With the vigorous development of intelligent campus construction, great changes have taken place in the development of information technology in colleges and universities from the previous digital to intelligent development. In the teaching process, the analysis of students' classroom learning has also changed from the previous manual observation…

English language learning students in China often feel challenged to learn English due to lack of motivation and confidence, pronunciation and grammar difference, lack of practice and people to communicate with etc., which affects students mental health. Adopting Big data and AI will help in overcoming these limitations as it provides personalized…

Developing tools to support students, educators, intuitions, and government in the educational environment has become an important task to improve the quality of education and learning outcomes. Information and communication technology (ICT) is adopted by educational institutions; one such instance is video interaction in flipped teaching.…

In order to study the big data of college students' employment, this paper takes the big data of college students' employment as the premise, analyzes the current employment data by establishing a DBN model, and puts forward relevant management measures, aiming to provide scientific basis for the management of graduates' employment data. The…

The objective of this work is to predict the employment rate of students based on the information in the SSM (student status management) in colleges and universities. Firstly, the relevant content of SSM is introduced. Secondly, the BP (Back Propagation) neural network, the LM (Levenberg Marquardt) algorithm, and the BR (Bayesian Regularization)…

Purpose: It has been demonstrated that AI-powered, data-driven tools' usage is not universal, but deeply linked to socio-cultural contexts. The purpose of this paper is to display the need of adopting situated lenses, relating to specific personal and professional learning about data protection and privacy. Design/methodology/approach: The authors…

Text analytics in education has evolved to form a critical component of the future SMART campus architecture. Sentiment analysis and qualitative feedback from students is now a crucial application domain of text analytics relevant to institutions. The implementation of sentiment analysis helps understand learners' appreciation of lessons, which…

This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…

Educational data mining has allowed for large improvements in educational outcomes and understanding of educational processes. However, there remains a constant tension between educational data mining advances and protecting student privacy while using educational datasets. Publicly available datasets have facilitated numerous research projects…

This paper outlines the development of a Hardware Development Kit (HDK) for a remote training platform on FPGA Devices designed to provide university students pursuing degrees in electronic and informatics engineering (at the bachelor's, master's, and PhD levels) with the tools they need to learn and develop systems related to artificial…

Open-ended responses to surveys can be highly beneficial to higher education institutions, providing clarity and context that quantitative data can sometimes lack. However, analyzing open-ended responses typically takes time and manpower most institutional assessment offices do not have to spare. This study focused on finding a potential solution…

Academic dropout remains a significant challenge for education systems, necessitating rigorous analysis and targeted interventions. This study employs machine learning techniques, specifically random forest (RF) and feature tokenizer transformer (FTT), to predict academic attrition. Utilizing a comprehensive dataset of over 40 000 students from an…

Pragmatically, machine learning techniques can improve educators' capacity to monitor students' learning progress when applied to quality data. For developing countries, the major obstacle has been the unavailability of quality data that fits the purpose. This is partly because the in-use information systems are either not properly managed or not…

With recent advances in artificial intelligence (AI), machine learning (ML) has been identified as particularly useful for organizations seeking to create value from data. However, as ML is commonly associated with technical professions, such as computer science and engineering, incorporating training in the use of ML into non-technical…

Although student advising is known to improve student success, its application is often inadequate in institutions that are resource constrained. Given recent advances in large language models (LLMs) such as Chat Generative Pre-trained Transformer (ChatGPT), automated approaches such as the AutoScholar Advisor system affords viable alternatives to…