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Qing Yu; Bao-min Li; Qi-yun Wang – Interactive Learning Environments, 2024
In recent years, 3D holographic technology (3DHT) has attracted more and more attention from the field of education, bringing new opportunities to reform the delivery of instruction and learning. Whether the application of 3D holographic technology can effectively improve student learning performance has become a pendent issue. In this study, a…
Descriptors: Visual Aids, Technology, Learning Processes, Meta Analysis
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
Li Xiangming; Xuening Li; Jingshun Zhang – Interactive Learning Environments, 2024
In this paper, we report a 12-week longitudinal study aiming at exploring the students' reading outcome and cognitive load with individual-based print, mobile app of Rain Classroom and collaboration-based social media of WeChat. Administered to 186 postgraduate students in a research university were the weekly reading materials and comprehension…
Descriptors: Outcomes of Education, Reading, Cognitive Processes, Difficulty Level
Ting-Chia Hsu; Ching Chang; Tien-Hsiu Jen – Interactive Learning Environments, 2024
Young learners' vocabulary learning needs interaction with language input when they are engaged in an activity. Given that AI-supported image recognition technologies offer hands-on learning in authentic contexts, and that self-regulated learning (SRL) enables learners to monitor and evaluate their learning when interacting with multi-sensory…
Descriptors: Metacognition, Multisensory Learning, Vocabulary Development, Learning Strategies
Kuo-Wei Kyle Lai; Hao-Jan Howard Chen – Interactive Learning Environments, 2024
Automatic speech recognition (ASR) technology affords language learners the ability to evaluate their pronunciation accuracy by comparing their intended spoken output with the transcribed text produced by ASR-based dictation applications. However, earlier dictation tools were criticized for producing low level recognition rates for non-native…
Descriptors: Computer Software, Computer Assisted Instruction, Technology Uses in Education, Assistive Technology
Ramirez, Hazel Joyce M.; Monterola, Sheryl Lyn C. – Interactive Learning Environments, 2022
Computer-supported collaborative learning (CSCL) is a technology-driven inquiry-based approach that encourages social interaction and shared knowledge construction in completing computer-aided tasks. Although there were researches carried out on CSCL, no research to date has extensively examined how CSCL enhanced with scripts containing…
Descriptors: Earth Science, Logical Thinking, Cooperative Learning, Computer Assisted Instruction
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
Lan, Pei-Shan; Liu, Ming-Chou; Baranwal, Divya – Interactive Learning Environments, 2023
This research is designed to find out whether joining an online community combined with contract learning can enhance the learning motivation of students, promote their self-regulated learning, and improve their academic achievements. In this regard, quasi-experiment research was conducted at primary level students in which the experimental group…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Elementary School Students
Mavroudi, Anna; Hadzilacos, Thanasis; Kalles, Dimitris; Gregoriades, Andreas – Interactive Learning Environments, 2016
This paper discusses a requirements engineering process that exemplifies teacher-led design in the case of an envisioned system for adaptive learning. Such a design poses various challenges and still remains an open research issue in the field of adaptive learning. Starting from a scenario-based elicitation method, the whole process was highly…
Descriptors: Educational Environment, Teacher Developed Materials, Computer System Design, Computer Assisted Instruction
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
Zydney, Janet Mannheimer; Bathke, Arne; Hasselbring, Ted S. – Interactive Learning Environments, 2014
This study investigated the effect of different methods of guidance with anchored instruction on students' mathematical problem-solving performance. The purpose of this research was to iteratively design a learning environment to find the optimal level of guidance. Two iterations of the software were compared. The first iteration used explicit…
Descriptors: Computer Assisted Instruction, Mathematics Instruction, Instructional Effectiveness, Problem Solving
Zhang, Lishan; VanLehn, Kurt – Interactive Learning Environments, 2017
The paper describes a biology tutoring system with adaptive question selection. Questions were selected for presentation to the student based on their utilities, which were estimated from the chance that the student's competence would increase if the questions were asked. Competence was represented by the probability of mastery of a set of biology…
Descriptors: Biology, Science Instruction, Intelligent Tutoring Systems, Probability
Lin, Yu-Tzu; Chang, Chia-Hu; Hou, Huei-Tse; Wu, Ke-Chou – Interactive Learning Environments, 2016
This study investigated the effectiveness of using Google Docs in collaborative concept mapping (CCM) by comparing it with a paper-and-pencil approach. A quasi-experimental study was conducted in a physics course. The control group drew concept maps using the paper-and-pencil method and face-to-face discussion, whereas the experimental group…
Descriptors: Internet, Search Engines, Concept Mapping, Cooperative Learning
Rodicio, H. Garcia; Sanchez, E. – Interactive Learning Environments, 2012
Learners are usually provided with support devices because they find it difficult to learn from multimedia presentations. A key question, with no clear answer so far, is how best to present these support devices. One possibility is to insert them into the multimedia presentation (canned support), while another is to have a human agent provide them…
Descriptors: Multimedia Instruction, Computer Assisted Instruction, Tutoring, Intermode Differences
Nogry, S.; Jean-Daubias, S.; Guin, N. – Interactive Learning Environments, 2012
This article deals with evaluating an interactive learning environment (ILE) during the iterative-design process. Various aspects of the system must be assessed and a number of evaluation methods are available. In designing the ILE Ambre-add, several techniques were combined to test and refine the system. In particular, we point out the merits of…
Descriptors: Foreign Countries, Educational Technology, Elementary School Students, Mathematics Instruction
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