Predicting Standardized Test Scores from Cognitive Tutor Interactions
نویسندگان
چکیده
Cognitive Tutors are primarily developed as instructional systems, with the goal of helping students learn. However, the systems are inherently also data collection and assessment systems. In this paper, we analyze data from over 3,000 students in a school district using Carnegie Learning’s Middle School Mathematics tutors and model performance on standardized tests. Combining a standardized pretest score with interaction data from Cognitive Tutor predicts outcomes of standardized tests better than the pretest alone. In addition, a model built using only 7th grade data and a single standardized test outcome (Virginia’s SOL) generalizes to additional grade levels (6 and 8) and standardized test outcomes (NWEA’s MAP).
منابع مشابه
Generalizing and Extending a Predictive Model for Standardized Test Scores Based On Cognitive Tutor Interactions
Recent work demonstrates that process data from intelligent tutoring systems (ITSs) can be used to predict student outcomes on high-stakes, standardized tests. Such models are important if ITSs are to be used for formative assessment and as replacements for external assessments. Recent work used various measures of learning efficiency and performance from problem-level, aggregate data from Carn...
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