Review: Adaptive Mobile Learning Algorithms Current State-of-the-Art

Adaptive mobile learning algorithms offer a promising approach to addressing the challenges of mobile learning, such as learner engagement and personalized learning. Recent advances and innovations in these algorithms have shown promising results in improving learning outcomes and user satisfaction. However, there are still several limitations and challenges to overcome, including the need for standard evaluation metrics, large-scale data and computational resources, and addressing ethical and privacy concerns. Future research should focus on developing effective and efficient adaptive mobile learning algorithms that can optimize the learning experience for all learners. To review and compare the current state-of-the-art adaptive mobile learning algorithms, we conducted a systematic literature review following the PRISMA guidelines. We searched multiple academic databases, including IEEE Xplore, ACM Digital Library, and ScienceDirect, using specific keywords related to adaptive mobile learning algorithms. After applying inclusion and exclusion criteria, we selected a total of 10 studies published between 2017 and 2021 for our analysis as in table 2, and studies that were published before the year 2010 as in table 1. We extracted data from each study, including the research design, sample size, algorithm used, evaluation metrics, and results. We then synthesized the data and identified common themes and patterns across the studies.