A Comparison of EDM Tools and Techniques

Alshehri, Eman; Alhakami, Hosam; Baz, Abdullah; Alsubait, Tahani

doi:10.14569/ijacsa.2020.0111295

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…The conventional approaches of DM cannot be readily applied to these types of data and challenges in educational environments [19]. As a result, different types of DM techniques are required for specific educational problems [20].…”

Section: Edm Techniquesmentioning

confidence: 99%

“…The problem is that these are not suited to handling educational data. Furthermore, these DM tools cannot be used by educators or teachers who do not have a basic understanding of DM concepts [19]. Methods for DM are derived from a wide variety of disciplines, including machine learning, statistical methods such as psychometrics, visualization techniques such as infographics, and computational modelling [21].…”

Section: Edm Techniquesmentioning

confidence: 99%

“…• Pattern Evaluation: In this stage, we can identify specific patterns and evaluate them to arrive at the desired goals [19].…”

Section: Figurementioning

confidence: 99%

See 2 more Smart Citations

Handling Big Data in Education: A Review of Educational Data Mining Techniques for Specific Educational Problems

Ampadu

2023

ACRT

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In the era of big data, where the amount of information is growing exponentially, the importance of data mining has never been greater. Educational institutions today collect and store vast amounts of data, such as student enrollment and attendance records, and their exam results. With the need to sift through enormous amounts of data and present it in a way that anyone can understand, educational institutions are at the forefront of this trend, and this calls for a more sophisticated set of algorithms. Data mining in education was born as a response to this problem. Traditional data mining methods cannot be directly applied to educational problems because of the special purpose and function they serve. Defining at-risk students, identifying priority learning requirements for varied groups of students, increasing graduation rates, monitoring institutional performance efficiently, managing campus resources, and optimizing curriculum renewal are just a few of the applications of educational data mining. This paper reviews methodologies used as knowledge extractors to tackle specific education challenges from large data sets of higher education institutions to the benefit of all educational stakeholders.

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Section: Edm Techniquesmentioning

confidence: 99%

Section: Edm Techniquesmentioning

confidence: 99%