Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
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This Author published in this journals
All Journal Journal of Research in Mathematics, Science, and Technology Education Journal of Technology-Assisted Learning
Onesme Niyibizi
Department of Sciences of the Faculty of Education, Institut Catholique de Kabgayi, Muhanga, Rwanda
Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Enhancing Independent Learning and Conceptual Understanding of Integration in Higher Education Mathematics with Photomath Onesme Niyibizi; Jean Nepomuscene Singirankabo
Journal of Research in Mathematics, Science, and Technology Education Vol. 3 No. 2 (2026): Journal of Research in Mathematics, Science, and Technology Education
Publisher : Scientia Publica Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70232/jrmste.v3i2.61

Abstract

This study investigated the potential of the Photomath application to promote self-directed learning and integrated conceptual understanding among first-year pre-service mathematics teachers studying at a private higher learning institution during the 2024–2025 academic year. Fastened in a quantitative quasi-experimental survey design, the study targeted 46 purposively selected students pursuing mathematics-related subject combinations. Data were collected using a validated Likert-scale questionnaire designed to capture students’ attitudes, learning autonomy, and levels of conceptual understanding. The instrument demonstrated acceptable internal consistency, with reliability coefficients ranging between 0.76 and 0.84. Statistical analysis using Multivariate Analysis of Variance (MANOVA) revealed statistically significant effects of Photomath usage on students’ attitudes toward mathematics, their conceptual understanding, and their capacity for independent learning. The findings indicated that the app positively influenced learners’ motivation and engagement by providing immediate feedback and step-by-step solution pathways. These features enabled students to follow logical problem-solving processes, thereby supporting deeper engagement with mathematical procedures and concepts. Participants further reported that Photomath facilitated the integration of mathematical ideas through its visual representations, symbolic explanations, and structured guidance. Such affordances supported self-directed learning by allowing learners to verify solutions independently, revisit explanations, and regulate their own pace of learning. While a minority of respondents expressed concerns regarding potential overreliance on the application and the risk of superficial understanding when used uncritically, the overall perceptions remained strongly positive. In conclusion, the study demonstrates that Photomath functions not only as a computational tool but also as a pedagogical aid that supports learner-centered instructional approaches. When used thoughtfully, the application has the potential to enhance conceptual understanding, foster autonomy, and complement formal mathematics instruction in higher education contexts.
Impact of Large Language Models on Personalized Learning, Assessment Automation, and Student Outcomes in Higher Learning Institution Onesme Niyibizi
Journal of Technology-Assisted Learning Vol. 2 No. 1 (2026): Journal of Technology-Assisted Learning
Publisher : Scientia Publica Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70232/jtal.v2i1.22

Abstract

This study investigated the multifaceted influence of Large Language Models (LLMs) on teaching and learning within a private higher education institution in Rwanda during the 2024–2025 academic year. A total of 658 students and 28 lecturers participated, providing a comprehensive perspective on both user experiences and professional concerns. Using a quantitative approach, the study employed Multivariate Analysis of Variance (MANOVA) to examine how the use of LLMs relates to students’ perceptions of personalized learning effectiveness, academic performance improvement, online engagement, satisfaction with assessment feedback, and motivation for lifelong learning. Findings from the student indicated that LLMs are widely perceived as beneficial across multiple dimensions of the learning process. Students reported that LLMs enhance personalized learning by providing adaptive guidance, improving academic performance through instant clarification and practice support, and increasing online engagement by offering interactive and accessible learning assistance. The results further showed that LLMs contribute to greater satisfaction with feedback mechanisms and stimulate motivation for continuous and self-directed learning. These statistically significant associations point to the strong potential of LLMs to enrich higher education outcomes. In contrast, the lecturers’ data revealed notable concerns related to data privacy, ethical use, and algorithmic bias. Lecturers expressed significant apprehension regarding students’ overreliance on LLMs, the risks associated with inaccurate or biased outputs, and the potential erosion of academic integrity. Their perceptions underscore the need for safeguards that ensure responsible and ethical use of AI in academic settings. Overall, the findings highlighted a dual reality: while LLMs hold transformative potential for improving learning experiences, their integration must be supported by robust institutional policies, targeted capacity-building initiatives, and ongoing research. Such measures are essential to promote equitable, ethical, and effective adoption of LLMs in higher education.
Co-Authors Jean Nepomuscene Singirankabo