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Alshurideh, Muhammad; Al Kurdi, Barween; Salloum, Said A.; Arpaci, Ibrahim; Al-Emran, Mostafa – Interactive Learning Environments, 2023
Despite the plethora of m-learning acceptance studies, few have tackled the importance of examining the actual use of m-learning systems from the lenses of social influence, expectation-confirmation, and satisfaction. Additionally, most of the prior technology adoption literature tends to use the structural equation modeling (SEM) technique in…
Descriptors: Electronic Learning, Prediction, Least Squares Statistics, Structural Equation Models
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Ji-Eun Lee; Amisha Jindal; Sanika Nitin Patki; Ashish Gurung; Reilly Norum; Erin Ottmar – Interactive Learning Environments, 2024
This paper demonstrated how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. Using a data-driven approach, we examined 1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance (i.e. posttest math knowledge scores) prediction and 2)…
Descriptors: Teaching Methods, Algorithms, Mathematics Tests, Computer Games
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Paquette, Luc; Baker, Ryan S. – Interactive Learning Environments, 2019
Learning analytics research has used both knowledge engineering and machine learning methods to model student behaviors within the context of digital learning environments. In this paper, we compare these two approaches, as well as a hybrid approach combining the two types of methods. We illustrate the strengths of each approach in the context of…
Descriptors: Comparative Analysis, Student Behavior, Models, Case Studies
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Radosavljevic, Slavica; Radosavljevic, Vitomir; Grgurovic, Biljana – Interactive Learning Environments, 2020
The goal of higher vocational education is professional training of students. Teaching contents related to practical training are the most important for preparing students for work in real-life conditions. The mobile learning model presented in this paper analyzes the possibility of implementing augmented reality in the process of educating…
Descriptors: Telecommunications, Handheld Devices, Computer Simulation, Models
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Li, Yushun; Zhao, Shuxia; Ma, Qingyan; Qian, Chunlan; Lin, Qun – Interactive Learning Environments, 2019
The overall appeal of ICT in education in China is promoting deep integration of ICT technology with teaching. From a regional point of view, intelligent terminals, such as laptop and tablet, were integrated into classroom, in Beijing, Shanghai and other developed cities. Interactive media equipment (whiteboards, interactive TVs and iPad, etc.)…
Descriptors: Foreign Countries, Interaction, Teaching Methods, Regional Schools
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Wetzel, Jon; VanLehn, Kurt; Butler, Dillan; Chaudhari, Pradeep; Desai, Avaneesh; Feng, Jingxian; Grover, Sachin; Joiner, Reid; Kong-Sivert, Mackenzie; Patade, Vallabh; Samala, Ritesh; Tiwari, Megha; van de Sande, Brett – Interactive Learning Environments, 2017
This paper describes Dragoon, a simple intelligent tutoring system which teaches the construction of models of dynamic systems. Modelling is one of seven practices dictated in two new sets of educational standards in the U.S.A., and Dragoon is one of the first systems for teaching model construction for dynamic systems. Dragoon can be classified…
Descriptors: Intelligent Tutoring Systems, Models, Computer Interfaces, Comparative Analysis
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Zou, Di; Wang, Minhong; Xie, Haoran; Cheng, Gary; Wang, Fu Lee; Lee, Lap-Kei – Interactive Learning Environments, 2021
Personalized learning has become an important and powerful paradigm catering for various needs, styles, preferences, and modes of learning. Several methods including task recommendations and path planning have recently emerged to effectively implement personalized learning using e-learning systems. The literature shows that the use of task…
Descriptors: Linguistic Theory, Vocabulary Development, Second Language Learning, Second Language Instruction
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Huang, Xiaoxia – Interactive Learning Environments, 2017
Previous research has indicated the disconnect between example-based research focusing on worked examples (WEs) and that focusing on modeling examples. The purpose of this study was to examine and compare the effect of four different types of examples from the two separate lines of research, including standard WEs, erroneous WEs, expert (masterly)…
Descriptors: Teaching Methods, Problem Solving, Academic Achievement, Cognitive Processes
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Chen, Chih-Ming; Wang, Jung-Ying; Chen, Yong-Ting; Wu, Jhih-Hao – Interactive Learning Environments, 2016
To reduce effectively the reading anxiety of learners while reading English articles, a C4.5 decision tree, a widely used data mining technique, was used to develop a personalized reading anxiety prediction model (PRAPM) based on individual learners' reading annotation behavior in a collaborative digital reading annotation system (CDRAS). In…
Descriptors: Reading Strategies, Prediction, Models, Quasiexperimental Design
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Chen, Min; Yu, Sheng Quan; Chiang, Feng Kuang – Interactive Learning Environments, 2017
Most ubiquitous learning researchers use resource recommendation and retrieving based on context to provide contextualized learning resources, but it is the kind of one-way context matching. Learners always obtain fixed digital learning resources, which present all learning contents in any context. This study proposed a dynamic ubiquitous learning…
Descriptors: Electronic Learning, Educational Technology, Instructional Materials, Models
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Scardamalia, M.; And Others – Interactive Learning Environments, 1992
Presents results from elementary school classroom uses of the Computer Supported Integrated Learning Environments (CSILE) software that stores student productions, including text and graphics, in one database to which all users have simultaneous access. Educational uses, effects, and outcomes of CSILE are described; and knowledge construction is…
Descriptors: Academic Achievement, Classroom Observation Techniques, Comparative Analysis, Computer Assisted Instruction
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Johnson, Scott D.; And Others – Interactive Learning Environments, 1993
This study examines the effect of the "Technical Troubleshooting Tutor," a computer-coached training program, on aircraft electrical system troubleshooting. Performance ability differences between control groups are noted, and troubleshooting models and flow diagram examples are included. The study demonstrates the possibilities for…
Descriptors: Aviation Education, Cognitive Processes, Comparative Analysis, Computer Assisted Instruction