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Tsutsumi, Emiko; Kinoshita, Ryo; Ueno, Maomi – International Educational Data Mining Society, 2021
Knowledge tracing (KT), the task of tracking the knowledge state of each student over time, has been assessed actively by artificial intelligence researchers. Recent reports have described that Deep-IRT, which combines Item Response Theory (IRT) with a deep learning model, provides superior performance. It can express the abilities of each student…
Descriptors: Item Response Theory, Prediction, Accuracy, Artificial Intelligence
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Fancsali, Stephen E.; Murphy, April; Ritter, Steve – International Educational Data Mining Society, 2022
Ten years after the announcement of the "rise of the super experiment" at Educational Data Mining 2012, challenges to implementing "internet scale" educational experiments often persist for educational technology providers, especially when they seek to test substantive instructional interventions. Studies that deploy and test…
Descriptors: Learning Analytics, Educational Technology, Barriers, Data Analysis
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Xu, Jia; Wei, Tingting; Lv, Pin – International Educational Data Mining Society, 2022
In an Intelligent Tutoring System (ITS), problem (or question) difficulty is one of the most critical parameters, directly impacting problem design, test paper organization, result analysis, and even the fairness guarantee. However, it is very difficult to evaluate the problem difficulty by organized pre-tests or by expertise, because these…
Descriptors: Prediction, Programming, Natural Language Processing, Databases
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Williamson, Kimberly; Kizilcec, René F. – International Educational Data Mining Society, 2021
Knowledge tracing algorithms such as Bayesian Knowledge Tracing (BKT) can provide students and teachers with helpful information about their progress towards learning objectives. Despite the popularity of BKT in the research community, the algorithm is not widely adopted in educational practice. This may be due to skepticism from users and…
Descriptors: Bayesian Statistics, Learning Processes, Computer Software, Learning Analytics
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Nasir, Jauwairia; Kothiyal, Aditi; Sheng, Haoyu; Dillenbourg, Pierre – International Educational Data Mining Society, 2023
Transactive discussion during collaborative learning is crucial for building on each other's reasoning and developing problem solving strategies. In a tabletop collaborative learning activity, student actions on the interface can drive their thinking and be used to ground discussions, thus affecting their problem-solving performance and learning.…
Descriptors: Cooperative Learning, Thinking Skills, Problem Solving, Learning Activities
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Matayoshi, Jeffrey; Cosyn, Eric; Uzun, Hasan – International Educational Data Mining Society, 2022
As outlined by Benjamin Bloom, students working within a mastery learning framework must demonstrate mastery of the core prerequisite material before learning any subsequent material. Since many learning systems in use today adhere to these principles, an important component of such systems is the set of rules or algorithms that determine when a…
Descriptors: Guidelines, Mastery Learning, Learning Processes, Correlation
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Rho, Jihyun; Rau, Martina A.; Van Veen, Barry D. – International Educational Data Mining Society, 2022
Instruction in many STEM domains heavily relies on visual representations, such as graphs, figures, and diagrams. However, students who lack representational competencies do not benefit from these visual representations. Therefore, students must learn not only content knowledge but also representational competencies. Further, as learning…
Descriptors: Learning Processes, Models, Introductory Courses, Engineering Education
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Švábenský, Valdemar; Baker, Ryan S.; Zambrano, Andrés; Zou, Yishan; Slater, Stefan – International Educational Data Mining Society, 2023
Students who take an online course, such as a MOOC, use the course's discussion forum to ask questions or reach out to instructors when encountering an issue. However, reading and responding to students' questions is difficult to scale because of the time needed to consider each message. As a result, critical issues may be left unresolved, and…
Descriptors: Generalization, Computer Mediated Communication, MOOCs, State Universities
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Zhou, Yuhao; Li, Xihua; Cao, Yunbo; Zhao, Xuemin; Ye, Qing; Lv, Jiancheng – International Educational Data Mining Society, 2021
In educational applications, "Knowledge Tracing" (KT) has been widely studied for decades as it is considered a fundamental task towards adaptive online learning. Among proposed KT methods, Deep Knowledge Tracing (DKT) and its variants are by far the most effective ones due to the high flexibility of the neural network. However, DKT…
Descriptors: Online Courses, Computer Assisted Instruction, Networks, Learning Analytics
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Azhar, Aqil Zainal; Segal, Avi; Gal, Kobi – International Educational Data Mining Society, 2022
This paper studies the use of Reinforcement Learning (RL) policies for optimizing the sequencing of online learning materials to students. Our approach provides an end to end pipeline for automatically deriving and evaluating robust representations of students' interactions and policies for content sequencing in online educational settings. We…
Descriptors: Reinforcement, Instructional Materials, Learning Analytics, Policy Analysis
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Barollet, Théo; Bouchez Tichadou, Florent; Rastello, Fabrice – International Educational Data Mining Society, 2021
In Intelligent Tutoring Systems (ITS), methods to choose the next exercise for a student are inspired from generic recommender systems, used, for instance, in online shopping or multimedia recommendation. As such, collaborative filtering, especially matrix factorization, is often included as a part of recommendation algorithms in ITS. One notable…
Descriptors: Intelligent Tutoring Systems, Prediction, Internet, Purchasing
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Mao, Ye; Shi, Yang; Marwan, Samiha; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2021
As students learn how to program, both their programming code and their understanding of it evolves over time. In this work, we present a general data-driven approach, named "Temporal-ASTNN" for modeling student learning progression in open-ended programming domains. Temporal-ASTNN combines a novel neural network model based on abstract…
Descriptors: Programming, Computer Science Education, Learning Processes, Learning Analytics
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Chu, Wei; Pavlik, Philip I., Jr. – International Educational Data Mining Society, 2023
In adaptive learning systems, various models are employed to obtain the optimal learning schedule and review for a specific learner. Models of learning are used to estimate the learner's current recall probability by incorporating features or predictors proposed by psychological theory or empirically relevant to learners' performance. Logistic…
Descriptors: Reaction Time, Accuracy, Models, Predictor Variables
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Rzepka, Nathalie; Müller, Hans-Georg; Simbeck, Katharina – International Educational Data Mining Society, 2021
The ability to spell correctly is a fundamental skill for participating in society and engaging in professional work. In the German language, the capitalization of nouns and proper names presents major difficulties for both native and nonnative learners, since the definition of what is a noun varies according to one's linguistic perspective. In…
Descriptors: Spelling, German, Punctuation, Nouns