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ERIC Number: ED560524
Record Type: Non-Journal
Publication Date: 2015-Jun
Pages: 8
Abstractor: As Provided
Reference Count: 17
Mixture Modeling of Individual Learning Curves
Streeter, Matthew
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015)
We show that student learning can be accurately modeled using a mixture of learning curves, each of which specifies error probability as a function of time. This approach generalizes Knowledge Tracing [7], which can be viewed as a mixture model in which the learning curves are step functions. We show that this generality yields order-of-magnitude improvements in prediction accuracy on real data. Furthermore, examination of the learning curves provides actionable insights into how different segments of the student population are learning. To make our mixture model more expressive, we allow the learning curves to be defined by generalized linear models with arbitrary features. This approach generalizes Additive Factor Models [4] and Performance Factors Analysis [16], and outperforms them on a large, real world dataset. [For complete proceedings, see ED560503.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: International Educational Data Mining Society