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Nan Wu – International Journal of Web-Based Learning and Teaching Technologies, 2024
Higher education is becoming increasingly competitive and all educational institutions are concentrating on improving quality and changing traditional higher education teaching methods. New-type classroom instruction has embraced a unique advancement opportunity with the arrival of the fifth generation (5G) era. It is critical to develop a…
Descriptors: Instructional Effectiveness, Internet, Computer Literacy, Artificial Intelligence
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Selma Tosun; Dilara Bakan Kalaycioglu – Journal of Educational Technology and Online Learning, 2024
Predicting and improving the academic achievement of university students is a multifactorial problem. Considering the low success rates and high dropout rates, particularly in open education programs characterized by mass enrollment, academic success is an important research area with its causes and consequences. This study aimed to solve a…
Descriptors: Academic Achievement, Open Education, Distance Education, Foreign Countries
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Md Akib Zabed Khan; Agoritsa Polyzou – Journal of Educational Data Mining, 2024
In higher education, academic advising is crucial to students' decision-making. Data-driven models can benefit students in making informed decisions by providing insightful recommendations for completing their degrees. To suggest courses for the upcoming semester, various course recommendation models have been proposed in the literature using…
Descriptors: Academic Advising, Courses, Data Use, Artificial Intelligence
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Chuan Cai; Adam Fleischhacker – Journal of Educational Data Mining, 2024
We propose a novel approach to address the issue of college student attrition by developing a hybrid model that combines a structural neural network with a piecewise exponential model. This hybrid model not only shows the potential to robustly identify students who are at high risk of dropout, but also provides insights into which factors are most…
Descriptors: College Students, Student Attrition, Dropouts, Potential Dropouts
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Jacqueline Nijenhuis-Voogt; Durdane Bayram; Paulien C. Meijer; Erik Barendsen – International Journal of Computer Science Education in Schools, 2024
A context-based approach to education aims to improve students' meaningful learning and uses authentic situations in which scientific concepts are applied. The use of contexts may contribute to the learning of abstract concepts such as algorithms. The selection of appropriate contexts, however, is challenging for teachers. It is therefore…
Descriptors: Secondary Education, Computer Science Education, Secondary School Science, Algorithms
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Jonathan K. Foster; Peter Youngs; Rachel van Aswegen; Samarth Singh; Ginger S. Watson; Scott T. Acton – Journal of Learning Analytics, 2024
Despite a tremendous increase in the use of video for conducting research in classrooms as well as preparing and evaluating teachers, there remain notable challenges to using classroom videos at scale, including time and financial costs. Recent advances in artificial intelligence could make the process of analyzing, scoring, and cataloguing videos…
Descriptors: Learning Analytics, Automation, Classification, Artificial Intelligence
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Chenchen Ma; Jing Ouyang; Chun Wang; Gongjun Xu – Grantee Submission, 2024
Survey instruments and assessments are frequently used in many domains of social science. When the constructs that these assessments try to measure become multifaceted, multidimensional item response theory (MIRT) provides a unified framework and convenient statistical tool for item analysis, calibration, and scoring. However, the computational…
Descriptors: Algorithms, Item Response Theory, Scoring, Accuracy
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Julia Tomanova; Martin Vozar; Dasa Munkova – International Journal of Education in Mathematics, Science and Technology, 2024
The study focuses on the identification of relationships and/or rules between computational thinking (CT) concepts among the undergraduate students of Applied Informatics due to their attitudes towards mathematics. We analyze three CT concepts -- decomposition, pattern recognition, and algorithmic thinking. We assume that students who have a…
Descriptors: Computation, Thinking Skills, Student Attitudes, Undergraduate Students
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Line Have Musaeus; Deborah Tatar; Peter Musaeus – Journal of Biological Education, 2024
Computational modelling is widely used in biological science. Therefore, biology students need to learn computational modelling. However, there is a lack of evidence about how to teach computational modelling in biology and what the effects are on student learning. The purpose of this intervention-control study was to investigate how knowledge in…
Descriptors: Computation, Models, High School Students, Biology
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Kather, Philipp; Duran, Rodrigo; Vahrenhold, Jan – ACM Transactions on Computing Education, 2022
Previous studies on writing and understanding programs presented evidence that programmers beyond a novice stage utilize plans or plan-like structures. Other studies on code composition showed that learners have difficulties with writing, reading, and debugging code where interacting plans are merged into a short piece of code. In this article, we…
Descriptors: Eye Movements, Coding, Algorithms, Schemata (Cognition)
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Yuguo Ke; Xiaozhen Zhou – SAGE Open, 2025
Focusing efficiently on potential weaknesses in the validity argument of writing assessments--such as writing subjectivity, content coverage, criteria vagueness, and raters' incompetence--has been shown to positively enhance teachers' overall writing assessment competence (AC). In this study, we propose a computational bootstrapping model of…
Descriptors: Writing Evaluation, Persuasive Discourse, Validity, Writing Teachers
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Kelli A. Bird; Benjamin L. Castleman; Yifeng Song – Journal of Policy Analysis and Management, 2025
Predictive analytics are increasingly pervasive in higher education. However, algorithmic bias has the potential to reinforce racial inequities in postsecondary success. We provide a comprehensive and translational investigation of algorithmic bias in two separate prediction models--one predicting course completion, the second predicting degree…
Descriptors: Algorithms, Technology Uses in Education, Bias, Racism
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Kayode Oyetade; Tranos Zuva – Educational Process: International Journal, 2025
Background/purpose: The integration of artificial intelligence (AI) in education has the potential to address inequalities and enhance teaching and learning outcomes. However, challenges such as AI biases, limited teacher literacy, and resource constraints hinder equitable implementation, especially in contexts like South Africa. This study…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Equal Education
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Sohail Ahmed Soomro; Halar Haleem; Bertrand Schneider; Georgi V. Georgiev – IEEE Transactions on Learning Technologies, 2025
This study presents a monocular approach for capturing students' prototyping activities and interactions in digital-fabrication-based makerspaces. The proposed method uses images from a single camera and applies object reidentification, tracking, and depth estimation algorithms to track and uniquely label participants in the space, extracting both…
Descriptors: Learning Activities, Shared Resources and Services, Manufacturing, Photography
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Ariely, Moriah; Nazaretsky, Tanya; Alexandron, Giora – International Journal of Artificial Intelligence in Education, 2023
Machine learning algorithms that automatically score scientific explanations can be used to measure students' conceptual understanding, identify gaps in their reasoning, and provide them with timely and individualized feedback. This paper presents the results of a study that uses Hebrew NLP to automatically score student explanations in Biology…
Descriptors: Artificial Intelligence, Algorithms, Natural Language Processing, Hebrew
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