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Zhongcheng Lei; Hong Zhou; Wenshan Hu; Guo-Ping Liu – IEEE Transactions on Learning Technologies, 2024
Online laboratories have been widely used in education, research, and industrial applications. For online laboratories, various architectures have been constructed to provide good user experience and powerful capabilities for online experimentation, in which the controller is a crucial part to connect the server with a controlled test rig.…
Descriptors: Online Courses, Laboratories, Distance Education, Computers
Lu, Yu; Chen, Penghe; Pian, Yang; Zheng, Vincent W. – IEEE Transactions on Learning Technologies, 2022
In this article, we advocate for and propose a novel concept map driven knowledge tracing (CMKT) model, which utilizes educational concept map for learner modeling. This article particularly addresses the issue of learner data sparseness caused by the unwillingness to practice and irregular learning behaviors on the learner side. CMKT considers…
Descriptors: Concept Mapping, Learning Processes, Prediction, Models
Yuang Wei; Bo Jiang – IEEE Transactions on Learning Technologies, 2024
Understanding student cognitive states is essential for assessing human learning. The deep neural networks (DNN)-inspired cognitive state prediction method improved prediction performance significantly; however, the lack of explainability with DNNs and the unitary scoring approach fail to reveal the factors influencing human learning. Identifying…
Descriptors: Cognitive Mapping, Models, Prediction, Short Term Memory
Zhao, Anping; Yu, Yu – IEEE Transactions on Learning Technologies, 2022
To provide insight into online learners' interests in various knowledge from course discussion texts, modeling learners' sentiments and interests at different granularities are of great importance. In this article, the proposed framework combines a deep convolutional neural network and a hierarchical topic model to discover the hidden structure of…
Descriptors: Online Courses, Student Attitudes, Knowledge Level, Networks
Babik, Dmytro; Stevens, Scott P.; Waters, Andrew; Tinapple, David – IEEE Transactions on Learning Technologies, 2020
Over the last 20 years, online peer review and assessment have become widely used and well-researched practices in education. Their use increased, especially with the proliferation of nonconventional large-scale and online modes of teaching and learning, such as Massive Open Online Courses (MOOCs). A well-designed peer-review system is expected to…
Descriptors: Peer Evaluation, Fidelity, Networks, Evaluation Methods
Emiko Tsutsumi; Yiming Guo; Ryo Kinoshita; Maomi Ueno – IEEE Transactions on Learning Technologies, 2024
Knowledge tracing (KT), the task of tracking the knowledge state of a 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 method, provides superior performance. It can express the abilities of each student…
Descriptors: Item Response Theory, Academic Ability, Intelligent Tutoring Systems, Artificial Intelligence
Jiang, Bo; Wu, Simin; Yin, Chengjiu; Zhang, Haifeng – IEEE Transactions on Learning Technologies, 2020
Accurately tracing the state of learner knowledge contributes to providing high-quality intelligent support for computer-supported programming learning. However, knowledge tracing is difficult when learners have only had a few practice opportunities, which is often common in block-based programming. This article proposed two knowledge tracing…
Descriptors: Programming, Computer Assisted Instruction, Problem Solving, Task Analysis
Wan, Pengfei; Wang, Xiaoming; Lin, Yaguang; Pang, Guangyao – IEEE Transactions on Learning Technologies, 2021
Learners' autonomous learning is at the heart of modern education, and the convenient network brings new opportunities for it. We notice that learners mainly use the combination of online and offline learning methods to complete the entire autonomous learning process, but most of the existing models cannot effectively describe the complex process…
Descriptors: Independent Study, Personal Autonomy, Learning Processes, Electronic Learning
Molontay, Roland; Horvath, Noemi; Bergmann, Julia; Szekrenyes, Dora; Szabo, Mihaly – IEEE Transactions on Learning Technologies, 2020
Curriculum prerequisite networks have a central role in shaping the course of university programs. The analysis of prerequisite networks has attracted a lot of research interest recently since designing an appropriate network is of great importance both academically and economically. It determines the learning goals of the program and also has a…
Descriptors: College Curriculum, Prerequisites, Networks, Time to Degree
Holmes, Mike; Latham, Annabel; Crockett, Keeley; O'Shea, James D. – IEEE Transactions on Learning Technologies, 2018
Comprehension is an important cognitive state for learning. Human tutors recognize comprehension and non-comprehension states by interpreting learner non-verbal behavior (NVB). Experienced tutors adapt pedagogy, materials, and instruction to provide additional learning scaffold in the context of perceived learner comprehension. Near real-time…
Descriptors: Comprehension, Classification, Artificial Intelligence, Networks
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
Olive, David Monllao; Huynh, Du Q.; Reynolds, Mark; Dougiamas, Martin; Wiese, Damyon – IEEE Transactions on Learning Technologies, 2019
A significant amount of research effort has been put into finding variables that can identify students at risk based on activity records available in learning management systems (LMS). These variables often depend on the context, for example, the course structure, how the activities are assessed or whether the course is entirely online or a…
Descriptors: Prediction, Identification, At Risk Students, Online Courses
Botelho, Anthony F.; Varatharaj, Ashvini; Patikorn, Thanaporn; Doherty, Diana; Adjei, Seth A.; Beck, Joseph E. – IEEE Transactions on Learning Technologies, 2019
The increased usage of computer-based learning platforms and online tools in classrooms presents new opportunities to not only study the underlying constructs involved in the learning process, but also use this information to identify and aid struggling students. Many learning platforms, particularly those driving or supplementing instruction, are…
Descriptors: Student Attrition, Student Behavior, Early Intervention, Identification