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Showing 1 to 15 of 38 results Save | Export
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Deho, Oscar Blessed; Joksimovic, Srecko; Li, Jiuyong; Zhan, Chen; Liu, Jixue; Liu, Lin – IEEE Transactions on Learning Technologies, 2023
Many educational institutions are using predictive models to leverage actionable insights using student data and drive student success. A common task has been predicting students at risk of dropping out for the necessary interventions to be made. However, issues of discrimination by these predictive models based on protected attributes of students…
Descriptors: Learning Analytics, Models, Student Records, Prediction
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Divasón, Jose; Martinez-de-Pison, Francisco Javier; Romero, Ana; Saenz-de-Cabezon, Eduardo – IEEE Transactions on Learning Technologies, 2023
The evaluation of student projects is a difficult task, especially when they involve both a technical and a creative component. We propose an artificial intelligence (AI)-based methodology to help in the evaluation of complex projects in engineering and computer science courses. This methodology is intended to evaluate the assessment process…
Descriptors: Student Projects, Student Evaluation, Artificial Intelligence, Models
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Baneres, David; Rodriguez-Gonzalez, M. Elena; Guerrero-Roldan, Ana Elena – IEEE Transactions on Learning Technologies, 2023
Course dropout is a concern in online higher education, mainly in first-year courses when different factors negatively influence the learners' engagement leading to an unsuccessful outcome or even dropping out from the university. The early identification of such potential at-risk learners is the key to intervening and trying to help them before…
Descriptors: Prediction, Models, Identification, Potential Dropouts
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Nan Zhang; Hongkai Wang; Tianqi Huang; Xinran Zhang; Hongen Liao – IEEE Transactions on Learning Technologies, 2024
Trunk anatomy education is critical in the training of the surgeon. Most trunk anatomy education systems use a personal or synthetic anatomical model. It remains difficult to obtain appropriate population anatomy information and create individualized anatomy customization based on a quantity of diverse data. Furthermore, the naked-eye virtual…
Descriptors: Computer Simulation, Simulated Environment, Human Body, Anatomy
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Andres Neyem; Luis A. Gonzalez; Marcelo Mendoza; Juan Pablo Sandoval Alcocer; Leonardo Centellas; Carlos Paredes – IEEE Transactions on Learning Technologies, 2024
Software assistants have significantly impacted software development for both practitioners and students, particularly in capstone projects. The effectiveness of these tools varies based on their knowledge sources; assistants with localized domain-specific knowledge may have limitations, while tools, such as ChatGPT, using broad datasets, might…
Descriptors: Computer Software, Artificial Intelligence, Intelligent Tutoring Systems, Capstone Experiences
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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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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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Fincham, Ed; Rozemberczki, Benedek; Kovanovic, Vitomir; Joksimovic, Srecko; Jovanovic, Jelena; Gasevic, Dragan – IEEE Transactions on Learning Technologies, 2021
In this article, we empirically validate Tinto's Student Integration model, in particular, the predictions the model makes regarding both students' academic outcomes and their dropout decisions. In doing so, we analyze three decades' worth of student enrollments at an Australian university and present a novel methodological approach using graph…
Descriptors: Models, Prediction, Outcomes of Education, Dropouts
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Rao, Dhawaleswar; Saha, Sujan Kumar – IEEE Transactions on Learning Technologies, 2020
Automatic multiple choice question (MCQ) generation from a text is a popular research area. MCQs are widely accepted for large-scale assessment in various domains and applications. However, manual generation of MCQs is expensive and time-consuming. Therefore, researchers have been attracted toward automatic MCQ generation since the late 90's.…
Descriptors: Multiple Choice Tests, Test Construction, Automation, Computer Software
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Sanchez-Ferreres, Josep; Delicado, Luis; Andaloussi, Amine Abbab; Burattin, Andrea; Calderon-Ruiz, Guillermo; Weber, Barbara; Carmona, Josep; Padro, Lluis – IEEE Transactions on Learning Technologies, 2020
The creation of a process model is primarily a formalization task that faces the challenge of constructing a syntactically correct entity, which accurately reflects the semantics of reality, and is understandable to the model reader. This article proposes a framework called "Model Judge," focused toward the two main actors in the process…
Descriptors: Models, Automation, Validity, Natural Language Processing
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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
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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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Ortigosa, Alvaro; Carro, Rosa M.; Bravo-Agapito, Javier; Lizcano, David; Alcolea, Juan Jesus; Blanco, Oscar – IEEE Transactions on Learning Technologies, 2019
This paper presents the work done to support student dropout risk prevention in a real online e-learning environment: A Spanish distance university with thousands of undergraduate students. The main goal is to prevent students from abandoning the university by means of retention actions focused on the most at-risk students, trying to maximize the…
Descriptors: At Risk Students, Dropout Prevention, Undergraduate Students, Distance Education
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Vrablecová, Petra; Šimko, Marián – IEEE Transactions on Learning Technologies, 2016
The domain model is an essential part of an adaptive learning system. For each educational course, it involves educational content and semantics, which is also viewed as a form of conceptual metadata about educational content. Due to the size of a domain model, manual domain model creation is a challenging and demanding task for teachers or…
Descriptors: Semantics, Models, Metadata, Programming
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VanLehn, Kurt; Wetzel, Jon; Grover, Sachin; van de Sande, Brett – IEEE Transactions on Learning Technologies, 2017
Constructing models of dynamic systems is an important skill in both mathematics and science instruction. However, it has proved difficult to teach. Dragoon is an intelligent tutoring system intended to quickly and effectively teach this important skill. This paper describes Dragoon and an evaluation of it. The evaluation randomly assigned…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Skill Development
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