. Sources include Google Scholar, UNNES, Emerald Insight, among others.
3. Provides theoretical framework and insights from previous research.

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• Primary Data - Survey Questionnaires:
• Method of choice for in-depth insights into academic motivation.
• Surveys designed with a mix of open-ended and closed-ended questions.
• Covers academic performance, self-directed learning, problem-solving, and innovation.
• Aims to comprehensively understand the influence of motivation on comprehensive abilities.

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Integration for Comprehensive Analysis:

• Combining Theoretical and Empirical Insights:
• Aims for a comprehensive analysis and interpretation.
• Explores how academic motivation shapes the development of comprehensive abilities in Malaysian secondary school students.
• Seeks nuanced and comprehensive research conclusions through the integration of secondary and primary data.

Data Analyse Method

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SPSS Statistical Analysis:

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1. Reliability Test:
1. Method to evaluate questionnaire reliability.
2. Examines all items for study variables.
3. Result: Cronbach Alpha value indicating internal coherence.

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• Descriptive Test:
• Utilizes frequencies analysis.
• Displays occurrences of each response.
• Obtains percentage and frequency for demographic items.
• Converts raw data for easy comprehension.
• Analyzes items for study variables, using mean and standard deviation.

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Data Analyse Method

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3. Normality Test:
1. Evaluates data distribution matching a standard normal distribution.
2. Can be graphical or mathematical.
3. Analyzes items for all study variables.
4. Result assesses whether the data set conforms to a normal distribution.

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• Multiple Linear Regression (MLR):
• Analyzes data with multiple independent and one dependent variable.
• Identifies relationships between independent variables (academic performance, self-directed learning skills, problem-solving, and innovation skills) and the dependent variable.
• Utilizes p-value to assess research hypotheses.

Thank you

LEE SHI EN

sharon070495@gmail.com

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