Data Mining in Higher Education : University Student Dropout Case Study

Abu-Oda, Ghadeer S.; El-Halees, Alaa M.

doi:10.5121/ijdkp.2015.5102

Cited by 58 publications

(21 citation statements)

References 11 publications

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“…In addition, you may know how to write and execute basic level computer programs. 2 Means you have studied one or more programming languages or you have sufficient knowledge in programming. In addition, you know how to write midlevel and or higher level computer programs.…”

Section: Prior Programming Knowledge (Ppk)mentioning

confidence: 99%

Prediction of Student Final Exam Performance in an Introductory Programming Course: Development and Validation of the Use of a Support Vector Machine-Regression Model

Veerasamy

D'Souza

Lindén

et al. 2019

AJEEL

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This paper presents a Support Vector Machine predictive model to determine if prior programming knowledge and completion of in-class and take home formative assessment tasks might be suitable predictors of examination performance. Student data from the academic years 2012 - 2016 for an introductory programming course was captured via ViLLE e-learning tool for analysis. The results revealed that student prior programming knowledge and assessment scores captured in a predictive model, is a good fit of the data. However, while overall success of the model is significant, predictions on identifying at-risk students is neither high nor low and that persuaded us to include two more research questions. However, our preliminary post analysis on these test results show that on average students who secured less than 70% in formative assessment scores with little or basic prior programming knowledge in programming may fail in the final programming exam and increase the prediction accuracy in identifying at-risk students from 46% to nearly 63%. Hence, these results provide immediate information for programming course instructors and students to enhance teaching and learning process.

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Section: Prior Programming Knowledge (Ppk)mentioning

confidence: 99%

Prediction of Student Final Exam Performance in an Introductory Programming Course: Development and Validation of the Use of a Support Vector Machine-Regression Model

Veerasamy

D'Souza

Lindén

et al. 2019

AJEEL

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“…In [6], the authors applied different data mining approaches for the purpose of examining and predicting student dropouts through their university programs. In their study, they use select a total of 1290 records of computer science students graduated from ALAQSA university between 2005 and 2011.…”

Section: *Author For Correspondencementioning

confidence: 99%

A proposed academic advisor model based on data mining classification techniques

Mohamed¹,

Waguih²

2018

IJACR

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“…Educational Data Mining has a variety of purposes. Some studies use EDM to predict academic patterns by reviewing the accuracy of the study period of students in their education such as research conducted by [4], [5], [2], [6], [7], [8], [9]. In addition there are studies with the aim of predicting academic patterns by reviewing student performance such as research conducted by [10], [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Graduation Prediction of S1 Industrial Engineering Students IST AKPRIND by Using Data Mining Method

Wibowo

2020

Logic

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There is data of students who experience Drop Out which raises the curiosity in IST AKPRIND's industrial engineering study program on students' graduation patterns. It is necessary to have research on how to classify the data held by industrial engineering study programs in order to obtain students' graduation patterns as evaluation material in the administration of study programs. This study also produced a design to set the goals of Educational Data Mining, this case as a student modeling that would be achieved by predicting using the Decision Tree method. The final results showed a mismatch between the general information data passed and the drop out of the rule obtained using the decision tree algorithm in the Rapidminer software which is shown by an accuracy of 95.83%. This value indicates that there is a match between the prediction of student identity data with the rule obtained using the decision tree algorithm.

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Data Mining in Higher Education : University Student Dropout Case Study

Cited by 58 publications

References 11 publications

Prediction of Student Final Exam Performance in an Introductory Programming Course: Development and Validation of the Use of a Support Vector Machine-Regression Model

Prediction of Student Final Exam Performance in an Introductory Programming Course: Development and Validation of the Use of a Support Vector Machine-Regression Model

A proposed academic advisor model based on data mining classification techniques

Graduation Prediction of S1 Industrial Engineering Students IST AKPRIND by Using Data Mining Method

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