Investigation of Performance Analysis for Data‐Driven Intelligent Model With Internet of Things in Higher Education Teaching System
Bibliographic Data
| ID | 21446484 |
|---|---|
| Authors | Eesa Alsolami (0000-0003-0132-6662, College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia, corresponding author) |
| Year | 2026 |
| Volume | 61 |
| Issue | 1 |
| Publication date | 2026-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | European Journal of Education (JOURNAL) |
| Journal identifiers | ISSN: 0141-8211 • E-ISSN: 1465-3435 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/ejed.70409 |
| OpenAlex | W7116940760 |
| Language | EN |
| References cited | 40 |
Enhancing the intelligence of the higher education teaching system and making multimedia teaching equipment based on the Internet of Things (IoT) has become a significant concern for universities and colleges. In addition, predicting students' academic performance has drawn substantial attention in higher education institutions. However, despite widespread agreement that the learning objectives should lead to better education, minimal study has been done on predicting whether students will achieve these objectives. Hence, in this article, the Internet of Things‐assisted Intelligent Higher Education Teaching Model (IoT‐IHETM) has been suggested to evaluate the educators' and learners' performance. The data are taken from the University of California, Irvine (UCI) Educational Process Mining dataset for analysing the performance analysis of students and teachers. The intelligence classroom is the crucial structure of knowledge education platforms. Therefore, an intelligent classroom model based on the Internet is intended, which links with conventional network services via IoT gateways. Furthermore, the suggested IoT‐assisted online learning system evaluates students' levels of engagement and focuses throughout online lessons. The numerical analysis demonstrates that the proposed IoT‐IHETM model increases the student performance ratio by 97.4%, the teaching evaluation ratio by 98.2%, the teacher‐student interaction ratio by 96.5%, and the student satisfaction ratio by 95.5% compared to popular models
E learning · Higher education · Internet of Things · Online Teaching · Process (computing) · Teaching method · The Internet · Advanced Technologies and Applied Computing · Advanced Technologies in Various Fields · Educational Technology and Pedagogy
Online teaching-learning in higher education during lockdown period of Covid-19 pandemic
The core components of education 4.0 in higher education
Online Teaching and Learning in Higher Education during the Coronavirus Pandemic
The datafication of teaching in Higher Education
Linking role definition rigidity to elementary school teachers’ e-service for in-service teacher development
Predicting academic performance of students from VLE big data using deep learning models
Perceived organisational support and university students’ career exploration
How does social support detected automatically in discussion forums relate to online learning burnout? The moderating role of students’ self-regulated learning
State of the art and practice in AI in education
Private Tutoring as a Space for Professional Fulfilment and Personal Renewal
The future of AI and education
Simulation-Based Learning in Higher Education
| Highly cited | No |
|---|