Discovering Theoretically Grounded Predictors of Deep vs. Shallow Level Learning
نویسندگان
چکیده
We investigated predictors of shallow and deep learning for 192 college students with high vs. low prior knowledge in a game-like intelligent tutoring system, OperationARA that has an eText, multiple-choice tests, case-based reasoning, and adaptive tutorial conversations. Students are expected to learn about 11 topics of research methodology across three modules that target factual information, application of reasoning to specific cases, and question generation. Our approach blends evidence-centered design (ECD) and educational data mining (EDM) methods to discover the best predictors of deep and shallow level learning for students of varying aptitudes within this game. Theoreticallygrounded constructs (e.g., time-on-task, generation, discrimination) were found to be significant predictors of deep vs. shallow knowledge acquisition.
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تاریخ انتشار 2014