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This paper explores how learning analytics data provided by a math problem-solving educational technology platform informed 5th and 6th grade teachers' instructional decisions around socioemotional learning (SEL). MathSpring is an educational technology tool that provides teachers with data on students' effort, progress, and emotions while students are solving math problems. This paper presents findings from qualitative analyses of 11 classroom observations, 10 observation follow-up chats, and 35 teacher interviews that were collected as part of a randomized control trial conducted in the northeastern United States. The analyses include teachers who used the program with their students (treatment group) and teachers who implemented math instruction as usual (control group). Teachers in the treatment group viewed analytic data on students' effort and progress, which informed the discussions they had with their students around SEL topics including setting goals, the importance of effort, and using productive problem-solving strategies. Findings from these analyses suggest that analytic data on effort and progress can inform teachers' discussions of math-related SEL concepts with their students. [This paper was published in: "The 14th Learning Analytics and Knowledge Conference (LAK '24), March 18-22, 2024, Kyoto, Japan," ACM, 2024.]

Intelligent tutoring systems (ITS) can help students learn successfully, yet little work has explored the role of caregivers in shaping that success. Past interventions to support caregivers in supporting their child's homework have been largely disjunct from educational technology. The paper presents prototyping design research with nine middle school caregivers. We ask: (1) what are caregivers' preferences for different prototypes incorporating data-driven recommendations into their math homework support? Integrating caregivers' preferences, we then ask: (2) what are caregivers' perceptions when interacting with a prototype of an intelligent chatbot tool to support students' homework? We found caregivers reported feeling comfortable integrating AI into their practices and appreciated chat-based support for understanding content and effective ITS use. Our results highlight the affordances of ITS data and AI to assist caregivers who would otherwise not be able to support their child's homework, paving the way for more effective and equitable mathematics learning. [This paper will be published in the ISLS2024 proceedings.]

Predictive analytics is widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque and incomprehensible to the officials who use them, reducing their trust and utility. Furthermore, predictive models may introduce or exacerbate bias and inequity, as they have done in many sectors of society. Therefore, there is a need for transparent, interpretable, and fair predictive models that can be easily adopted and adapted by different stakeholders. In this paper, we propose a fair predictive model based on multivariate adaptive regression splines (MARS) that incorporates fairness measures in the learning process. MARS is a non-parametric regression model that performs feature selection, handles non-linear relationships, generates interpretable decision rules, and derives optimal splitting criteria on the variables. Specifically, we integrate fairness into the knot optimization algorithm and provide theoretical and empirical evidence of how it results in a fair knot placement. We apply our "fair"MARS model to real-world data and demonstrate its effectiveness in terms of accuracy and equity. Our paper contributes to the advancement of responsible and ethical predictive analytics for social good. [This paper was presented at an Association for the Advancement of Artificial Intelligence conference.]

Teachers often rely on curricular materials as support for implementing reform-based science practices and as educative materials to continue developing ways in which they teach and approach science. Therefore, it is important to understand how teachers develop and use curricular materials and how they learn from their experiences. However, we lack deeper dives into longitudinal relationships that teachers have with curricular materials. This study explores the ways a teacher designs and adapts curriculum over three years. The study tracks the teacher's involvement in co-designing curricular materials alongside other teachers, researchers, and curriculum designers and adapting and using these materials in her own classroom. This study will trace curricular values that the teacher uses to guide her relationships with the curriculum and the adaptations she chooses to make for her own classroom context. The study focuses on a moment in class that the teacher was particularly proud of because it created a palpable shift in the students' engagement with the content she was teaching. This study aims to draw a narrative of this teacher's journey across her three years of writing and adapting materials she was using. [This paper was published in: "Hawaii International Conference on Education Proceedings 2024."]

Teacher learning in asynchronous online professional development (PD) is often assessed through self-reported instruments, despite uncertainties regarding teachers' ability to accurately self-assess their learning gains in such settings. This study compared middle school mathematics teachers' self-perceived and observed learning gains in content knowledge (CK) and pedagogical content knowledge (PCK) after participating in an asynchronous online PD program. By analyzing data collected from 57 teachers, we found a weak and nonsignificant correlation between teachers' perceived learning gains and observed learning gains. Teachers who frequently monitored their understanding of the learning content tended to underestimate their improvement of CK from the PD program while those using cognitive learning strategies (i.e., organize and elaborate) often overestimated it, controlling for their baseline knowledge level. These findings highlight methodological concerns in using self-reports to evaluate online learning outcomes and the potential distortion in identifying effective learning strategies.