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Fu Chen; Chang Lu; Ying Cui – Education and Information Technologies, 2024
Successful computer-based assessments for learning greatly rely on an effective learner modeling approach to analyze learner data and evaluate learner behaviors. In addition to explicit learning performance (i.e., product data), the process data logged by computer-based assessments provide a treasure trove of information about how learners solve…
Descriptors: Computer Assisted Testing, Problem Solving, Learning Analytics, Learning Processes
Athitaya Nitchot; Lester Gilbert – Education and Information Technologies, 2025
Learning programming is a complex process that requires understanding abstract concepts and solving problems efficiently. To support and motivate students, educators can use technology-enhanced learning (TEL) in the form of visual tools for knowledge mapping. Mytelemap, a prototype tool, uses TEL to organize and visualize information, enhancing…
Descriptors: Learning Motivation, Concept Mapping, Programming, Computer Science Education
Ayça Fidan; Yasemin Koçak Usluel – Education and Information Technologies, 2024
It is pointed out that one of the main problems of online learning environments is determining whether students engage or not. As engagement is a complex and multifaceted concept, researchers have stated that engagement is effected by many factors (environmental conditions and learner characteristics) and changes according to the context. Among…
Descriptors: Online Courses, Electronic Learning, Metacognition, Emotional Response
Melissa T. A. Simarmata; Gwo-Guang Lee; Hoky Ajicahyadi; Kung-Jeng Wang – Education and Information Technologies, 2024
Teaching computer programming language remotely presents particular difficulties due to its requirement for abstract and logical thinking. There is a dearth of research specifically examining the potential factors that determine student performance when distance self-learning is conducted for programming language education. This study aims to…
Descriptors: Distance Education, Independent Study, Computer Science Education, Programming
Kevser Hava; Özgür Babayigit – Education and Information Technologies, 2025
In recent years, there has been a growing emphasis on integrating Artificial Intelligence (AI) applications in educational settings. As a result, it is essential to assess teachers' competencies in Technological, Pedagogical, and Content Knowledge (TPACK) as it pertains to AI and examine the factors that influence these competencies. This study…
Descriptors: Technological Literacy, Pedagogical Content Knowledge, Artificial Intelligence, Technology Integration
Afef Saihi; Mohamed Ben-Daya; Moncer Hariga – Education and Information Technologies, 2025
The integration of AI-chatbots into higher education offers the potential to enhance learning practices. This research aims to explore the factors influencing AI-chatbots adoption within higher education, with a focus on the moderating roles of technological proficiency and academic discipline. Utilizing a survey-based approach and advanced…
Descriptors: Technology Uses in Education, Artificial Intelligence, Higher Education, Technology Integration
Ana M. Gallardo-Guerrero; María J. Maciá-Andreu; Noelia González-Gálvez; Raquel Vaquero-Cristóbal; Marta García-Tascón – Education and Information Technologies, 2025
The main objectives of this research were to analyze the impact of the use of augmented reality (AR) for analyzing the safety of sport equipment, on the motivational climate, behavior, and intention to use, and to validate a theoretical model for predicting continuance intention to use AR among students. The sample consisted of 254 university…
Descriptors: Simulated Environment, Safety, Athletics, Equipment
Thomas Mgonja – Education and Information Technologies, 2024
The successful completion of remedial mathematics is widely recognized as a crucial factor for college success. However, there is considerable concern and ongoing debate surrounding the low completion rates observed in remedial mathematics courses across various parts of the world. This study applies explainable artificial intelligence (XAI) tools…
Descriptors: Higher Education, Remedial Mathematics, Artificial Intelligence, Predictor Variables
Xingsu Wu; Chunyang Xu – Education and Information Technologies, 2025
This study, anchored in the empirical domain of student learning experiences, employs the Chaoxing Fanya network teaching platform to delineate a comprehensive model of factors that influence student learning experiences within the framework of blended collaborative learning. Through a rigorous synthesis of extant literature and qualitative…
Descriptors: Learning Experience, Blended Learning, Cooperative Learning, College Students
Chenyue Wang; Chang Lu; Fu Chen; Xueliang Liu; Qin Zhao; Shuai Wang – Education and Information Technologies, 2024
Computational thinking (CT) competency is essential for K-12 students in the digital societies. Understanding the relationship between students' CT and relevant factors contributes to implementing and improving CT education. Most previous studies investigated the effect of demographic or attitudinal factors on CT performance; whereas few research…
Descriptors: Self Efficacy, Thinking Skills, Problem Solving, Computation
Kheira Ouassif; Benameur Ziani – Education and Information Technologies, 2025
The integration of educational data mining and deep neural networks, along with the adoption of the Apriori algorithm for generating association rules, focuses to resolve the problem of misdirection of students in the university, leading to their failure and dropout. This is reached through the development of an intelligent model that predicts the…
Descriptors: Predictor Variables, College Students, Majors (Students), Decision Making
Jingxian Li; Yasemin Copur-Gencturk – Education and Information Technologies, 2025
Teacher learning in asynchronous online professional development (PD) is often measured by self-reported instruments, despite uncertainties regarding teachers' accuracy in their self-assessment of learning. This study explored whether teachers' self-reported "gains" in content knowledge for teaching aligned with those measured by direct…
Descriptors: Faculty Development, Teacher Education, Asynchronous Communication, Online Courses
Zhengze Li; Hui Chen; Xin Gao – Education and Information Technologies, 2024
Online supplementary education has been prevalent in recent years due to the advent of technology (e.g., live streaming) and the COVID-19 pandemic. However, the performance of students in this mode of education varies greatly, and the underlying reasons are yet to be investigated. This study aims to understand the impact of various factors and…
Descriptors: Predictor Variables, Elementary School Students, Electronic Learning, Supplementary Education
Giulio Marchena Sekli; May Portuguez-Castro – Education and Information Technologies, 2025
This study presents an in-depth examination of the role of Generative Artificial Intelligence in enhancing entrepreneurial success, situated within the educational context of a leading business school in Peru. Utilizing the Technology-to-Performance Chain framework, the research integrates both qualitative and quantitative methodologies to explore…
Descriptors: Entrepreneurship, Success, Artificial Intelligence, Technology Uses in Education
Using Analytics to Predict Students' Interactions with Learning Management Systems in Online Courses
Ali Alshammari – Education and Information Technologies, 2024
In online education, it is widely recognized that interaction and engagement have an impact on students' academic performance. While previous research has extensively explored interactions between students, instructors, and content, there has been limited exploration of course design elements that promote the fourth type of interaction:…
Descriptors: Learning Analytics, Learning Management Systems, Academic Achievement, Correlation