Leveraging Educational Data Mining for Real-time Performance Assessment of Scientific Inquiry Skills within Microworlds

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Published Oct 1, 2012
Janice D. Gobert Michael A. Sao Pedro Ryan S.J.d. Baker Ermal Toto Orlando Montalvo

Abstract

We present Science Assistments, an interactive environment, which assesses students’ inquiry skills as they engage in inquiry using science microworlds. We frame our variables, tasks, assessments, and methods of analyzing data in terms of evidence-centered design. Specifically, we focus on the student model, the task model, and the evidence model in the conceptual assessment framework. In order to support both assessment and the provision of scaffolding, the environment makes inferences about student inquiry skills using models developed through a combination of text replay tagging [cf. Sao Pedro et al. 2011], a method for rapid manual coding of student log files, and educational data mining. Models were developed for multiple inquiry skills, with particular focus on detecting if students are testing their articulated hypotheses, and if they are designing controlled experiments. Student-level cross-validation was applied to validate that this approach can automatically and accurately identify these inquiry skills for new students. The resulting detectors also can be applied at run-time to drive scaffolding intervention.

How to Cite

Gobert, J. D., Sao Pedro, M. A., Baker, R. S., Toto, E., & Montalvo, O. (2012). Leveraging Educational Data Mining for Real-time Performance Assessment of Scientific Inquiry Skills within Microworlds. JEDM | Journal of Educational Data Mining, 4(1), 111-143. https://doi.org/10.5281/zenodo.3554645
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Keywords

performance assessment, inquiry skills, educational data mining, machine learning, text replay tagging

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