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Muhammad Fawad Akbar Khan; Max Ramsdell; Erik Falor; Hamid Karimi – International Educational Data Mining Society, 2024
This paper undertakes a thorough evaluation of ChatGPT's code generation capabilities, contrasting them with those of human programmers from both educational and software engineering standpoints. The emphasis is placed on elucidating its importance in these intertwined domains. To facilitate a robust analysis, we curated a novel dataset comprising…
Descriptors: Artificial Intelligence, Automation, Computer Science Education, Programming
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Andreea Dutulescu; Stefan Ruseti; Mihai Dascalu; Danielle McNamara – International Educational Data Mining Society, 2025
The assessment of student responses to learning-strategy prompts, such as self-explanation, summarization, and paraphrasing, is essential for evaluating cognitive engagement and comprehension. However, manual scoring is resource-intensive, limiting its scalability in educational settings. This study investigates the use of Large Language Models…
Descriptors: Scoring, Computational Linguistics, Computer Software, Artificial Intelligence
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Doewes, Afrizal; Kurdhi, Nughthoh Arfawi; Saxena, Akrati – International Educational Data Mining Society, 2023
Automated Essay Scoring (AES) tools aim to improve the efficiency and consistency of essay scoring by using machine learning algorithms. In the existing research work on this topic, most researchers agree that human-automated score agreement remains the benchmark for assessing the accuracy of machine-generated scores. To measure the performance of…
Descriptors: Essays, Writing Evaluation, Evaluators, Accuracy
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Zhang, Mengxue; Baral, Sami; Heffernan, Neil; Lan, Andrew – International Educational Data Mining Society, 2022
Automatic short answer grading is an important research direction in the exploration of how to use artificial intelligence (AI)-based tools to improve education. Current state-of-the-art approaches use neural language models to create vectorized representations of students responses, followed by classifiers to predict the score. However, these…
Descriptors: Grading, Mathematics Instruction, Artificial Intelligence, Form Classes (Languages)
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Baral, Sami; Botelho, Anthony; Santhanam, Abhishek; Gurung, Ashish; Cheng, Li; Heffernan, Neil – International Educational Data Mining Society, 2023
Teachers often rely on the use of a range of open-ended problems to assess students' understanding of mathematical concepts. Beyond traditional conceptions of student open-ended work, commonly in the form of textual short-answer or essay responses, the use of figures, tables, number lines, graphs, and pictographs are other examples of open-ended…
Descriptors: Mathematics Instruction, Mathematical Concepts, Problem Solving, Test Format
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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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Jia, Qinjin; Young, Mitchell; Xiao, Yunkai; Cui, Jialin; Liu, Chengyuan; Rashid, Parvez; Gehringer, Edward – International Educational Data Mining Society, 2022
Providing timely feedback is crucial in promoting academic achievement and student success. However, for multifarious reasons (e.g., limited teaching resources), feedback often arrives too late for learners to act on the feedback and improve learning. Thus, automated feedback systems have emerged to tackle educational tasks in various domains,…
Descriptors: Student Projects, Feedback (Response), Natural Language Processing, Guidelines
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Zhou, Yiqiu; Kang, Jina – International Educational Data Mining Society, 2022
The complex and dynamic nature of collaboration makes it challenging to find indicators of productive learning and quality collaboration. This exploratory study developed a collaboration metric to capture temporal patterns of joint attention (JA) based on log files generated as students interacted with an immersive astronomy simulation using…
Descriptors: Astronomy, Problem Solving, Science Instruction, Cooperative Learning
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Sha, Lele; Rakovic, Mladen; Li, Yuheng; Whitelock-Wainwright, Alexander; Carroll, David; Gaševic, Dragan; Chen, Guanliang – International Educational Data Mining Society, 2021
Classifying educational forum posts is a longstanding task in the research of Learning Analytics and Educational Data Mining. Though this task has been tackled by applying both traditional Machine Learning (ML) approaches (e.g., Logistics Regression and Random Forest) and up-to-date Deep Learning (DL) approaches, there lacks a systematic…
Descriptors: Classification, Computer Mediated Communication, Learning Analytics, Data Analysis
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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
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Tack, Anaïs; Piech, Chris – International Educational Data Mining Society, 2022
How can we test whether state-of-the-art generative models, such as Blender and GPT-3, are good AI teachers, capable of replying to a student in an educational dialogue? Designing an AI teacher test is challenging: although evaluation methods are much-needed, there is no off-the-shelf solution to measuring pedagogical ability. This paper reports…
Descriptors: Artificial Intelligence, Dialogs (Language), Bayesian Statistics, Decision Making
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Shimmei, Machi; Matsuda, Noboru – International Educational Data Mining Society, 2020
One of the most challenging issues for online courseware engineering is to maintain the quality of instructional components, such as written text, video, and assessments. Learning engineers would like to know how individual instructional components contributed to students' learning. However, it is a hard task because it requires significant…
Descriptors: Teaching Methods, Engineering, Outcomes of Education, Courseware
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Kovalkov, Anastasia; Paassen, Benjamin; Segal, Avi; Gal, Kobi; Pinkwart, Niels – International Educational Data Mining Society, 2021
Promoting creativity is considered an important goal of education, but creativity is notoriously hard to define and measure. In this paper, we make the journey from defining a formal creativity and applying the measure in a practical domain. The measure relies on core theoretical concepts in creativity theory, namely fluency, flexibility, and…
Descriptors: Creativity, Theory Practice Relationship, Evaluators, Specialists
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Unnam, Abhishek; Takhar, Rohit; Aggarwal, Varun – International Educational Data Mining Society, 2019
Email has become the most preferred form of business communication. Writing "good" email has become an essential skill required in the industry. "Good" email writing not only facilitates clear communication, but also makes a positive impression on the recipient, whether it be one's colleague or a customer. The aim of this paper…
Descriptors: Grading, Electronic Mail, Feedback (Response), Written 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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