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Research Article
Enhancing Student Engagement in the One-to-Five Peer Learning Model Among Second-year Chemistry and Mathematics Students at Wolkite University
Shewa Getachew*
,
Endalkachew Hunde,
Tsegaye Atnaf,
Mekash Zewdu,
Waltengus Dagne,
Zemedkun Siraj
Issue:
Volume 10, Issue 4, August 2025
Pages:
128-140
Received:
6 May 2025
Accepted:
26 May 2025
Published:
26 June 2025
Abstract: This action research aimed to enhance student engagement in the One-to-Five peer learning model among second-year Chemistry and Mathematics students at Wolkite University. Although peer learning strategies have been introduced across Ethiopian higher education institutions, actual student participation has remained limited due to several persistent challenges. These included low awareness of the model’s benefits, inadequate mentorship from instructors, poorly structured peer groups, lack of learning resources, and generally negative attitudes toward collaborative learning environments. To address these issues, the study employed a mixed-methods approach, utilizing questionnaires, direct classroom observations, focus group discussions, and academic performance records to gather comprehensive data. A census method was used, involving all 21 students from the target departments to ensure inclusiveness and accuracy. The intervention strategy consisted of structured awareness-raising sessions, consistent mentorship support, provision of relevant educational materials, and improved time allocation for peer group activities. Post-intervention evaluations demonstrated significant improvements in student attitudes, participation levels, and academic confidence. Students reported a stronger sense of ownership in the learning process and improved collaboration within their groups. These findings indicate that with the right support mechanisms, the One-to-Five peer learning model can be an effective strategy for enhancing student engagement and academic performance in university settings.
Abstract: This action research aimed to enhance student engagement in the One-to-Five peer learning model among second-year Chemistry and Mathematics students at Wolkite University. Although peer learning strategies have been introduced across Ethiopian higher education institutions, actual student participation has remained limited due to several persistent...
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Research Article
Effectiveness of Collaborative Problem Based Learning on Critical Thinking Among Secondary School Students of Kerala
Linisha Cheekkilott Kuniyil*,
Fathima Jaseena Mancheriputhuserimuhamed
Issue:
Volume 10, Issue 4, August 2025
Pages:
141-147
Received:
8 May 2025
Accepted:
21 May 2025
Published:
7 July 2025
Abstract: The 21st century is a technology-driven century that enhances a knowledge-based society, knowledge-based economy, and technology-enhanced teaching-learning process. Conventional teaching methods do not meet up to the intellectual and emotional needs of the children. Those methods are insufficient to solve real life problems. Critical thinking is a higher order thinking skill, which is essential for solving real life problems. Each person needs practical skill and problem-solving skill rather than theoretical knowledge to succeed in life. In this world of competition, everyone, especially adolescents, face more stress and strain. So, adolescent students need ability to face challenge of new era and must equip with more soft skills. Inculcation of such skills in learners has become a great challenge for the teachers and parents to make learners as critically objective and creative thinkers, for that teacher can use variety of methods to make innovation, solve real world problems and motivate them to learn by using original and innovative methods. Collaborative Problem Based Learning is such a method, based on the 3C3R framework, which is the most innovative frame work in Problem Based Learning. The study aimed to assess the effectiveness of Collaborative Problem Based Learning modules on enhancing critical thinking among the children. Pretest-post test nonequivalent group design from the Quasi-Experimental family was used for the study.3C3R Model for Collaborative Problem-Based Learning modules, Critical Thinking Test and lesson Transcript on the existing method of teaching were the tools used for the study. A total of 89,8th standard students were the participants of the study. Experimental group consisted of 43 student s and control group consisted of 46 students. The result of the study revealed that CPBL is effective than existing (activity based) method. It enhances Critical Thinking, among secondary school students. CPBL has a significant and large effect on Critical Thinking and it’s components Inference and Recognition of Assumption and significant and medium effect on components Deduction, Interpretation and Evaluation of Arguments.
Abstract: The 21st century is a technology-driven century that enhances a knowledge-based society, knowledge-based economy, and technology-enhanced teaching-learning process. Conventional teaching methods do not meet up to the intellectual and emotional needs of the children. Those methods are insufficient to solve real life problems. Critical thinking is a ...
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Methodology Article
Machine Learning Model for Prediction of Student Attrition in E-learning Environment: Research Methodology
Issue:
Volume 10, Issue 4, August 2025
Pages:
148-156
Received:
10 May 2025
Accepted:
29 May 2025
Published:
23 July 2025
Abstract: This paper presents a novel machine learning methodology for predicting student attrition in eLearning environments. Recognizing the limitations of traditional approaches, research exploring the power of ensemble machine learning, combining the strengths of Naïve Bayes, Gradient Boosting, and Random Forest algorithms. To further enhance predictive accuracy based on machine learning, integrating Simulated Annealing for parameter optimization and validation, allowing for fine-tuning of each individual model within the ensemble. An investigation of why students in Kenya public universities dropout from particular course, early identification and mitigation procedures of students attrition. The ensemble weights are iteratively adjusted and optimized to create a robust predictive machine learning model. This paper allows the machine learning model to learn complex patterns within the data that contribute to student’s attrition identification. Using a mixed method research design for optimal predictive machine learning in student attrition identification offers a robust approach to understanding and addressing the multifaceted issue of student dropout. Both quantitative and qualitative methods, researchers can develop more accurate, interpretable, and actionable models, ultimately leading to more effective interventions and improved student retention rates. Research validate the proposed framework using real-world eLearning datasets, comparing its performance against standalone models. The results demonstrate the effectiveness of combining ensemble learning with optimization techniques, highlighting the potential for improved precision in identifying at-risk students. This methodology contributes to the field of educational data mining by pioneering the use of Simulated Annealing for attrition prediction, offering a scalable solution for institutions to proactively support student retention and improve eLearning outcomes.
Abstract: This paper presents a novel machine learning methodology for predicting student attrition in eLearning environments. Recognizing the limitations of traditional approaches, research exploring the power of ensemble machine learning, combining the strengths of Naïve Bayes, Gradient Boosting, and Random Forest algorithms. To further enhance predictive ...
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