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Senthil Kumaran, V.; Malar, B. – Interactive Learning Environments, 2023
Churn in e-learning refers to learners who gradually perform less and become lethargic and may potentially drop out from the course. Churn prediction is a highly sensitive and critical task in an e-learning system because inaccurate predictions might cause undesired consequences. A lot of approaches proposed in the literature analyzed and modeled…
Descriptors: Electronic Learning, Dropouts, Accuracy, Classification
Ya Xiao; Khe Foon Hew – Interactive Learning Environments, 2024
In recent years, many studies have highlighted the need to go beyond the "one-size-fits-all" gamification approach to tailored or personalised gamification to optimise students' engagement based on their user attributes. However, little is known about its effectiveness on student engagement. To advance the understanding of personalized…
Descriptors: Individualized Instruction, Gamification, Student Participation, Learner Engagement
Ritter, Frank E.; Qin, Michael; MacDougall, Korey; Chae, Chungil – Interactive Learning Environments, 2023
We created a list of more than 140 tools that can be used to create tutoring systems, from complete tutoring systems to low-level tools for preparing instructional materials. Based on this list, we present a preliminary ontology of system dimensions that can serve as a base for a comprehensive review or in building systems. We also note that: (a)…
Descriptors: Educational Resources, Intelligent Tutoring Systems, Computer Managed Instruction, Programmed Tutoring
Luna, J. M.; Fardoun, H. M.; Padillo, F.; Romero, C.; Ventura, S. – Interactive Learning Environments, 2022
The aim of this paper is to categorize and describe different types of learners in massive open online courses (MOOCs) by means of a subgroup discovery (SD) approach based on MapReduce. The proposed SD approach, which is an extension of the well-known FP-Growth algorithm, considers emerging parallel methodologies like MapReduce to be able to cope…
Descriptors: Online Courses, Student Characteristics, Classification, Student Behavior
O. S. Adewale; O. C. Agbonifo; E. O. Ibam; A. I. Makinde; O. K. Boyinbode; B. A. Ojokoh; O. Olabode; M. S. Omirin; S. O. Olatunji – Interactive Learning Environments, 2024
With the advent of technological advancement in learning, such as context-awareness, ubiquity and personalisation, various innovations in teaching and learning have led to improved learning. This research paper aims to develop a system that supports personalised learning through adaptive content, adaptive learning path and context awareness to…
Descriptors: Cognitive Style, Individualized Instruction, Learning Processes, Preferences
Cömert, Zeynep; Samur, Yavuz – Interactive Learning Environments, 2023
Almost in every aspect of life, classification and categorization make it easier for humans to analyze complex structures and systems. In games, the classification of the players based on their demographics, behaviors, expectations and preferences of the game is important to increase players' motivation and satisfaction. Likewise, knowing the…
Descriptors: Classification, Student Characteristics, Models, Student Motivation
Zhang, Lishan; Huang, Yuwei; Yang, Xi; Yu, Shengquan; Zhuang, Fuzhen – Interactive Learning Environments, 2022
Automatic short-answer grading has been studied for more than a decade. The technique has been used for implementing auto assessment as well as building the assessor module for intelligent tutoring systems. Many early works automatically grade mainly based on the similarity between a student answer and the reference answer to the question. This…
Descriptors: Automation, Grading, Models, Artificial Intelligence
Eisuke Saito; Jennifer Mansfield; Richard O'Donovan – Interactive Learning Environments, 2024
By assessing student engagement with learning tasks along with students' understanding of subject matter before and during teaching, teachers are able to shift their teaching approaches through improvisational pedagogical reasoning in real time. However, if a teacher does not know how to respond to students' cues, their capacity to effectively…
Descriptors: Educational Practices, Teaching Methods, Reflective Teaching, Decision Making
Kanwal Zahoor; Narmeen Zakaria Bawany – Interactive Learning Environments, 2024
Mobile application developers rely largely on user reviews for identifying issues in mobile applications and meeting the users' expectations. User reviews are unstructured, unorganized and very informal. Identifying and classifying issues by extracting required information from reviews is difficult due to a large number of reviews. To automate the…
Descriptors: Artificial Intelligence, Computer Oriented Programs, Courseware, Learning Processes
Soomaiya Hamid; Narmeen Zakaria Bawany – Interactive Learning Environments, 2024
E-learning is the process of sharing knowledge out of the traditional classrooms through different online tools using internet. The availability and use of these tools are not easy for every student. Many institutions gather e-learning feedback to know the problems of students to improve their systems. In e-learning systems, typically a high…
Descriptors: Feedback (Response), Electronic Learning, Automation, Classification
Eisuke Saito; Percy Lai Yin Kwok; Richard O'Donovan – Interactive Learning Environments, 2024
With an increased emphasis being placed on the importance of postgraduate students publishing articles in international journals, many students may feel a need to organise learning communities with faculty members or their peers to support this aspirational activity. Most research in this area is related to doctoral students, but places relatively…
Descriptors: Communities of Practice, Graduate Students, Writing for Publication, Journal Articles
Shaheen, Muhammad – Interactive Learning Environments, 2023
Outcome-based education (OBE) is uniquely adapted by most of the educators across the world for objective processing, evaluation and assessment of computing programs and its students. However, the extraction of knowledge from OBE in common is a challenging task because of the scattered nature of the data obtained through Program Educational…
Descriptors: Undergraduate Students, Programming, Computer Science Education, Educational Objectives
Hangyan Yu; Jie Hu – Interactive Learning Environments, 2023
In the digital era, traditional communication has undergone a drastic transformation into computer-mediated communication (CMC), which can be classified into synchronous CMC (SCMC) and asynchronous CMC (ASCMC). This study compared the effects of extracurricular CMC among students about schoolwork on students' digital reading achievement between…
Descriptors: Reading Achievement, Synchronous Communication, Asynchronous Communication, Computer Mediated Communication
Xing, Wanli; Pei, Bo; Li, Shan; Chen, Guanhua; Xie, Charles – Interactive Learning Environments, 2023
Engineering design plays an important role in education. However, due to its open nature and complexity, providing timely support to students has been challenging using the traditional assessment methods. This study takes an initial step to employ learning analytics to build performance prediction models to help struggling students. It allows…
Descriptors: Learning Analytics, Engineering Education, Prediction, Design
Abbas, Muhammad Azeem; Hammad, Shiza; Hwang, Gwo-Jen; Khan, Sharifullah; Gilani, Syed Mushhad Mustuzhar – Interactive Learning Environments, 2023
Writing an English research article for novice English as an additional language (EAL) writers is a challenging task that requires experience and training at both the sentence and meaning levels. One strategy that EAL writers employ when writing a research article is the use of formulaic sequences (FSs). However, available FS corpora are general…
Descriptors: English (Second Language), Second Language Instruction, Writing Strategies, Writing Instruction
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