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  1. Sosnovsky, S. ; Brusilovsky, P ; Baraniuk, R. G. ; Lan, A. S. (Ed.)
    As students read textbooks, they often highlight the material they deem to be most important. We analyze students’ highlights to predict their subsequent performance on quiz questions. Past research in this area has encoded highlights in terms of where the highlights appear in the stream of text—a positional representation. In this work, we construct a semantic representation based on a state-of-the-art deep-learning sentence embedding technique (SBERT) that captures the content-based similarity between quiz questions and highlighted (as well as non-highlighted) sentences in the text. We construct regression models that include latent variables for student skill level and question difficulty andmore »augment the models with highlighting features. We find that highlighting features reliably boost model performance. We conduct experiments that validate models on held-out questions, students, and student-questions and find strong generalization for the latter two but not for held-out questions. Surprisingly, highlighting features improve models for questions at all levels of the Bloom taxonomy, from straightforward recall questions to inferential synthesis/evaluation/creation questions.« less
  2. Sosnovsky, S. ; Brusilovsky, P. ; Baraniuk, R. ; Lan, A. (Ed.)
    An intelligent textbook may be considered to be an interaction layer that lies between the text and the student, helping the student to master the content in the text. The Mobile Fact and Concept Training System (MoFaCTS) is an adaptive instructional system for simple content that has been developed into an interaction layer to mediate textbook instruction and so is being transformed into the Mobile Fact and Concept Textbook System (MoFaCTS). In this project, MoFaCTS is being completely retooled to accept texts from a textbook and to automatically create cloze sentence practice content to help the student learn the materialmore »in the text. Additional features in the prototype stage include automatically generated refutational feedback for incorrect cloze responses and a dialog system, which will trigger a short conversation by a tutor to correct conceptual misunderstandings. MoFaCTS administers this content via a web browser, providing the teacher with score reports and class management tools. Because the "optimal practice" module is interchangeable and the cloze content can come from any text, the system is highly configurable for different grade levels, populations, and academic subjects. To foster faster research progress, data export supports the DataShop transaction format, which allows quick analysis of data using the DataShop tools.« less