Educational Data Mining and Learning Analytics: Overview of Benefits and Challenges

Javidi, Giti; Rajabion, Lila; Sheybani, Ehsan

doi:10.1109/csci.2017.360

Cited by 16 publications

(12 citation statements)

References 4 publications

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“…He discusses how to approach big data and how big data can be used in education policy research. [19] In his book he describes how education is becoming increasingly 'digitized' and 'datafield'. which being conducted through software programs that have been written in code, he details how digitization and datafication support each other, and how they have started to animate the visions of the social member.…”

Section: Related Workmentioning

confidence: 99%

“…which being conducted through software programs that have been written in code, he details how digitization and datafication support each other, and how they have started to animate the visions of the social member. They introduce the concept of "sociotechnical imaginaries," how "desirable future visions for education based on digital data are now being projected and enacted" [19]. [19] He also provides a historical and conceptual map to an understanding of big data.…”

Section: Related Workmentioning

confidence: 99%

“…They introduce the concept of "sociotechnical imaginaries," how "desirable future visions for education based on digital data are now being projected and enacted" [19]. [19] He also provides a historical and conceptual map to an understanding of big data. This demonstrates how a concern with data has become central to the activities of businesses, governments, and social scientific practice.…”

Section: Related Workmentioning

confidence: 99%

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Big Data Application and its Impact on Education

Khan

Alqahtani

2020

Int. J. Emerg. Technol. Learn.

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Big data is employed in widely different fields; we here study how education uses big data. We review the literature of the research about big data in education in the time interval from 2010 to 2020 then review the process of big educational data mining, the tools, and the applications of big data in education. This paper, with the help of these applications, explores the idea to improve the education process. Two methods are applied to validate education process and many parameters are discussed to complete the research.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Big Data Application and its Impact on Education

Khan

Alqahtani

2020

Int. J. Emerg. Technol. Learn.

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“…In [58] authors have made overviews about the impact of ICT, digital tools and cloud computing on students social and educational dynamics. In [33] it is showed the importance of Big Data as a way to have a knowledge about students and the context where they act. This knowledge can be useful in guiding frail students, identifying special learning needs for different groups of students, identifying student behavior and learning patterns, increasing their achievements.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for the design of context-aware services for social inclusion and education

Grimaldi¹,

Attanasio

Corte

2021

HSM

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BACKGROUND:The new generation networks (5G and beyond) will allow us to collect and process real-time information about a user and his context. Analyzing the adolescents' behaviour and taking into account relations between their psychological frailty and socio-cultural context, it is possible to highlight situations of vulnerability. OBJECTIVE: It is crucial to shed light on how the nature of social relationships and the similarity among individuals play a role in the collective dynamics. METHODS: To understand these dynamics, Evolutionary Game Theory and the analysis of social networks, modeled as multiplex networks, are useful. RESULTS: Thanks to a simulative approach we evaluate the emergence and maintenance of cooperation within a class, assessing the role of social network structure and of the homophily on the dynamics. CONCLUSION: Exploiting these tools it is possible to design innovative ICT context-aware services based on collective cooperation and aimed at improving social inclusion, education and support for frail people.

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“…Keeping in view, the identification of student demographic, academic, social, personal, and others features some latest literature proved that machine learning played a much significant role in predictive modeling. The learning activities differentiated with the academic activities of students using big data in EDM [3]. Using the data set kalboard 360, out of five algorithms, MLP outperformed with an accuracy of 76.07% and also SVM gained 75.40% accuracy [4].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Machine Learning-Based Student’s Native Place Identification for Real-Time

et al. 2020

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Mindset reading of a student towards technology is a challenging task. The student's demographic features prediction has a significant aspect for the learning activities in educational institutions. The current studies predicted the student's native place based on technological awareness having various features such as development, availability, usability, educational benefits, etc. However,these studies have not explored the identification of sentiment identification about the technology through ML,optimization,etc.Motivated from these facts,in this paper, we propose a machine learning (ML) model with optimizing techniques to tune the hyper-parameters. In the proposed model, a primary dataset gathered from Indian and Hungarian universities, which analyzed with a Multi-Layer-Perceptron (MLP) with three popular optimization algorithms, such as Adaptive Moment Estimation (Adam), Stochastic Gradient Descent (SGD), Limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS). The optimized MLP has compared with the Support Vector Machine (SVM). Besides, numerous testing methods and to select the most prominent features, Principal Component Analysis (PCA) trained both models. Association of the Adam optimizer with the ReLu activation function in the MLP proved significant play in prediction with regularization. The PCA components covering most of the variance improved the optimized MLP accuracy with 2.3% and boosted the accuracy of the SVM with 2.9%. The Gain-ratio and the Info-gain suggested 11 features with significant weights. Both predictive models are found not only competitive but also outperformed with an identical prediction accuracy of 94% to identify the native place of the student. The Statistical t-test supported the equal predictive strength of both models and proved the significant enhancement in the SVM performance using the PCA components. Further, a considerable reduction is also achieved in the prediction error and prediction time to support the institute's web-based real-time system. Based on deep experiments, we recommend the optimistic native identification models for the higher educational institutions to analyze the attitude and technical awareness among students based on their native place.

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Educational Data Mining and Learning Analytics: Overview of Benefits and Challenges

Cited by 16 publications

References 4 publications

Big Data Application and its Impact on Education

Big Data Application and its Impact on Education

A novel approach for the design of context-aware services for social inclusion and education

Machine Learning-Based Student’s Native Place Identification for Real-Time

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