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Alejandra J. Magana; Syed Tanzim Mubarrat; Dominic Kao; Bedrich Benes – IEEE Transactions on Learning Technologies, 2024
Fostering productive engagement within teams has been found to improve student learning outcomes. Consequently, characterizing productive and unproductive time during teamwork sessions is a critical preliminary step to increase engagement in teamwork meetings. However, research from the cognitive sciences has mainly focused on characterizing…
Descriptors: Artificial Intelligence, Technology Uses in Education, Teamwork, Learner Engagement
Heng Zhang; Minhong Wang – Knowledge Management & E-Learning, 2024
With the fast development of artificial intelligence and emerging technologies, automatic recognition of students' facial expressions has received increased attention. Facial expressions are a kind of external manifestation of emotional states. It is important for teachers to assess students' emotional states and adjust teaching activities…
Descriptors: Artificial Intelligence, Models, Recognition (Psychology), Nonverbal Communication
Acheampong Owusu – Education and Information Technologies, 2024
Knowledge Management Systems (KMS) have been used to provide automated assistance to customers in different organizations from diverse sectors. In the context of higher educational institutions (HEIs) especially in universities, research indicates that KMS assist universities with quicker response time to key issues, share vital knowledge, improve…
Descriptors: Foreign Countries, Knowledge Management, College Students, Academic Achievement
Saida Ulfa; Ence Surahman; Izzul Fatawi; Hirashima Tsukasa – Electronic Journal of e-Learning, 2024
The purpose of this study was to evaluate the factors that influence behavioural intention (BI) to use the Online Summary-with Automated Feedback (OSAF) in a MOOCs platform. Task-Technology Fit (TTF) was the main framework used to analyse the match between task requirements and technology characteristics, predictng the utilisation of the…
Descriptors: MOOCs, Intention, Automation, Feedback (Response)
Sun, Xiaohua; Liu, Haochen; Liang, Peng – IEEE Transactions on Education, 2023
Contribution: This research designs an experimental education project of an automatic material magnetism properties measurement system. It explores how the do-it-yourself (DIY), hands-on establishment, and hardware-software integration experiment system could be leveraged to enhance the understanding of the electromagnetism theory and…
Descriptors: Magnets, Hands on Science, Technology Uses in Education, Automation
Elisabeth Bauer; Michael Sailer; Frank Niklas; Samuel Greiff; Sven Sarbu-Rothsching; Jan M. Zottmann; Jan Kiesewetter; Matthias Stadler; Martin R. Fischer; Tina Seidel; Detlef Urhahne; Maximilian Sailer; Frank Fischer – Journal of Computer Assisted Learning, 2025
Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks…
Descriptors: Artificial Intelligence, Feedback (Response), Computer Simulation, Natural 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
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
Pearson, Christopher; Penna, Nigel – Assessment & Evaluation in Higher Education, 2023
E-assessments are becoming increasingly common and progressively more complex. Consequently, how these longer, more complex questions are designed and marked is imperative. This article uses the NUMBAS e-assessment tool to investigate the best practice for creating longer questions and their mark schemes on surveying modules taken by engineering…
Descriptors: Automation, Scoring, Engineering Education, Foreign Countries
Samantha Yanosko; Grant Valentine; Matthew W. Liberatore – Chemical Engineering Education, 2025
An interactive textbook for a material and energy balances course measured over 1,300 reading interactions and hundreds of auto-graded problems per student each term. Specifically, seven cohorts and 601 students completed over 700,000 reading interactions and 150,000 auto-graded problems. Median reading participation was over 93%. Median correct…
Descriptors: Chemical Engineering, Textbooks, Computer Uses in Education, Grading
Davidson, Jason L. – ProQuest LLC, 2023
Student enrollment in online courses has nearly tripled over the last decade, with 72% of college students participating in at least one online course. There are many advantages to online education such as increased classroom diversity, the reduction of geographical limitations, and overall convenience. However, studies have shown students…
Descriptors: Automation, Online Courses, Nonverbal Communication, Learner Engagement
Albreiki, Balqis – International Journal of Educational Technology in Higher Education, 2022
Higher education institutions often struggle with increased dropout rates, academic underachievement, and delayed graduations. One way in which these challenges can potentially be addressed is by better leveraging the student data stored in institutional databases and online learning platforms to predict students' academic performance early using…
Descriptors: Automation, Remedial Instruction, At Risk Students, College Students
Samuel S. Davidson – ProQuest LLC, 2024
Automated corrective feedback (ACF), in which a computer system helps language learners identify and correct errors in their writing or speech, is considered an important tool for language instruction by many researchers. Such systems allow learners to correct their own mistakes, thereby reducing teacher workload and potentially preventing issues…
Descriptors: Computer Assisted Testing, Automation, Student Evaluation, Feedback (Response)
Jessie S. Barrot – Technology, Knowledge and Learning, 2025
Significant advancements in artificial intelligence (AI) technologies have led to the development of Google Gemini, which can be used to provide automated writing assistance. Within higher education, this feature extends to research writing. However, skepticism is particularly evident as students, teachers, and researchers in universities explore…
Descriptors: Artificial Intelligence, Computer Software, Computer Uses in Education, Educational Research
Motz, Benjamin A.; Mallon, Matthew G.; Quick, Joshua D. – IEEE Transactions on Learning Technologies, 2021
As institutions of higher education increasingly utilize online learning management systems, college students are asked to submit more assignments online. Under this regime, when most assignments are posted and submitted online, it is possible to know if a student is missing a submission for an imminent deadline, and to intervene proactively to…
Descriptors: Automation, Assignments, Cues, Time Management