The effectiveness of personalized technology-enhanced learning in higher education: A meta-analysis with association rule mining

Abstract

Personalized technology-enhanced learning (TEL) has emerged as a prominent tool used by universities to cater to students' diverse individual needs. Many higher education researchers and educators have explored the adoption of personalized TEL as an important tool to foster student learning outcomes from diverse perspectives. However, despite its significance and the substantial body of existing research, a notable gap exists in systematically evaluating the effectiveness of personalized TEL with meta-analysis approach within the higher education. To address the research gap, we investigated the effectiveness of personalized TEL in developing students' cognitive skills and non-cognitive characteristics in higher education context by utilizing the methods of meta-analysis and association rule mining. Our study reveals that the cognitive skills are reported more than non-cognitive characteristics as the learning outcomes of adopting personalized TEL. Overall, utilizing personalized TEL can improve students' cognitive skills and non-cognitive characteristics at the medium level effect size. Factors of research settings, mean of delivery, and modelled characteristics can influence students’ non-cognitive characteristics while using personalized TEL. Based on our rule mining findings, future teachers, researchers, and instructional designers can consider combining the modelling of learners' skills/knowledge or preferences with adaptive learning support strategies, such as recommending materials and scaffolding, to achieve positive effects, particularly in the fields of Social Sciences and Engineering.