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Zhenchang Xia; Nan Dong; Jia Wu; Chuanguo Ma – IEEE Transactions on Learning Technologies, 2024
As an excellent means of improving students' effective learning, knowledge tracking can assess the level of knowledge mastery and discover latent learning patterns based on students' historical learning evaluation of related questions. The advantage of knowledge tracking is that it can better organize and adjust students' learning plans, provide…
Descriptors: Graphs, Artificial Intelligence, Multivariate Analysis, Prediction
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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
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Hua Ma; Wen Zhao; Yuqi Tang; Peiji Huang; Haibin Zhu; Wensheng Tang; Keqin Li – IEEE Transactions on Learning Technologies, 2024
To prevent students from learning risks and improve teachers' teaching quality, it is of great significance to provide accurate early warning of learning performance to students by analyzing their interactions through an e-learning system. In existing research, the correlations between learning risks and students' changing cognitive abilities or…
Descriptors: College Students, Learning Analytics, Learning Management Systems, Academic Achievement
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Mario Vallarino; Saverio Iacono; Edoardo Bellanti; Gianni V. Vercelli – IEEE Transactions on Learning Technologies, 2024
This article introduces a novel approach to remote laboratory instruction, specifically designed for teaching three-dimensional modeling using Blender software. The lab uses virtual machines to provide students with the necessary computational power to carry out the course activities, along with the correct version of the software. The flipped…
Descriptors: Flipped Classroom, Distance Education, Laboratories, Peer Evaluation
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Bull, Susan – IEEE Transactions on Learning Technologies, 2020
This overview outlines key issues in learning with an open learner model (OLM). Originally, learner models remained hidden, as their primary role was to enable a system to personalize the educational interaction. Opening the model in an understandable form provides additional methods of prompting reflection, planning, and other metacognitive…
Descriptors: Intelligent Tutoring Systems, Models, Student Characteristics, Educational Research
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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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Crowston, Kevin; Ă˜sterlund, Carsten; Lee, Tae Kyoung; Jackson, Corey; Harandi, Mahboobeh; Allen, Sarah; Bahaadini, Sara; Coughlin, Scott; Katsaggelos, Aggelos K.; Larson, Shane L.; Rohani, Neda; Smith, Joshua R.; Trouille, Laura; Zevin, Michael – IEEE Transactions on Learning Technologies, 2020
We present the design of a citizen science system that uses machine learning to guide the presentation of image classification tasks to newcomers to help them more quickly learn how to do the task while still contributing to the work of the project. A Bayesian model for tracking volunteer learning for training with tasks with uncertain outcomes is…
Descriptors: Citizen Participation, Scientific Research, Man Machine Systems, Training
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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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Capuano, Nicola; Loia, Vincenzo; Orciuoli, Francesco – IEEE Transactions on Learning Technologies, 2017
Massive Open Online Courses (MOOCs) are becoming an increasingly popular choice for education but, to reach their full extent, they require the resolution of new issues like assessing students at scale. A feasible approach to tackle this problem is peer assessment, in which students also play the role of assessor for assignments submitted by…
Descriptors: Participative Decision Making, Models, Peer Evaluation, Online Courses
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Achumba, I. E.; Azzi, D.; Dunn, V. L.; Chukwudebe, G. A. – IEEE Transactions on Learning Technologies, 2013
Laboratory work is critical in undergraduate engineering courses. It is used to integrate theory and practice. This demands that laboratory activities are synchronized with lectures to maximize their derivable learning outcomes, which are measurable through assessment. The typical high costs of the traditional engineering laboratory, which often…
Descriptors: Laboratories, Electronics, Engineering Education, Undergraduate Study
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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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Scheuer, O.; McLaren, B. M. – IEEE Transactions on Learning Technologies, 2013
One of the main challenges in tapping the full potential of modern educational software is to devise mechanisms to automatically analyze and adaptively support students' problem solving and learning. A number of such approaches have been developed to teach argumentation skills in domains as diverse as science, the Law, and ethics. Yet,…
Descriptors: Intelligent Tutoring Systems, Persuasive Discourse, Cooperative Learning, Legal Education (Professions)