Machine Learning Techniques for Determining Students' Academic Performance: A Sustainable Development Case for Engineering Education

Poudyal, Sujan; Nagahi, Morteza; Nagahisarchoghaei, Mohammad; Ghanbari, Ghodsieh

doi:10.1109/dasa51403.2020.9317178

Cited by 11 publications

(10 citation statements)

References 8 publications

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“…For example, some authors argue about the relevance of technology in sustainability teaching in HE (Bonini, 2020). Other studies focus on how the leading technologies related to data science, for example, the creation of new algorithms and statistical models, are helping in sustainability research by using different ways of monitoring, collecting, and analysing data (Poudyal et al, 2020). Another strand in this cluster uses data science techniques to understand the impact of HE courses and programmes in several dimensions of SD (Arango‐Uribe et al, 2022).…”

Section: Resultsmentioning

confidence: 99%

Using data science for sustainable development in higher education

Filho

Eustachio

Danila

et al. 2023

Sustainable Development

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Despite the abundance of studies focused on how higher education institutions (HEIs) are implementing sustainable development (SD) in their educational programmes, there is a paucity of interdisciplinary studies exploring the role of technology, such as data science, in an SD context. Further research is thus needed to identify how SD is being deployed in higher education (HE), generating positive externalities for society and the environment. This study aims to address this research gap by exploring various ways in which data science may support university efforts towards SD. The methodology relied on a bibliometric analysis to understand and visualise the connections between data science and SD in HE, as well as reporting on selected case studies showing how data science may be deployed for creating SD impact in HE and in the community. The results from the bibliometric analysis unveil five research strands driving this field, and the case studies exemplify them. This study can be considered innovative since it follows previous research on artificial intelligence and SD. Moreover, the combination of bibliometric analysis and case studies provides an overview of trends, which may be useful to researchers and decision‐makers who wish to explore the use of data science for SD in HEIs. Finally, the findings highlight how data science can be used in HEIs, combined with a framework developed to support further research into SD in HE.

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Section: Resultsmentioning

confidence: 99%

Using data science for sustainable development in higher education

Filho

Eustachio

Danila

et al. 2023

Sustainable Development

View full text Add to dashboard Cite

show abstract

“…Research in this domain has also explored the transformative impact of information and communication technology (ICT) on sustainability-focused engineering education, particularly in the development of smart campuses [85,88,89]. Scholars have identified five ICT drivers behind this change [85]: (1) data computing and storage technologies (e.g., cloud and edge computing) [90,91]; (2) Internet of Things technologies (e.g., smart sensors and communication protocols) [92][93][94]; (3) intelligent technologies (e.g., artificial intelligence, machine learning, and computation intelligence) [95][96][97]; (4) immersive technologies (e.g., augmented and virtual reality) [98][99][100][101]; and (5) mobile technologies (e.g., mobile phones and tablets) [100,102]. These studies shed light on the transformative potential of these technologies, offering data-driven insights into their pivotal role in shaping the future of sustainability-focused engineering education programs.…”

Section: Intellectual Structure Of Research On Engineering Education ...mentioning

confidence: 99%

Traversing the Evolution of Research on Engineering Education for Sustainability: A Bibliometric Review (1991–2022)

Narong,

Hallinger

2024

Sustainability

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Engineering education plays a pivotal role in cultivating the engineering capacity for sustainable development. Nonetheless, there has been no comprehensive review that examines sustainability as a distinct knowledge domain within engineering education. This review filled this gap by conducting a bibliometric review to document the research landscape, analyze the intellectual structure of the literature, and identify emerging research themes. The review sourced 2738 Scopus-indexed documents published between 1991 and 2022. Data analyses included descriptive statistics, co-citation analysis, and keyword co-occurrence analysis. The study identified consistent growth in research output and geographic diversity. Four predominant conceptual themes were identified in the literature: (1) Engineering Education Reform, (2) Engineering Competencies, Pedagogy, and Curriculum, (3) Curriculum Assessment and Benchmarks, and (4) Sustainable Technologies. Findings emphasize the need for defining precise engineering competencies related to sustainability, incorporating diverse teaching methods, and ensuring that sustainability learning outcomes align with changing industry norms, regulations, and accreditation criteria. The study also highlights a growing focus on the use of Industry 4.0 technologies as a means of achieving sustainability outcomes. The review underscores the need for sustained curriculum reform to successfully transform engineering education toward sustainability.

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“…It transforms potentially redundant data into a new reduced set of features. In [Poudyal et al 2020] a study was performed to predict student performance in higher education degrees using the Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) algorithms for feature extraction. The results obtained showed that the choice of which method to employ is dependent on the classifier utilized.…”

Section: Related Workmentioning

confidence: 99%

A Genetic Algorithm with Flexible Fitness Function for Feature Selection in Educational Data: Comparative Evaluation

Albuquerque

Tarrataca

Brandão

et al. 2022

jidm

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Educational Data Mining is an interdisciplinary field that helps understand educational phenomena through computational techniques. The databases of educational institutions are usually extensive, possessing many descriptive attributes that make the prediction process complex. In addition, the data can be sparse, redundant, irrelevant, and noisy, which can degrade the predictive quality of the models and affect computational performance. One way to simplify the problem is to identify the least important attributes and omit them from the modeling process. This can be performed by employing attribute selection techniques. This work evaluates different feature selection techniques applied to open educational data and paired alongside a genetic algorithm with a flexible fitness function. The methods and results described herein extend a previously published paper by: (i) describing a larger set of computational experiments; (ii) performing a hypothesis test over different classifiers; and (iii) presenting a more in-depth literature revision. The results obtained indicate an improvement in the classification process.

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Machine Learning Techniques for Determining Students' Academic Performance: A Sustainable Development Case for Engineering Education

Cited by 11 publications

References 8 publications

Using data science for sustainable development in higher education

Using data science for sustainable development in higher education

Traversing the Evolution of Research on Engineering Education for Sustainability: A Bibliometric Review (1991–2022)

A Genetic Algorithm with Flexible Fitness Function for Feature Selection in Educational Data: Comparative Evaluation

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