Survey on Evaluation of Student's Performance in Educational Data Mining

Bonde, Sharayu N.; Kirange, D. K.

doi:10.1109/icicct.2018.8473228

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…It is found that 22 out of the 33 review articles were not characterized as systematic reviews because they did not illustrate the detailed process of the examined data collections. These non-systematic review articles mainly introduced and reviewed EDM methods and tools (Salter et al , 2017; Anoopkumar and Rahman, 2016; Venkatachalapathy et al , 2017; Sukhija et al , 2015; Bonde and Kirange, 2018; Sachin and Vijay, 2012; Roy and Garg, 2018; Ganesh and Christy, 2015; Marwaha and Ahuja, 2017; Zaffar et al , 2018; Burman et al , 2017), data sources and main applications (Romero and Ventura, 2007; Romero and Ventura, 2010; Romero and Ventura, 2013; Baker and Kisor, 2009; Algarni, 2016). They claimed that EDM methods included not only prediction, clustering, relationship mining, distillation of data for human judgment and discovery with models but also outlier detections, text mining and social network analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Educational data mining: a systematic review of research and emerging trends

Yang

Hung

et al. 2020

IDD

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Purpose Educational data mining (EDM) and learning analytics, which are highly related subjects but have different definitions and focuses, have enabled instructors to obtain a holistic view of student progress and trigger corresponding decision-making. Furthermore, the automation part of EDM is closer to the concept of artificial intelligence. Due to the wide applications of artificial intelligence in assorted fields, the authors are curious about the state-of-art of related applications in Education. Design/methodology/approach This study focused on systematically reviewing 1,219 EDM studies that were searched from five digital databases based on a strict search procedure. Although 33 reviews were attempted to synthesize research literature, several research gaps were identified. A comprehensive and systematic review report is needed to show us: what research trends can be revealed and what major research topics and open issues are existed in EDM research. Findings Results show that the EDM research has moved toward the early majority stage; EDM publications are mainly contributed by “actual analysis” category; machine learning or even deep learning algorithms have been widely adopted, but collecting actual larger data sets for EDM research is rare, especially in K-12. Four major research topics, including prediction of performance, decision support for teachers and learners, detection of behaviors and learner modeling and comparison or optimization of algorithms, have been identified. Some open issues and future research directions in EDM field are also put forward. Research limitations/implications Limitations for this search method include the likelihood of missing EDM research that was not captured through these portals. Originality/value This systematic review has not only reported the research trends of EDM but also discussed open issues to direct future research. Finally, it is concluded that the state-of-art of EDM research is far from the ideal of artificial intelligence and the automatic support part for teaching and learning in EDM may need improvement in the future work.

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