A Prediction of Engineering Students Performance from Core Engineering Course Using Classification

Rachburee, Nachirat; Punlumjeak, Wattana; Rugtanom, Sitti; Jaithavil, Deachrut; Pracha, Manoch

doi:10.1007/978-3-662-46578-3_76

Cited by 5 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data preprocessing and missing data handling Data preprocessing. Very few studies we reviewed reported briefly accuracy checks of the data (Jayaprakash et al, 2014;Rachburee et al, 2015). No studies we reviewed provided detailed procedures for checking data quality and errors.…”

Section: Ils 1203/4mentioning

confidence: 99%

“…With more nodes in the hidden layer, the relationship between predictors and outcome variables becomes more nonlinear in the MLP model. It has been mathematically demonstrated that the MLP, given a sufficient number of hidden nodes, can approximate any nonlinear function to any desired level of accuracy (Dawson and Wilby, 2004;Hornik et al, 1989)., Rachburee et al (2015) developed predictive models with five classification techniques, namely, DT, NBC, k-nearest neighbors, SVM and MLP. The results show that MLP generates the best prediction with 89.29 per cent accuracy.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Predictive analytic models of student success in higher education

Cui

Shiri

et al. 2019

ILS

View full text Add to dashboard Cite

Purpose Many higher education institutions are investigating the possibility of developing predictive student success models that use different sources of data available to identify students that might be at risk of failing a course or program. The purpose of this paper is to review the methodological components related to the predictive models that have been developed or currently implemented in learning analytics applications in higher education. Design/methodology/approach Literature review was completed in three stages. First, the authors conducted searches and collected related full-text documents using various search terms and keywords. Second, they developed inclusion and exclusion criteria to identify the most relevant citations for the purpose of the current review. Third, they reviewed each document from the final compiled bibliography and focused on identifying information that was needed to answer the research questions Findings In this review, the authors identify methodological strengths and weaknesses of current predictive learning analytics applications and provide the most up-to-date recommendations on predictive model development, use and evaluation. The review results can inform important future areas of research that could strengthen the development of predictive learning analytics for the purpose of generating valuable feedback to students to help them succeed in higher education. Originality/value This review provides an overview of the methodological considerations for researchers and practitioners who are planning to develop or currently in the process of developing predictive student success models in the context of higher education.

show abstract

Section: Ils 1203/4mentioning

confidence: 99%