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1. Can Instructional Prompts Support Effective Learner Behaviors with Tangible and Digital 3D Models?

   Orr, Matthew; Butcher, Kirsten (January 2020, The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020)

   Gresalfi, M.; Horn, I. S. (Ed.)

   This study investigated the impact of instructional prompts on how learners interacted with two forms of 3D models during a science education task: tangible (3D prints) or digital (desktop-based) 3D models of fossils from a natural history museum collection. Learners observed and reasoned with 3D models to answer a scientific question. Two forms of instructional prompts were compared: functional prompts to manipulate the models in ways that explored how the fossil may have functioned, and general prompts to manipulate the models in ways that help answer the scientific question. Results suggest that functional prompts encouraged different participant interactions with tangible models, but not with digital models. 

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2. Leveraging Prediction and Reflection in a Computational Setting to Enrich Undergraduate Students’ Combinatorial Thinking

   https://doi.org/10.1080/07370008.2021.2020793  

   Lockwood, Elise (January 2022, Cognition and Instruction)

   Enyedy, Noel (Ed.)

   In this paper, I discuss undergraduate students’ engagement in basic Python programming while solving combinatorial problems. Students solved tasks that were designed to involve programming, and they were encouraged to engage in activities of prediction and reflection. I provide data from two paired teaching experiments, and I outline how the task design and instructional interventions particularly supported students’ combinatorial reasoning. I argue that emergent computational representations and the prompts for prediction and reflection were especially useful in supporting students’ reasoning about fundamental combinatorial ideas. I argue that this particular mathematical example informs broader notions of disciplinary reflexivity and representational heterogeneity, providing insight into computational thinking practices in the domain of mathematics. Ultimately, I aim to explore the nature of computing and enumeration, shedding light on why the two disciplines are particularly well-suited to support each other. I conclude with implications and avenues for future research. 

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3. Exploring Free Digital Tools in Education

   Mucundanyi, G.; Woodley, X. (January 2021, International journal of education and development using information and communication technology)

   Technology can assist instructional designers and teachers in meeting the needs of learners in traditional classrooms and virtual course environments. During the COVID-19 pandemic, many teachers and instructional designers began looking for resources they could use for hybrid and online course delivery. Many found that the cost of some technology tools was well outside of their financial means to assist them in meeting student learning outcomes. However, some digital tools provide free access for educators and are beneficial to students. In this article, the authors shared five tools they have used in developing and teaching online and traditional technology courses at the college level. They share how they used a learning management system tool, a collaboration tool, a search engine tool, a content creation tool, and a content sharing tool to engage students in their courses. As teachers look for alternatives to use as they move content from classroom teaching to online instruction, this article can help them consider the recommended tools for instruction. Teachers, instructors, and instructional designers may explore the free digital tools in this article and do further research on other digital tools to support student learning in their disciplines. 

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4. Focused self-explanations lead to the best learning outcomes in a digital learning game

   McLaren, Bruce M.; Richey, J. Elizabeth; Nguyen, Huy Anh; Mogessie, Michael (June 2022, Proceedings of the 16th International Conference on Learning Science (ICLS 2022))

   Prompted self-explanation, in which learners are induced to explain how they have solved problems, is a powerful instructional technique. Self-explanation can be prompted within learning technology by asking learners to construct their own self-explanations or select explanations from a menu. The menu-based approach has led to the best learning outcomes in the relatively few cases it has been studied in the context of digital learning games, contrary to some self-explanation theory. In a classroom study of 214 5th and 6th graders, in which the students played a digital learning game, we compared three forms of prompted self-explanation: menu-based, scaffolded, and focused (i.e., open-ended text entry, but with a focused prompt). Students in the focused condition learned more than students in the menu-based condition at delayed posttest, with no other learning differences between the conditions. This suggests that focused self-explanations may be especially beneficial for retention and deeper knowledge. 

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5. How instructors frame students' interactions with educational technologies can enhance or reduce learning with multiple representations

   https://doi.org/https://doi.org/10.1016/j.compedu.2018.09.012  

   Wu, S. P.; Corr, J.; Rau, M. A. (April 2019, Computers & education)

   Instructors in STEM classrooms often frame students' interactions with technologies to help them learn content. For instance, in many STEM domains, instructors commonly help students translate physical 3D models into 2D drawings by prompting them to focus on (a) orienting physical 3D models and (b) generating 2D drawings. We investigate whether framing prompts that target either of these practices enhance the effectiveness of an educational technology that supports collaborative translation among multiple representations. To this end, we conducted a quasi-experiment with 565 undergraduate chemistry students. All students collaboratively built physical 3D models of molecules and translated them into 2D drawings. In a business-as-usual control condition, students drew on paper, without support from an educational technology. In two experimental conditions, students drew in an educational technology that provided feedback and prompted collaboration. One condition received framing prompts to focus on physical models (model condition); another received prompts to generate intermediary drawings on paper (draw condition). Compared to the control condition, the model condition showed higher learning gains, but the draw condition showed lower learning gains. Analyses of log data showed that students made more model-based errors, and the prompts in the model condition reduced these model-based errors. However, interviews with instructors showed that they prefer drawing-focused prompts, in contrast to our results. These findings offer theoretical insights into how students learn to translate among representations. Furthermore, they yield practical recommendations for the use of educational technologies that support learning with multiple representations. 

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