Adaptive Content Sequencing without Domain Information

Carlotta Schatten, Lars Schmidt-Thieme


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- opment. 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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Paper Citation

in Harvard Style

Schatten C. and Schmidt-Thieme L. (2014). Adaptive Content Sequencing without Domain Information . In Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-020-8, pages 25-33. DOI: 10.5220/0004753000250033

in Bibtex Style

author={Carlotta Schatten and Lars Schmidt-Thieme},
title={Adaptive Content Sequencing without Domain Information},
booktitle={Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU,},

in EndNote Style

JO - Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Adaptive Content Sequencing without Domain Information
SN - 978-989-758-020-8
AU - Schatten C.
AU - Schmidt-Thieme L.
PY - 2014
SP - 25
EP - 33
DO - 10.5220/0004753000250033