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Muntasir Hoq; Ananya Rao; Reisha Jaishankar; Krish Piryani; Nithya Janapati; Jessica Vandenberg; Bradford Mott; Narges Norouzi; James Lester; Bita Akram – International Educational Data Mining Society, 2025
In Computer Science (CS) education, understanding factors contributing to students' programming difficulties is crucial for effective learning support. By identifying specific issues students face, educators can provide targeted assistance to help them overcome obstacles and improve learning outcomes. While identifying sources of struggle, such as…
Descriptors: Computer Science Education, Programming, Misconceptions, Error Patterns
Yunsung Kim; Jadon Geathers; Chris Piech – International Educational Data Mining Society, 2024
"Stochastic programs," which are programs that produce probabilistic output, are a pivotal paradigm in various areas of CS education from introductory programming to machine learning and data science. Despite their importance, the problem of automatically grading such programs remains surprisingly unexplored. In this paper, we formalize…
Descriptors: Grading, Automation, Accuracy, Programming
Cai, Zhiqiang; Marquart, Cody; Shaffer, David W. – International Educational Data Mining Society, 2022
Regular expression (regex) coding has advantages for text analysis. Humans are often able to quickly construct intelligible coding rules with high precision. That is, researchers can identify words and word patterns that correctly classify examples of a particular concept. And, it is often easy to identify false positives and improve the regex…
Descriptors: Coding, Classification, Artificial Intelligence, Engineering Education
Jahnke, Maximilian; Höppner, Frank – International Educational Data Mining Society, 2022
The value of an instructor is that she exactly recognizes what the learner is struggling with and provides constructive feedback straight to the point. This work aims at a step towards this type of feedback in the context of an introductory programming course, where students perform program execution tracing to align their understanding of Java…
Descriptors: Programming, Coding, Computer Science Education, Error Patterns
Ali Sartaz Khan; Tolulope Ogunremi; Ahmed Attia; Dorottya Demszky – International Educational Data Mining Society, 2025
Speaker diarization, the process of identifying "who spoke when" in audio recordings, is essential for understanding classroom dynamics. However, classroom settings present distinct challenges, including poor recording quality, high levels of background noise, overlapping speech, and the difficulty of accurately capturing children's…
Descriptors: Audio Equipment, Acoustics, Classroom Environment, Models
Gorgun, Guher; Yildirim-Erbasli, Seyma N.; Epp, Carrie Demmans – International Educational Data Mining Society, 2022
The need to identify student cognitive engagement in online-learning settings has increased with our use of online learning approaches because engagement plays an important role in ensuring student success in these environments. Engaged students are more likely to complete online courses successfully, but this setting makes it more difficult for…
Descriptors: Online Courses, Group Discussion, Learner Engagement, Student Participation
Baral, Sami; Botelho, Anthony F.; Erickson, John A.; Benachamardi, Priyanka; Heffernan, Neil T. – International Educational Data Mining Society, 2021
Open-ended questions in mathematics are commonly used by teachers to monitor and assess students' deeper conceptual understanding of content. Student answers to these types of questions often exhibit a combination of language, drawn diagrams and tables, and mathematical formulas and expressions that supply teachers with insight into the processes…
Descriptors: Scoring, Automation, Mathematics Tests, Student Evaluation
Miao, Dezhuang; Dong, Yu; Lu, Xuesong – International Educational Data Mining Society, 2020
In colleges, programming is increasingly becoming a general education course of almost all STEM majors as well as some art majors, resulting in an emerging demand for scalable programming education. To support scalable education, teaching activities such as grading and feedback have to be automated. Recently, online judge systems have been…
Descriptors: Programming, Prediction, Error Patterns, Models
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
Southwell, Rosy; Pugh, Samuel; Perkoff, E. Margaret; Clevenger, Charis; Bush, Jeffrey B.; Lieber, Rachel; Ward, Wayne; Foltz, Peter; D'Mello, Sidney – International Educational Data Mining Society, 2022
Automatic speech recognition (ASR) has considerable potential to model aspects of classroom discourse with the goals of automated assessment, feedback, and instructional support. However, modeling student talk is besieged by numerous challenges including a lack of data for child speech, low signal to noise ratio, speech disfluencies, and…
Descriptors: Audio Equipment, Error Analysis (Language), Classroom Communication, Feedback (Response)
Zhang, Mengxue; Wang, Zichao; Baraniuk, Richard; Lan, Andrew – International Educational Data Mining Society, 2021
Feedback on student answers and even during intermediate steps in their solutions to open-ended questions is an important element in math education. Such feedback can help students correct their errors and ultimately lead to improved learning outcomes. Most existing approaches for automated student solution analysis and feedback require manually…
Descriptors: Mathematics Instruction, Teaching Methods, Intelligent Tutoring Systems, Error Patterns
Liu, Chengyuan; Cui, Jialin; Shang, Ruixuan; Xiao, Yunkai; Jia, Qinjin; Gehringer, Edward – International Educational Data Mining Society, 2022
An online peer-assessment system typically allows students to give textual feedback to their peers, with the goal of helping the peers improve their work. The amount of help that students receive is highly dependent on the quality of the reviews. Previous studies have investigated using machine learning to detect characteristics of reviews (e.g.,…
Descriptors: Peer Evaluation, Feedback (Response), Computer Mediated Communication, Teaching Methods
PaaBen, Benjamin; Bertsch, Andreas; Langer-Fischer, Katharina; Rüdian, Sylvio; Wang, Xia; Sinha, Rupali; Kuzilek, Jakub; Britsch, Stefan; Pinkwart, Niels – International Educational Data Mining Society, 2021
Many modern anatomy curricula teach histology using virtual microscopes, where students inspect tissue slices in a computer program (e.g. a web browser). However, the educational data mining (EDM) potential of these virtual microscopes remains under-utilized. In this paper, we use EDM techniques to investigate three research questions on a virtual…
Descriptors: Anatomy, Science Instruction, Computer Simulation, Computer Software
Nguyen, Huy; Liew, Chun Wai – International Educational Data Mining Society, 2018
Recent works on Intelligent Tutoring Systems have focused on more complicated knowledge domains, which pose challenges in automated assessment of student performance. In particular, while the system can log every user action and keep track of the student's solution state, it is unable to determine the hidden intermediate steps leading to such…
Descriptors: Bayesian Statistics, Intelligent Tutoring Systems, Data Analysis, Error Patterns
Li, Tiffany Wenting; Paquette, Luc – International Educational Data Mining Society, 2020
Spatial visualization skills are essential and fundamental to studying STEM subjects, and sketching is an effective way to practice those skills. One significant challenge of supporting practice using sketching questions is the vast number of possible mistakes, making it time-consuming for instructors to provide customized and actionable feedback…
Descriptors: Error Patterns, Cluster Grouping, Visualization, Spatial Ability
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