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Xia, Xiaona – Interactive Learning Environments, 2023
The interactive learning is a continuous process, which is full of a large number of learning interaction activities. The data generated between learners and learning interaction activities can reflect the online learning behaviors. Through the correlation analysis among learning interaction activities, this paper discusses the potential…
Descriptors: Behavior Patterns, Learning Analytics, Decision Making, Correlation
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
Meng, Lingling; Zhang, Mingxin; Zhang, Wanxue; Chu, Yu – Interactive Learning Environments, 2021
Bayesian knowledge tracing model (BKT) is a typical student knowledge assessment method. It is widely used in intelligent tutoring systems. In the standard BKT model, all knowledge and skills are independent of each other. However, in the process of student learning, they have a very close relation. A student may understand knowledge B better when…
Descriptors: Bayesian Statistics, Intelligent Tutoring Systems, Student Evaluation, Knowledge Level
Rezaei, Mohammadsadegh; Bobarshad, Hossein; Badie, Kambiz – Interactive Learning Environments, 2021
The development of information technology and social networks has created new opportunities to access lifelong learning in the form of informal learning. In an informal learning environment, learning takes place via Communities of Practice (CoP). The learning success factors in online CoPs are learners' similarity in learning interests and…
Descriptors: Prediction, Electronic Learning, Communities of Practice, Information Technology
Cai, Su; Liu, Enrui; Shen, Yang; Liu, Changhao; Li, Shuhui; Shen, Yihua – Interactive Learning Environments, 2020
The development of Augmented Reality technologies has enabled students to learn in an environment that combines learning resources from the real and digital world. This paper integrates three mobile Augmented Reality-based applications into a series of mathematics lessons on probabilities in a junior high school. This paper aims to examine how…
Descriptors: Probability, Mathematics Instruction, Computer Simulation, Instructional Effectiveness
Liu, Zhi; Yang, Chongyang; Rüdian, Sylvio; Liu, Sannyuya; Zhao, Liang; Wang, Tai – Interactive Learning Environments, 2019
Textual data, as a key carrier of learning feedback, is continuously produced by many students within course forums. The temporal nature of discussion requires students' emotions and concerned aspects (e.g. teaching styles, learning activities, etc.) to be dynamically tracked for understanding learning requirements. To characterize dynamics of…
Descriptors: Online Courses, Student Attitudes, Emotional Response, Models
Hsu, Chung-Yuan; Tsai, Chin-Chung; Wang, Hung-Yuan – Interactive Learning Environments, 2016
The purpose of this study was to examine the impacts of embedding collaboration into a game with a self-explanation design for supporting the acquisition of light and shadow concepts. The participants were 184 fourth graders who were randomly assigned to three conditions: a solitary mode of the game with self-explanation, a collaborative mode with…
Descriptors: Science Education, Scientific Concepts, Cooperative Learning, Grade 4
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
Poelmans, Stephan; Wessa, Patrick – Interactive Learning Environments, 2015
In this study, we report on the students' evaluation of a self-constructed constructivist e-learning environment for statistics, the compendium platform (CP). The system was built to endorse deeper learning with the incorporation of statistical reproducibility and peer review practices. The deployment of the CP, with interactive workshops and…
Descriptors: Electronic Learning, Constructivism (Learning), Blended Learning, Business Administration Education
Aharony, Noa; Bronstein, Jenny – Interactive Learning Environments, 2014
The purpose of this study is to investigate the views and opinions of e-learning experts regarding future trends in the e-learning arena. The Delphi technique was chosen as a method of study. This technique is an efficient and effective group communication process designed to systematically elicit judgments from experts in their selected area of…
Descriptors: Foreign Countries, Electronic Learning, Futures (of Society), Trend Analysis
Tseng, Shian-Shyong; Weng, Jui-Feng – Interactive Learning Environments, 2010
The most important thing for learners in Programming Language subject is problem solving. During the practical programming project, various problems may occur and learners usually need consultation from the senior programmers (i.e. the experts) to assist them in solving the problems. Thus, the inquiry-based learning with learning forum is applied…
Descriptors: Programming Languages, Inquiry, Active Learning, Problem Solving

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