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Abstract The origin of switchbacks in the solar wind is discussed in two classes of theory that differ in the location of the source being either near the transition region near the Sun or in the solar wind itself. The two classes of theory differ in their predictions of the switchback rate (the number of switchbacks observed per hour) as a function of distance from the Sun. To distinguish between these theories, one-hour averages of Parker Solar Probe data were averaged over five orbits to find the following: (1) The hourly averaged switchback rate was independent of distance from themore »Free, publicly-accessible full text available September 1, 2022
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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 »
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We explore the behavior of a standard convolutional neural net in a continual-learning setting that introduces visual classification tasks sequentially and requires the net to master new tasks while preserving mastery of previously learned tasks. This setting corresponds to that which human learners face as they acquire domain expertise serially, for example, as an individual studies a textbook. Through simulations involving sequences of ten related visual tasks, we find reason for optimism that nets will scale well as they advance from having a single skill to becoming multi-skill domain experts. We observe two key phenomena. First, forward facilitation—the accelerated learningmore »
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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 ofmore »
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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 ablemore »
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Personalized learning environments requiring the elicitation of a student’s knowledge state have inspired researchers to propose distinct models to understand that knowledge state. Recently, the spotlight has shone on comparisons between traditional, interpretable models such as Bayesian Knowledge Tracing (BKT) and complex, opaque neural network models such as Deep Knowledge Tracing (DKT). Although DKT appears to be a powerful predictive model, little effort has been expended to dissect the source of its strength. We begin with the observation that DKT differs from BKT along three dimensions: (1) DKT is a neural network with many free parameters, whereas BKT is amore »
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Boosting engagement with educational software has been promoted as a means of improving student performance. Various engagement factors have been explored, including choice, personalization, badges, bonuses, and competition. We examine two promising and relatively understudied manipulations from the realm of gambling: the nearwin effect and anticipation. The near-win effect occurs when an individual comes close to achieving a goal, e.g., getting two cherries and a lemon in a slot machine. Anticipation refers to the build-up of suspense as an outcome is revealed, e.g., revealing cherry-cherry-lemon in that order drives expectations of winning more than revealing lemon-cherrycherry. Gambling psychologists have longmore »