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Modeling, explanation, argumentation, and conceptual change
... and simulation.Foster and Keane (2015) andMunnich et al. (2019) advanced a sensemaking account of surprise as a metacognitive estimate of the effort needed to explain an anomaly.Reisenzein et al. (2019) provided an alternative description of surprise, not as the effort involved but in terms of discrepancies and prediction failure. See alsoChinn and Samarapungavan (2008) andChinn (2017) for discussions of the process of surprise and conceptual change. ...
When people make plausibility judgments about an assertion, an event, or a piece of evidence, they are gauging whether it makes sense that the event could transpire as it did. Therefore, we can treat plausibility judgments as a part of sensemaking. In this paper, we review the research literature, presenting the different ways that plausibility has been defined and measured. Then we describe the naturalistic research that allowed us to model how plausibility judgments are engaged during the sensemaking process. The model is based on an analysis of 23 cases in which people tried to make sense of complex situations. The model describes the user’s attempts to construct a narrative as a state transition string, relying on plausibility judgments for each transition point. The model has implications for measurement and for training.
College is a critical time when changes in students' attitudes, knowledge, personality characteristics, and self-concepts are affected by their face-to-face and online interactions with educators, peers, and the campus climate (Astin, 1997). The growing use of big data and analytics in higher education has fostered research that supports human judgement in the analysis of information about learning and the application of interventions that can aid students in their development and improve retention rates (Siemens & Baker, 2012). This information is often displayed in the form of learning analytics dashboards (LADs), which are individual displays with multiple visualizations of indicators about learners, their learning activities, and/or features of the learning context both at the individual and group levels (Schwendimann et al., 2017). The information presented in LADs is intended to support students' learning competencies that include metacognitive, cognitive, behavioral, and emotional self-regulation (Jivet et al., 2018). We investigated the impact of a student-facing LAD on students' self-concepts and viewing preferences to address the following questions: What are students' viewing preferences (i.e., for individual vs. comparative performance feedback)? How does viewing performance information affect the development of students' metacognitive skills and self-concepts? And, what are students' perceptions about the usability of LADs? In an end-of-term survey, 111 students at a large research university responded to 10 Likert scale and three open-ended questions. Overall, the students reported understanding the information that was presented to them through the LAD and that it was useful, although some students expressed concerns about its accuracy and wanted more detailed information. Students also reported that they preferred to view comparisons to other students over just viewing their own performance information, and that LAD use increased positive affect about performance. Students also reported that dashboard use affected how much they believed they understood the course material, the time and effort they were willing to put into the course, and that it lessened their anxiety. We concluded that course-specific or program-specific related outcomes may require different LAD design and evaluation approaches, and the nonuse of the LAD may be linked to self-confidence, forgetfulness, and a lack of innovative dashboard features. Our study was limited by the analysis of survey data (without trace data), and the sample size. This research contributes to the literature on student-facing learning analytics dashboards (LADs) by investigating students' reasons for interacting with dashboards, their viewing preferences, and how their interactions affect their performance and tying these insights to educational concepts that were a part of the LAD design. Further research is needed to determine whether presenting students with the option to turn on the dashboard for any or all of their courses over the course of the semester is important,