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Jesper Dannath; Alina Deriyeva; Benjamin Paaßen – International Educational Data Mining Society, 2025
Research on the effectiveness of Intelligent Tutoring Systems (ITSs) suggests that automatic hint generation has the best effect on learning outcomes when hints are provided on the level of intermediate steps. However, ITSs for programming tasks face the challenge to decide on the granularity of steps for feedback, since it is not a priori clear…
Descriptors: Intelligent Tutoring Systems, Programming, Computer Science Education, Undergraduate Students
Sen, Ayon; Patel, Purav; Rau, Martina A.; Mason, Blake; Nowak, Robert; Rogers, Timothy T.; Zhu, Xiaojin – International Educational Data Mining Society, 2018
In STEM domains, students are expected to acquire domain knowledge from visual representations that they may not yet be able to interpret. Such learning requires perceptual fluency: the ability to intuitively and rapidly see which concepts visuals show and to translate among multiple visuals. Instructional problems that engage students in…
Descriptors: Visual Aids, Visual Perception, Data Analysis, Artificial Intelligence
Labutov, Igor; Lipson, Hod – International Educational Data Mining Society, 2016
A growing subset of the web today is aimed at "teaching" and "explaining" technical concepts with varying degrees of detail and to a broad range of target audiences. Content such as tutorials, blog articles and lecture notes is becoming more prevalent in many technical disciplines and provides up-to-date technical coverage with…
Descriptors: Educational Resources, Internet, Sequential Learning, Classification
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

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