Title

Using an additive factor model and performance factor analysis to assess learning gains in a tutoring system to help adults with reading difficulties

Abstract

After developing an intelligent tutoring system (ITS), or any other class of learning environments, one of the first questions that should be asked is whether the system was effective in helping students learn the targeted skills or subject matter. In this study, we employed two educational data mining models (Additive Factor Model, AFM and Performance Factor Analysis, PFA) which are available in Datashop (LearnSphere) to assess the learning gains on 5 theoretical levels of adults. With AFM, for the KC models tested, the results showed positive learning gains for the Rhetorical Structure knowledge component in contrast, for the PFA model, adults did not learn from either successes or failures.

Publication Title

Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017

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