Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners' levels of prior knowledge in hypermedia-learning environments?

Michelle Taub, Roger Azevedo, François Bouchet, Babak Khosravifar
2014 Computers in Human Behavior  
Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners' levels of prior knowledge in hypermedia-learning environments?. Computers in Human Behavior, Elsevier, 2014, 39, pp. Abstract Research on self-regulated learning (SRL) in hypermedia-learning environments is a growing area of interest, and prior knowledge can influence how students interact with these systems. One hundred twelve (N = 112) undergraduate students' interactions with MetaTutor, a
more » ... ulti-agent, hypermedia-based learning environment, were investigated, including how prior knowledge affected their use of SRL strategies. We expected that students with high prior knowledge would engage in significantly more cognitive and metacognitive SRL strategies, engage in different sequences of SRL strategies, spend more time engaging in SRL processes, and visit more pages that were relevant to their sub-goals than students with low prior knowledge. Results showed significant differences in the total use of SRL strategies between prior knowledge groups, and more specifically, revealed significant differences in the use of each metacognitive strategy (e.g., judgment of learning), but not each cognitive strategy (e.g., taking notes) between prior knowledge groups. Results also revealed different sequences of use of SRL strategies between prior knowledge groups, and that students spent different amounts of time engaging in SRL processes; however, all students visited similar numbers of relevant pages. These results have important implications on designing multi-agent, hypermedia environments; we can design pedagogical agents that adapt to students' learning needs, based on their prior knowledge levels.
doi:10.1016/j.chb.2014.07.018 fatcat:us2tiunglvbwldpde4iiwpyod4