Publication Date
In 2025 | 0 |
Since 2024 | 2 |
Since 2021 (last 5 years) | 8 |
Since 2016 (last 10 years) | 9 |
Since 2006 (last 20 years) | 9 |
Descriptor
Bayesian Statistics | 9 |
Prediction | 5 |
Learning Analytics | 4 |
Accuracy | 3 |
Classification | 3 |
Electronic Learning | 3 |
Learning Processes | 3 |
Probability | 3 |
Algorithms | 2 |
Cognitive Measurement | 2 |
Cognitive Style | 2 |
More ▼ |
Source
Interactive Learning… | 9 |
Author
A. I. Makinde | 1 |
B. A. Ojokoh | 1 |
Badie, Kambiz | 1 |
Bobarshad, Hossein | 1 |
Chang, Kuo-En | 1 |
Chang, Maiga | 1 |
Chen, Guanhua | 1 |
Chen, Nian-Shing | 1 |
Chu, Yu | 1 |
Denden, Mouna | 1 |
Dharani, B. | 1 |
More ▼ |
Publication Type
Journal Articles | 9 |
Reports - Research | 9 |
Education Level
Higher Education | 3 |
Postsecondary Education | 3 |
Elementary Education | 1 |
Grade 8 | 1 |
Junior High Schools | 1 |
Middle Schools | 1 |
Secondary Education | 1 |
Audience
Location
Massachusetts (Boston) | 1 |
Taiwan (Taipei) | 1 |
Tunisia | 1 |
Laws, Policies, & Programs
Assessments and Surveys
Big Five Inventory | 1 |
What Works Clearinghouse Rating
Xia, Xiaona – Interactive Learning Environments, 2023
The research of multi-category learning behaviors is a hot issue in interactive learning environment, and there are many challenges in data statistics and relationship modeling. We select the massive learning behaviors data of multiple periods and courses and study the decision application of regression analysis. First, based on the definition of…
Descriptors: Learning Analytics, Decision Making, Regression (Statistics), Bayesian Statistics
O. S. Adewale; O. C. Agbonifo; E. O. Ibam; A. I. Makinde; O. K. Boyinbode; B. A. Ojokoh; O. Olabode; M. S. Omirin; S. O. Olatunji – Interactive Learning Environments, 2024
With the advent of technological advancement in learning, such as context-awareness, ubiquity and personalisation, various innovations in teaching and learning have led to improved learning. This research paper aims to develop a system that supports personalised learning through adaptive content, adaptive learning path and context awareness to…
Descriptors: Cognitive Style, Individualized Instruction, Learning Processes, Preferences
MOOC Performance Prediction and Analysis via Bayesian Network and Maslow's Hierarchical Needs Theory
Luyu 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
Wu, Lin-Jung; Chang, Kuo-En – Interactive Learning Environments, 2023
To achieve adaptive learning, a dynamic assessment system equipped with a cognitive diagnosis was developed for this study, which adopts a three-stage model of diagnosis-intervention-assessment. To examine how this system influenced spatial geometry learning, the study used a quasi-experimental method to investigate student learning outcomes…
Descriptors: Cognitive Measurement, Alternative Assessment, Spatial Ability, Geometry
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
Tlili, Ahmed; Denden, Mouna; Essalmi, Fathi; Jemni, Mohamed; Chang, Maiga; Kinshuk; Chen, Nian-Shing – Interactive Learning Environments, 2023
The ability of automatically modeling learners' personalities is an important step in building adaptive learning environments. Several studies showed that knowing the personality of each learner can make the learning interaction with the provided learning contents and activities within learning systems more effective. However, the traditional…
Descriptors: Learning Analytics, Learning Management Systems, Intelligent Tutoring Systems, Bayesian Statistics
Premlatha, K. R.; Dharani, B.; Geetha, T. V. – Interactive Learning Environments, 2016
E-learning allows learners individually to learn "anywhere, anytime" and offers immediate access to specific information. However, learners have different behaviors, learning styles, attitudes, and aptitudes, which affect their learning process, and therefore learning environments need to adapt according to these differences, so as to…
Descriptors: Electronic Learning, Profiles, Automation, Classification