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Shabrina, Preya; Mostafavi, Behrooz; Tithi, Sutapa Dey; Chi, Min; Barnes, Tiffany – International Educational Data Mining Society, 2023
Problem decomposition into sub-problems or subgoals and recomposition of the solutions to the subgoals into one complete solution is a common strategy to reduce difficulties in structured problem solving. In this study, we use a datadriven graph-mining-based method to decompose historical student solutions of logic-proof problems into Chunks. We…
Descriptors: Intelligent Tutoring Systems, Problem Solving, Graphs, Data Analysis
Zhou, Guojing; Moulder, Robert G.; Sun, Chen; D'Mello, Sidney K. – International Educational Data Mining Society, 2022
In collaborative problem solving (CPS), people's actions are interactive, interdependent, and temporal. However, it is unclear how actions temporally relate to each other and what are the temporal similarities and differences between successful vs. unsuccessful CPS processes. As such, we apply a temporal analysis approach, Multilevel Vector…
Descriptors: Cooperative Learning, Problem Solving, College Students, Physics
Samadi, Mohammad Amin; Cavazos, Jacqueline G.; Lin, Yiwen; Nixon, Nia – International Educational Data Mining Society, 2022
Collaborative problem solving (CPS) is a 21st-century skill essential for learning gains, workplace success, and tackling increasingly complicated global problems. Group diversity plays a vital role during collaborative activities, especially in a digital space. Although CPS involves dynamic communication behaviors, few studies have considered the…
Descriptors: Systems Approach, Cultural Pluralism, Teamwork, Communication (Thought Transfer)
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
Prihar, Ethan; Heffernan, Neil – International Educational Data Mining Society, 2021
Similar content has tremendous utility in classroom and online learning environments. For example, similar content can be used to combat cheating, track students' learning over time, and model students' latent knowledge. These different use cases for similar content all rely on different notions of similarity, which make it difficult to determine…
Descriptors: Computer Software, Middle School Teachers, Mathematics Teachers, College Students
Zhou, Guojing; Wang, Jianxun; Lynch, Collin F.; Chi, Min – International Educational Data Mining Society, 2017
In this study, we applied decision trees (DT) to extract a compact set of pedagogical decision-making rules from an original "full" set of 3,702 Reinforcement Learning (RL)- induced rules, referred to as the DT-RL rules and Full-RL rules respectively. We then evaluated the effectiveness of the two rule sets against a baseline Random…
Descriptors: Learning Theories, Teaching Methods, Decision Making, Intelligent Tutoring Systems
Wan, Hao; Beck, Joseph Barbosa – International Educational Data Mining Society, 2015
The phenomenon of wheel spinning refers to students attempting to solve problems on a particular skill, but becoming stuck due to an inability to learn the skill. Past research has found that students who do not master a skill quickly tend not to master it at all. One question is why do students wheel spin? A plausible hypothesis is that students…
Descriptors: Skill Development, Problem Solving, Knowledge Level, Learning Processes
Ye, Cheng; Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam – International Educational Data Mining Society, 2015
This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features…
Descriptors: Learning Activities, Learning Processes, Data Collection, Student Behavior
Rau, Martina A.; Scheines, Richard – International Educational Data Mining Society, 2012
Although learning from multiple representations has been shown to be effective in a variety of domains, little is known about the mechanisms by which it occurs. We analyzed log data on error-rate, hint-use, and time-spent obtained from two experiments with a Cognitive Tutor for fractions. The goal of the experiments was to compare learning from…
Descriptors: Experiments, Mathematics, Comparative Analysis, Outcomes of Education
Jiang, Yang; Paquette, Luc; Baker, Ryan S.; Clarke-Midura, Jody – International Educational Data Mining Society, 2015
Inquiry skills are an important part of science education standards. There has been particular interest in verifying that these skills can transfer across domains and instructional contexts [4,15,16]. In this paper, we study transfer of inquiry skills, and the effects of prior practice of inquiry skills, using data from over 2000 middle school…
Descriptors: Performance Based Assessment, Virtual Classrooms, Comparative Analysis, Novices
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection

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