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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 and 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. 

Knowledge tracing refers to a family of methods that estimate each student’s knowledge component/skill mastery level from their past responses to questions. One key limitation of most existing knowledge tracing methods is that they can only estimate an overall knowledge level of a student per knowledge component/skill since they analyze only the (usually binary-valued) correctness of student responses. Therefore, it is hard to use them to diagnose specific student errors. In this paper, we extend existing knowledge tracing methods beyond correctness prediction to the task of predicting the exact option students select in multiple choice questions. We quantitatively evaluate the performance of our option tracing methods on two large-scale student response datasets. We also qualitatively evaluate their ability in identifying common student errors in the form of clusters of incorrect options across different questions that correspond to the same error. 

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 material 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. 

Scaffolding and providing feedback on problem-solving activities during online learning has consistently been shown to improve performance in younger learners. However, less is known about the impacts of feedback strategies on adult learners. This paper investigates how two computer-based support strategies, hints and required scaffolding questions, contribute to performance and behavior in an edX MOOC with integrated assignments from ASSISTments, a web-based platform that implements diverse student supports. Results from a sample of 188 adult learners indicated that those given scaffolds benefited less from ASSISTments support and were more likely to request the correct answer from the system.