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Jing Chen; Bei Fang; Hao Zhang; Xia Xue – Interactive Learning Environments, 2024
High dropout rate exists universally in massive open online courses (MOOCs) due to the separation of teachers and learners in space and time. Dropout prediction using the machine learning method is an extremely important prerequisite to identify potential at-risk learners to improve learning. It has attracted much attention and there have emerged…
Descriptors: MOOCs, Potential Dropouts, Prediction, Artificial Intelligence
Meng, Lingling; Zhang, Mingxin; Zhang, Wanxue; Chu, Yu – Interactive Learning Environments, 2021
Bayesian knowledge tracing model (BKT) is a typical student knowledge assessment method. It is widely used in intelligent tutoring systems. In the standard BKT model, all knowledge and skills are independent of each other. However, in the process of student learning, they have a very close relation. A student may understand knowledge B better when…
Descriptors: Bayesian Statistics, Intelligent Tutoring Systems, Student Evaluation, Knowledge Level
Tran, Tuan M.; Hasegawa, Shinobu – International Association for Development of the Information Society, 2022
A learner model reflects learning patterns and characteristics of a learner. A learner model with learning history and its effectiveness plays a significant role in supporting a learner's understanding of their strengths and weaknesses of their way of learning in order to make proper adjustments for improvement. Nowadays, learners have been…
Descriptors: Markov Processes, Learning Processes, Models, Scores
Mbouzao, Boniface; Desmarais, Michel C.; Shrier, Ian – International Educational Data Mining Society, 2020
Massive online Open Courses (MOOCs) make extensive use of videos. Students interact with them by pausing, seeking forward or backward, replaying segments, etc. We can reasonably assume that students have different patterns of video interactions, but it remains hard to compare student video interactions. Some methods were developed, such as Markov…
Descriptors: Comparative Analysis, Video Technology, Interaction, Measurement Techniques
Gupta, Anika; Garg, Deepak; Kumar, Parteek – IEEE Transactions on Learning Technologies, 2022
With the onset of online education via technology-enhanced learning platforms, large amount of educational data is being generated in the form of logs, clickstreams, performance, etc. These Virtual Learning Environments provide an opportunity to the researchers for the application of educational data mining and learning analytics, for mining the…
Descriptors: Markov Processes, Online Courses, Learning Management Systems, Learning Analytics
Ryo Maie – ProQuest LLC, 2022
Skill acquisition theorists conceptualize second language (L2) learning as the acquisition of a set of perceptual, cognitive, and motor skills. The dominant view in skill acquisition theory is to regard L2 skill acquisition as a three-stage process "from initial representation of knowledge through initial changes in behavior to eventual…
Descriptors: Second Language Learning, Second Language Instruction, Linguistic Theory, Learning Processes
Halpern, David; Tubridy, Shannon; Wang, Hong Yu; Gasser, Camille; Popp, Pamela Osborn; Davachi, Lila; Gureckis, Todd M. – International Educational Data Mining Society, 2018
Knowledge tracing is a popular and successful approach to modeling student learning. In this paper we investigate whether the addition of neuroimaging observations to a knowledge tracing model enables accurate prediction of memory performance in held-out data. We propose a Hidden Markov Model of memory acquisition related to Bayesian Knowledge…
Descriptors: Learning Processes, Memory, Prediction, Second Language Learning
Kaser, Tanja; Klingler, Severin; Schwing, Alexander G.; Gross, Markus – IEEE Transactions on Learning Technologies, 2017
Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore, an accurate representation and prediction of student knowledge is essential. Bayesian Knowledge Tracing (BKT) is a popular approach for student modeling. The structure of BKT models, however, makes it impossible to represent the hierarchy and…
Descriptors: Bayesian Statistics, Models, Intelligent Tutoring Systems, Networks
Wang, Shiyu; Zhang, Susu; Douglas, Jeff; Culpepper, Steven – Measurement: Interdisciplinary Research and Perspectives, 2018
Analyzing students' growth remains an important topic in educational research. Most recently, Diagnostic Classification Models (DCMs) have been used to track skill acquisition in a longitudinal fashion, with the purpose to provide an estimate of students' learning trajectories in terms of the change of fine-grained skills overtime. Response time…
Descriptors: Reaction Time, Markov Processes, Computer Assisted Instruction, Spatial Ability
Okubo, Fumiya; Shimada, Atsushi; Taniguchi, Yuta – International Association for Development of the Information Society, 2017
In this paper, we present a system for visualizing learning logs of a course in progress together with predictions of learning activities of the following week and the final grades of students by state transition graphs. Data are collected from 236 students attending the course in progress and from 209 students attending the past course for…
Descriptors: Learning Activities, Graphs, Prediction, Grades (Scholastic)
Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco – Cognitive Science, 2016
Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in…
Descriptors: Orthographic Symbols, Neurological Organization, Models, Probability