Authors:
Carlotta Schatten
and
Lars Schmidt-Thieme
Affiliation:
University of Hildesheim, Germany
Keyword(s):
Sequencing, Performance Prediction, Intelligent Tutoring Systems, Matrix Factorization.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Computer-Supported Education
;
e-Learning
;
Enterprise Information Systems
;
Information Technologies Supporting Learning
;
Intelligent Tutoring Systems
;
Learning Analytics
Abstract:
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 sequenc-
ing 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 predic-
tion 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 Devel-
opmen
t. In simulation experiments, this approach compares favorably to common domain informed sequenc-
ing 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.
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