Open AccessPredicting University Students’ Exam Performance Using a Model-Based Adaptive Fact-Learning System
Author(s) -
Florian Sense,
Marco van der Velde,
Hedderik van Rijn
Publication year - 2021
Publication title -
journal of learning analytics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.084
H-Index - 7
ISSN - 1929-7750
DOI - 10.18608/jla.2021.6590
Subject(s) - forgetting , learning analytics , adaptive learning , computer science , context (archaeology) , tracing , educational data mining , educational technology , cognition , mathematics education , psychology , artificial intelligence , data science , cognitive psychology , biology , neuroscience , paleontology , operating system
Modern educational technology has the potential to support students to use their study time more effectively. Learning analytics can indicate relevant individual differences between learners, which adaptive learning systems can use to tailor the learning experience to individual learners. For fact learning, cognitive models of human memory are well suited to tracing learners’ acquisition and forgetting of knowledge over time. Such models have shown great promise in controlled laboratory studies. To work in realistic educational settings, however, they need to be easy to deploy and their adaptive components should be based on individual differences relevant to the educational context and outcomes. Here, we focus on predicting university students’ exam performance using a model-based adaptive fact-learning system. The data presented here indicate that the system provides tangible benefits to students in naturalistic settings. The model’s estimate of a learner’s rate of forgetting predicts overall grades and performance on individual exam questions. This encouraging case study highlights the value of model-based adaptive fact-learning systems in classrooms.