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Rho, Jihyun; Rau, Martina A.; Van Veen, Barry D. – International Educational Data Mining Society, 2022
Instruction in many STEM domains heavily relies on visual representations, such as graphs, figures, and diagrams. However, students who lack representational competencies do not benefit from these visual representations. Therefore, students must learn not only content knowledge but also representational competencies. Further, as learning…
Descriptors: Learning Processes, Models, Introductory Courses, Engineering Education
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Zhang, Qiao; Maclellan, Christopher J. – International Educational Data Mining Society, 2021
Knowledge tracing algorithms are embedded in Intelligent Tutoring Systems (ITS) to keep track of students' learning process. While knowledge tracing models have been extensively studied in offline settings, very little work has explored their use in online settings. This is primarily because conducting experiments to evaluate and select knowledge…
Descriptors: Electronic Learning, Mastery Learning, Computer Simulation, Intelligent Tutoring Systems
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PaaBen, Benjamin; Dywel, Malwina; Fleckenstein, Melanie; Pinkwart, Niels – International Educational Data Mining Society, 2022
Item response theory (IRT) is a popular method to infer student abilities and item difficulties from observed test responses. However, IRT struggles with two challenges: How to map items to skills if multiple skills are present? And how to infer the ability of new students that have not been part of the training data? Inspired by recent advances…
Descriptors: Item Response Theory, Test Items, Item Analysis, Inferences
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Chen, Li; Lu, Min; Goda, Yoshiko; Yamada, Masanori – International Association for Development of the Information Society, 2019
Metacognition is an aspect in self-regulated learning and is necessary to achieve such learning in an effective and efficient manner. However, it is not always easy and accurate for learners to monitor or assess their own metacognition. In this study, we designed a learning analytics dashboard to improve self-regulated learning in online…
Descriptors: Learning Analytics, Metacognition, Learning Strategies, Information Management
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Morsy, Sara; Karypis, George – International Educational Data Mining Society, 2019
Grade prediction for future courses not yet taken by students is important as it can help them and their advisers during the process of course selection as well as for designing personalized degree plans and modifying them based on their performance. One of the successful approaches for accurately predicting a student's grades in future courses is…
Descriptors: Grades (Scholastic), Models, Prediction, Predictor Variables
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Yang, Kexin Bella; Echeverria, Vanessa; Wang, Xuejian; Lawrence, LuEttaMae; Holstein, Kenneth; Rummel, Nikol; Aleven, Vincent – International Educational Data Mining Society, 2021
Constructing effective and well-balanced learning groups is important for collaborative learning. Past research explored how group formation policies affect learners' behaviors and performance. With the different classroom contexts, many group formation policies work in theory, yet their feasibility is rarely investigated in authentic class…
Descriptors: Grouping (Instructional Purposes), Cooperative Learning, Teaching Methods, Kindergarten
Gobert, Janice Darlene; Sao Pedro, Michael A.; Baker, Ryan S. – Grantee Submission, 2012
In this paper we explored whether engaging in two inquiry skills associated with data collection, designing controlled experiments and testing stated hypotheses, within microworlds for one physical science domain (density) impacted the acquisition of inquiry skills in another domain (phase change). To do so, we leveraged educational data mining…
Descriptors: Data Collection, Learning Analytics, Inquiry, Science Process Skills