Ranking of Influencing Factors in Predicting Students’ Academic Performance

Affendey, Lilly Suriani; Paris, Ikmal Hisyam Mohd; Mustapha, Norwati; Sulaiman, Md. Nasir; Muda, Zaiton

doi:10.3923/itj.2010.832.837

Cited by 34 publications

(17 citation statements)

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“…In consequence, it has enjoyed substantial uptake (Nikora 2005;Camporelli 2006;Flikka et al 2006;Orhan and Altan 2006;Lasko et al 2006;Hunt 2006;Ferrari and Aitken 2006;Birzele and Kramer 2006;Kunchevaa et al 2007;Lau et al 2007;Masegosa et al 2007;Wang et al 2007;Garcia et al 2008;Tian et al 2008;Kurz et al 2009;Leon et al 2009;Shahri and Jamil 2009;Simpson et al 2009;Affendey et al 2010;García-Jiménez et al 2010;Hopfgartner et al 2010;Liew et al 2010). …”

Section: Aodementioning

confidence: 99%

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

et al. 2011

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Averaged n-Dependence Estimators (AnDE) is an approach to probabilistic classification learning that learns by extrapolation from marginal to full-multivariate probability distributions. It utilizes a single parameter that transforms the approach between a lowvariance high-bias learner (Naive Bayes) and a high-variance low-bias learner with Bayes optimal asymptotic error. It extends the underlying strategy of Averaged One-Dependence Estimators (AODE), which relaxes the Naive Bayes independence assumption while retaining many of Naive Bayes' desirable computational and theoretical properties. AnDE further relaxes the independence assumption by generalizing AODE to higher-levels of dependence. Extensive experimental evaluation shows that the bias-variance trade-off for Averaged 2-Dependence Estimators results in strong predictive accuracy over a wide range of data sets. It has training time linear with respect to the number of examples, learns in a single pass through the training data, supports incremental learning, handles directly missing values, and is robust in the face of noise. Beyond the practical utility of its lower-dimensional variants, AnDE is of interest in that it demonstrates that it is possible to create low-bias high-variance generative learners and suggests strategies for developing even more powerful classifiers.

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Section: Aodementioning

confidence: 99%

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

et al. 2011

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“…Prediction of the achieved success and the fi nal grade in the exam can be performed applying data mining algorithms. In [1], the ranking of factors that infl uence the prediction of academic performance in order to identify students who will need to study harder to pass the exam was performed by the application of data mining methods. An experiment with pattern classifi cation for student performance prediction is performed in [2].…”

Section: Introductionmentioning

confidence: 99%

Using Decision Tree Classifier for Analyzing Students’ Activities

Milinković¹,

Maksimović²

2013

JITA

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“…How the data is used, and what actions are taken, depends on the needs of the group, and their placement in the hierarchy of the educational process (Jayaprakash et al, 2014;Shelton et al, 2015). Learning analytics data are used in three areas: descriptive, predictive, and prescriptive analyses (Affendey, Paris, Mustapha, Sulaiman, & Muda, 2010;Brown, 2011). Descriptive analysis helps create a portrait of past students, instructors, or other stakeholders, while predictive analysis predicts likely trends and outcomes for students prior to their experience (Affendey et al, 2010;Brown, 2011;Verbert, Manouselis, Drachsler, & Duval, 2012).…”

Section: Uses Of Datamentioning

confidence: 99%

“…Learning analytics data are used in three areas: descriptive, predictive, and prescriptive analyses (Affendey, Paris, Mustapha, Sulaiman, & Muda, 2010;Brown, 2011). Descriptive analysis helps create a portrait of past students, instructors, or other stakeholders, while predictive analysis predicts likely trends and outcomes for students prior to their experience (Affendey et al, 2010;Brown, 2011;Verbert, Manouselis, Drachsler, & Duval, 2012). Prescriptive analysis dictates interventions for various stakeholders within the educational community (Brown, 2011).…”

Section: Uses Of Datamentioning

confidence: 99%

Using Learning Analytics to Predict Academic Success in Online and Face-to-Face Learning Environments

Berry¹

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This learning analytics study looked at the various student characteristics of all on-campus students who were enrolled in 100 and 200 level courses that were offered in both online and face-to-face formats during a two-year period. There is a perception that online education is either not as successful as face-to-face instruction, or it is more difficult for students. The results of this study show this is not the case.The goal of this study was to complete an in-depth analysis of student profiles addressing a variety of demographic categories as well as several academic and course related variables to reveal any patterns for student success in either online or face-to-face courses as measured by final grade. There were large enough differences within different demographic and academic categories to be considered significant for the study population, but overwhelmingly, the most significant predictor of success was found to be past educational success, as reflected in a student's cumulative grade point average.Further analysis was completed on students who declared high school credit as their primary major based on significantly different levels of success. These students were concurrent enrollment students or those who completed college courses for both high school and university credit. Since most of these students were new to the university, they did not have a cumulative GPA, so other predictive factors were explored. The study concludes with recommendations for action based on the logistic regression prediction tool that resulted from the data analysis.

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Ranking of Influencing Factors in Predicting Students’ Academic Performance

Cited by 34 publications

References 0 publications

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

Using Decision Tree Classifier for Analyzing Students’ Activities

Using Learning Analytics to Predict Academic Success in Online and Face-to-Face Learning Environments

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