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1. Design of an expansively-framed board game-based unit to introduce computer programming to upper elementary students

   Lee, Victor R.; Poole, Frederick; Clarke-Midura, Jody; Recker, Mimi (January 2020, The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020)

   Gresalfi, Melissa; Horn, Ilana Seidel (Ed.)

   This paper presents an instructional design using expansive framing to introduce computer programming to upper elementary students. By using a tabletop board game as the context for learning, bridging connections between the learning in the board game and its digital instantiation, and privileging student authorship, we show how two students developed and transferred their understanding of several computational practices, including procedures and conditional logic, from the board game into their design of digital games in Scratch. 

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2. Cracking the Code of Learning Gains: Using Ordered Network Analysis to Understand the Influence of Prior Knowledge

   https://doi.org/10.1007/978-3-031-47014-1_2  

   Zambrano, A; Barany, A; Ocumpaugh, J; Nasiar, N; Hutt, S; Goslen, A; Rowe, J; Lester, J; Weibe, E; Mott, B (October 2023, Proceedings of the 5th International Conference on Quantitative Ethnography)

   Prior research has shown that digital games can enhance STEM education by providing learners with immersive and authentic scientific experiences. However, optimizing the learning outcomes of students engaged in game-based environments requires aligning the game design with diverse student needs. Therefore, an in-depth understanding of player behavior is crucial for identifying students who need additional support or modifications to the game design. This study applies an Ordered Network Analysis (ONA)—a specific kind of Epistemic Network Analysis (ENA)—to examine the game trace log data of student interactions, to gain insights into how learning gains relate to the different ways that students move through an open-ended virtual world for learning microbiology. Our findings reveal that differences between students with high and low learning gains are mediated by their prior knowledge. Specifically, level of prior knowledge is related to behaviors that resemble wheel-spinning, which warrant the development of future interventions. Results also have implications for discovery with modeling approaches and for enhancing in-game support for learners and improving game design. 

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3. Cracking the code of learning gains: Using Ordered Network Analysis to Understand the Influence of Prior Knowledge.

   https://doi.org/10.1007/978-3-031-47014-1_2  

   Zambrano, A; Barany, A; Ocumpaugh, J; Nasiar, N; Hutt, S; Goslen, A; Rowe, J; Lester, J; Weibe, E; Mott, B (October 2023, Proceedings of the 5th International Conference on Quantitative Ethnography)

   rior research has shown that digital games can enhance STEM education by providing learners with immersive and authentic scientific experiences. However, optimizing the learning outcomes of students engaged in game-based environments requires aligning the game design with diverse student needs. Therefore, an in-depth understanding of player behavior is crucial for identifying students who need additional support or modifications to the game design. This study applies an Ordered Network Analysis (ONA)—a specific kind of Epistemic Network Analysis (ENA)—to examine the game trace log data of student interactions, to gain insights into how learning gains relate to the different ways that students move through an open-ended virtual world for learning microbiology. Our findings reveal that differences between students with high and low learning gains are mediated by their prior knowledge. Specifically, level of prior knowledge is related to behaviors that resemble wheel-spinning, which warrant the development of future interventions. Results also have implications for discovery with modeling approaches and for enhancing in-game support for learners and improving game design. 

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4. RoboMath: Designing a Learning Companion Robot to Support Children’s Numerical Skills

   https://doi.org/10.1145/3459990.3460709  

   Ho, Hui-Ru; Cagiltay, Bengisu; White, Nathan Thomas; Hubbard, Edward M.; Mutlu, Bilge (June 2021, IDC '21: Interaction Design and Children)

   null (Ed.)

   Children’s early numerical knowledge establishes a foundation for later development of mathematics achievement and playing linear number board games is effective in improving basic numeri- cal abilities. Besides the visuo-spatial cues provided by traditional number board games, learning companion robots can integrate multi-sensory information and offer social cues that can support children’s learning experiences. We explored how young children experience sensory feedback (audio and visual) and social expressions from a robot when playing a linear number board game, “RoboMath.” We present the interaction design of the game and our investigation of children’s (n = 19, aged 4) and parents’ experiences under three conditions: (1) visual-only, (2) audio-visual, and (3) audio- visual-social robot interaction. We report our qualitative analysis, including the themes observed from interviews with families on their perceptions of the game and the interaction with the robot, their child’s experiences, and their design recommendations. 

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5. The Contour to Classification Game

   Lee, Irene; Ali, Safinah (May 2021, Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   The Contour to Classification game is a browser-based game that teaches middle school students basic concepts in supervised learning. The game is an online variant of the Neural Network game that was presented at AAAI Fall Symposium Teaching AI in K-12 track in 2019. We share preliminary findings from implementing the online version of the original Neural Network game in a pilot research study and describe the game’s evolution to the Contour to Classification game. The new game uses a simulation of a neural network to engage students, through digital drawing and selection interactions, in the classification of images. The players act as nodes in a multi-step process of compositing salient smaller features to form larger features and ultimately a partial contour of an object that is used to make a prediction. After evaluating the prediction, information is sent back through the network in processes mimicking back propagation and gradient descent. Additional rounds of the game can be played to witness how the network evolves and gets “better” at classifying images from contours. Through this game, we aimed for students to learn the structure, components, and functioning of a neural network, and the processes involved in supervised learning. The Contour to Classification game supports online student learning by providing the image classification experience using purely visual inputs to each layer. We will conclude with a discussion of if and how the evolving design addresses classroom needs and scaling considerations. 

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