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Direct linkConstance A. Lightner; Carin A. Lightner-Laws – Interactive Learning Environments, 2024
As COVID-19 continues to impact various business sectors, university administrators have steadily pushed for all academic units to resume on campus operations and activities; conversely, faculty and students have expressed increased interest in continuing online teaching/learning. We aim to mitigate this "tug-of-war" between…
Descriptors: Blended Learning, Flexible Scheduling, Business Administration Education, Statistics
Wu, Jiun-Yu; Hsiao, Yi-Cheng; Nian, Mei-Wen – Interactive Learning Environments, 2020
This paper demonstrated the use of the supervised Machine Learning (ML) for text classification to predict students' final course grades in a hybrid Advanced Statistics course and exhibited the potential of using ML classified messages to identify students at risk of course failure. We built three classification models with training data of 76,936…
Descriptors: Social Media, Discussion Groups, Artificial Intelligence, Classification
Delaval, Marine; Michinov, Nicolas; Le Bohec, Olivier; Le Hénaff, Benjamin – Interactive Learning Environments, 2017
The aim of this study was to examine how social or temporal-self comparison feedback, delivered in real-time in a web-based training environment, could influence the academic performance of students in a statistics examination. First-year psychology students were given the opportunity to train for a statistics examination during a semester by…
Descriptors: Mathematics Achievement, Statistics, Self Evaluation (Individuals), Feedback (Response)
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
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
Novak, Elena; Johnson, Tristan E.; Tenenbaum, Gershon; Shute, Valerie J. – Interactive Learning Environments, 2016
The study explored instructional benefits of a storyline gaming characteristic (GC) on learning effectiveness, efficiency, and engagement with the use of an online instructional simulation for graduate students in an introductory statistics course. A storyline is a game-design element that connects scenes with the educational content. In order to…
Descriptors: Teaching Methods, Statistics, Educational Games, Instructional Effectiveness
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
Hwang, Wu-Yuin; Huang, Yueh-Min; Wu, Sheng-Yi – Interactive Learning Environments, 2011
The use of instant messaging to support e-learning will continue to gain importance because of its speed, effectiveness, and low cost. This study developed an MSN agent to mediate and facilitate students' learning in a Web-based course. The students' acceptance of the MSN agent and its effect on learning community identification and learning…
Descriptors: Electronic Learning, Feedback (Response), Web Based Instruction, Identification
