Enhance intelligent tutoring systems by individualized mastery estimation to guide instructional adaptation¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends exploiting individualized knowledge estimates to enhance intelligent tutoring systems' mastery decisions. Its stated implication is "the ability to enhance existing intelligent tutoring systems to more accurately estimate when a student has reached mastery of a skill." The authors also discuss "Adaptation of instruction based on individualized knowledge and learning speed" as well as open research questions for exploiting student and skill information in user models.

Design Implications¶

Context¶

Requirements¶

• An intelligent tutoring system using an individualizable knowledge tracing model whose parameter estimates can feed mastery decisions

Constraints¶

• The article presents this as implication and discussion rather than as an intervention tested with learning outcomes

Target Learners¶

• Students using intelligent tutoring systems

Target Learning Goals¶

• Skill mastery estimation and adaptive instruction based on individualized knowledge and learning speed

Affordances¶

• Individualized Bayesian Knowledge Tracing Framework

Related Strategies¶

Examples¶

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Key Sources¶

• Pardos, Z.A., Heffernan, N.T. (2010). Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing. UMAP 2010, Lecture Notes in Computer Science, vol 6075. Springer. https://doi.org/10.1007/978-3-642-13470-8_24