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1. Improving Stealth Assessment in Game-Based Learning with LSTM-Based Analytics

   Akram, B.; Min, W.; Wiebe, E.; Mott, B.; Boyer, K. E.; Lester, J. (July 2018, International Conference on Educational Data Mining)

   A key affordance of game-based learning environments is their potential to unobtrusively assess student learning without interfering with gameplay. In this paper, we introduce a temporal analytics framework for stealth assessment that analyzes students' problem-solving strategies. The strategy-based temporal analytic framework uses long short-term memory network-based evidence models and clusters sequences of students' problem-solving behaviors across consecutive tasks. We investigate this strategy based temporal analytics framework on a dataset of problem solving behaviors collected from student interactions with a game-based learning environment for middle school computational thinking. The results of an evaluation indicate that the strategy-based temporal analytics framework significantly outperforms competitive baseline models with respect to stealth assessment predictive accuracy. 

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2. Problem solving and perseverance in geometry: Revelations from think alouds with middle grades students

   Mannix, J. P.; Bentley, B.; Krupa, E. E. (November 2022, Proceedings of the forty-fourth annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education)

   Lischka, A. E.; Dyer, E. B.; Jones, R. S.; Lovett, J. N.; Strayer, J.; Drown, S. (Ed.)

   Problem solving is a very important skill for students to learn (e.g., Bonilla-Rius, 2020; NGA, 2010), and part of developing problem solving skills is learning to persevere. One strategy for learning how to persevere is by providing students with materials that allow them the opportunity to engage with challenging problems (e.g., Kapur, 2010; Middleton et al., 2015). This study of the Volume unit of the AC2inG materials analyzes students’ strategies for problem solving and persevering. Findings from these think-aloud interviews indicate that different students will utilize one or more methods for solving challenging problems, such as asking clarifying questions, talking themselves through the problem, and attempting various mathematical approaches. 

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3. Unpacking strategy efficiency: Examining the relations between pre-solving pause time and productivity in a digital mathematics game

   Lee, J E; Thorp, C S; Kamberi, A; Ottmar, E (August 2025, Metacognition and learning)

   This study examined the relations among strategic planning, execution, and strategy efficiency during problem-solving in a digital algebra learning game with 7th-grade students. We used pre-solving pause time as a proxy indicator of strategic planning, and the productivity of the initial strategy as a measure of effective strategy execution. Additionally, we explored how these variables correlated with students’ posttest scores assessing algebraic knowledge. Mediation analyses at both the problem and student levels indicated that longer pre-solving pause times were associated with greater strategy efficiency. When considering both the direct and indirect effects of pre-solving pause time on strategy efficiency, the results revealed a partial positive mediation through the productivity of the initial strategy. Lastly, the results of a path analysis showed that strategy efficiency significantly predicted algebraic knowledge with a positive effect. These findings suggest that longer pause times are associated with more efficient problem solving as they increase the likelihood of a productive initial step, highlighting a positive mediating role of execution in the relation between planning and strategy efficiency in algebraic problem solving. 

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4. Effects of Guidance on Learning about Ill-defined Problems

   https://doi.org/10.1007/978-3-031-09680-8_28  

   An, S. (June 2022, Proceedings of the 18th International Conference on Intelligent Tutoring Systems.)

   We present a study that examines the effects of guidance on learning about addressing ill-defined problems in undergraduate bi- ology education. Two groups of college students used an online labo- ratory named VERA to learn about ill-defined ecological phenomena. While one group received guidance, such as giving the learners a specific problem and instruction on problem-solving methods, the other group re- ceived minimal guidance. The results indicate that, while performance in a problem-solving task was not different between groups receiving more vs. minimal guidance, the group that received minimal guidance adopted a more exploratory strategy and generated more interesting models of the given phenomena in a problem-solving task. 

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5. Authentic Learning on Machine Learning for Cybersecurity

   https://doi.org/10.1145/3545947.3576245  

   Lo, Dan; Shahriar, Hossain; Qian, Kai; Whitman, Michael; Wu, Fan; Thomas, Cassandra (March 2022, Proceedings of the 54th ACM Technical Symposium on Computer Science Education (SIGCSE))

   The primary goal of the authentic learning approach is to engage and motivate students in learning real world problem solving. We report our experience in developing k-nearest neighbor (KNN) classification for anomaly user behavior detection, one of the authentic machine learning for cybersecurity (ML4Cybr) learning modules based on 10 cybersecurity (CybrS) cases with machine learning (ML) solutions. All portable labs are made available on Google CoLab. So students can access and practice these hands-on labs anywhere and anytime without software installation and configuration which will engage students in learning concepts immediately and getting more experience for hands-on problem solving skills. 

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