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Zhan, Zehui; Mei, Hu; Liang, Ting; Huo, Liming; Bonk, Curtis; Hu, Qintai – Interactive Learning Environments, 2023
The purpose of this study is to examine the effect of material incentives on the knowledge-sharing networks and information lifecycles of a public online forum. The users' interaction data were collected from two similar sub-forums, and one of them provided material incentives for sharing knowledge. After collecting user behaviors for one natural…
Descriptors: Longitudinal Studies, Incentives, Networks, Computer Mediated Communication
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
Tan, Hongye; Wang, Chong; Duan, Qinglong; Lu, Yu; Zhang, Hu; Li, Ru – Interactive Learning Environments, 2023
Automatic short answer grading (ASAG) is a challenging task that aims to predict a score for a given student response. Previous works on ASAG mainly use nonneural or neural methods. However, the former depends on handcrafted features and is limited by its inflexibility and high cost, and the latter ignores global word cooccurrence in a corpus and…
Descriptors: Automation, Grading, Computer Assisted Testing, Graphs
Lin, Wen-Shan; Chen, Hong-Ren; Huang, Yueh-Min – Interactive Learning Environments, 2023
This paper aims to investigate the antecedents of how crowdfunded projects succeed over crowdsourcing platforms (CFPs) on the Internet. As CFPs make quite a large number of open innovations feasible, little is known about knowledge sharing and cross-project learning (CPL) in association with the success of crowdfunded projects. No study has…
Descriptors: Internet, Social Support Groups, Teamwork, Cooperation
Troussas, Christos; Giannakas, Filippos; Sgouropoulou, Cleo; Voyiatzis, Ioannis – Interactive Learning Environments, 2023
Computer-Supported Collaborative Learning is a promising innovation that ameliorates tutoring through modern technologies. However, the way of recommending collaborative activities to learners, by taking into account their learning needs and preferences, is an important issue of increasing interest. In this context, this paper presents a framework…
Descriptors: Computer Assisted Instruction, Cognitive Style, Cooperative Learning, Models
Çelikbilek, Yakup; Adigüzel Tüylü, Ayse Nur – Interactive Learning Environments, 2022
Institutions and universities have started using e-learning systems to reach the potential students from all over the world by decreasing costs of investments. The speed of technological developments increases the importance of e-learning systems and their technology-based components. E-learning systems also decrease the costs of both institutions…
Descriptors: Electronic Learning, Technology Uses in Education, Distance Education, Artificial Intelligence
Luke, Karl – Interactive Learning Environments, 2022
Lecture recording is an increasingly common practice in UK universities, whereby audio, video, and multimedia content from lecture theatres can be captured and distributed online. Despite a large body of recent lecture capture literature, much of the empirical research adopts positivist paradigms, which overlooks the complex and unpredictable…
Descriptors: Lecture Method, Audiovisual Aids, Video Technology, Multimedia Materials
Perez-Ramirez, Miguel; Arroyo-Figueroa, G.; Ayala, A. – Interactive Learning Environments, 2021
The application of virtual reality (VR) technologies is beneficial to the training related to industrial processes. Mainly because the technologies allow training complex threatening tasks within a safe environment. The interactive three-dimensional (3D) representation of a real world seems to be a more effective learning medium than other…
Descriptors: Computer Simulation, Educational Technology, Technology Uses in Education, Training
Poitras, Eric G.; Doleck, Tenzin; Huang, Lingyun; Dias, Laurel; Lajoie, Susanne P. – Interactive Learning Environments, 2023
This study applies a time-driven approach to model self-regulated learning (SRL) on the basis of elapsed time metrics in the context of open-ended learning environments (OELEs), specifically, network-based tutors. In doing so, we examine how students allocated attentional resources to distinct phases of SRL as a measure of depth of information…
Descriptors: Independent Study, Self Management, Time, Networks
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
Ai-Jou Pan; Yu-Che Huang; Chin-Feng Lai – Interactive Learning Environments, 2024
Engineering education emphasizes experiential learning and laboratory experience, an approach which has faced significant challenges during the COVID-19 pandemic. The inability to conduct hands-on laboratory experiments in engineering courses can significantly impede the student's learning experience, as well as their acquisition and retention of…
Descriptors: Learning Management Systems, Hands on Science, Distance Education, Laboratories
Zhang, Lishan; VanLehn, Kurt – Interactive Learning Environments, 2021
Despite their drawback, multiple-choice questions are an enduring feature in instruction because they can be answered more rapidly than open response questions and they are easily scored. However, it can be difficult to generate good incorrect choices (called "distractors"). We designed an algorithm to generate distractors from a…
Descriptors: Semantics, Networks, Multiple Choice Tests, Teaching Methods
Premlatha, K. R.; Dharani, B.; Geetha, T. V. – Interactive Learning Environments, 2016
E-learning allows learners individually to learn "anywhere, anytime" and offers immediate access to specific information. However, learners have different behaviors, learning styles, attitudes, and aptitudes, which affect their learning process, and therefore learning environments need to adapt according to these differences, so as to…
Descriptors: Electronic Learning, Profiles, Automation, Classification