Educational Data Mining and Learning Analytics for Improving Online Learning Environments

Paredes, Yancy Vance; Siegle, Robert F.; Hsiao, I-Han; Craig, Scotty D.

doi:10.1177/1071181320641113

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…EDM aims to study this information and solve education research problems. EDM acts toward the development of modern technology to examine the education information, and utilize DM tools for better understanding student learning environments [4][5][6]. The EDM method converts transform raw data coming from the education system into helpful data that might have a greater impact on education practice and research.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

Hamza¹,

Hassine²,

Abunadi³

et al. 2022

Computers, Materials &Amp; Continua

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Data mining in the educational field can be used to optimize the teaching and learning performance among the students. The recently developed machine learning (ML) and deep learning (DL) approaches can be utilized to mine the data effectively. This study proposes an Improved Sailfish Optimizer-based Feature Selection with Optimal Stacked Sparse Autoencoder (ISOFS-OSSAE) for data mining and pattern recognition in the educational sector. The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process. Moreover, the ISOFS-OSSAE model involves the design of the ISOFS technique to choose an optimal subset of features. Moreover, the swallow swarm optimization (SSO) with the SSAE model is derived to perform the classification process. To showcase the enhanced outcomes of the ISOFS-OSSAE model, a wide range of experiments were taken place on a benchmark dataset from the University of California Irvine (UCI) Machine Learning Repository. The simulation results pointed out the improved classification performance of the ISOFS-OSSAE model over the recent state of art approaches interms of different performance measures.

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Section: Introductionmentioning

confidence: 99%

Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

Hamza¹,

Hassine²,

Abunadi³

et al. 2022

Computers, Materials &Amp; Continua

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A critical review of data mining in education on the levels and aspects of education

Fuseini,

Missah

2024

QEA

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Purpose This systematic literature review aims to identify the pattern of data mining (DM) research by looking at the levels and aspects of education. Design/methodology/approach This paper reviews 113 conference and research papers from well-known publishers of educational data mining (EDM) and learning analytics-related research using a recognized literature review in computer science by Carrera-Rivera et al. (2022a). Two major stages, planning and conducting the review, were used. The databases of Elsevier, Springer, IEEE, SAI, Hindawi, MDPI, Wiley, Emerald and Sage were searched to retrieve EDM papers from the period 2017 to 2023. The papers retrieved were then filtered based on the application of DM to the three educational levels – basic, pre-tertiary and tertiary education. Findings EDM is concentrated on higher education. Basic education is not given the needed attention in EDM. This does not enhance inclusivity and equity. Learner performance is given much attention. Resource availability and teaching and learning are not given the needed attention. Research limitations/implications This review is limited to only EDM. Literature from the year 2017 to 2023 is covered. Other aspects of DM and other relevant literature published in EDM outside the research period are not considered. Practical implications As the current trend of EDM shows an increase in zeal, future research in EDM should concentrate on the lower levels of education to identify the challenges of basic education which serves as the core of education. This will enable addressing the challenges of education at an early stage and facilitate getting a quality education at all levels of education. Appropriate EDM techniques for mining the data at this level should be the focus of the research. Specifically, techniques that can cater for the variation in learner abilities and the appropriate identification of learner needs should be considered. Social implications Content sequencing is necessary in facilitating an easy understanding of concepts. Curriculum design from basic to higher education dwells much on this. Identifying the challenge of learning at the early stages will facilitate efficient learning. At the basic level of learning, data on learning should be collected by educational institutions just as it is done at the tertiary level. This will enable EDM to accurately identify the challenges and appropriate solutions to educational problems. Resource availability is a catalyst for effective teaching and learning. The attributes of a learner will enable knowing the true nature of the learner to determine the prospects of the learner. Originality/value This research has not been published in any journal. The information presented is the original knowledge of the authors. However, a pre-print of the work is in Research Square.

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Analysis of Multi-dimensional Image Fusion and Joint Information Mining in Landscape Design with Vision Information Mining

Liu

2022

2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT)

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Educational Data Mining and Learning Analytics for Improving Online Learning Environments

Cited by 5 publications

References 30 publications

Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

A critical review of data mining in education on the levels and aspects of education

Analysis of Multi-dimensional Image Fusion and Joint Information Mining in Landscape Design with Vision Information Mining

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