Machine Learning and Education in the Human Age: A Review of Emerging Technologies

Bacos, Catherine A.

doi:10.1007/978-3-030-17798-0_43

Cited by 7 publications

(6 citation statements)

References 27 publications

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“…Whether a technology is considered emergent depends on where it is being used, the domain and knowledge of its operation, and the application given to it [33]. An important characteristic of these technologies is that they do not need to have a limited life, even when they have been used for a long period of time.…”

Section: Emerging Digital Technologies In Educationmentioning

confidence: 99%

“…Emerging technologies for education (ELTs) are those that are being incorporated into education, which favor communication and allow easy access to educational information [36]. Some of the ELTs that contribute to an innovation system in higher education are mobile learning, MOOCs, social networks, gamification, augmented reality, virtual reality, and web applications [14,33]. The use of ELTs is quite diverse, for example, one research proposes the use of smartphones to run educational applications and contribute to improving the quality of teaching and learning.…”

Section: Emerging Digital Technologies In Educationmentioning

confidence: 99%

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Towards the Integration of Emerging Technologies as Support for the Teaching and Learning Model in Higher Education

et al. 2023

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The pandemic caused by COVID-19 impacted the entire world, but the significant challenges to be faced during this crisis opened an opportunity for organizations to evolve toward a digital transformation. Educational institutions were a concrete example of the use of technologies, which were abruptly incorporated into the teaching–learning model. Although this initiative was initially a challenge for teachers and students, it has now become a tool for new innovative teaching models, such as hybrid, online, and flexible models. The impact of technology used in education has been beneficial due to emerging technologies (virtual reality, augmented reality, games, web applications, mobile applications, etc.), which have served as tools to facilitate and motivate studying. These educational trends contribute directly to the fourth Sustainable Development Goal (SDG). This research analyzes whether the use of a web application, as a support in the educational model, can make students better understand the subjects of network infrastructure and be more efficient when configuring equipment in a data network. Therefore, this research is based on the design of an educational web application based on Python libraries, which allows the configuration of networking equipment based on the concept of network automation with the application of a graphical user interface (GUI). The web application can be deployed with communication equipment or in conjunction with the GNS3 simulator. This versatility allows this web tool to be applied to the teaching of network equipment configuration in any mode of study (classroom, online, hybrid, or flexible). The results obtained in this research are encouraging and open the way for the implementation of network automation and Python libraries for educational applications that can be important tools within the teaching and learning models of higher education.

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Section: Emerging Digital Technologies In Educationmentioning

confidence: 99%

Section: Emerging Digital Technologies In Educationmentioning

confidence: 99%

Towards the Integration of Emerging Technologies as Support for the Teaching and Learning Model in Higher Education

et al. 2023

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“…Lin and Kao used machine learning algorithms to perform mental effort detection in e-learning using EEG data [20]. Bacos explored human-centered techniques of using technology in education [21]. Raheel, Majid, and Anwar used EEG data and from the data thirteen different features were extracted to classify five different expressions namely looking up, looking down, smile, eye-wink and eye blink using four different classifiers namely K-nearest neighbor, naïve bayes, SVM and MLP [22].…”

Section: Related Workmentioning

confidence: 99%

Classification of Student's Confusion Level in E-Learning using Machine Learning

2019

IJITEE

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With the advancement of technology, the traditional mode of teaching-learning pedagogy has evolved into online education system as it is easily accessible. But, it is very difficult to detect whether the students are ‘confused’ or ‘not confused’ while watching online videos. Getting confused while watching online videos is one of the root causes of less performance of the students. Keeping in mind the above statements, we would like to investigate whether the students are ‘confused’ or ‘not confused’ while watching Massive Open Online Course (MOOC) videos. There are a lot of studies that prove electroencephalogram (EEG) signals behave differently as we are in different conditions such as happy, sad, angry, etc. So, in this paper, we have applied several supervised learning algorithms to detect if the students are ‘confused’ or ‘not confused’ while watching MOOC videos using EEG data. The results of this paper show that machine learning is a potential technique, for the analysis of EEG data that can detect the confusion level of the students which is comparable to human observation for predicting the confusion level of the students that can improve the quality of online education system.

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“…Data mining and machine learning have been used in several studies ( Naidu et al, 2020 ; Pérez & Rubio, 2020 ) to achieve successful education development. Educational data mining ( Bacos, 2019 ) is key technique that aims to get better educational practices and improves learning outcomes. Software engineering ( Akhlaq & Yousaf, 2010 ; Hayes et al, 2016 ; Rubinstein, 2007 ) aims to understand the overall SDLC to increase the poor-quality of software product.…”

Section: Introductionmentioning

confidence: 99%

Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects

Al‐Ahmad

Al-Zoubi

Kabir

et al. 2022

PeerJ Computer Science

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Software engineering is one of the most significant areas, which extensively used in educational and industrial fields. Software engineering education plays an essential role in keeping students up to date with software technologies, products, and processes that are commonly applied in the software industry. The software development project is one of the most important parts of the software engineering course, because it covers the practical side of the course. This type of project helps strengthening students’ skills to collaborate in a team spirit to work on software projects. Software project involves the composition of software product and process parts. Software product part represents software deliverables at each phase of Software Development Life Cycle (SDLC) while software process part captures team activities and behaviors during SDLC. The low-expectation teams face challenges during different stages of software project. Consequently, predicting performance of such teams is one of the most important tasks for learning process in software engineering education. The early prediction of performance for low-expectation teams would help instructors to address difficulties and challenges related to such teams at earliest possible phases of software project to avoid project failure. Several studies attempted to early predict the performance for low-expectation teams at different phases of SDLC. This study introduces swarm intelligence -based model which essentially aims to improve the prediction performance for low-expectation teams at earliest possible phases of SDLC by implementing Particle Swarm Optimization-K Nearest Neighbours (PSO-KNN), and it attempts to reduce the number of selected software product and process features to reach higher accuracy with identifying less than 40 relevant features. Experiments were conducted on the Software Engineering Team Assessment and Prediction (SETAP) project dataset. The proposed model was compared with the related studies and the state-of-the-art Machine Learning (ML) classifiers: Sequential Minimal Optimization (SMO), Simple Linear Regression (SLR), Naïve Bayes (NB), Multilayer Perceptron (MLP), standard KNN, and J48. The proposed model provides superior results compared to the traditional ML classifiers and state-of-the-art studies in the investigated phases of software product and process development.

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Machine Learning and Education in the Human Age: A Review of Emerging Technologies

Cited by 7 publications

References 27 publications

Towards the Integration of Emerging Technologies as Support for the Teaching and Learning Model in Higher Education

Towards the Integration of Emerging Technologies as Support for the Teaching and Learning Model in Higher Education

Classification of Student's Confusion Level in E-Learning using Machine Learning

Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects

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