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A goal of the AIED community is to create equitable systems; yet, we lack a cohesive viewpoint on how to do so. In the present work, we propose power as this organizing principle. We utilize the data feminism framework to showcase how we might balance power, focusing on learner engagement. We utilize multimodal data from ten middle school girls in a virtual computer science camp to discuss how the AIED community might create systems of equity that support all learners. 

While existing student modeling methods focus on predicting students’ knowledge states, they often overlook the underlying cognitive processes contributing to learning. In this work, we integrate cognitive processes, specifically phases of rule learning, into student modeling, drawing inspiration from cognitive science. Rule learning involves rule search, discovery, and following, providing a systematic framework for understanding how individuals acquire and apply knowledge. We conduct two studies to explore rule learning phases in a real-world learning context. Moreover, we present a two-step approach to first predict the phases of rule learning students experience during problem solving with an intelligent tutoring system and then estimate the time spent on each predicted phase. Furthermore, we identify the relationships between the time spent on specific phases of rule learning and student performance. Our findings underscore the importance of integrating cognitive processes into student modeling for more targeted interventions and personalized support. 

Many educational recommender systems (EdRecSys) rely on commercial recommendation strategies that emphasize content relevance while neglecting learners’ views on recommendation effectiveness. To address this, we conducted a co-design study with computer science students in an introductory programming course to explore their vision of an ideal EdRecSys. The subjects shared preferences and concerns related to three areas: recommendation approaches, transparency, and control. We used Zimmerman’s model of self-regulated learning to contextualize their expectations within a broader educational framework. Findings offer actionable insights for designing learner-centered AIED systems that foster engagement, agency, and self-regulation. 

Open-ended learning environments (OELEs) involve high learner agency in defining learning goals and multiple pathways to achieve those goals. These tasks involve learners transitioning through self-regulated learning (SRL) phases by actively setting goals, applying different strategies for those goals, and monitoring performance to update their strategies. However, because of the flexibility, how learners react to impasses and errors has a critical influence on their learning. An intelligent pedagogical agent (IPA) continuously modeling learner activities could help support learners in these environments. However, this continuous comprehension of behaviors and strategies is difficult in OELEs with evolving goals, ill-defined problem structures, and learning sequences. In this paper, we draw from the literature on SRL phases and cognitive states to investigate the utility of two different methods, Sequence Mapping, and Hidden Markov Models, in building learner activity models from log data collected from a summer camp with 14 middle school girls in an open-design environment. We evaluate the effectiveness of these models separately, and combined, in identifying 7 states: Forethought, Engaged Concentration, Acting, Monitoring, Wheel Spinning, Mind Wandering, and Reflect and Repair. Lastly, we recommend dialogue intervention strategies for an IPA to support learning in OELEs. 

One of the keys to the success of collaborative learning is balanced participation by all learners, but this does not always happen naturally. Pedagogical robots have the potential to facilitate balance. However, it remains unclear what participation balance robots should aim at; various metrics have been proposed, but it is still an open question whether we should balance human participation in human-human interactions (HHI) or human-robot interactions (HRI) and whether we should consider robots' participation in collaborative learning involving multiple humans and a robot. This paper examines collaborative learning between a pair of students and a teachable robot that acts as a peer tutee to answer the aforementioned question. Through an exploratory study, we hypothesize which balance metrics in the literature and which portions of dialogues (including vs. excluding robots' participation and human participation in HHI vs. HRI) will better predict learning as a group. We test the hypotheses with another study and replicate them with automatically obtained units of participation to simulate the information available to robots when they adaptively fix imbalances in real-time. Finally, we discuss recommendations on which metrics learning science researchers should choose when trying to understand how to facilitate collaboration. 

Culturally responsive STEM and computing initiatives aim to engage and embolden a diverse range of learners, center their identity and experiences in curriculum, and connect learners to each other and their communities. With an abrupt pivot to online learning at the beginning of 2020, more educational experiences have taken place virtually. We ran a virtual synchronous culturally responsive computing camp and saw that establishing the right environment online to support a good sense of connectedness was challenging. To investigate this further, we interviewed eight K-12 instructors of culturally responsive STEM and computing programs. Three themes emerged on defining and cultivating connectedness in learning experiences, the role of equity in supporting community online, and affordances of being online specific to culturally responsive perspectives. We support our thematic findings with vignettes from the camp data. In this study, we address K-12 culturally responsive STEM and computing instructors' beliefs, experiences, and approaches regarding cultivating connectedness online. This work fills a gap in understanding instructor perspectives on building in-program and broader community connections online from a culturally responsive STEM and computing lens.