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Felipe de Morais; Patricia A. Jaques – International Journal of Artificial Intelligence in Education, 2024
Affect dynamics in digital learning systems (DLS) research investigates how students' emotions evolve while using these systems. This information can reveal the most common emotions experienced by students and how they transition between them. Recent studies indicate that affect dynamics in DLS vary depending on students' demographic and personal…
Descriptors: Student Attitudes, Mathematics Instruction, Gender Differences, Psychological Patterns
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André Seixas de Novais; José Alexandre Matelli; Messias Borges Silva – International Journal of Artificial Intelligence in Education, 2024
This research aims to present a Fuzzy Expert System with psychologist expertise that seeks to assist professors, researchers and educational institutions in assessing the level of incorporation of students' Soft Skills while attending Active Learning sessions. The difficulties encountered by higher education institutions, researchers and…
Descriptors: Soft Skills, Active Learning, Teaching Methods, Evaluation Methods
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Bao Wang; Philippe J. Giabbanelli – International Journal of Artificial Intelligence in Education, 2024
Knowledge maps have been widely used in knowledge elicitation and representation to evaluate and guide students' learning. To effectively evaluate maps, instructors must select the most informative map features that capture students' knowledge constructs. However, there is currently no clear and consistent criteria to select such features, as…
Descriptors: Concept Mapping, Evaluation Methods, Student Evaluation, Algorithms
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Ilana Ram; Sara Harris; Ido Roll – International Journal of Artificial Intelligence in Education, 2024
Personalization in education describes instruction that is tailored to learners' interests, attributes, or background and can be applied in various ways, one of which is through choice. In choice-based personalization, learners choose topics or resources that fit them the most. Personalization may be especially important (and under-used) with…
Descriptors: MOOCs, Individualized Instruction, Student Interests, Active Learning
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Luiz Rodrigues; Paula T. Palomino; Armando M. Toda; Ana C. T. Klock; Marcela Pessoa; Filipe D. Pereira; Elaine H. T. Oliveira; David F. Oliveira; Alexandra I. Cristea; Isabela Gasparini; Seiji Isotani – International Journal of Artificial Intelligence in Education, 2024
Personalized gamification aims to address shortcomings of the one-size-fits-all (OSFA) approach in improving students' motivations throughout the learning process. However, studies still focus on personalizing to a single user dimension, ignoring multiple individual and contextual factors that affect user motivation. Unlike prior research, we…
Descriptors: Individualized Instruction, Student Motivation, Gamification, Student Evaluation
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Rebecka Weegar; Peter Idestam-Almquist – International Journal of Artificial Intelligence in Education, 2024
Machine learning methods can be used to reduce the manual workload in exam grading, making it possible for teachers to spend more time on other tasks. However, when it comes to grading exams, fully eliminating manual work is not yet possible even with very accurate automated grading, as any grading mistakes could have significant consequences for…
Descriptors: Grading, Computer Assisted Testing, Introductory Courses, Computer Science Education
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Ramon Mayor Martins; Christiane Gresse von Wangenheim; Marcelo Fernando Rauber; Jean Carlo Hauck – International Journal of Artificial Intelligence in Education, 2024
Although Machine Learning (ML) is found practically everywhere, few understand the technology behind it. This presents new challenges to extend computing education by including ML concepts in order to help students to understand its potential and limits and empowering them to become creators of intelligent solutions. Therefore, we developed an…
Descriptors: Artificial Intelligence, Information Technology, Technology Uses in Education, Computer Software
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Archana Praveen Kumar; Ashalatha Nayak; Manjula Shenoy K.; Chaitanya; Kaustav Ghosh – International Journal of Artificial Intelligence in Education, 2024
Multiple Choice Questions (MCQs) are a popular assessment method because they enable automated evaluation, flexible administration and use with huge groups. Despite these benefits, the manual construction of MCQs is challenging, time-consuming and error-prone. This is because each MCQ is comprised of a question called the "stem", a…
Descriptors: Multiple Choice Tests, Test Construction, Test Items, Semantics
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Tanja Käser; Giora Alexandron – International Journal of Artificial Intelligence in Education, 2024
Simulation is a powerful approach that plays a significant role in science and technology. Computational models that simulate learner interactions and data hold great promise for educational technology as well. Amongst others, simulated learners can be used for teacher training, for generating and evaluating hypotheses on human learning, for…
Descriptors: Computer Simulation, Educational Technology, Artificial Intelligence, Algorithms
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Anna Filighera; Sebastian Ochs; Tim Steuer; Thomas Tregel – International Journal of Artificial Intelligence in Education, 2024
Automatic grading models are valued for the time and effort saved during the instruction of large student bodies. Especially with the increasing digitization of education and interest in large-scale standardized testing, the popularity of automatic grading has risen to the point where commercial solutions are widely available and used. However,…
Descriptors: Cheating, Grading, Form Classes (Languages), Computer Software
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Kurt VanLehn; Fabio Milner; Chandrani Banerjee; Jon Wetzel – International Journal of Artificial Intelligence in Education, 2024
An algebraic model uses a set of algebraic equations to describe a situation. Constructing such models is a fundamental skill, but many students still lack the skill, even after taking several algebra courses in high school and college. For such students, we developed instruction that taught students to decompose the to-be-modelled situation into…
Descriptors: Algebra, Mathematical Models, Low Achievement, Mathematics Instruction
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Teo Susnjak – International Journal of Artificial Intelligence in Education, 2024
A significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate timely interventions and thereby elevate retention and completion rates. The overarching feature of the majority of these research studies has been on the science of…
Descriptors: Prediction, Learning Analytics, Artificial Intelligence, At Risk Students
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Verena Dornauer; Michael Netzer; Éva Kaczkó; Lisa-Maria Norz; Elske Ammenwerth – International Journal of Artificial Intelligence in Education, 2024
Cognitive presence is a core construct of the Community of Inquiry (CoI) framework. It is considered crucial for deep and meaningful online-based learning. CoI-based real-time dashboards visualizing students' cognitive presence may help instructors to monitor and support students' learning progress. Such real-time classifiers are often based on…
Descriptors: Electronic Learning, Discussion, Classification, Automation
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Marcell Nagy; Roland Molontay – International Journal of Artificial Intelligence in Education, 2024
Student drop-out is one of the most burning issues in STEM higher education, which induces considerable social and economic costs. Using machine learning tools for the early identification of students at risk of dropping out has gained a lot of interest recently. However, there has been little discussion on dropout prediction using interpretable…
Descriptors: Dropout Characteristics, Dropout Research, Intervention, At Risk Students
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Michael DeBuse; Dallin Clayton; Brooks Butler; Sean Warnick – International Journal of Artificial Intelligence in Education, 2024
This work considers group discussion data, as recorded in video conferencing settings, and demonstrates the ability to use readily available computational tools to glean important information characterizing the dynamics of the group discussion. In particular, our toolbox reveals a number of important characteristics of a discussion, including who…
Descriptors: Discussion (Teaching Technique), Discussion Groups, Group Dynamics, Group Discussion
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