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Huang, Tao; Hu, Shengze; Yang, Huali; Geng, Jing; Liu, Sannyuya; Zhang, Hao; Yang, Zongkai – IEEE Transactions on Learning Technologies, 2023
The global outbreak of the new coronavirus epidemic has promoted the development of intelligent education and the utilization of online learning systems. In order to provide students with intelligent services, such as cognitive diagnosis and personalized exercises recommendation, a fundamental task is the concept tagging for exercises, which…
Descriptors: Educational Technology, Prediction, Electronic Learning, Intelligent Tutoring Systems
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Behzad Mirzababaei; Viktoria Pammer-Schindler – IEEE Transactions on Learning Technologies, 2024
In this article, we investigate a systematic workflow that supports the learning engineering process of formulating the starting question for a conversational module based on existing learning materials, specifying the input that transformer-based language models need to function as classifiers, and specifying the adaptive dialogue structure,…
Descriptors: Learning Processes, Electronic Learning, Artificial Intelligence, Natural Language Processing
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Sha, Lele; Rakovic, Mladen; Lin, Jionghao; Guan, Quanlong; Whitelock-Wainwright, Alexander; Gasevic, Dragan; Chen, Guanliang – IEEE Transactions on Learning Technologies, 2023
In online courses, discussion forums play a key role in enhancing student interaction with peers and instructors. Due to large enrolment sizes, instructors often struggle to respond to students in a timely manner. To address this problem, both traditional machine learning (ML) (e.g., Random Forest) and deep learning (DL) approaches have been…
Descriptors: Computer Mediated Communication, Discussion Groups, Artificial Intelligence, Intelligent Tutoring Systems
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Jionghao Lin; Wei Tan; Lan Du; Wray Buntine; David Lang; Dragan Gasevic; Guanliang Chen – IEEE Transactions on Learning Technologies, 2024
Automating the classification of instructional strategies from a large-scale online tutorial dialogue corpus is indispensable to the design of dialogue-based intelligent tutoring systems. Despite many existing studies employing supervised machine learning (ML) models to automate the classification process, they concluded that building a…
Descriptors: Classification, Dialogs (Language), Teaching Methods, Computer Assisted Instruction
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Sahu, Archana; Bhowmick, Plaban Kumar – IEEE Transactions on Learning Technologies, 2020
In this paper, we studied different automatic short answer grading (ASAG) systems to provide a comprehensive view of the feature spaces explored by previous works. While the performance reported in previous works have been encouraging, systematic study of the features is lacking. Apart from providing systematic feature space exploration, we also…
Descriptors: Automation, Grading, Test Format, Artificial Intelligence
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Pelanek, Radek – IEEE Transactions on Learning Technologies, 2020
Learning systems can utilize many practice exercises, ranging from simple multiple-choice questions to complex problem-solving activities. In this article, we propose a classification framework for such exercises. The framework classifies exercises in three main aspects: (1) the primary type of interaction; (2) the presentation mode; and (3) the…
Descriptors: Integrated Learning Systems, Classification, Multiple Choice Tests, Problem Solving
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Geller, Shay A.; Gal, Kobi; Segal, Avi; Sripathi, Kamali; Kim, Hyunsoo G.; Facciotti, Marc T.; Igo, Michele; Hoernle, Nicholas; Karger, David – IEEE Transactions on Learning Technologies, 2021
This article provides computational and rule-based approaches for detecting confusion that is expressed in students' comments in couse forums. To obtain reliable, ground truth data about which posts exhibit student confusion, we designed a decision tree that facilitates the manual labeling of forum posts by experts. However, manual labeling is…
Descriptors: Identification, Misconceptions, Student Attitudes, Computer Mediated Communication
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Jimenez, Fernando; Paoletti, Alessia; Sanchez, Gracia; Sciavicco, Guido – IEEE Transactions on Learning Technologies, 2019
In the European academic systems, the public funding to single universities depends on many factors, which are periodically evaluated. One of such factors is the rate of success, that is, the rate of students that do complete their course of study. At many levels, therefore, there is an increasing interest in being able to predict the risk that a…
Descriptors: Prediction, Risk, Dropouts, College Students
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Srba, Ivan; Bielikova, Maria – IEEE Transactions on Learning Technologies, 2015
In the current time of globalization, collaboration among people in virtual environments is becoming an important precondition of success. This trend is reflected also in the educational domain where students collaborate in various short-term groups created repetitively but changing in each round (e.g. in MOOCs). Students in these kind of dynamic…
Descriptors: Cooperative Learning, Online Courses, Group Dynamics, Feedback (Response)
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Cetintas, Suleyman; Si, Luo; Aagard, Hans Peter; Bowen, Kyle; Cordova-Sanchez, Mariheida – IEEE Transactions on Learning Technologies, 2011
Microblogging is a popular technology in social networking applications that lets users publish online short text messages (e.g., less than 200 characters) in real time via the web, SMS, instant messaging clients, etc. Microblogging can be an effective tool in the classroom and has lately gained notable interest from the education community. This…
Descriptors: Information Retrieval, Accuracy, Synchronous Communication, Classification