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Kajal Mahawar; Punam Rattan – Education and Information Technologies, 2025
Higher education institutions have consistently strived to provide students with top-notch education. To achieve better outcomes, machine learning (ML) algorithms greatly simplify the prediction process. ML can be utilized by academicians to obtain insight into student data and mine data for forecasting the performance. In this paper, the authors…
Descriptors: Electronic Learning, Artificial Intelligence, Academic Achievement, Prediction
Khaleel Asyraaf Mat Sanusi; Deniz Iren; Nardie Fanchamps; Mai Geisen; Roland Klemke – Educational Technology Research and Development, 2025
Psychomotor skill training has traditionally relied on the physical presence of both teacher and learner for effective demonstrations and feedback, posing challenges for remote and self-directed learning. Technological advancements have given rise to immersive learning environments (ILEs), offering novel solutions for interactive and multimodal…
Descriptors: Psychomotor Skills, Skill Development, Computer Simulation, Electronic Learning
Mirmoghtadaie, Zohresadat; Keshavarz, Mohsen; Mohammadimehr, Mojgan; Rasouli, Davood – International Review of Research in Open and Distributed Learning, 2023
In peer observation of teaching, an experienced colleague in the educational environment of a faculty member observes the educational performance of that faculty member and provides appropriate feedback. The use of peer review as an alternative source of evidence of teaching effectiveness is increasing. However, no research has been done in the…
Descriptors: Learning Management Systems, Academic Achievement, Peer Evaluation, Teacher Evaluation
Li, Li; Titsworth, Scott – American Journal of Distance Education, 2015
The current program of research included two studies that developed the Student Online Misbehaviors (SOMs) scale and explored relationships between the SOMs and various classroom communication processes and outcomes. The first study inductively developed initial SOM typologies and tested factor structure via an exploratory factor analysis.…
Descriptors: Student Behavior, Behavior Problems, Online Courses, Electronic Learning
Agrawal, Akshay; Venkatraman, Jagadish; Leonard, Shane; Paepcke, Andreas – International Educational Data Mining Society, 2015
In Massive Open Online Courses (MOOCs), struggling learners often seek help by posting questions in discussion forums. Unfortunately, given the large volume of discussion in MOOCs, instructors may overlook these learners' posts, detrimentally impacting the learning process and exacerbating attrition. In this paper, we present YouEDU, an…
Descriptors: Large Group Instruction, Distance Education, Technology Uses in Education, Educational Technology
Fanfarelli, Joseph R.; McDaniel, Rudy – Journal of Educational Technology Systems, 2015
Badge use has rapidly expanded in recent years and has benefited a variety of applications. However, a large portion of the research has applied a binary "useful" or "not useful" approach to badging. Few studies examine the characteristics of the user and the impact of those characteristics on the effectiveness of the badging…
Descriptors: College Students, Recognition (Achievement), Individual Differences, Student Characteristics
Kim, Jin-Young – Computers & Education, 2012
This study explores and describes different viewpoints on blended e-Education by using Q methodology to identify students' perspectives and classify them into perceptional types. It is also designed to examine possible relationships among learner's perceptional type, characteristics (i.e., academic self-efficacy, interest in blended e-Education,…
Descriptors: Foreign Countries, Undergraduate Students, College Instruction, Self Efficacy
Bose, Eric V. – ProQuest LLC, 2012
This study sought to examine and extend the existing knowledge of IBSTPI competencies of instructional design (International Board of Standards for Training, Performance and Instruction, 2000), the instructional rubric standard expectations of Quality Matters (2010), the identified categories of online learning affordances (Kirschner, Strijbos,…
Descriptors: Instructional Design, Standards, Scoring Rubrics, Educational Quality
Carle, Adam C. – Computers & Education, 2009
Aims: Do college students' ratings of a professor's teaching effectiveness suggest that a professor's teaching improves with time? Does anything predict which instructors receive the highest ratings or improve the fastest? And, importantly, do the correlates of change differ across face-to-face and online courses? Methods: I used data from 10,392…
Descriptors: College Students, Student Evaluation of Teacher Performance, Online Courses, Correlation
An Investigation of the Effect of Network Latency on Pedagogic Efficacy: A Comparison of Disciplines
Bush, H. Francis; Squire, James; Sullivan, Gerald; Walsh, Vonda; English, Anthony; Bolen, Rosie – Contemporary Issues in Education Research, 2008
E-learning has become a mainstream educational opportunity, as noted in "U.S. News & World Report." Further, differences among college students have been documented in various disciplines. An experiment was conducted to determine the effects of network latency on pedagogical efficacy based on the students who were classified as in…
Descriptors: Comparative Analysis, Efficiency, Humanities, Networks
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection
International Association for Development of the Information Society, 2012
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…
Descriptors: Academic Achievement, Academic Persistence, Academic Support Services, Access to Computers