Visual Anomaly Detection in Educational Data

Géryk, Jan; Popelínský, Lubomír; Triščík, Jozef

doi:10.1007/978-3-319-44748-3_10

Cited by 3 publications

(1 citation statement)

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“…Therefore, technological support can be an appropriate way to address this issue. Researchers in the past have acknowledged that anomaly detection has been sparingly applied to educational data (G eryk et al, 2016;Salami et al, 2016) and has a scope for further research, and there exists a need for research on more sophisticated techniques for identification of the evaluation anomaly. Anomaly detection was applied in text data, sensor networks, fraud detection (Chadyšas et al, 2022), and a few other domains but it is hardly ever applied to educational data.…”

Section: Research Gap and Questionsmentioning

confidence: 99%

Anomaly detection in the course evaluation process: a learning analytics–based approach

Vaidya

Sharma

2023

ITSE

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Purpose Course evaluations are formative and are used to evaluate learnings of the students for a course. Anomalies in the evaluation process can lead to a faulty educational outcome. Learning analytics and educational data mining provide a set of techniques that can be conveniently applied to extensive data collected as part of the evaluation process to ensure remedial actions. This study aims to conduct an experimental research to detect anomalies in the evaluation methods. Design/methodology/approach Experimental research is conducted with scientific approach and design. The researchers categorized anomaly into three categories, namely, an anomaly in criteria assessment, subject anomaly and anomaly in subject marks allocation. The different anomaly detection algorithms are used to educate data through the software R, and the results are summarized in the tables. Findings The data points occurring in all algorithms are finally detected as an anomaly. The anomaly identifies the data points that deviate from the data set’s normal behavior. The subject which is consistently identified as anomalous by the different techniques is marked as an anomaly in evaluation. After identification, one can drill down to more details into the title of anomalies in the evaluation criteria. Originality/value This paper proposes an analytical model for the course evaluation process and demonstrates the use of actionable analytics to detect anomalies in the evaluation process.

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Section: Research Gap and Questionsmentioning

confidence: 99%