Using naïve bayes algorithm to students' bachelor academic performances analysis

Razaque, Fahad; Soomro, Nareena; Shaikh, Shoaib Ahmed; Soomro, Safeeullah; Samo, Javed Ahmed; Kumar, Natesh; Dharejo, Huma

doi:10.1109/icetas.2017.8277884

Cited by 29 publications

(16 citation statements)

References 9 publications

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“…Another study that also utilize the Naïve Bayes algorithm is the study of Razaque et al, (2018) which applied the Naïve Bayes algorithm on the bachelor of Computing students dataset. The performances of NB were observed on the data clusters labeled C1 to C4 and the results showed the best predictive accuracy of 98.8% in cluster C3.…”

Section: Naïve Bayes (Nb)mentioning

confidence: 99%

Students’ Academic Performance and Dropout Prediction

Ameen¹,

Alarape²,

Adewole³

2019

MJoC

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Students’ Academic Performance (SAP) is an important metric in determining the status of students in any academic institution. It allows the instructors and other education managers to get an accurate evaluation of the students in different courses in a particular semester and also serve as an indicator to the students to review their strategies for better performance in the subsequent semesters. Predicting SAP is therefore important to help learners in obtaining the best from their studies. A number of researches in Educational Psychology (EP), Learning Analytics (LA) and Educational Data Mining (EDM) has been carried out to study and predict SAP, most especially in determining failures or dropouts with the goal of preventing the occurrence of the negative final outcome. This paper presents a comprehensive review of related studies that deal with SAP and dropout predictions. To group the studies, this review proposes taxonomy of the methods and features used in the literature for SAP and dropout prediction. The paper identifies some key issues and challenges for SAP and dropout predictions that require substantial research efforts. Limitations of the existing approaches for SAP and dropout prediction are identified. Finally, the paper exposes the current research directions in the area.

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Section: Naïve Bayes (Nb)mentioning

confidence: 99%

Students’ Academic Performance and Dropout Prediction

Ameen¹,

Alarape²,

Adewole³

2019

MJoC

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“…For example, student background data and some more implicit indicator factors [24] in the very early stage of student course study could be considered based on more experiments. Secondly, in addition to external behavioural data about student studying process, some internal psychological data about student studying emotion such as eye movement data [31,33,34] could be integrated into the model.…”

Section: Discussionmentioning

confidence: 99%

A Dynamic Early Warning Method of Student Study Failure Risk based on Fuzzy Synthetic Evaluation

Mi¹

2018

IJPE

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As more and more students fail in course studies, higher education is now facing challenges regarding increasingly lower course completion rates as well as overall graduation rates. However, failures in course studies is a comprehensive result of various factors, which is characterized by uncertainty. To deal with this issue, fuzzy sets theory and fuzzy logic are advantageous compared with traditional methods. In this study, based on dynamic student study process data, a fuzzy synthetic evaluation method for dynamic early warning student study failure risk is provided. For each student, three specific early warning factors: 1) student course participation, 2) assignment earned points, and 3) student attendance record, are selected as risk indicators, and the overall risk level is determined by a fuzzy synthetic evaluation approach, which can dynamically give the situation of risk as the evaluation time point changes. Finally, our obtained results show that the employed method is good for identifying at-risk students and exploring the risk reasons by showing the degrees of each early warning factors to the overall risk level. It is of significance for educators to timely apply corresponding strategic pedagogical interventions to help at-risk students avoid academic failure.

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“…It utilizes variables separately included in the data sample by analyzing each of them. The Naïve Bayes classifier depends on the conditional probability obtained from the Bayes Law [9]. It utilizes all the variables included in the data, and then allows an individual analysis as they are equally important and independent of each other.…”

Section: Naive Bayes Classifiermentioning

confidence: 99%

Predicting Students' Dropout Indicators in Public School using Data Mining Approaches

Gil¹

2020

IJATCSE

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Currently, one of the challenges of academic institutions is dropout student issues. It is critical to recognize which students are at risk of losing in school and what are the underlying factors of dropout. The study of students' dropout in Lawigan National High School enables teachers to identify the influential factors of dropout cases in school preemptively, to respond to it immediately, and to assist prospective dropout students in continuing studies and gaining knowledge for a better future. Lawigan National High School is a public school supervised with the Department of Education. The said school holds all the records of the students. The study aims to identify the underlying factors of dropout students that need to have an intervention to lessen the value of dropouts. The Weka Experiment Environment platform used to run simulations with machine learning algorithms from ready datasets. Two classification algorithms C4.5 and Naïve Bayes (NB) tested on a dataset containing student academic and demographic details, e.g., age, gender, marital status, written, performance, and quarterly examination grade, attendance percentage, home distance, parents' income, mothers' education, fathers' education, and dropout status, using a 10-fold cross-validation to estimate generalization accuracy. The experiment result shows that the top indicator for students' dropout cases is the academic performance with 98.9474% accuracy as perceived by the C4.5 algorithm, as shown in Table 2.

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Using naïve bayes algorithm to students' bachelor academic performances analysis

Cited by 29 publications

References 9 publications

Students’ Academic Performance and Dropout Prediction

Students’ Academic Performance and Dropout Prediction

A Dynamic Early Warning Method of Student Study Failure Risk based on Fuzzy Synthetic Evaluation

Predicting Students' Dropout Indicators in Public School using Data Mining Approaches

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