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Sophia Mavridi – Technology in Language Teaching & Learning, 2025
This article proposes a critical typology of five emerging responses to artificial intelligence (AI) in language education, from prohibition and hype to critical engagement, highlighting the assumptions, tensions, and possibilities each orientation embodies. This typology serves as a reflective tool to examine how educators and institutions are…
Descriptors: Artificial Intelligence, Classification, Responses, Language Teachers
Mohanad Halaweh – Journal of Information Systems Education, 2025
Artificial intelligence (AI) and its subfield, machine learning, have become indispensable across various industries. With the aid of low-code/no-code development platform like KNIME, understanding and applying machine learning algorithms has been simplified for various fields, including business and information systems, as these platforms reduce…
Descriptors: Artificial Intelligence, Computer Uses in Education, Business Education, Information Systems
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
Jennifer Campbell; Katie Ansell; Tim Stelzer – Physical Review Physics Education Research, 2024
Recent advances in publicly available natural language processors (NLP) may enhance the efficiency of analyzing student short-answer responses in physics education research (PER). We train a state-of-the-art NLP, IBM's Watson, and test its agreement with human coders using two different studies that gathered text responses in which students…
Descriptors: Artificial Intelligence, Physics, Natural Language Processing, Computer Uses in Education
Hamal, Oussama; El Faddouli, Nour-Eddine; Harouni, Moulay Hachem Alaoui – World Journal on Educational Technology: Current Issues, 2021
Nowadays, AI is a real springboard for finding solutions to optimize and improve learning and teaching processes. This issue has been a focus of humanity for millennia, and very significant advances have been made in this quest. This article aims to address the issue of optimizing and improving learning and teaching processes through AI…
Descriptors: Artificial Intelligence, Learning Analytics, Computer Uses in Education, Classification
Han Zhang; Yilang Peng – Sociological Methods & Research, 2024
Automated image analysis has received increasing attention in social scientific research, yet existing scholarship has mostly covered the application of supervised learning to classify images into predefined categories. This study focuses on the task of unsupervised image clustering, which aims to automatically discover categories from unlabelled…
Descriptors: Social Science Research, Visual Aids, Visual Learning, Cluster Grouping
Gao, Zhikai; Lynch, Collin; Heckman, Sarah; Barnes, Tiffany – International Educational Data Mining Society, 2021
As Computer Science has increased in popularity so too have class sizes and demands on faculty to provide support. It is therefore more important than ever for us to identify new ways to triage student questions, identify common problems, target students who need the most help, and better manage instructors' time. By analyzing interaction data…
Descriptors: Automation, Classification, Help Seeking, Computer Science Education
Bülent BASARAN; Ömer SIMSEK – Journal of Computer Assisted Learning, 2024
Background: Their ubiquity is particularly notable as video games become increasingly intertwined with the technological revolution. Despite this prominence, gender disparities in adolescent video gaming remain under-explored. Objectives: This research aims to determine the frequency classes of video game playing based on gender, analyse the…
Descriptors: Gender Differences, Video Games, Play, Academic Achievement
Lonneke Boels; Enrique Garcia Moreno-Esteva; Arthur Bakker; Paul Drijvers – International Journal of Artificial Intelligence in Education, 2024
As a first step toward automatic feedback based on students' strategies for solving histogram tasks we investigated how strategy recognition can be automated based on students' gazes. A previous study showed how students' task-specific strategies can be inferred from their gazes. The research question addressed in the present article is how data…
Descriptors: Eye Movements, Learning Strategies, Problem Solving, Automation
Shahzad, Areeba; Wali, Aamir – Education and Information Technologies, 2022
Checking essays written by students is a very time consuming task. Besides spellings and grammar, they also need to be evaluated on their semantic content such as cohesion, coherence, etc. In this study we focus on one such aspect of semantic content which is the topic of the essay. Putting it formally, given a prompt or essay-statement and an…
Descriptors: Computer Uses in Education, Essays, Writing Evaluation, Semantics
Hodges, Jaret; Mohan, Soumya – Gifted Child Quarterly, 2019
Machine learning algorithms are used in language processing, automated driving, and for prediction. Though the theory of machine learning has existed since the 1950s, it was not until the advent of advanced computing that their potential has begun to be realized. Gifted education is a field where machine learning has yet to be utilized, even…
Descriptors: Gifted Education, Artificial Intelligence, Classification, Computer Uses in Education
Noyes, Keenan; McKay, Robert L.; Neumann, Matthew; Haudek, Kevin C.; Cooper, Melanie M. – Journal of Chemical Education, 2020
Computer-assisted analysis of students' written responses to questions is becoming a possibility due to developments in technology. This could make such constructed response questions more feasible for use in large classrooms where multiple choice assessments are often considered a more practical option. In this study, we use a previously…
Descriptors: Automation, Artificial Intelligence, Computer Uses in Education, Classification
System-Based Ontology for Assessing Learner's Programming Practical Works Activities (S_Onto_ALPPWA)
Boussaha, Karima; Mokhati, Farid; Hanneche, Amira – International Journal of Web-Based Learning and Teaching Technologies, 2021
This article introduces a new learner's self-assessment environment as CEHL that allows comparison of learners' programs with those elaborated by the teacher. The subjacent idea is to indirectly compare programs through their graphical representations described by ontologies. So, CEHL developed so-called S_Onto_ALPPWA which allows comparing…
Descriptors: Self Evaluation (Individuals), Programming, Computer Uses in Education, Comparative Analysis
Xiao, Yunkai; Zingle, Gabriel; Jia, Qinjin; Akbar, Shoaib; Song, Yang; Dong, Muyao; Qi, Li; Gehringer, Edward – International Educational Data Mining Society, 2020
Peer assessment adds value when students provide "helpful" feedback to their peers. But, this begs the question of how we determine "helpfulness." One important aspect is whether the review detects problems in the submitted work. To recognize problem detection, researchers have employed NLP and machine-learning text…
Descriptors: Peer Evaluation, Problems, Identification, Natural Language Processing
Stapleton, Suzanne Cady; Dinsmore, Chelsea S.; Van Kleeck, David; Ma, Xiaoli – College & Research Libraries, 2021
Discovery of digital items by scholars and the public is highly dependent upon effective metadata to ensure inclusion and prioritization in search engines. Subject descriptions based on controlled vocabulary, such as Library of Congress subject headings (LCSH), are particularly useful to enhance discovery, but they may be expensive to provide. In…
Descriptors: Indexing, Metadata, Computer Use, Electronic Publishing

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