Analyzing students records to identify patterns of students' performance

Hoe, Alan Cheah Kah; Ahmad, Mohd Sharifuddin; Hooi, Tan Chin; Shanmugam, Mohana; Gunasekaran, Saraswathy Shamini; Cob, Zaihisma Che; Ramasamy, Ammuthavali

doi:10.1109/icriis.2013.6716767

Cited by 21 publications

(18 citation statements)

References 3 publications

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“…For example, Hoe et al [15] employed a CHAID algorithm to identify the important variables that influence the performance of undergraduate students. The study examined the patterns obtained using the data of students demographics and past performances.…”

Section: Related Workmentioning

confidence: 99%

Using data mining techniques to predict students at risk of poor performance

Alharbi

Cornford

Dolder

et al. 2016

2016 SAI Computing Conference (SAI)

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Abstract-The achievement of good honours in Undergraduate degrees is important in the context of Higher Education (HE), both for students and for the institutions that host them. In this paper, we look at whether data mining can be used to highlight performance problems early on and propose remedial actions. Furthermore, some of the methods may also form the basis for recommender systems that may guide students towards their module choices to increase their chances of a good outcome. We use data collected through the admission process and through the students' degrees. In this paper, we predict good honours outcomes based on data at admission and on the first year module results. To validate the proposed results, we evaluate data relating to students with different characteristics from different schools. The analysis is achieved by using historical data from the Data Warehouse of a specific University. The methods used, however, are fairly general and can be used in any HE institution. Our results highlight groups of students at considerable risk of obtaining poor outcomes. For example, using admissions and first year module performance data we can isolate groups for one of the studied schools in which only 24% of students achieve good honour degrees. Over 67% of all low achievers in the school can be identified within this group.

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Section: Related Workmentioning

confidence: 99%

Using data mining techniques to predict students at risk of poor performance

Alharbi

Cornford

Dolder

et al. 2016

2016 SAI Computing Conference (SAI)

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“…However, in recent time, emphasis on the predicting student performance has been on the use of their cognitive ability, log activities in learning management system as well as the student demographic attributes. [13], [14], [15] and [16] used demographic data along with students scores to predict their performance, using machine learning languages such as Artificial Neural Network, Support Vector Machine and Naïve Bayes algorithms. This technique is a move away from the commonly used traditional logistic regression.…”

Section: Literature Review Of Related Workmentioning

confidence: 99%

An Integrated System Framework for Predicting Students' Academic Performance in Higher Educational Institutions

Adejo

Connolly

2017

IJCSIT

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“…[13], [14], [15] and [16] used demographic data along with students scores to predict their performance, using machine learning languages such as Artificial Neural Network, Support Vector Machine and Naïve Bayes algorithms. This technique is a move away from the commonly used traditional logistic regression.…”

Section: Literature Review Of Related Workmentioning

confidence: 99%

Holistic Approach to Predicting Students Performance in Higher Educational Institutions - A Conceptual Framework

Adejo

Connolly

2017

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Accurate prediction and early identification of student at-risk of attrition are of high concern for higher educational institutions (HEIs). It is of a great importance not only to the students but also to the educational administrators and the institutions in the areas of improving academic quality and efficient utilisation of the available resources for effective intervention. However, despite the different frameworks and models that various researchers have used across institutions for predicting performance, only negligible success has been recorded in terms of accuracy, efficiency and reduction of student attrition. This has been attributed to the inadequate and selective use of variables for the predictive models. This paper presents a multidimensional and holistic framework for predicting student academic performance and intervention in HEIs.The purpose and functionality of the framework are to produce a comprehensive, unbiased and efficient way of predicting student performance that its implementation is based upon multi-sources data and database system. The proposed approach will be generalizable and possibly give a prediction at a higher level of accuracy that educational administrators can rely on for providing timely intervention to students.

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Analyzing students records to identify patterns of students' performance

Cited by 21 publications

References 3 publications

Using data mining techniques to predict students at risk of poor performance

Using data mining techniques to predict students at risk of poor performance

An Integrated System Framework for Predicting Students' Academic Performance in Higher Educational Institutions

Holistic Approach to Predicting Students Performance in Higher Educational Institutions - A Conceptual Framework

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