Recently, machine learning (ML) has evolved and finds its application in higher education (HE) for various data analysis. Studies have shown that such an emerging field in educational technology provides meaningful insights into several dimensions of educational quality. An in-depth analysis of the application of ML could have a positive impact on the HE sector. However, there is a scarcity of a systematic review of HE literature to gain from the overarching trends and patterns discovered using ML. This paper conducts a systematic review and meta-analyses of research studies that have reported on the application of ML in HE. The differentiating factors of this study are primarily vested in the meta-analyses including a specific focus on student academic performance, at-risk, and attrition in HE. Our detailed investigation adopts an evidence-based framework called PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for reporting the findings of our systematic review and meta-analyses of literature on the use of ML models, algorithms, evaluation metrics, and other criteria, including demographics for assessing student academic performance, at-risk and attrition in HE. After undergoing the PRISMA steps such as selection criteria and filtering, we arrive at a small-scale dataset of 89 relevant studies published from 2010 to 2020 for an in-depth analysis. The results show the outcomes of the quantitative analysis of the application of ML types, models, evaluation metrics, and other related demographics and provide quality insights of publication patterns and future trends towards predicting and monitoring student academic progress in HE.

Application of machine learning in higher education to assess student academic performance, at-risk, and attrition: A meta-analysis of literature

A small, affordable computer, the size of a debit card called Raspberry Pi, that uses a regular mouse and keyboard and connects to either a desktop screen or television. It makes it possible for people of all generations to learn about computer devices and various programming languages like Python and Scratch. It has all the characteristics of a desktop computer, including the potential to browse the internet, view high-definition video, work with spreadsheets, write documents, and play games. In addition, the Raspberry Pi was involved with several digital maker projects, such as song players and climate forecasting. It is therefore effective in communicating with people around the world. In this article, detailed features and information of the Raspberry Pi have been discussed very clearly as well as each version along with its alternative boards. The hardware components utilized, as well as the software needed to program it, will be discussed in this article (Raspberry Pi board). This article will offer a broad overview of the Raspberry Pi and is helpful for individuals who want to learn and do projects using the Raspberry Pi by giving them a proper guide and details regarding the Pi boards. It serves as the comprehensive guide for the basic ideas and uses of the Raspberry Pi boards. 

A Systematic Analysis on Raspberry Pi Prototyping: Uses, Challenges, Benefits, and Drawbacks

The paper presents smart blended learning for fostering students’ performance. It uses models of student activity and detects causes of a possible drop out, using learning analytics and real-time data. Taking advantage of embedded sensors (non-invasive, low-cost, flexible and distraction free) built into wearable devices real-time data can be used to support students’ success, well-being and health. In our contribution to the research topic we evaluate the students' experience with smart blended learning using biofeedback methods, sensor data extracted from wearables for five specific lectures, during study periods and in examinations. The preliminary results indicate a correlation between the physiological signals and the grades in examination.

Education 4.0: Smart Blended Learning Assisted by Artificial Intelligence, Biofeedback and Sensors

Education 4.0 uses digital technologies, innovative methodologies, and learning spaces to improve the quality of education. It reflects the changes brought about by the fourth industrial revolution and aims to develop skills relevant to current and future technology scenarios. The transformation of education to Education 4.0 presents challenges and opportunities, and requires the collaborative participation of industry, governments, universities, and society. This work aims to synthesize concepts of Education 4.0, and identify the innovative technologies, key competencies, learning approaches, trend topics, and how it is being applied in engineering education. A scoping review was conducted following the PRISMA statement. The Scopus and Web of Science databases were used in the review. Bibliometric tools in R Studio and VOSviewer software were employed to summarize the bibliometric data and analyze the results. Bibliometric analysis of the 528 selected documents revealed a significant growth in publications from 2017 to September 2023. Nearly half of the selected literature originated from conference proceedings, and 42.6% of the selected papers have not yet been cited. Moreover, five research clusters were identified from the select publications related to studying Education 4.0 in higher education, specifically engineering education: concept, competency-based learning, specific technologies applied to education, learning system, and competency-based learning. The use of Industry 4.0 technology, such as Artificial Intelligence, the Internet of Things, and Virtual Labs, combined with active learning teaching methodologies, such as gamification and blended learning, has been identified as points of convergence and trending research topics within Education 4.0. 

Concepts, innovative technologies, learning approaches and trend topics in education 4.0: A scoping literature review

Wearable Biosensor Technology (WBT) has emerged as a transformative tool in the educational system over the past decade. This systematic review encompasses a comprehensive analysis of WBT utilization in educational settings over a 10-year span (2012–2022), highlighting the evolution of this field to address challenges in education by integrating technology to solve specific educational challenges, such as enhancing student engagement, monitoring stress and cognitive load, improving learning experiences, and providing real-time feedback for both students and educators. By exploring these aspects, this review sheds light on the potential implications of WBT on the future of learning. A rigorous and systematic search of major academic databases, including Google Scholar and Scopus, was conducted in accordance with the PRISMA guidelines. Relevant studies were selected based on predefined inclusion and exclusion criteria. The articles selected were assessed for methodological quality and bias using established tools. The process of data extraction and synthesis followed a structured framework. Key findings include the shift from theoretical exploration to practical implementation, with EEG being the predominant measurement, aiming to explore mental states, physiological constructs, and teaching effectiveness. Wearable biosensors are significantly impacting the educational field, serving as an important resource for educators and a tool for students. Their application has the potential to transform and optimize academic practices through sensors that capture biometric data, enabling the implementation of metrics and models to understand the development and performance of students and professors in an academic environment, as well as to gain insights into the learning process.

Wearable Biosensor Technology in Education: A Systematic Review

The Education 4.0 process can be used to foster students' performance, to motivate them by means of adaptive and personalized learning, to automate answering routine questions and to improve the quality of online examinations. It releases teachers from routine tasks, and it enables them to be more involved in the individualization of the educational process and in innovation. We apply artificial intelligence methodology to motivate students to improve their performance, and to identify early in course which students are at-risk of dropping out. In the adaptive Education 4.0 loT framework, the environmental and embedded sensors from wearables examine biosignals with biofeedback, such as pulse (HR) and heart rate variability (HRV), thereby increasing students' self-reflection, and allowing for a more personalized learning experience. The first experiments corroborate meaningful correlations between the data-points reached in the self-assessments, and values of the biosignals.

Education 4.0: An Adaptive Framework with Artificial Intelligence, Raspberry Pi and Wearables - Innovation for Creating Value

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Paul Svasta

Author Tools

Chat about Author

Papers

Education 4.0: An Adaptive Framework with Artificial Intelligence, Raspberry Pi and Wearables - Innovation for Creating Value