In this demo we showcase an interactive application to support the learning of "TikTok dance challenge" short dance choreographies. Our system utilizes dance challenge videos as the information source, performing music analysis and pose estimation to segment the dance into learnable chunks and generate a practice plan that implements motor learning techniques such as incremental part-learning and fading guidance. These plans are presented in a web app that implements video demonstration, augmented webcam mirroring, practice recording/review functionality, and both concurrent and terminal feedback. By operating on a ubiquitous information source, generating the lessons automatically, and requiring only a web browser and webcam in the user interface, our system is a step towards significantly expanding the reach of dance choreography learning and a platform for further research into dance HCI.
Developing physical motion skills is an essential and empowering aspect of the human experience, and dance learning in particular has been shown to be widely useful with benefits ranging from promoting socioemotional learning in children [3] to increasing motivation and curiosity in college students [2] and improving quality of life for patients with Parkinson's disease [5]. Yet human dance instructors are expensive and electronically available guided dance instruction often requires a subscription. Many dances are shared on video-first social media sites such as TikTok and now Instagram, however a limitation to expanding access to dance instruction is the human effort that must be put into creating high quality dance tutorials.
When we learn conceptual knowledge, we not only remember the pieces of information, but also understand how they are related to capture the deeper meaning of underlying concepts. Previous studies have shown that the network structure of narratives can predict the information that will be recalled, but it is currently unknown whether the same network properties relate to conceptual learning in an academic lecture. In this study, participants watched a YouTube video to learn several concepts about Newtonian physics and subsequently were prompted to recall what they remembered and learned from the lesson. By transforming the transcript of the lecture into a semantic network graph using a text embedding model (SBERT), we demonstrated that the more degree central the information is in the network, the more likely it is to be remembered. Additionally, the recall of more central information predicts how well a given student understands the concepts, as reflected in their conceptual essays. Lastly, we investigated whether there was information that predicted learning that was not as semantically central. Our findings indicate the inherent semantic relations between concepts in the lecture shape the way we construct conceptual knowledge.
Current debate surrounds the promise of neuroscience for education, including whether learning-related neural changes can predict learning transfer better than traditional performance-based learning assessments. Longstanding debate in philosophy and psychology concerns the proposition that spatial processes underlie seemingly nonspatial/verbal reasoning (mental model theory). If so, education that fosters spatial cognition might improve verbal reasoning. Here, in a quasi-experimental design in real-world STEM classrooms, a curriculum devised to foster spatial cognition yielded transfer to improved verbal reasoning. Further indicating a spatial basis for verbal transfer, students’ spatial cognition gains predicted and mediated their reasoning improvement. Longitudinal fMRI detected learning-related changes in neural activity, connectivity, and representational similarity in spatial cognition–implicated regions. Neural changes predicted and mediated learning transfer. Ensemble modeling demonstrated better prediction of transfer from neural change than from traditional measures (tests and grades). Results support in-school “spatial education” and suggest that neural change can inform future development of transferable curricula.
Semantic concepts relate to each other to varying degrees to form a network of first-order relations, and these first-order relations serve as input into networks of general relation types as well as higher order relations. Previous work has studied the neural mapping of semantic concepts across domains, though much work remains to be done to understand how the localization and structure of those architectures differ depending on various individual differences in attentional bias towards different content presentation formats. Using an item-wise model of semantic distance of first-order relations (word2vec) between stimuli (presented both in word and picture forms), we used representational similarity analysis to identify individual differences in the neural localization of semantic concepts, and how those localization differences can be predicted by individual variance in the degree to which individuals attend to word information instead of pictures. Importantly, there were no reliable representations of this first-order semantic relational network when looking at the full group, and it was only through considering individual differences that a stable localization difference became evident. These results indicate that individual differences in the degree to which a person habitually attends to word information instead of picture information substantially affects the neural localization of first-order semantic representations.
Students with math anxiety experience excessive levels of negative emotion, including intrusive and distracting thoughts, when attempting to learn about math or complete a math assignment. Consequently, math anxiety is associated with maladaptive study skills, such as avoidance of homework and test preparation, creating significant impediments for students to fulfill their potential in math classes. To combat the impact of math anxiety on academic performance, we introduced two classroom-based interventions across two samples of high school math students: one intervention focused on emotion regulation (ER) using cognitive reappraisal, a technique for reframing an anxious situation, and the other intervention encouraged students to improve their study habits. The Study Skills (SS) intervention was associated with increased grades for highly anxious students during the intervention period, whereas the ER intervention was less efficacious in countering anxiety-related decreases in grade performance. The SS intervention encouraged highly math-anxious students to incorporate self-testing and overcome avoidant behaviors, increasing academic performance and ameliorating performance deficits associated with increased anxiety that were observed in both groups prior to intervention, and that persisted in the ER group. Notably, the benefits observed for the SS group extended to the post-intervention quarter, indicating the potential lasting effects of this intervention. These results support the hypothesis that using better study strategies and encouraging more frequent engagement with math resources would help highly-anxious students habituate to their math anxiety and ameliorate the negative effects of anxiety on performance, ultimately increasing their math comprehension and academic achievement.
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