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1. Using Semantics of Textbook Highlights to Predict Student Comprehension and Knowledge Retention

   Kim, David Y.J.; Scott, Tyler R.; Basu Mallick, Debshila; Mozer, Michael C. (June 2021, Proceedings of the Third Workshop on Intelligent Textbooks (iTextbooks) at the 22nd International Conference on Artificial Intelligence in Education (AIED 2021))

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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. 

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2. Can textbook annotations serve as an early predictor of student learning?

   Winchell, A.; Mozer, M. C.; Lan, A.; Grimaldi, P.; Pashler, H. (July 2018, Proceedings of the 11th International Conference on Educational Data Mining)

   When engaging with a textbook, students are inclined to highlight key content. Although students believe that highlighting and subsequent review of the highlights will further their educational goals, the psychological literature provides no evidence of benefits. Nonetheless, a student’s choice of text for highlighting may serve as a window into their mental state—their level of comprehension, grasp of the key ideas, reading goals, etc. We explore this hypothesis via an experiment in which 198 participants read sections from a college-level biology text, briefly reviewed the text, and then took a quiz on the material. During initial reading, participants were able to highlight words, phrases, and sentences, and these highlights were displayed along with the complete text during the subsequent review. Consistent with past research, the amount of highlighted material is unrelated to quiz performance. However, our main goal is to examine highlighting as a data source for inferring student understanding. We explored multiple representations of the highlighting patterns and tested Bayesian linear regression and neural network models, but we found little or no relationship between a student’s highlights and quiz performance. Our long-term goal is to design digital textbooks that serve not only as conduits of information into the mind of the reader, but also allow us to draw inferences about the reader at a point where interventions may increase the effectiveness of the material. 

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3. Inferring student comprehension from highlighting patterns in digital textbooks: An exploration in an authentic learning platform

   Kim, D. Y.; Winchell, A.; Water, A. E.; Grimaldi, P. J.; Baraniuk, R.; Mozer, M. C. (July 2020, Intelligent Textbooks 2020)

   We investigate whether student comprehension and knowledge retention can be predicted from textbook annotations, specifically the material that students choose to highlight. Using a digital open-access textbook platform, Openstax, students enrolled in Biology, Physics, and Sociology courses read sections of their introductory text as part of required coursework, optionally highlighted the text to flag key material, and then took brief quizzes as the end of each section. We find that when students choose to highlight, the specific pattern of highlights can explain about 13% of the variance in observed quiz scores. We explore many different representations of the pattern of highlights and discover that a low-dimensional logistic principal component based vector is most effective as input to a ridge regression model. Considering the many sources of uncontrolled variability affecting student performance, we are encouraged by the strong signal that highlights provide as to a student’s knowledge state. 

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4. Text embedding models yield high-resolution insights into conceptual knowledge from short multiple-choice quizzes

   Fitzpatrick, Paxton C; Heusser, Andrew C; Manning, Jeremy R (January 2026, Nature communications)

   We develop a mathematical framework, based on natural language processing models, for tracking and characterizing the acquisition of conceptual knowledge. Our approach embeds each concept in a high-dimensional representation space, where nearby coordinates reflect similar or related concepts. We test our approach using behavioral data from participants who answered small sets of multiple-choice quiz questions, interleaved between watching two course videos from the Khan Academy platform. We apply our framework to the videos' transcripts and the text of the quiz questions to quantify the content of each moment of video and each quiz question. We use these embeddings, along with participants' quiz responses, to track how the learners' knowledge changed after watching each video. Our findings show how a small set of quiz questions may be used to obtain rich and meaningful high-resolution insights into what each learner knows, and how their knowledge changes over time as they learn. 

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5. Introducing desirable difficulty in engineering mathematics with spaced retrieval practice

   Bego, Campbell R.; Ralston, Patricia A.; Lyle, Keith B.; Immekus, Jason C. (July 2021, Proceedings of the 128th ASEE Annual Conference and Exposition)

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   This study examined the difficulty introduced by spaced retrieval practice in Calculus I for undergraduate engineering students. Spaced retrieval practice is an instructional technique in which students engage in multiple recall exercises on the same topic with intermittent temporal delays in between. Spacing out retrieval practice increases the difficulty of the exercises, reducing student performance on them. However, empirical research indicates that spaced retrieval practice is associated with improvements in students’ long-term memory for the retrieved information. The short-term costs and long-term benefits of spaced retrieval practice is an example of desirable difficulty, when more difficult exercises during the early stages of learning result in longer-lasting memory [1]. With support from the National Science Foundation (NSF), we sought to address: Does spacing decrease performance on retrieval practice exercises in an engineering mathematics course? Results showed that student performance was significantly lower for questions in the spaced condition than questions in the massed condition, indicating that we successfully increased the difficulty of the questions by spacing them out over time. Future work will assess final quiz performance to determine whether spacing improved long-term course performance, i.e., whether the difficulty imposed by spacing was desirable. 

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