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Esteban Villalobos; Mar Perez-Sanagustin; Roger Azevedo; Cedric Sanza; Julien Broisin – IEEE Transactions on Learning Technologies, 2024
Blended learning (BL) has become increasingly popular in higher education institutions. Despite its popularity and the advances in methodologies for the detection of learning tactics and strategies from trace data, little is known about how they apply to BL settings and, therefore, how students use them to plan, organize, monitor, and regulate…
Descriptors: Metacognition, Learning Strategies, Blended Learning, Instructional Design
Seunghan Lee; Amar Sadanand Shetty; Lora A. Cavuoto – IEEE Transactions on Learning Technologies, 2024
Recent usage of virtual reality (VR) technology in surgical training has emerged because of its cost-effectiveness, time savings, and cognition-based feedback generation. However, the quantitative evaluation of its effectiveness in training is still not thoroughly studied. This article demonstrates the effectiveness of a VR-based surgical training…
Descriptors: Markov Processes, Computer Simulation, Teaching Methods, Surgery
Ramesh, Arti; Goldwasser, Dan; Huang, Bert; Daume, Hal; Getoor, Lise – IEEE Transactions on Learning Technologies, 2020
Maintaining and cultivating student engagement is critical for learning. Understanding factors affecting student engagement can help in designing better courses and improving student retention. The large number of participants in massive open online courses (MOOCs) and data collected from their interactions on the MOOC open up avenues for studying…
Descriptors: Online Courses, Learner Engagement, Student Behavior, Success
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
Whitehill, Jacob; Movellan, Javier – IEEE Transactions on Learning Technologies, 2018
We propose a method of generating teaching policies for use in intelligent tutoring systems (ITS) for concept learning tasks [1], e.g., teaching students the meanings of words by showing images that exemplify their meanings à la Rosetta Stone [2] and Duo Lingo [3]. The approach is grounded in control theory and capitalizes on recent work by [4],…
Descriptors: Intelligent Tutoring Systems, Second Language Learning, Educational Policy, Comparative Analysis
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
Jain, G. Panka; Gurupur, Varadraj P.; Schroeder, Jennifer L.; Faulkenberry, Eileen D. – IEEE Transactions on Learning Technologies, 2014
In this paper, we describe a tool coined as artificial intelligence-based student learning evaluation tool (AISLE). The main purpose of this tool is to improve the use of artificial intelligence techniques in evaluating a student's understanding of a particular topic of study using concept maps. Here, we calculate the probability distribution of…
Descriptors: Artificial Intelligence, Concept Mapping, Teaching Methods, Student Evaluation