Quantifying Scientific Thinking Using Multichannel Data With Crystal Island: Implications for Individualized Game-Learning Analytics

Cloude, Elizabeth B.; Dever, Daryn A.; Wiedbusch, Megan; Azevedo, Roger

doi:10.3389/feduc.2020.572546

Cited by 15 publications

(22 citation statements)

References 53 publications

(36 reference statements)

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“…However, considerable research is still required. Implementing game-learning analytics in the classroom for instructional decision making will also require additional support and technological resources, such as a dashboard to illustrate data visualizations for sense making that are currently unavailable to most teachers (Perez-Colado et al, 2017;Cloude, Dever, Wiedbusch, & Azevedo, 2020;Wiedbusch et al, 2021;Roll & Winne, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…When is the ideal time to prompt learners to reflect, and does that time vary based on the learning goal and environment? Researchers should also consider the role of adolescents' motivation, cognitive load, or level of knowledge of problem solving, since game-learning analytics data could help pinpoint when learners are not engaging in reflection (e.g., if the quality of solution-based reflection and motivation is low) to inform instructional decision making (Winne et al, 2019;Winne, 2017;Cloude et al, 2020;Wiedbusch et al, 2021).…”

Section: To What Extent Do the Quantity And Quality Of Reflections Predict Post-test Scores While Controllingmentioning

confidence: 99%

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Game-Based Learning Analytics for Supporting Adolescents’ Reflection

Cloude

Carpenter

Dever

et al. 2021

JLA

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Reflection is critical for adolescents’ problem solving and learning in game-based learning environments (GBLEs). Yet challenges exist in the literature because most studies lack a theoretical perspective and clear operational definition to inform how and when reflection should be scaffolded during game-based learning. In this paper, we address these issues by studying the quantity and quality of 120 adolescents’ written reflections and their relation to their learning and problem solving with Crystal Island, a GBLE. Specifically, we (1) define reflection and how it relates to skill and knowledge acquisition; (2) review studies examining reflection and its relation to problem solving and learning with emerging technologies; and (3) provide direction for building reflection prompts into GBLEs that are aligned with the learning goals built into the learning session (e.g., learn about microbiology versus successfully solve a problem) to maximize adolescents’ reflection, learning, and performance. Overall, our findings emphasize how important it is to examine not only the quantity of reflection but also the depth of written reflection as it relates to specific learning goals. We discuss the implications of using game-learning analytics to guide instructional decision making in the classroom.

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Section: Discussionmentioning

confidence: 99%

Section: To What Extent Do the Quantity And Quality Of Reflections Predict Post-test Scores While Controllingmentioning

confidence: 99%

Game-Based Learning Analytics for Supporting Adolescents’ Reflection

Cloude

Carpenter

Dever

et al. 2021

JLA

Self Cite

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“…Learning analytics is a crucial facilitator for the personalization of learning, both in regular class-based teaching (Baker, 2016;de Quincey et al, 2019) and in the teaching of large-scale classes (Westervelt, 2017;Matz et al, 2021). In particular, in large-class settings, where teachers cannot learn the specific backgrounds and needs of all their students, the use of multi-modal or multichannel (Cloude et al, 2020;Matz et al, 2021) data can be of great benefit. These multi-modal data can help educators to understand the learning processes that take place and the derivation of prediction models for these learning processes.…”

Section: Personalized Learning and Multi-modal Data Sourcesmentioning

confidence: 99%

“…Examples of such product data are the outcomes of formative assessments or diagnostic entry tests. In applications of DLA, a third data source is provided by the self-report surveys applied to measure learning dispositions; although attempts are being made to measure dispositions through the observation of learning behaviors (Buckingham Shum and Deakin Crick, 2016;Cloude et al, 2020;Jivet et al, 2021;Salehian Kia et al, 2021), the survey method is still dominant (Shum and Crick, 2012).…”

Section: Personalized Learning and Multi-modal Data Sourcesmentioning

confidence: 99%

“…LA systems typically take so-called trace data, the digital footprints students leave in technology-enhanced learning environments while studying, as inputs for prediction models. As one example of such an approach, Cloude et al (2020) use gaze behaviors and in-game actions to describe the learning processes of different students. However, complex trace-based LA models risk turning into "black box" modeling with limited options to generalize beyond the data they are built on (Rosé et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Dispositional Learning Analytics for Supporting Individualized Learning Feedback

2021

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An important goal of learning analytics (LA) is to improve learning by providing students with meaningful feedback. Feedback is often generated by prediction models of student success using data about students and their learning processes based on digital traces of learning activities. However, early in the learning process, when feedback is most fruitful, trace-data-based prediction models often have limited information about the initial ability of students, making it difficult to produce accurate prediction and personalized feedback to individual students. Furthermore, feedback generated from trace data without appropriate consideration of learners’ dispositions might hamper effective interventions. By providing an example of the role of learning dispositions in an LA application directed at predictive modeling in an introductory mathematics and statistics module, we make a plea for applying dispositional learning analytics (DLA) to make LA precise and actionable. DLA combines learning data with learners’ disposition data measured through for example self-report surveys. The advantage of DLA is twofold: first, to improve the accuracy of early predictions; and second, to link LA predictions with meaningful learning interventions that focus on addressing less developed learning dispositions. Dispositions in our DLA example include students’ mindsets, operationalized as entity and incremental theories of intelligence, and corresponding effort beliefs. These dispositions were inputs for a cluster analysis generating different learning profiles. These profiles were compared for other dispositions and module performance. The finding of profile differences suggests that the inclusion of disposition data and mindset data, in particular, adds predictive power to LA applications.

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