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Ting Wang; Keith Stelter; Thomas O’Neill; Nathaniel Hendrix; Andrew Bazemore; Kevin Rode; Warren P. Newton – Journal of Applied Testing Technology, 2025
Precise item categorisation is essential in aligning exam questions with content domains outlined in assessment blueprints. Traditional methods, such as manual classification or supervised machine learning, are often time-consuming, error-prone, or limited by the need for large training datasets. This study presents a novel approach using…
Descriptors: Test Items, Automation, Classification, Artificial Intelligence
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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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Hanshu Zhang; Ran Zhou; Cheng-You Cheng; Sheng-Hsu Huang; Ming-Hui Cheng; Cheng-Ta Yang – Cognitive Research: Principles and Implications, 2025
Although it is commonly believed that automation aids human decision-making, conflicting evidence raises questions about whether individuals would gain greater advantages from automation in difficult tasks. Our study examines the combined influence of task difficulty and automation reliability on aided decision-making. We assessed decision…
Descriptors: Task Analysis, Difficulty Level, Decision Making, Automation
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Anderson Pinheiro Cavalcanti; Rafael Ferreira Mello; Dragan Gaševic; Fred Freitas – International Journal of Artificial Intelligence in Education, 2024
Educational feedback is a crucial factor in the student's learning journey, as through it, students are able to identify their areas of deficiencies and improve self-regulation. However, the literature shows that this is an area of great dissatisfaction, especially in higher education. Providing effective feedback becomes an increasingly…
Descriptors: Prediction, Feedback (Response), Artificial Intelligence, Automation
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Halim Acosta; Seung Lee; Haesol Bae; Chen Feng; Jonathan Rowe; Krista Glazewski; Cindy Hmelo-Silver; Bradford Mott; James C. Lester – International Journal of Artificial Intelligence in Education, 2025
Understanding students' multi-party epistemic and topic based-dialogue contributions, or how students present knowledge in group-based chat interactions during collaborative game-based learning, offers valuable insights into group dynamics and learning processes. However, manually annotating these contributions is labor-intensive and challenging.…
Descriptors: Game Based Learning, Artificial Intelligence, Technology Uses in Education, Cooperative Learning
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Putnikovic, Marko; Jovanovic, Jelena – IEEE Transactions on Learning Technologies, 2023
Automatic grading of short answers is an important task in computer-assisted assessment (CAA). Recently, embeddings, as semantic-rich textual representations, have been increasingly used to represent short answers and predict the grade. Despite the recent trend of applying embeddings in automatic short answer grading (ASAG), there are no…
Descriptors: Automation, Computer Assisted Testing, Grading, Natural Language Processing
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Yangna Hu; Cindy Sing Bik Ngai; Sihui Chen – Journal of Speech, Language, and Hearing Research, 2025
Purpose: This study examines existing automatic screening methods for developmental language disorder (DLD), a neurodevelopmental language deficit without known biomedical etiologies, focusing on languages, data sets, extracted features, performance metrics, and classification methods. Additionally, it summarizes the strengths and weaknesses of…
Descriptors: Developmental Disabilities, Language Impairments, Automation, Screening Tests
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Koen Suzelis; Gabriel Mott; John Curiel – Journal of Academic Ethics, 2025
Student evaluations of teaching (SET) act as the primary means to gauge instructor effectiveness. Likewise, SETs provide the primary qualitative feedback to instructors via student comments. However, mostly students with strong feelings tend to write comments. Among the most recallable are toxic comments: comments that are unhelpful/hurtful in…
Descriptors: Student Evaluation of Teacher Performance, Automation, Identification, Student Attitudes
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Ceylan, Hasan Can; Hardalaç, Naciye; Kara, Ali Can; Hardalaç Firat – World Journal of Education, 2021
Because the classification saves time in the learning process and enables this process to take place more easily, its contribution to music learning cannot be denied. One of the most valid and effective methods in music classification is music genre classification. Given the rapid progress of music production in the world and the significant…
Descriptors: Music, Classification, Automation, Music Education
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Muller, Ashley Elizabeth; Ames, Heather Melanie R.; Jardim, Patricia Sofia Jacobsen; Rose, Christopher James – Research Synthesis Methods, 2022
Systematic reviews are resource-intensive. The machine learning tools being developed mostly focus on the study identification process, but tools to assist in analysis and categorization are also needed. One possibility is to use unsupervised automatic text clustering, in which each study is automatically assigned to one or more meaningful…
Descriptors: Artificial Intelligence, Man Machine Systems, Automation, Literature Reviews
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Luke Strickland; Simon Farrell; Micah K. Wilson; Jack Hutchinson; Shayne Loft – Cognitive Research: Principles and Implications, 2024
In a range of settings, human operators make decisions with the assistance of automation, the reliability of which can vary depending upon context. Currently, the processes by which humans track the level of reliability of automation are unclear. In the current study, we test cognitive models of learning that could potentially explain how humans…
Descriptors: Automation, Reliability, Man Machine Systems, Learning Processes
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Jonathan Liu; Seth Poulsen; Erica Goodwin; Hongxuan Chen; Grace Williams; Yael Gertner; Diana Franklin – ACM Transactions on Computing Education, 2025
Algorithm design is a vital skill developed in most undergraduate Computer Science (CS) programs, but few research studies focus on pedagogy related to algorithms coursework. To understand the work that has been done in the area, we present a systematic survey and literature review of CS Education studies. We search for research that is both…
Descriptors: Teaching Methods, Algorithms, Design, Computer Science Education
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Jiang, Shiyan; Tang, Hengtao; Tatar, Cansu; Rosé, Carolyn P.; Chao, Jie – Learning, Media and Technology, 2023
It's critical to foster artificial intelligence (AI) literacy for high school students, the first generation to grow up surrounded by AI, to understand working mechanism of data-driven AI technologies and critically evaluate automated decisions from predictive models. While efforts have been made to engage youth in understanding AI through…
Descriptors: Artificial Intelligence, High School Students, Models, Classification
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Zhang, Lishan; Huang, Yuwei; Yang, Xi; Yu, Shengquan; Zhuang, Fuzhen – Interactive Learning Environments, 2022
Automatic short-answer grading has been studied for more than a decade. The technique has been used for implementing auto assessment as well as building the assessor module for intelligent tutoring systems. Many early works automatically grade mainly based on the similarity between a student answer and the reference answer to the question. This…
Descriptors: Automation, Grading, Models, Artificial Intelligence
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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