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Yaneva, Victoria; Clauser, Brian E.; Morales, Amy; Paniagua, Miguel – Advances in Health Sciences Education, 2022
Understanding the response process used by test takers when responding to multiple-choice questions (MCQs) is particularly important in evaluating the validity of score interpretations. Previous authors have recommended eye-tracking technology as a useful approach for collecting data on the processes test taker's use to respond to test questions.…
Descriptors: Eye Movements, Artificial Intelligence, Scores, Test Interpretation
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Despujol, Ignacio; Castañeda, Linda; Marín, Victoria I.; Turró, Carlos – International Journal of Educational Technology in Higher Education, 2022
By the end of 2020, over 16,300 Massive Open Online Courses (MOOCs) from 950 universities worldwide had enrolled over 180 million students. Interest in MOOCs has been matched by significant research on the topic, including a considerable number of reviews. This study uses Machine Learning techniques and human expert supervision to generate a…
Descriptors: MOOCs, Artificial Intelligence, Literature Reviews, Teaching Methods
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Hamim, Touria; Benabbou, Faouzia; Sael, Nawal – International Journal of Web-Based Learning and Teaching Technologies, 2022
The student profile has become an important component of education systems. Many systems objectives, as e-recommendation, e-orientation, e-recruitment and dropout prediction are essentially based on the profile for decision support. Machine learning plays an important role in this context and several studies have been carried out either for…
Descriptors: Mathematics, Artificial Intelligence, Man Machine Systems, Student Characteristics
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Sebbaq, Hanane; El Faddouli, Nour-eddine – Interactive Technology and Smart Education, 2022
Purpose: The purpose of this study is, First, to leverage the limitation of annotated data and to identify the cognitive level of learning objectives efficiently, this study adopts transfer learning by using word2vec and a bidirectional gated recurrent units (GRU) that can fully take into account the context and improves the classification of the…
Descriptors: MOOCs, Classification, Electronic Learning, Educational Objectives
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Holmes, Wayne; Tuomi, Ilkka – European Journal of Education, 2022
Recent developments in Artificial Intelligence (AI) have generated great expectations for the future impact of AI in education and learning (AIED). Often these expectations have been based on misunderstanding current technical possibilities, lack of knowledge about state-of-the-art AI in education, and exceedingly narrow views on the functions of…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Educational Trends
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Karimah, Shofiyati Nur; Hasegawa, Shinobu – Smart Learning Environments, 2022
Recognizing learners' engagement during learning processes is important for providing personalized pedagogical support and preventing dropouts. As learning processes shift from traditional offline classrooms to distance learning, methods for automatically identifying engagement levels should be developed. This article aims to present a literature…
Descriptors: Learner Engagement, Automation, Electronic Learning, Literature Reviews
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Shao, Lucy; Ieong, Martin; Levine, Richard A.; Stronach, Jeanne; Fan, Juanjuan – Strategic Enrollment Management Quarterly, 2022
Accurately forecasting course enrollment rates in higher education is of great concern in order to minimize unnecessary administrative costs as well as burden to both students and faculty. This research aimed to first recreate course enrollment predictions based on a conditional probability analysis using student data from San Diego State…
Descriptors: Artificial Intelligence, Prediction, Enrollment, Courses
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Feng, Luxi; Hancock, Roeland; Watson, Christa; Bogley, Rian; Miller, Zachary A.; Gorno-Tempini, Maria Luisa; Briggs-Gowan, Margaret J.; Hoeft, Fumiko – Journal of Learning Disabilities, 2022
Several crucial reasons exist to determine whether an adult has had a reading disorder (RD) and to predict a child's likelihood of developing RD. The Adult Reading History Questionnaire (ARHQ) is among the most commonly used self-reported questionnaires. High ARHQ scores indicate an increased likelihood that an adult had RD as a child and that…
Descriptors: Test Construction, Questionnaires, Artificial Intelligence, Adults
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Amane, Meryem; Aissaoui, Karima; Berrada, Mohammed – Education and Information Technologies, 2022
In distance learning, recommendation system (RS) aims to generate personalized recommendations to learners, which allows them an easy access to various contents at any time. This paper discusses the main RSs employed in E-learning and identifies new research directions to overcome their weaknesses. Existing RSs such as content-based, collaborative…
Descriptors: Electronic Learning, Artificial Intelligence, Distance Education, Individualized Instruction
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Johri, Aditya – Research in Learning Technology, 2022
There has been a conscious effort in the past decade to produce a more theoretical account of the use of technology for learning. At the same time, advances in artificial intelligence (AI) are being rapidly incorporated into learning technologies, significantly changing their affordances for teaching and learning. In this article I address the…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Affordances
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Godwin-Jones, Robert – Research-publishing.net, 2022
The use of chatbots in language learning has been on the rise. In recent Computer-Assisted Language Learning (CALL) research, there is a consensus that rule-based, scripted voice systems are optimal for language learning. Such systems integrate well into instructed language learning in that interactions with the user are predictable and…
Descriptors: Second Language Learning, Artificial Intelligence, Computer Assisted Instruction, Technology Uses in Education
Michael Hermann Hahn – ProQuest LLC, 2022
As humans, we use language with ease and speed, solving the complex computational problem of processing form and meaning seemingly without effort. This dissertation studies how the properties of language enable us to achieve this, by investigating what is computationally difficult about language, and what is easy. We first investigate the…
Descriptors: Language Usage, Difficulty Level, Artificial Intelligence, Language Processing
Jessica Andrews-Todd; Jonathan Steinberg; Michael Flor; Carolyn M. Forsyth – Grantee Submission, 2022
Competency in skills associated with collaborative problem solving (CPS) is critical for many contexts, including school, the workplace, and the military. Innovative approaches for assessing individuals' CPS competency are necessary, as traditional assessment types such as multiple-choice items are not well suited for such a process-oriented…
Descriptors: Automation, Classification, Cooperative Learning, Problem Solving
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Jessica Andrews-Todd; Jonathan Steinberg; Michael Flor; Carolyn M. Forsyth – Journal of Intelligence, 2022
Competency in skills associated with collaborative problem solving (CPS) is critical for many contexts, including school, the workplace, and the military. Innovative approaches for assessing individuals' CPS competency are necessary, as traditional assessment types such as multiple-choice items are not well suited for such a process-oriented…
Descriptors: Automation, Classification, Cooperative Learning, Problem Solving
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Diego G. Campos; Tim Fütterer; Thomas Gfrörer; Rosa Lavelle-Hill; Kou Murayama; Lars König; Martin Hecht; Steffen Zitzmann; Ronny Scherer – Educational Psychology Review, 2024
Systematic reviews and meta-analyses are crucial for advancing research, yet they are time-consuming and resource-demanding. Although machine learning and natural language processing algorithms may reduce this time and these resources, their performance has not been tested in education and educational psychology, and there is a lack of clear…
Descriptors: Artificial Intelligence, Algorithms, Computer System Design, Natural Language Processing
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