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MOOC Performance Prediction and Analysis via Bayesian Network and Maslow's Hierarchical Needs Theory
Peer reviewed
Direct linkLuyu Zhu; Jia Hao; Jianhou Gan – Interactive Learning Environments, 2024
Nowadays, Massive Open Online Courses (MOOC) has been gradually accepted by the public as a new type of education and teaching method. However, due to the lack of timely intervention and guidance from educators, learners' performance is not as effective as it could be. To address this problem, predicting MOOC learners' performance and providing…
Descriptors: MOOCs, Academic Achievement, Prediction, Bayesian Statistics
Nazish Shahid – Interactive Learning Environments, 2023
The current investigation reflects on the insufficient performance of students in virtual exams in the era of transformation of the physical mode of education to digital one due to the passive adaptation to the technical drive of the virtual environment, the tenuous command of platform's technical language, especially in mathematics and physics…
Descriptors: Evaluation Methods, Computer Assisted Testing, Academic Achievement, Electronic Learning
Li-Chen Cheng; Wei Li; Judy C. R. Tseng – Interactive Learning Environments, 2023
Programming ability is the core ability of this era and can be obtained and improved through practice. In this paper, an Automated Programming Assessment system based on Mastery learning and Peer competition (APAMP) was proposed and developed. APAMP allows students to practice repeatedly by providing immediate feedback after their programs are…
Descriptors: High School Freshmen, High School Seniors, Programming Languages, Foreign Countries
Gwo-Jen Hwang; An-Chi Lin; Shao-Chen Chang – Interactive Learning Environments, 2024
Digital games have been used in various disciplines to enhance students' learning interest and effectiveness through the gaming contexts. However, most of the digital educational games use multiple-choice questions to confirm students' learning status, implying the challenge of understanding the actual learning status of students. Also, the gaming…
Descriptors: Game Based Learning, Academic Achievement, Student Behavior, Horticulture
Li, Juan; Li, Zhe; Liu, Shuo-Fang; Cheng, Meng – Interactive Learning Environments, 2020
Traditional methods for conducting performance evaluations of academic courses are somewhat limited in that they are unable to account for both quantitative and qualitative data. For example, the data used to assess student performance in a typical industrial design course are generally complex, multi-criteria, multi-variable, and frequently vague…
Descriptors: Student Evaluation, College Students, Industrial Education, Design
Yu, Fu-Yun – Interactive Learning Environments, 2019
In view of contribution-based pedagogy and observational learning theory, students' perceived uses, preferences, usage, and selection considerations with regard to citing peers' work were examined in an online learning environment targeting student-constructed tests. Data were collected from 84 fifth-grade students who participated in online…
Descriptors: Computer Assisted Testing, Student Developed Materials, Peer Evaluation, Test Items
Cho, Moon-Heum; Yoo, Jin Soung – Interactive Learning Environments, 2017
Many researchers who are interested in studying students' online self-regulated learning (SRL) have heavily relied on self-reported surveys. Data mining is an alternative technique that can be used to discover students' SRL patterns from large data logs saved on a course management system. The purpose of this study was to identify students' online…
Descriptors: Online Courses, Self Management, Active Learning, Data Analysis
