Author(s):
This talk presents preliminary results of a meta-analysis that examines personalized adaptive learning (PAL) in undergraduate mathematics and shares insight on conducting a large systematic review. PAL implements intelligent learning systems, integrates learner preferences, and analyzes individual learning data to create a unique learner path personalized to the needs of students. PAL may benefit students in mathematics such as college algebra and calculus as they are gatekeeper courses. Research questions: 1) What is the average effect of PAL on undergraduate mathematics outcomes broadly (study 1) and specifically on algebra (study 2), based on the empirical literature? 2) To what extent is the effect of PAL on mathematics outcomes moderated by institutional-related, course-related, PAL-related, student-related, and study-related factors? Thirteen databases were searched, 12,734 studies were identified, and 2,162 duplicates excluded, resulting in 10,572 studies at the abstract and title screening phase (all have been double screened). Of these studies, 269 were moved to full text eligibility screening; 185 studies have been double screened with 153 of those studies excluded and 32 studies included. Of the studies included to date, 50% are dissertations. All but one have been published since 2002 with about 50% published in the last ten years. Next steps include completing the second screening for the remaining full text eligibility and then extracting data from the included studies. Insight into conducting a large-scale meta-analysis will also be shared, such as lessons learned and best practices resources that will assist applied researchers interested in conducting large systematic reviews.
