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Tsabari, 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
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de Alfaro, Luca; Shavlovsky, Michael – International Educational Data Mining Society, 2016
Peer grading is widely used in MOOCs and in standard university settings. The quality of grades obtained via peer grading is essential for the educational process. In this work, we study the factors that influence errors in peer grading. We analyze 288 assignments with 25,633 submissions and 113,169 reviews conducted with CrowdGrader, a web based…
Descriptors: Peer Evaluation, Grading, Error Patterns, Accuracy
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Sato, Masatoshi; McDonough, Kim – Studies in Second Language Acquisition, 2019
This study explored the impact of contextualized practice on second language (L2) learners' production of wh-questions in the L2 classroom. It examined the quality of practice (correct vs. incorrect production) and the contribution of declarative knowledge to proceduralization. Thirty-four university-level English as a foreign language learners…
Descriptors: Second Language Learning, Second Language Instruction, Questioning Techniques, English (Second Language)
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Feng, Mingyu, Ed.; Käser, Tanja, Ed.; Talukdar, Partha, Ed. – International Educational Data Mining Society, 2023
The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for…
Descriptors: Information Retrieval, Data Analysis, Computer Assisted Testing, Cheating
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Liben, Lynn S.; Kastens, Kim A.; Christensen, Adam E. – Cognition and Instruction, 2011
To study the role of spatial concepts in science learning, 125 college students with high, medium, or low scores on a horizontality (water-level) spatial task were given information about geological strike and dip using existing educational materials. Participants mapped an outcrop's strike and dip, a rod's orientation, pointed to a distant…
Descriptors: Student Evaluation, Error Patterns, Spatial Ability, Teaching Methods
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Sundermann, Michael J. – Journal of Chemical Education, 2008
A statistical analysis of multiple-choice answers is performed to identify anomalies that can be used as evidence of student cheating. The ratio of exact errors in common (EEIC: two students put the same wrong answer for a question) to differences (D: two students get different answers) was found to be a good indicator of cheating under a wide…
Descriptors: College Students, Cheating, Multiple Choice Tests, Statistical Analysis
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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
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Rath, Alex; Brown, David E. – Journal of Educational Computing Research, 1995
Presents a human-computer interaction (HCI) conceptions model designed to help in the understanding of the cognitive processes involved when college students learn to program computers. Examines syntactic and algorithmic HCI operational errors and reviews conceptions based on natural language reasoning, independent computer reasoning, and…
Descriptors: Cognitive Processes, College Students, Computers, Designers
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Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
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Jadud, Matthew C. – Computer Science Education, 2005
Syntactically correct code does not fall from the sky; the process that leads to a student's first executable program is not well understood. At the University of Kent we have begun to explore the "compilation behaviours" of novice programmers, or the behaviours that students exhibit while authoring code; in our initial study, we have…
Descriptors: Introductory Courses, Programming, Student Behavior, Educational Technology