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Cody, Christa; Maniktala, Mehak; Lytle, Nicholas; Chi, Min; Barnes, Tiffany – International Journal of Artificial Intelligence in Education, 2022
Research has shown assistance can provide many benefits to novices lacking the mental models needed for problem solving in a new domain. However, varying approaches to assistance, such as subgoals and next-step hints, have been implemented with mixed results. Next-Step hints are common in data-driven tutors due to their straightforward generation…
Descriptors: Comparative Analysis, Prior Learning, Intelligent Tutoring Systems, Problem Solving
Jia, Qinjin; Young, Mitchell; Xiao, Yunkai; Cui, Jialin; Liu, Chengyuan; Rashid, Parvez; Gehringer, Edward – Journal of Educational Data Mining, 2022
Instant feedback plays a vital role in promoting academic achievement and student success. In practice, however, delivering timely feedback to students can be challenging for instructors for a variety of reasons (e.g., limited teaching resources). In many cases, feedback arrives too late for learners to act on the advice and reinforce their…
Descriptors: Student Projects, Learning Analytics, Intelligent Tutoring Systems, Feedback (Response)
Jia, Qinjin; Young, Mitchell; Xiao, Yunkai; Cui, Jialin; Liu, Chengyuan; Rashid, Parvez; Gehringer, Edward – International Educational Data Mining Society, 2022
Providing timely feedback is crucial in promoting academic achievement and student success. However, for multifarious reasons (e.g., limited teaching resources), feedback often arrives too late for learners to act on the feedback and improve learning. Thus, automated feedback systems have emerged to tackle educational tasks in various domains,…
Descriptors: Student Projects, Feedback (Response), Natural Language Processing, Guidelines
Paquette, Luc; Baker, Ryan S. – Interactive Learning Environments, 2019
Learning analytics research has used both knowledge engineering and machine learning methods to model student behaviors within the context of digital learning environments. In this paper, we compare these two approaches, as well as a hybrid approach combining the two types of methods. We illustrate the strengths of each approach in the context of…
Descriptors: Comparative Analysis, Student Behavior, Models, Case Studies
Barollet, Théo; Bouchez Tichadou, Florent; Rastello, Fabrice – International Educational Data Mining Society, 2021
In Intelligent Tutoring Systems (ITS), methods to choose the next exercise for a student are inspired from generic recommender systems, used, for instance, in online shopping or multimedia recommendation. As such, collaborative filtering, especially matrix factorization, is often included as a part of recommendation algorithms in ITS. One notable…
Descriptors: Intelligent Tutoring Systems, Prediction, Internet, Purchasing
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
Sao Pedro, Michael; Jiang, Yang; Paquette, Luc; Baker, Ryan S.; Gobert, Janice – Grantee Submission, 2014
Students conducted inquiry using simulations within a rich learning environment for 4 science topics. By applying educational data mining to students' log data, assessment metrics were generated for two key inquiry skills, testing stated hypotheses and designing controlled experiments. Three models were then developed to analyze the transfer of…
Descriptors: Simulation, Transfer of Training, Bayesian Statistics, Inquiry

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