Adaptive Content Sequencing without Domain Information

In Intelligent Tutoring Systems, adaptive sequencers can take past student performances into account to select the next task which best fits the student’s learning needs. In order to do so, the system has to assess student skills and match them to the required skills and difficulties of available tasks. In this scenario two problems arise: (i) Tagging tasks with required skills and difficulties necessitate experts and thus is time-consuming, costly, and, especially for fine-grained skill levels, also potentially subjective. (ii) Learning adaptive sequencing models requires online experiments with real students, that have to be diligently ethically monitored. In this paper we address these two problems. First, we show that Matrix Factorization, as performance prediction model, can be employed to uncover unknown skill requirements and difficulties of tasks. It thus enables sequencing without explicit domain knowledge, exploiting the Vygotski concept of Zone of Proximal Development. In simulation experiments, this approach compares favorably to common domain informed sequencing strategies, making tagging tasks obsolete. Second, we propose a simulation model for synthetic learning processes, discuss its plausibility and show how it can be used to facilitate preliminary testing of sequencers before real students are involved.