☆ 4.5 Article

Extracting topological features to identify at-risk students using machine learning and graph convolutional network models

INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION (2023)

Related references

Note: Only part of the references are listed.

Article Computer Science, Artificial Intelligence

Modeling students' performance using graph convolutional networks

Ahmed A. Mubarak et al.

Summary: This study proposes a model based on Graph Convolutional Network to classify students into different behavioral patterns based on their engagement with course videos. By introducing a label function to label datasets, the model provides a learning foundation for future data processing. Experimental results show that the proposed model achieves high predictive accuracy and f1-score.

COMPLEX & INTELLIGENT SYSTEMS (2022)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

Prediction of Students' Performance in E-learning Environments Based on Link Prediction in a Knowledge Graph

Antonia Ettorre et al.

Summary: In recent years, the development of intelligent learning environments has been stimulated by the increasing demand for easily accessible high-quality educational resources, supported by advancements in AI and Web technologies. This paper proposes a model to predict students' performance based on a RDF Knowledge Graph, and reformulates the Knowledge Tracing task as a Link Prediction problem. Preliminary experiments show promising results comparable to state-of-the-art models.

ARTIFICIAL INTELLIGENCE IN EDUCATION: POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS AND DOCTORAL CONSORTIUM, PT II (2022)

Add to Collection

Article Education & Educational Research

Prediction of students' early dropout based on their interaction logs in online learning environment

Ahmed A. Mubarak et al.

Summary: Online learning is popular in higher education but faces challenges with high dropout rate, this study aims to construct a predictive model to early predict at-risk students, and the proposed models achieved an accuracy of 84%.

INTERACTIVE LEARNING ENVIRONMENTS (2022)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Visual analytics of video-clickstream data and prediction of learners' performance using deep learning models in MOOCs' courses

Ahmed A. Mubarak et al.

Summary: The paper focuses on using LSTM to predict learner performance based on video-clickstreams data for online courses. The accuracy rate of the proposed model ranges from 89% to 95% throughout the course weeks, outperforming baseline deep learning models such as GRU and simple recurrent neural network. In specific courses like Mining of Massive Datasets and Automata Theory, the LSTM model achieved accuracy rates of 90.30% and 89% respectively.

COMPUTER APPLICATIONS IN ENGINEERING EDUCATION (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Customized Rule-Based Model to Identify At-Risk Students and Propose Rational Remedial Actions

Balqis Albreiki et al.

Summary: Detecting at-risk students early and taking appropriate actions can improve students' achievement and success, with positive impacts on educators, academic institutions, and underperforming students.

BIG DATA AND COGNITIVE COMPUTING (2021)

Add to Collection

Review Education & Educational Research

A Systematic Literature Review of Student' Performance Prediction Using Machine Learning Techniques

Balqis Albreiki et al.

Summary: Educational Data Mining plays a critical role in advancing the learning environment by providing valuable tools for understanding and utilizing student learning environments. Universities face challenges in analyzing performance, providing high-quality education, evaluating student performance, and implementing student intervention plans. The use of machine learning techniques can help predict students at risk and dropout rates.

EDUCATION SCIENCES (2021)

Add to Collection

Article Computer Science, Software Engineering

Semantics of the Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable and Explainable?

Manas Gaur et al.

Summary: The recent innovations in deep learning have shown great potential but also brought challenges, including interpretability and utilization of knowledge. Knowledge-infused learning (K-iL) provides a solution by incorporating knowledge graphs.

IEEE INTERNET COMPUTING (2021)

Add to Collection

Article Chemistry, Multidisciplinary

Analyzing and Predicting Students' Performance by Means of Machine Learning: A Review

Juan L. Rastrollo-Guerrero et al.

APPLIED SCIENCES-BASEL (2020)

Add to Collection

Article Education & Educational Research

Student Engagement Level in e-Learning Environment: Clustering Using K-means

Abdallah Moubayed et al.

AMERICAN JOURNAL OF DISTANCE EDUCATION (2020)

Add to Collection

Article Education & Educational Research

Fifty years of British Journal of Educational Technology: A topic modeling based bibliometric perspective

Xieling Chen et al.

BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY (2020)

Add to Collection

Article Education & Educational Research

Digital Transformation Challenges: a Case Study Regarding the MOOC Development and Operations at Higher Education Institutions in China

Mingzhuo Liu et al.

TECHTRENDS (2019)

Add to Collection

Article Computer Science, Information Systems

Towards Automatic Mathematical Exercise Solving

Tianyu Zhao et al.

DATA SCIENCE AND ENGINEERING (2019)

Add to Collection

Article Multidisciplinary Sciences

Modelling, prediction and classification of student academic performance using artificial neural networks

E. T. Lau et al.

SN APPLIED SCIENCES (2019)

Add to Collection

Article Computer Science, Information Systems

Contextual local primitives for binary patent image retrieval

Naeem Bhatti et al.

MULTIMEDIA TOOLS AND APPLICATIONS (2018)

Add to Collection

Article Computer Science, Information Systems

KnowEdu: A System to Construct Knowledge Graph for Education

Penghe Chen et al.

IEEE ACCESS (2018)

Add to Collection

Article Economics

Decision trees for predicting the academic success of students

Josip Mesaric et al.

CROATIAN OPERATIONAL RESEARCH REVIEW (2016)

Add to Collection

Proceedings Paper Computer Science, Interdisciplinary Applications

Towards Automatic Prediction of Student Performance in STEM Undergraduate Degree Programs

Laci Mary Barbosa Manhaes et al.

30TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, VOLS I AND II (2015)

Add to Collection

Review Psychology, Multidisciplinary

Statistical assumptions of substantive analyses across the general linear model: a mini-review

Kim F. Nimon

FRONTIERS IN PSYCHOLOGY (2012)

Add to Collection

Article Education & Educational Research

Learning analytics: drivers, developments and challenges

Rebecca Ferguson

INTERNATIONAL JOURNAL OF TECHNOLOGY ENHANCED LEARNING (2012)

Add to Collection

Article Computer Science, Information Systems

Graph-based semi-supervised learning with multiple labels

Zheng-Jun Zha et al.

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION (2009)

Add to Collection

Article Physics, Fluids & Plasmas

Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality

MEJ Newman

PHYSICAL REVIEW E (2001)

Add to Collection

Article Physics, Multidisciplinary

Harmony in the small-world

M Marchiori et al.

PHYSICA A (2000)

Add to Collection

© Peeref 2019-2024. All rights reserved.