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This article reports findings of implementing a novel learning technology called WearableLearning to teach geometry transformations in a math classroom. The paper aims to answer RQ1) To what extent do students learn math with embodied games facilitated by WearableLearning? and RQ2) How do students learn math differently with embodied games enabled by WearableLearning, compared to traditional learning technology? Quantitative results indicate a trend of improvement from pre-test to post-test (t = 1.5, p < 0.1). Qualitative results indicate that games through WearableLearning increase the opportunities for mathematical thinking between students, physical objects, and the learning environment. Qualitative results also indicate that students benefit from additional affordances of support when utilizing WearableLearning compared to traditional learning technologies 

Digital learning games can help address gender disparities in math by promoting better learning experiences and outcomes for girls. However, there is a need for more research to understand why some digital learning games might be especially effective for girls studying mathematics. In this study, we assess two possible pathways: that girls might benefit from math games because they reduce the anxiety and evaluation apprehension that girls are more likely to experience when doing math; and that girls might benefit from math games when they enjoy the narrative and thus experience greater engagement. To evaluate these pathways, our work uses multiple dimensions of gender (e.g., gender identity and gender-typed interests, activities, and traits) and surveys of affective experiences to examine the impact of three learning systems with identical learning content: a digital learning game, Decimal Point, that has consistently led to better learning for girls over boys; a new masculine-typed game, Ocean Adventure, developed based on a survey of over 300 students; and a conventional tutoring system. We predicted that girls and students with stronger feminine-typed characteristics would experience less math anxiety in both Decimal Point and Ocean Adventure compared to the tutor. We also predicted that girls and students with stronger feminine-typed characteristics would experience greater engagement and learning with Decimal Point while boys and students with stronger masculine-typed characteristics would experience greater engagement and learning with Ocean Adventure. Consistent with predictions, students with stronger feminine-typed characteristics experienced less anxiety and evaluation apprehension in both games compared to the tutor. This suggests that math learning games may provide a way to address these negative affective experiences. In terms of our measures of engagement, we found that students with stronger masculine-typed characteristics reported greater experience of mastery in the masculine Ocean Adventure; however, this was the only indicator that the more masculine narrative of Ocean Adventure led to different experiences based on gender. This suggests that narrative alone may not have a strong enough effect on students based on gender, especially when other game features are kept constant. Contrary to our predictions, there were no effects of gender identity or condition on learning outcomes, although both masculine-typed and feminine-typed characteristics were negatively associated with learning. Overall, these results point to the value of a multi-dimensional model of gender in assessing learning with a game, the important role learning games can have in reducing math anxiety and evaluation apprehension for girls and students with feminine-typed characteristics, and the nuanced effects of game narratives on experiences with game-based learning. 

This emerging technology report introduces the WearableLearning (WL) platform as a tool to exercise computational thinking and STEM learning for 5-12th grade students through mobile technology-augmented active game play and game creation. Freely available at WearableLearning.org, it allows students and teachers to play, create, debug, and manage multiplayer, active games. To date, WearableLearning has been used in schools and afterschool programs by roughly 500 students and 25 teachers to create games covering STEM curricular content. WearableLearning enables the creation of physically active and social games, while offering possibilities for research on computational thinking, embodied cognition, collaborative learning, game-based learning, and practical applications of technology in STEM classrooms. 

A major challenge for online learning is the inability of systems to support student emotion and to maintain student engagement. In response to this challenge, computer vision has become an embedded feature in some instructional applications. In this paper, we propose a video dataset of college students solving math problems on the educational platform MathSpring.org with a front facing camera collecting visual feedback of student gestures. The video dataset is annotated to indicate whether students’ attention at specific frames is engaged or wandering. In addition, we train baselines for a computer vision module that determines the extent of student engagement during remote learning. Baselines include state-of-the-art deep learning image classifiers and traditional conditional and logistic regression for head pose estimation. We then incorporate a gaze baseline into the MathSpring learning platform, and we are evaluating its performance with the currently implemented approach. 

In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS). By analyzing a student's face and gestures, our method predicts the outcome of a student answering a problem in an ITS from a video feed. Our work is motivated by the reasoning that the ability to predict such outcomes enables tutoring systems to adjust interventions, such as hints and encouragement, and to ultimately yield improved student learning. We collected a large labeled dataset of student interactions with an intelligent online math tutor consisting of 68 sessions, where 54 individual students solved 2,749 problems. We will release this dataset publicly upon publication of this paper. It will be available at https://www.cs.bu.edu/faculty/betke/research/learning/. Working with this dataset, our transfer-learning challenge was to design a representation in the source domain of pictures obtained “in the wild” for the task of facial expression analysis, and transferring this learned representation to the task of human behavior prediction in the domain of webcam videos of students in a classroom environment. We developed a novel facial affect representation and a user-personalized training scheme that unlocks the potential of this representation. We designed several variants of a recurrent neural network that models the temporal structure of video sequences of students solving math problems. Our final model, named ATL-BP for Affect Transfer Learning for Behavior Prediction, achieves a relative increase in mean F -score of 50 % over the state-of-the-art method on this new dataset. 

We present a new technology-based paradigm to support embodied mathematics educational games, using wearable devices in the form of SmartPhones and SmartWatches for math learning, for full classes of students in formal in- school education settings. The Wearable Learning Games Engine is web based infrastructure that enables students to carry one mobile device per child, as they embark on math team-based activities that require physical engagement with the environment. These Wearable Tutors serve as guides and assistants while students manipulate, measure, estimate, discern, discard and find mathematical objects that satisfy specified constraints. Multi-player math games that use this infrastructure have yielded both cognitive and affective benefits. Beyond math game play, the Wearable Games Engine Authoring Tool enables students to create games themselves for other students to play; in this process, students engage in computational thinking and learn about finite-state machines. We present the infrastructure, games, and results for a series of experiments on both game play and game creation.