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Attasit Wiangkham; Rattawut Vongvit – Education and Information Technologies, 2024
The rapid development of metaverse technology provides countless opportunities for social interaction, collaboration, communication, and knowledge-sharing that will significantly impact human life. To ensure widespread adoption and acceptance, however, issues concerning approval, accessibility, privacy, and user behavior must be resolved.…
Descriptors: Technology Integration, Computer Simulation, Engineering Education, Models
Zhang, Wei; Wang, Yu; Wang, Suyu – Education and Information Technologies, 2022
Educational data mining (DEM) provides valuable educational information by applying data mining tools and techniques to analyze data at educational institutions. In this paper, tree-based machine learning algorithms are used to predict students' overall academic performance in their bachelor's program. The transcript data of the students in the…
Descriptors: Grade Prediction, Academic Achievement, Models, Artificial Intelligence
Bekir Yildirim – Education and Information Technologies, 2024
This study aimed to examine the effects of TRIZ-STEM applications within an online flipped learning model on teachers' problem-solving skills, creative thinking dispositions, STEM teaching, and their understanding of the nature of engineering. The sample consisted of 57 teachers (24 in the control group and 33 in the experimental group) recruited…
Descriptors: Blended Learning, STEM Education, Teacher Education, Instructional Innovation
Deepti Prit Kaur; Archana Mantri – Education and Information Technologies, 2024
Student perception is an essential component in education; especially in engineering courses, which involve complex spatial processes, manipulation and interpretation of graphs, diagrams, and concepts. Incorporation of special training instructions improve spatial skills of learners, assisting them to acquire enhanced conceptual knowledge. Through…
Descriptors: Computer Simulation, Interaction, Visual Aids, Engineering Education
Wai Tong Chor; Kam Meng Goh; Li Li Lim; Kin Yun Lum; Tsung Heng Chiew – Education and Information Technologies, 2024
The programme outcomes are broad statements of knowledge, skills, and competencies that the students should be able to demonstrate upon graduation from a programme, while the Educational Taxonomy classifies learning objectives into different domains. The precise mapping of a course outcomes to the programme outcome and the educational taxonomy…
Descriptors: Artificial Intelligence, Engineering Education, Taxonomy, Educational Objectives
Catalina Ramírez-Aristizábal; Renato de Oliveira Moraes – Education and Information Technologies, 2024
Learning Management Systems (LMS) have gained importance in the last few years; however, the emergence of COVID-19 disease made these systems indispensable for educational systems. In the post-pandemic scenario and blended learning contexts, this kind of information system is also becoming more critical as a support system. Therefore, this…
Descriptors: Learning Management Systems, Success, COVID-19, Pandemics
Kim, Ji-Yun; Seo, Jae Seon; Kim, Kwihoon – Education and Information Technologies, 2022
Attaining equitable education for all is one of the sustainable development goals. Not only the gap of education between urban and rural students but also the gap of access to information and communications technology also exists. Thus, an effort to resolve this gap is necessary. This study combines novel engineering, which is the fusion of…
Descriptors: Models, Problem Solving, Problem Based Learning, Engineering Education
Nahar, Khaledun; Shova, Boishakhe Islam; Ria, Tahmina; Rashid, Humayara Binte; Islam, A. H. M. Saiful – Education and Information Technologies, 2021
Information is everywhere in a hidden and scattered way. It becomes useful when we apply Data mining to extracts the hidden, meaningful, and potentially useful patterns from these vast data resources. Educational data mining ensures a quality education by analyzing educational data based on various aspects. In this paper, we have analyzed the…
Descriptors: Learning Analytics, College Students, Engineering Education, Data Collection
Khan, Mohd Javed; Mustafa, Khurram – Education and Information Technologies, 2019
AHIS equation describes a new model for instructional system design and develops a system based on Merrill's Component Display Theory incorporating appropriate selection of Media, Ergonomics and Navigation Structures to produce learner engaging and effective learning outcome. A significant component of the proposed model is the integration of…
Descriptors: Hypermedia, Instructional Systems, Models, Human Factors Engineering
Gottipati, Swapna; Shankararaman, Venky – Education and Information Technologies, 2018
The applications of learning outcomes and competency frameworks have brought better clarity to engineering programs in many universities. Several frameworks have been proposed to integrate outcomes and competencies into course design, delivery and assessment. However, in many cases, competencies are course-specific and their overall impact on the…
Descriptors: Outcomes of Education, Models, Engineering Education, Curriculum Design
Carroll, John M.; Jiang, Hao; Borge, Marcela – Education and Information Technologies, 2015
Teams of students in an upper-division undergraduate Usability Engineering course used a collaborative environment to carry out a series of three distributed collaborative homework assignments. Assignments were case-based analyses structured using a jigsaw design; students were provided a collaborative software environment and introduced to a…
Descriptors: Problem Based Learning, Homework, Cooperative Learning, Usability