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Prihar, Ethan; Vanacore, Kirk; Sales, Adam; Heffernan, Neil – International Educational Data Mining Society, 2023
There is a growing need to empirically evaluate the quality of online instructional interventions at scale. In response, some online learning platforms have begun to implement rapid A/B testing of instructional interventions. In these scenarios, students participate in series of randomized experiments that evaluate problem-level interventions in…
Descriptors: Electronic Learning, Intervention, Instructional Effectiveness, Data Collection
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Feng, Tianying; Chung, Gregory K. W. K. – Grantee Submission, 2022
A critical issue in using fine-grained gameplay data to measure learning processes is the development of indicators and the algorithms used to derive such indicators. Successful development--that is, developing traceable, interpretable, and sensitive-to-learning indicators--requires understanding the underlying theory, how the theory is…
Descriptors: Games, Data Collection, Learning Processes, Measurement
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Juliette Woodrow; Sanmi Koyejo; Chris Piech – International Educational Data Mining Society, 2025
High-quality feedback requires understanding of a student's work, insights into what concepts would help them improve, and language that matches the preferences of the specific teaching team. While Large Language Models (LLMs) can generate coherent feedback, adapting these responses to align with specific teacher preferences remains an open…
Descriptors: Feedback (Response), Artificial Intelligence, Teacher Attitudes, Preferences
Fitzallen, Noleine; Watson, Jane – Mathematics Education Research Group of Australasia, 2023
This paper reports on students' experiences of describing and representing variation in hypothetical data. Fifty-six students (8-9 years-old) experienced collecting and working with quantitative data for two years as part of a STEM education project. The task described here was an end-of-year survey question, with three parts about a hypothetical…
Descriptors: Elementary School Students, STEM Education, Foreign Countries, Data Analysis
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John Y. H. Bai; Olaf Zawacki-Richter; Wolfgang Muskens – Turkish Online Journal of Distance Education, 2024
Artificial intelligence in education (AIEd) is a fast-growing field of research. In previous work, we described efforts to explore the possible futures of AIEd by identifying key variables and their future prospects. This paper re-examines our discussions on the governance of data and the role of students and teachers by considering the…
Descriptors: Artificial Intelligence, Technology Uses in Education, Natural Language Processing, Governance
Ali Yildiz; Bünyamin Ispir – Online Submission, 2024
The study aims to reveal the thoughts of the researchers about the data collection process in the studies whose data were provided by the participants' views. The case study approach, one of the qualitative research designs, was used in the study. The study group of the research consists of a total of 12 experienced researchers, 9 women, and 3…
Descriptors: Doctoral Students, College Graduates, Student Research, Student Attitudes
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Xiaolu Liu; Jingwen Liu; Rachel Gurvitch; Yonggi Son – AERA Online Paper Repository, 2024
This study aimed to examine pre-service physical education teachers' (PPETs) subjective theories of assessment as they formed prior to the professional training of assessment during PETE. Using a phenomenological approach, 11 PPETs were interviewed. Data were analyzed in Nvivo 12. Findings revealed that (a) PPETs understood assessment as data…
Descriptors: Preservice Teachers, Physical Education Teachers, Student Attitudes, Student Evaluation
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Conrad Borchers; Yinuo Xu; Zachary A. Pardos – International Educational Data Mining Society, 2024
Educational data mining increasingly leverages enrollment data for higher education applications. However, these data describe final end-of-semester course selections, not the often complex enrollment activities leading up to a finalized schedule. Fine-grain transaction data of student waitlist, add, and drop actions during academic semester…
Descriptors: College Enrollment, Student Behavior, Enrollment Trends, Decision Making
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Aziman Abdullah; Pang Jieyu – International Society for Technology, Education, and Science, 2023
It is essential to save lives during emergencies not only in hospitals but also in colleges and universities. Failure to identify risks and take prompt action during catastrophes and emergency situations could result in the loss of life and property for the campus community. This research aims to explore the feasibility of using data analytics to…
Descriptors: Emergency Programs, Information Technology, Planning, Higher Education
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Tianyu Ma; Jennifer Beth Kahn; Lisa Aileen Hardy; Sarah C. Radke – AERA Online Paper Repository, 2024
This paper reports on systematic literature review that examined learning theories and data collection and analysis methods used to study game-based learning in research on educational digital games for K-12 populations. Through electronic database, hand, and ancestral searches, we identified 25 empirical studies (29 educational games) published…
Descriptors: Data Collection, Data Analysis, Elementary Secondary Education, Educational Games
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Oliver-Quelennec, Katia; Bouchet, François; Carron, Thibault; Pinçon, Claire – International Association for Development of the Information Society, 2021
In-person sessions of participative design are commonly used in the field of Learning Analytics, but to reach students not always available on-site (e.g. during a pandemic), they have to be adapted to online-only context. Card-based tools are a common co-design method to collect users' needs, but this tangible format limits data collection and…
Descriptors: Learning Analytics, Educational Technology, Data Collection, Universities
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Portnoff, Lucy; Gustafson, Erin; Rollinson, Joseph; Bicknell, Klinton – International Educational Data Mining Society, 2021
Students using self-directed learning platforms, such as Duolingo, cannot be adequately assessed relying solely on responses to standard learning exercises due to a lack of control over learners' choices in how to utilize the platform: for example, how learners choose to sequence their studying and how much they choose to revisit old material. To…
Descriptors: Second Language Learning, Language Tests, Educational Technology, Electronic Learning
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Hutt, Stephen; Das, Sanchari; Baker, Ryan S. – International Educational Data Mining Society, 2023
The General Data Protection Regulation (GDPR) in the European Union contains directions on how user data may be collected, stored, and when it must be deleted. As similar legislation is developed around the globe, there is the potential for repercussions across multiple fields of research, including educational data mining (EDM). Over the past two…
Descriptors: Data Analysis, Decision Making, Data Collection, Foreign Countries
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Marwan, Samiha; Shi, Yang; Menezes, Ian; Chi, Min; Barnes, Tiffany; Price, Thomas W. – International Educational Data Mining Society, 2021
Feedback on how students progress through completing subgoals can improve students' learning and motivation in programming. Detecting subgoal completion is a challenging task, and most learning environments do so either with "expert-authored" models or with "data-driven" models. Both models have advantages that are…
Descriptors: Expertise, Models, Feedback (Response), Identification
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Shreya Singhal; Andres Felipe Zambrano; Maciej Pankiewicz; Xiner Liu; Chelsea Porter; Ryan S. Baker – International Educational Data Mining Society, 2024
Education is increasingly taking place in learning environments mediated by technology. This transition has made it easier to collect student-generated data including comments in discussion forums and chats. Although this data is extremely valuable to researchers, it often contains sensitive information like names, locations, social media links,…
Descriptors: MOOCs, Privacy, Confidential Records, Student Records
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