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Ye Jia; Xiangzhi Eric Wang; Zackary P. T. Sin; Chen Li; Peter H. F. Ng; Xiao Huang; George Baciu; Jiannong Cao; Qing Li – IEEE Transactions on Learning Technologies, 2024
One of the promises of edu-metaverse is its ability to provide a virtual environment that enables us to engage in learning activities that are similar to or on par with reality. The digital enhancements introduced in a virtual environment contribute to our increased expectations of novel learning experiences. However, despite its promising…
Descriptors: Computer Simulation, Educational Technology, Learning Processes, Socialization
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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
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Lee, Chia-An; Huang, Nen-Fu; Tzeng, Jian-Wei; Tsai, Pin-Han – IEEE Transactions on Learning Technologies, 2023
Massive open online courses offer a valuable platform for efficient and flexible learning. They can improve teaching and learning effectiveness by enabling the evaluation of learning behaviors and the collection of feedback from students. The knowledge map approach constitutes a suitable tool for evaluating and presenting students' learning…
Descriptors: Artificial Intelligence, MOOCs, Concept Mapping, Student Evaluation
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Kroeze, Karel A.; van den Berg, Stephanie M.; Veldkamp, Bernard P.; de Jong, Ton – IEEE Transactions on Learning Technologies, 2021
A tool is presented that can automatically assess the quality of students' concept maps and provide feedback based on a reference concept map. It is shown that this tool can effectively assess the quality of concept maps, and that it can provide accurate and helpful feedback on a number of specific shortcomings often evident in students' concept…
Descriptors: Feedback (Response), Concept Mapping, Student Evaluation, Teaching Methods
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Furtado, Pedro Gabriel Fonteles; Hirashima, Tsukasa; Hayashi, Yusuke – IEEE Transactions on Learning Technologies, 2019
Computer-assisted concept map building from provided pieces by using Kit-build is an activity which can promote comprehension and retention. However, users are burdened with searching for pieces and organizing the layout, which are believed to increase the overall cognitive load of the activity. In this paper, we describe the Airmap interface,…
Descriptors: Cognitive Processes, Difficulty Level, Concept Mapping, Reading Comprehension
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Sun, Bo; Zhu, Yunzong; Xiao, Yongkang; Xiao, Rong; Wei, Yungang – IEEE Transactions on Learning Technologies, 2019
In recent years, computerized adaptive testing (CAT) has gained popularity as an important means to evaluate students' ability. Assigning tags to test questions is crucial in CAT. Manual tagging is widely used for constructing question banks; however, this approach is time-consuming and might lead to consistency issues. Automatic question tagging,…
Descriptors: Computer Assisted Testing, Student Evaluation, Test Items, Multiple Choice Tests
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Liu, Qingtang; Zhang, Si; Wang, Qiyun; Chen, Wenli – IEEE Transactions on Learning Technologies, 2018
Teachers' online discussion text data shed light on their reflective thinking. With the growing scale of text data, the traditional way of manual coding, however, has been challenged. In order to process the large-scale unstructured text data, it is necessary to integrate the inductive content analysis method and educational data mining…
Descriptors: Information Retrieval, Data Collection, Data Analysis, Discourse Analysis
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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
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Martinez-Maldonado, Roberto; Clayphan, Andrew; Yacef, Kalina; Kay, Judy – IEEE Transactions on Learning Technologies, 2015
The teacher has very important roles in the classroom, particularly as manager of most resources for learning activities and in providing timely feedback that can enhance learning. But teachers need to be aware of students' achievements and weaknesses to decide how to time feedback. We present MTFeedback, a system that harnesses the new…
Descriptors: Small Group Instruction, Feedback (Response), Teacher Role, Teaching Methods
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Liu, Ming; Calvo, R. A.; Aditomo, A.; Pizzato, L. A. – IEEE Transactions on Learning Technologies, 2012
In this paper, we present a novel approach for semiautomatic question generation to support academic writing. Our system first extracts key phrases from students' literature review papers. Each key phrase is matched with a Wikipedia article and classified into one of five abstract concept categories: Research Field, Technology, System, Term, and…
Descriptors: Foreign Countries, Computer Assisted Instruction, Web 2.0 Technologies, Automation