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Mozer, F. S.; Bale, S. D.; Bonnell, J. W.; Drake, J. F.; Hanson, E. L.; Mozer, M. C.(
, The Astrophysical Journal)
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 »Sun. (2) The average switchback rate increased with solar wind speed. (3) The switchback size perpendicular to the flow increased as R , the distance from the Sun, while the radial size increased as R 2 , resulting in an increasing switchback aspect ratio with distance from the Sun. (4) The hourly averaged and maximum switchback rotation angles did not depend on the solar wind speed or distance from the Sun. These results are consistent with switchback formation in the transition region because their increase of tangential size with radius compensates for the radial falloff of their equatorial density to produce switchback rates that are independent of radial distance. This constant switchback rate is inconsistent with an in situ source. The switchback size and aspect ratio, but not their hourly average or maximum rotation angle, increased with radial distance to 100 solar radii. Additionally, quiet intervals between switchback patches occurred at the lowest solar wind speeds.« less
Free, publicly-accessible full text available September 1, 2022
Kim, D. Y.; Scott, T. R.; Mallick, D.; Mozer, M. C.(
, Proceedings of the Third International Workshop on Intelligent Textbooks (iTextbooks))
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
Davidson, G.; Mozer, M. C.(
, IEEE Computer Society Conference on Computer Vision and Pattern Recognition)
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 »of task n+1 having learned n previous tasks—grows with n. Second, backward interference— the forgetting of the n previous tasks when learning task n + 1—diminishes with n. Amplifying forward facilitation is the goal of research on metalearning, and attenuating backward interference is the goal of research on catastrophic forgetting. We find that both of these goals are attained simply through broader exposure to a domain.« less
Kim, D. Y.; Winchell, A.; Water, A. E.; Grimaldi, P. J.; Baraniuk, R.; Mozer, M. C.(
, 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 ofmore »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.« less
Winchell, A.; Mozer, M. C.; Lan, A.; Grimaldi, P.; Pashler, H.(
, 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 ablemore »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.« less
Montero, S.; Arora, A.; Kelly, S.; Milne, B.; Mozer, M. C.(
, Proceedings of the Eleventh International Conference on Educational Data Mining)
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 »probabilistic model with few free parameters; (2) a single instance of DKT is used to model all skills in a domain, whereas a separate instance of BKT is constructed for each skill; and (3) the input to DKT interlaces practice from multiple skills, whereas the input to BKT is separated by skill. We tease apart these three dimensions by constructing versions of DKT which are trained on single skills and which are trained on sequences separated by skill. Exploration of three data sets reveals that dimensions (1) and (3) are critical; dimension (2) is not. Our investigation gives us insight into the structural regularities in the data that DKT is able to exploit but that BKT cannot.« less
Khajah, M. M.; Mozer, M. C.; Kelly, S.; Milne, B.(
, Nineteenth International Conference on Artificial Intelligence in Education)
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 »studied how near-wins affect engagement in pure-chance games but it is difficult to do the same in an educational context where outcomes are based on skill. In this paper, we manipulate the display of outcomes in a manner that allows us to introduce artificial near-wins largely independent of a student’s performance. In a study involving thousands of students using an online math tutor, we examine how this manipulation affects a behavioral measure of engagement—whether or not a student repeats a lesson. We find a near-win effect on engagement when the ‘win’ indicates to the student that they have attained critical competence on a lesson—the competence that allows them to continue to the next lesson. Nonetheless, when we experimentally induce near wins in a randomized controlled trial, we do not obtain a reliable effect of the near win. We discuss this mismatch of results in terms of the role of anticipation on making near wins effective. We conclude by describing manipulations that might increase the effect of near wins on engagement.« less
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