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Marco Lünich; Birte Keller; Frank Marcinkowski – Technology, Knowledge and Learning, 2024
Artificial intelligence in higher education is becoming more prevalent as it promises improvements and acceleration of administrative processes concerning student support, aiming for increasing student success and graduation rates. For instance, Academic Performance Prediction (APP) provides individual feedback and serves as the foundation for…
Descriptors: Predictor Variables, Artificial Intelligence, Computer Software, Higher Education
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Elizeth Mayrene Flores Hinostroza; Derling Jose Mendoza; Mercedes Navarro Cejas; Edinson Patricio Palacios Trujillo – International Electronic Journal of Mathematics Education, 2025
This study builds on the increasing relevance of technology integration in higher education, specifically in artificial intelligence (AI) usage in educational contexts. Background research highlights the limited exploration of AI training in educational programs, particularly within Latin America. AI has become increasingly pivotal in educational…
Descriptors: Science Instruction, Artificial Intelligence, Technology Integration, Technology Uses in Education
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Jumoke I. Oladele – Online Submission, 2023
The aim of the study was to examine self-motivation and study ethics as predictors of academic achievement among undergraduates in a Nigerian University. The study employed the correlation research design in the quantitative approach. Purposive sampling technique was used to draw a sample of 320 students out of which 228 students consented and…
Descriptors: Self Motivation, Undergraduate Students, Computer Software, Study Habits
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Niklas Humble; Jonas Boustedt; Hanna Holmgren; Goran Milutinovic; Stefan Seipel; Ann-Sofie Östberg – Electronic Journal of e-Learning, 2024
Artificial Intelligence (AI) and related technologies have a long history of being used in education for motivating learners and enhancing learning. However, there have also been critiques for a too uncritical and naïve implementation of AI in education (AIED) and the potential misuse of the technology. With the release of the virtual assistant…
Descriptors: Cheating, Artificial Intelligence, Technology Uses in Education, Computer Science Education
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Heddy, Benjamin C.; Danielson, Robert W.; Ross, Kelly; Goldman, Jacqueline A. – Journal of Engineering Education, 2023
Background: Promoting engagement and motivation in early engineering experiences is important for fostering interest and retention in engineering. One method that has been effective for doing so is facilitating transformative experiences (TEs), which occurs when students apply academic content to everyday experience. Our goal was to explore the…
Descriptors: Engineering Education, Transformative Learning, Program Effectiveness, Middle School Students
Christina Elizabeth Pigg – ProQuest LLC, 2024
The purpose of this ex post facto quantitative study was to examine the correlation between the scores of preservice teachers on 240 Tutoring STR practice tests and their scores on the actual STR exam and to explore the extent to which test preparation programs predicted performance on certification exams. In addition, this study compared the…
Descriptors: Test Preparation, Preservice Teachers, Teacher Certification, Licensing Examinations (Professions)
Loo, Michelle – ProQuest LLC, 2023
Based on the Technology Acceptance Model (TAM) (Davis et al., 1989), this research examines the variables of perceived usefulness and perceived ease of use as predictors of video creation usage after software training. To gain a comprehensive understanding of the technology adoption process, the study also employed the Learning Adoption Trajectory…
Descriptors: Value Judgment, Usability, Predictor Variables, Computer Software
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Artur Strzelecki – Interactive Learning Environments, 2024
ChatGPT is an AI tool that assisted in writing, learning, solving assessments and could do so in a conversational way. The purpose of the study was to develop a model that examined the predictors of adoption and use of ChatGPT among higher education students. The proposed model was based on a previous theory of technology adoption. Seven…
Descriptors: Computer Software, Artificial Intelligence, Synchronous Communication, Technology Uses in Education
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Nagy, Gabriel; Ulitzsch, Esther – Educational and Psychological Measurement, 2022
Disengaged item responses pose a threat to the validity of the results provided by large-scale assessments. Several procedures for identifying disengaged responses on the basis of observed response times have been suggested, and item response theory (IRT) models for response engagement have been proposed. We outline that response time-based…
Descriptors: Item Response Theory, Hierarchical Linear Modeling, Predictor Variables, Classification
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Crossley, Scott; Wan, Qian; Allen, Laura; McNamara, Danielle – Reading and Writing: An Interdisciplinary Journal, 2023
Synthesis writing is widely taught across domains and serves as an important means of assessing writing ability, text comprehension, and content learning. Synthesis writing differs from other types of writing in terms of both cognitive and task demands because it requires writers to integrate information across source materials. However, little is…
Descriptors: Writing Skills, Cognitive Processes, Essays, Cues
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Tsabari, Stav; Segal, Avi; Gal, Kobi – International Educational Data Mining Society, 2023
Automatically identifying struggling students learning to program can assist teachers in providing timely and focused help. This work presents a new deep-learning language model for predicting "bug-fix-time", the expected duration between when a software bug occurs and the time it will be fixed by the student. Such information can guide…
Descriptors: College Students, Computer Science Education, Programming, Error Patterns
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Smolinski, Pawel Robert; Szostakowski, Marcin; Winiarski, Jacek – Electronic Journal of e-Learning, 2023
The COVID-19 pandemic has caused an increase in the use of e-learning software. From the perspective of the decision-makers (school/university administration), it is crucial to understand what characteristics of the software are perceived by the users (teachers) as necessary for a task (e-learning). A popular method of determining these…
Descriptors: Electronic Learning, Computer Software, Use Studies, Teacher Behavior
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Demir, Ahmet; Maroof, Lubna; Sabbah Khan, Noor Us; Ali, Bayad Jamal – Journal of Applied Research in Higher Education, 2021
Purpose: In this study, we have collected the response from 200 private university lecturers in Kurdistan Region of Iraq. In order to test the hypotheses, we have proposed structural equations modeling (SEM). Design/methodology/approach: The purpose of this paper is to elaborate the direct and indirect effects of e-service quality on perceived…
Descriptors: Technology Uses in Education, College Faculty, Foreign Countries, Teacher Attitudes
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Fang Huang; Dingyang Peng; Timothy Teo – European Journal of Education, 2025
Contextualised in the AI--supported English-speaking learning, this study examined the roles of AI affordances in influencing EFL learners' emotional, cognitive, and behavioural speaking engagement, and explored the moderating roles of gender and learner types (on-campus vs. on-job) in influencing AI-supported English-speaking engagement. Data…
Descriptors: Learner Engagement, Second Language Learning, Second Language Instruction, English (Second Language)
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Jiaozhi Liang; Fang Huang; Timothy Teo – International Journal of Computer-Assisted Language Learning and Teaching, 2024
Artificial intelligence (AI) is useful to English as a foreign language (EFL) learners, but there is a paucity of research on how they perceive AI. Contextualized in a Chinese university setting, this study investigated Chinese university EFL learners' perceptions of Grammarly in English writing. Based on an extended technology acceptance model…
Descriptors: English (Second Language), Second Language Instruction, Second Language Learning, Writing Processes
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