Search for: All records

Learner models are used to support the implementation of personalization features in Adaptive Instructional Systems (AISs; e.g., adaptive sequencing of activities, adaptive feedback), which are important aspects of Intelligent Adaptive Systems. With the increased computational power, more advanced methodologies, and more available data, learner models include a variety of Artificial Intelligence techniques. These techniques have different levels of complexity, which influence interpretability and explainability of learner models. Interpretable and explainable learner models can facilitate appropriate use of the learner modeling information in AISs, their adoption, and scalability. This chapter elaborates on the definitions of interpretability and explainability, describes interpretability and explainability levels of different models, elaborates on the levels of explainability to produce needed information for teachers and learners, and discusses implications and future work in this area.