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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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Kerstin Wagner; Agathe Merceron; Petra Sauer; Niels Pinkwart – Journal of Educational Data Mining, 2024
In this paper, we present an extended evaluation of a course recommender system designed to support students who struggle in the first semesters of their studies and are at risk of dropping out. The system, which was developed in earlier work using a student-centered design, is based on the explainable k-nearest neighbor algorithm and recommends a…
Descriptors: At Risk Students, Algorithms, Foreign Countries, Course Selection (Students)
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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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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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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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Counsell, Alyssa; Cribbie, Robert A. – Psychology Teaching Review, 2020
Statistics play an important role in psychology, but statistics modules are notoriously unpopular amongst psychology students. We examined attitudes toward statistics and attitudes toward the statistical software package R in both undergraduate and postgraduate students across the duration of a statistics module. Participants' responses were…
Descriptors: College Students, Student Attitudes, Statistics, Psychology
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Alleyne, Philmore; Soleyn, Sherlexis; Harris, Terry – Journal of Academic Ethics, 2015
The purpose of this study is to investigate the salient factors that influence accounting students to engage in software and music piracy. This study uses the theory of reasoned action (TRA) and the theory of planned behavior (TPB), and extends these models to incorporate other variables (such as moral obligation and perceived prosecution risk) to…
Descriptors: Predictor Variables, College Students, Accounting, Intention
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Kung, LeeAnn; Kung, Hsiang-Jui – Information Systems Education Journal, 2017
This paper identifies factors that motivate students to pursue a vendor-endorsed ERP award by integrating concepts from motivation theory and constructs from technology acceptance literature. We developed a web-based survey with closed- and open-ended questions to collect both quantitative and qualitative data, respectively. Students in…
Descriptors: Student Attitudes, Student Surveys, Online Surveys, Predictor Variables
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Burstein, Jill; McCaffrey, Dan; Beigman Klebanov, Beata; Ling, Guangming – Grantee Submission, 2017
No significant body of research examines writing achievement and the specific skills and knowledge in the writing domain for postsecondary (college) students in the U.S., even though many at-risk students lack the prerequisite writing skills required to persist in their education. This paper addresses this gap through a novel…
Descriptors: Computer Software, Writing Evaluation, Writing Achievement, College Students
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Barakat, Asia; Othman, Afaf – Journal of Education and Practice, 2015
The present study aims to identify the relationship between the five-factor model of personality and its relationship to cognitive style (rush and prudence) and academic achievement among a sample of students. The study is based on descriptive approach for studying the relationship between the variables of the study, results and analysis. The…
Descriptors: Foreign Countries, Cognitive Style, Academic Achievement, Correlation
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Kay, Robin Holding; Lauricella, Sharon – Canadian Journal of Learning and Technology, 2014
The purpose of this study was to investigate the benefits and challenges using laptop computers (hereafter referred to as laptops) inside and outside higher education classrooms. Quantitative and qualitative data were collected from 156 university students (54 males, 102 females) enrolled in either education or communication studies. Benefits of…
Descriptors: Laptop Computers, Higher Education, College Students, Computer Software
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Monaghan, Conal; Bizumic, Boris; Reynolds, Katherine; Smithson, Michael; Johns-Boast, Lynette; van Rooy, Dirk – European Journal of Engineering Education, 2015
One prominent approach in the exploration of the variations in project team performance has been to study two components of the aggregate personalities of the team members: conscientiousness and agreeableness. A second line of research, known as self-categorisation theory, argues that identifying as team members and the team's performance norms…
Descriptors: Performance Based Assessment, Student Projects, Computer Software, Programming
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Shin, Dong-Hee; An, Hyeri; Kim, Jang Hyun – Interactive Learning Environments, 2016
The use of a second screen can enhance information processing and the execution of search tasks within a given period. In this study, we examined the learner's attentional shift (AS) between two screens and controlled secondary tasks (STs) in the media multitasking setting and its effect on the learning process. In particular, we analyzed how…
Descriptors: Cognitive Processes, Search Strategies, Attention Control, Learning Processes
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Damnik, Gregor; Proske, Antje; Narciss, Susanne; Körndle, Hermann – Journal of Educational Research, 2013
Especially in the context of technology-enhanced informal learning, it is crucial to understand how to design information sources in such a way that learners are not overwhelmed by the demands of the learning process, but at the same time are engaged in higher order thinking processes. Guidance aids learners in dealing with the demands of a…
Descriptors: Educational Technology, Learning Processes, College Students, Retention (Psychology)
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Dishaw, Mark T.; Eierman, Michael A.; Iversen, Jacob H.; Philip, George – Journal of Information Technology Education: Research, 2013
As collaboration among teams that are distributed in time and space is becoming increasingly important, there is a need to understand the efficacy of tools available to support that collaboration. This study employs a combination of the Technology Acceptance Model (TAM) and the Task-Technology Fit (TTF) model to compare four different technologies…
Descriptors: Cooperation, Collaborative Writing, Electronic Publishing, Word Processing
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