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Download full textTsabari, Stav; Segal, Avi; Gal, Kobi – International Educational Data Mining Society, 2023
Automatically identifying struggling students learning to program can assist teachers in providing timely and focused help. This work presents a new deep-learning language model for predicting "bug-fix-time", the expected duration between when a software bug occurs and the time it will be fixed by the student. Such information can guide…
Descriptors: College Students, Computer Science Education, Programming, Error Patterns
Akpinar, Nil-Jana; Ramdas, Aaditya; Acar, Umut – International Educational Data Mining Society, 2020
Educational software data promises unique insights into students' study behaviors and drivers of success. While much work has been dedicated to performance prediction in massive open online courses, it is unclear if the same methods can be applied to blended courses and a deeper understanding of student strategies is often missing. We use pattern…
Descriptors: Learning Strategies, Blended Learning, Learning Analytics, Student Behavior
Sheshadri, Adithya; Gitinabard, Niki; Lynch, Collin F.; Barnes, Tiffany; Heckman, Sarah – International Educational Data Mining Society, 2018
Online tools provide unique access to research students' study habits and problem-solving behavior. In MOOCs [Massive Open Online Courses], this online data can be used to inform instructors and to provide automatic guidance to students. However, these techniques may not apply in blended courses with face to face and online components. We report…
Descriptors: Online Courses, Large Group Instruction, Educational Technology, Technology Uses in Education
Bihani, Ankita; Paepcke, Andreas – International Educational Data Mining Society, 2018
We develop a random forest classifier that helps assign academic credit for a student's class forum participation. The classification target are the four classes created by student rank quartiles. Course content experts provided ground truth by ranking a limited number of post pairs. We expand this labeled set via data augmentation. We compute the…
Descriptors: College Credits, Classification, Computer Mediated Communication, Student Participation
Ezen-Can, Aysu; Boyer, Kristy Elizabeth – International Educational Data Mining Society, 2015
The tremendous effectiveness of intelligent tutoring systems is due in large part to their interactivity. However, when learners are free to choose the extent to which they interact with a tutoring system, not all learners do so actively. This paper examines a study with a natural language tutorial dialogue system for computer science, in which…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Computer Science Education, Problem Solving
Boyer, Kristy Elizabeth, Ed.; Yudelson, Michael, Ed. – International Educational Data Mining Society, 2018
The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37…
Descriptors: Data Collection, Data Analysis, Computer Science Education, Program Proposals
