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Robot technologies have been introduced to computing education to engage learners. This study introduces the concept of co-creation with a robot agent into culturally-responsive computing (CRC). Co- creation with computer agents has previously focused on creating external artifacts. Our work differs by making the robot agent itself the co-created product. Through participatory design activities, we positioned adolescent girls and an agentic social robot as co- creators of the robot’s identity. Taking a thematic analysis approach, we examined how girls embody the role of creator and co-creator in this space. We identified themes surrounding who has the power to make decisions, what decisions are made, and how to maintain social relationship. Our findings suggest that co-creation with robot technology is a promising implementation vehicle for realizing CRC. 

Eye movements provide a window into cognitive processes, but much of the research harnessing this data has been confined to the laboratory. We address whether eye gaze can be passively, reliably, and privately recorded in real-world environments across extended timeframes using commercial-off-the-shelf (COTS) sensors. We recorded eye gaze data from a COTS tracker embedded in participants (N=20) work environments at pseudorandom intervals across a two-week period. We found that valid samples were recorded approximately 30% of the time despite calibrating the eye tracker only once and without placing any other restrictions on participants. The number of valid samples decreased over days with the degree of decrease dependent on contextual variables (i.e., frequency of video conferencing) and individual difference attributes (e.g., sleep quality and multitasking ability). Participants reported that sensors did not change or impact their work. Our findings suggest the potential for the collection of eye-gaze in authentic environments. 

We investigated the generalizability of language-based analytics models across two collaborative problem solving (CPS) tasks: an educational physics game and a block programming challenge. We analyzed a dataset of 95 triads (N=285) who used videoconferencing to collaborate on both tasks for an hour. We trained supervised natural language processing classifiers on automatic speech recognition transcripts to predict the human-coded CPS facets (skills) of constructing shared knowledge, negotiation / coordination, and maintaining team function. We tested three methods for representing collaborative discourse: (1) deep transfer learning (using BERT), (2) n-grams (counts of words/phrases), and (3) word categories (using the Linguistic Inquiry Word Count [LIWC] dictionary). We found that the BERT and LIWC methods generalized across tasks with only a small degradation in performance (Transfer Ratio of .93 with 1 indicating perfect transfer), while the n-grams had limited generalizability (Transfer Ratio of .86), suggesting overfitting to task-specific language. We discuss the implications of our findings for deploying language-based collaboration analytics in authentic educational environments. 

We model coordination and coregulation patterns in 33 triads engaged in collaboratively solving a challenging computer programming task for approximately 20 minutes. Our goal is to prospectively model speech rate (words/sec) – an important signal of turn taking and active participation – of one teammate (A or B or C) from time lagged nonverbal signals (speech rate and acoustic-prosodic features) of the other two (i.e., A + B → C; A + C → B; B + C → A) and task-related context features. We trained feed-forward neural networks (FFNNs) and long short- term memory recurrent neural networks (LSTMs) using group- level nested cross-validation. LSTMs outperformed FFNNs and a chance baseline and could predict speech rate up to 6s into the future. A multimodal combination of speech rate, acoustic- prosodic, and task context features outperformed unimodal and bimodal signals. The extent to which the models could predict an individual’s speech rate was positively related to that individual’s scores on a subsequent posttest, suggesting a link between coordination/coregulation and collaborative learning outcomes. We discuss applications of the models for real-time systems that monitor the collaborative process and intervene to promote positive collaborative outcomes.