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Research into student affect detection has historically relied on ground truth measures of emotion that utilize one of three sources of data: (1) self-report data, (2) classroom observations, or (3) sen- sor data that is retrospectively labeled. Although a few studies have compared sensor- and observation-based approaches to student af- fective modeling, less work has explored the relationship between self-report and classroom observations. In this study, we use both recurring self-reports (SR) and classroom observation (BROMP) to measure student emotion during a study involving middle school students interacting with a game-based learning environment for microbiology education. We use supervised machine learning to develop two sets of affect detectors corresponding to SR and BROMP-based measures of student emotion, respectively. We compare the two sets of detectors in terms of their most relevant features, as well as correlations of their output with measures of student learning and interest. Results show that highly predictive features in the SR detectors are different from those selected for BROMP-based detectors. The associations with interest and moti- vation measures show that while SR detectors captured underlying motivations, the BROMP detectors seemed to capture more in-the- moment information about the student’s experience. Evidence sug- gests that there is benefit of using both sources of data to model different components of student affect. 

Integration of computational thinking (CT) within STEM subjects is common, although not often at the elementary school level where teachers have minimal experience with CT. We have designed and are refining INFUSECS, a narrative-centered digital learning environment to support upper elementary students’ CT and science knowledge construction as they create digital stories. We used orchestration as our theoretical framework, to examine how elementary teachers planned to approach this multidisciplinary implementation. Through a series of three focus groups, we learned that teachers planned for their students to take notes or utilize other graphic organizers to align the science content with the narrative planning, to engage in collaborative sense-making, and to observe the teacher modeling use of the INFUSECS system. Ultimately, the results have informed the next phase of our research design as we collect teacher and student level data as INFUSECS is utilized in authentic classroom settings. 

Abstract: Given that pair programming has proved to be an effective pedagogical approach for teaching programming skills, it is now important to explore alternative collaborative configurations. One popular configuration is where dyads collaborate by sharing a single computer sitting side-by-side. However, prior research points to potential challenges for elementary students when sharing a single computer when collaborating. This prompted us to explore another configuration where dyads sit side by side but collaborate on a shared virtual platform with individual computers. We compared the discourse of students’ collaboration under these two settings. Results show that although there are no significant differences in the amount of collaborative talk between the two configurations, there is qualitative evidence of how differing affordances of two configurations shape collaborative elementary students’ practices.