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    Enhancing students' learning motivation and comprehension by reflecting on the practical applications of learning materials in an education learning journal
    (Institute of Electrical and Electronics Engineers Inc., 2024) Ajibade, Samuel-Soma Mofoluwa; Valle, Lislee; General, Edralin; Baclayon-Lim, Joshlen; Colina, Sarah Jane; Akintoye, Kayode Akinlekan; Adediran, Anthonia Oluwatosin
    Evaluating the learning materials' personal utility and worth is vital for cultivating motivation and involvement in high-quality learning experiences. The researchers set out to find out how using a personal-utility prompt in journal writing affected the students' motivation to learn and their understanding of biological concepts in the classroom. Sixty high school students from a Nigerian school participated in the quasi-experimental field investigation. Students were asked to maintain a weekly learning journal starting from the first week of the semester for a total of eight weeks. Students were given short instructions on how to write their journal entries, which might or might not have included a suggestion about how the entries could benefit them individually. The results show that having students think about the educational resources' personal value was a great success with the personal-utility prompt. Consequently, students whose teachers allowed them to employ a personal-utility prompt had more interest in learning and had more success with comprehension than those whose teachers did not. A clear and effective strategy for improving students' motivation and understanding in secondary science education is to have them write reflective journals in which they consider the relevance and usefulness of various learning materials.
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    Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis
    (Elsevier B.V., 2024) Ajibade, Samuel-Soma Mofoluwa; Alhassan, Gloria Nnadwa; Zaidi, Abdelhamid; Oki, Olukayode Ayodele; Awotunde, Joseph Bamidele; Ogbuju, Emeka; Akintoye, Kayode Akinlekan
    This bibliometric research explores the global evolution of machine learning applications in medical and healthcare research for 3 decades (1994 to 2023). The study applies data mining techniques to a comprehensive dataset of published articles related to machine learning applications in the medical and healthcare sectors. The data extraction process includes the retrieval of relevant information from the source sources such as journals, books, and conference proceedings. An analysis of the extracted data is then conducted to identify the trends in the machine learning applications in medical and healthcare research. The Results revealed the publications published and indexed in the Scopus and PubMed database over the last 30 years. Bibliometric Analysis revealed that funding played a more significant role in publication productivity compared to collaboration (co-authorships), particularly at the country level. Hotspots analysis revealed three core research themes on MLHC research hence demonstrating the importance of machine learning applications to medical and healthcare research. Further, the study showed that the MLHC research landscape has largely focused on ML applications to tackle various issues ranging from chronic medical challenges (e.g., cardiological diseases) to patient data security. The findings of this research may be useful to policy makers and practitioners in the medical and healthcare sectors and to global research endeavours in the field. Future studies could include addressing issues such as growing ethical considerations, integration, and practical applications in wearable technology, IoT, and smart healthcare systems.