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1. Promoting students’ self-regulated choices in learning with visual representations in intelligent tutoring software

   Nagashima, T.; Zheng, B.; Tseng, S.; Ling, E.; Aleven, V. (January 2023, Proceedings of the Annual Meeting for the International Society of the Learning Sciences (ISLS2023))

   Although students’ self-regulated learning has been studied extensively, past research has not investigated students’ fine-grained, self-regulated choice-making processes during learning with visual representations and strategies to support such processes. We conducted design and experimental studies with 148 students to develop and evaluate an intervention package for supporting students’ self-regulated choice-making in using diagrammatic scaffolding in algebra tutoring software. A classroom experiment showed that students with the intervention learned greater conceptual and procedural knowledge in algebra than students in the control condition whose choices were not supported. Also, students with the intervention chose to use diagrams less frequently overall but showed distinctive use patterns that changed over time, indicating a form of self-regulated diagram use. This study demonstrates the importance of understanding and supporting choice behaviors that change over time during learning, going beyond simply measuring the frequency of choice behaviors and encouraging students to engage in these behaviors more frequently. 

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2. Students’ Self-Regulated Use of Diagrams in a Choice-based Intelligent Tutoring System

   Nagashima, T.; Tseng, S.; Ling, E.; Bartel, A. N.; Vest, N. A.; Silla, E. M.; Alibali, M. W.; Aleven, V. (January 2022, Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022)

   Chinn, C.; Tan, E.; Chao, C.; Kali, Y. (Ed.)

   Learners’ choices as to whether and how to use visual representations during learning are an important yet understudied aspect of self-regulated learning. To gain insight, we developed a choice-based intelligent tutor in which students can choose whether and when to use diagrams to aid their problem solving in algebra. In an exploratory classroom study with 26 students, we investigated how learners choose diagrams and how their choice behaviors relate to learning outcomes. Students who proactively chose to use diagrams achieved higher learning outcomes than those who reactively used diagrams when they made incorrect attempts. This study contributes to understanding of self-regulated use of visual representations during problem solving. 

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3. Students’ self-regulated use of diagrams in a choice-based intelligent tutoring system

   Nagashima, T.; Tseng, S.; Ling, E.; Bartel, A. N.; Vest, N. A.; Silla, E. M.; Alibali, M. W.; Aleven, V. (June 2022, Proceedings of the 16th International Conference of the Learning Sciences , ICLS 2022)

   Chinn, C.; Tan, E.; Chan, C.; Kali, Y. (Ed.)

   Learners’ choices as to whether and how to use visual representations during learning are an important yet understudied aspect of self-regulated learning. To gain insight, we developed a choice-based intelligent tutor in which students can choose whether and when to use diagrams to aid their problem solving in algebra. In an exploratory classroom study with 26 students, we investigated how learners choose diagrams and how their choice behaviors relate to learning outcomes. Students who proactively chose to use diagrams achieved higher learning outcomes than those who reactively used diagrams when they made incorrect attempts. This study contributes to understanding of self-regulated use of visual representations during problem solving. 

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4. Adding self-regulated learning instruction to an introductory physics class

   https://doi.org/10.1119/perc.2023.pr.Maldonado  

   Maldonado, Danielle; Stewart, John (October 2023, 2023 PERC Proceedings)

   Self-regulated learning (SRL) is an essential factor in academic success. Self-regulated learning is a process where learners set clear goals, monitor progress toward attainment of those goals, and adapt their strategies to improve their learning. Because SRL is often not explicitly integrated into the classroom, students struggle to identify and use learning techniques empirically proven to be more successful than others. SRL is a learned skill students can develop over time that has been found to be related to high achievement and self-efficacy. This paper examines the effects of introducing SRL strategies into an undergraduate introductory physics classroom. The degree to which the students were self-regulated learners was correlated with their test averages (r = 0.23, p < 0.05). Students reported that they found the SRL instruction helpful (3.5 out of 5.0 on a 5-point scale) and 86% of the students felt the time spent on the instruction was generally appropriate. Students’ preferred study methods changed over the course of the semester, indicating that students applied SRL by adapting their learning processes based on which methods were most effective in helping them study for an upcoming exam and opting not to use techniques no longer perceived as useful. Higher achieving students were more likely to settle on highly effective techniques by the end of the semester, while lower achieving students continued to modify their learning processes. 

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5. Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning

   Taub, M.; Azevedo, R. (December 2018, Journal of educational data mining)

   Self-regulated learning conducted through metacognitive monitoring and scientific inquiry can be influenced by many factors, such as emotions and motivation, and are necessary skills needed to engage in efficient hypothesis testing during game-based learning. Although many studies have investigated metacognitive monitoring and scientific inquiry skills during game-based learning, few studies have investigated how the sequence of behaviors involved during hypothesis testing with game-based learning differ based on both efficiency level and emotions during gameplay. For this study, we analyzed 59 undergraduate students’ (59% female) metacognitive monitoring and hypothesis testing behavior during learning and gameplay with CRYSTAL ISLAND, a game-based learning environment that teaches students about microbiology. Specifically, we used sequential pattern mining and differential sequence mining to determine if there were sequences of hypothesis testing behaviors and to determine if the frequencies of occurrence of these sequences differed between high or low levels of efficiency at finishing the game and high or low levels of facial expressions of emotions during gameplay. Results revealed that students with low levels of efficiency and high levels of facial expressions of emotions had the most sequences of testing behaviors overall, specifically engaging in more sequences that were indicative of less strategic hypothesis testing behavior than the other students, where students who were more efficient with both levels of emotions demonstrated strategic testing behavior. These results have implications for the strengths of using educational data mining techniques for determining the processes underlying patterns of engaging in self-regulated learning conducted through hypothesis testing as they unfold over time; for training students on how to engage in the self-regulation, scientific inquiry, and emotion regulation processes that can result in efficient gameplay; and for developing adaptive game-based learning environments that foster effective and efficient self-regulation and scientific inquiry during learning. 

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