Use traced error causes as personalized intervention recommendations in online learning systems¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article proposes that diagnosing the causes of a student's systematic errors can guide adapting education to individual needs: the SET model "returns predicted probabilities for all considered causes, which may function as intervention recommendations for a tutor." Because a single observed error can have multiple possible causes that differ across individuals and over time, recommendations should be based on a student's own pattern of successive errors rather than on population-level error frequencies alone. The approach suits online learning systems where errors can be automatically detected and mapped to causes.

Design Implications¶

Context¶

Requirements¶

• An error-to-cause bigraph for the domain and software that can automatically detect errors

Constraints¶

• Error responses are not ubiquitous, not necessarily consistent, and their causes may change over time, so inference relies on limited data

Target Learners¶

• individual students in online adaptive practice systems

Target Learning Goals¶

• targeted remediation of systematic error causes, e.g., in arithmetic

Affordances¶

• Systematic Error Tracing Model

Related Strategies¶

Examples¶

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Key Sources¶

• Savi, A. O., Deonovic, B. E., Bolsinova, M., van der Maas, H. L. J., & Maris, G. K. J. (2021). Tracing Systematic Errors to Personalize Recommendations in Single Digit Multiplication and Beyond. Journal of Educational Data Mining, Volume 13, No 4. https://jedm.educationaldatamining.org/index.php/JEDM/article/view/382