Predicting Student Success: A Naïve Bayesian Application to Community College Data

Ornelas, Fermin; Ordóñez, Carlos

doi:10.1007/s10758-017-9334-z

Cited by 18 publications

(6 citation statements)

References 9 publications

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“…A review of previous research that aimed to predict academic achievement indicates that researchers have applied a range of machine learning algorithms, including multiple, probit and logistic regression, neural networks, and C4.5 and J48 decision trees. However, random forests (Zabriskie et al, 2019), genetic programming (Xing et al, 2015), and Naive Bayes algorithms (Ornelas & Ordonez, 2017) were used in recent studies. The prediction accuracy of these models reaches very high levels.…”

Section: Literaturementioning

confidence: 99%

Educational data mining: prediction of students' academic performance using machine learning algorithms

Yağcı

2022

Smart Learn. Environ.

261

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Educational data mining has become an effective tool for exploring the hidden relationships in educational data and predicting students' academic achievements. This study proposes a new model based on machine learning algorithms to predict the final exam grades of undergraduate students, taking their midterm exam grades as the source data. The performances of the random forests, nearest neighbour, support vector machines, logistic regression, Naïve Bayes, and k-nearest neighbour algorithms, which are among the machine learning algorithms, were calculated and compared to predict the final exam grades of the students. The dataset consisted of the academic achievement grades of 1854 students who took the Turkish Language-I course in a state University in Turkey during the fall semester of 2019–2020. The results show that the proposed model achieved a classification accuracy of 70–75%. The predictions were made using only three types of parameters; midterm exam grades, Department data and Faculty data. Such data-driven studies are very important in terms of establishing a learning analysis framework in higher education and contributing to the decision-making processes. Finally, this study presents a contribution to the early prediction of students at high risk of failure and determines the most effective machine learning methods.

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Section: Literaturementioning

confidence: 99%

Educational data mining: prediction of students' academic performance using machine learning algorithms

Yağcı

2022

Smart Learn. Environ.

261

View full text Add to dashboard Cite

show abstract

“…In the high end of the scale, there are tools tested with several thousand students. Two studies stand out above the rest in terms of population size: Ornelas and Ordonez had data of 8658 students belonging to 13 different courses [10], while Waddington and Nam collected data regarding 8762 students across 10 semesters [27]. On the other hand, the system presented by Chen was tested on a class of only 38 students [21], while Akcapinar et al included only 90 students in their study [36], which can be regarded as insufficient in order to make a solid evaluation of a predictor's performance.…”

Section: Discussionmentioning

confidence: 99%

“…Ornelas and Ordonez developed a Naïve Bayesian classifier that was applied in 13 different courses at Rio Salado Community College (Tempe, AZ, USA) [10]. The courses belonged to degree programs in the fields of science and humanities.…”

Section: Predictorsmentioning

confidence: 99%

Systematic Literature Review of Predictive Analysis Tools in Higher Education

et al. 2019

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The topic of predictive algorithms is often regarded among the most relevant fields of study within the data analytics discipline. They have applications in multiple contexts, education being an important one of them. Focusing on higher education scenarios, most notably universities, predictive analysis techniques are present in studies that estimate academic outcomes using different kinds of student-related data. Furthermore, predictive algorithms are the basis of tools such as early warning systems (EWS): applications able to foresee future risks, such as the likelihood of students failing or dropping out of a course, and alert of such risks so that corrective measures can be taken. The purpose of this literature review is to provide an overview of the current state of research activity regarding predictive analytics in higher education, highlighting the most relevant instances of predictors and EWS that have been used in practice. The PRISMA guidelines for systematic literature reviews were followed in this study. The document search process yielded 1382 results, out of which 26 applications were selected as relevant examples of predictors and EWS, each of them defined by the contexts where they were applied and the data that they used. However, one common shortcoming is that they are usually applied in limited scenarios, such as a single course, evidencing that building a predictive application able to work well under different teaching and learning methodologies is an arduous task.

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“…These models achieve remarkably high levels of prediction accuracy. To accurately predict the academic achievement of students, a thorough comprehension of the variables and characteristics influencing student outcomes is essential in (Ornelas & Ordonez 2017). In this study (Alshanqiti & Namoun 2020), they analysed 357 articles focusing on student performance and examining the effects of 29 different features.…”

Section: Review On Machine Learning Methodsmentioning

confidence: 99%

Exploring Software Infrastructures for Enhanced Learning Environments to Empowering Education

Hernández,

Ugaz,

Tarrillo

et al. 2024

JOWUA

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Recently, the education sector has undergone a notable change due to the incorporation of technology, resulting in the emergence of Educational Technology (EdTech). This new trend has completely transformed the learning process for students, teaching methods for educators, and operations of educational institutions. Due to EdTech, education has become simpler to access, engaging, and efficient, providing customized and diverse learning experiences. This article explores into the significant influence of EdTech on the field of education and the promising opportunities it offers for the future. EdTech has become a significant enabler, allowing institutions to meet evolving student needs and cultivate new skillsets without being limited by geographical barriers. EdTech integrates digital and technological media with conventional methods of instruction to enable various forms of learning, offering adaptability, enhancing engagement, and providing high-quality educational solutions. EdTech tools empower educators to track student engagement, encourage interactive and creative learning experiences, and stand for human-centred education focusing on critical thinking, innovation, and entrepreneurial activity. Exploring deeper relationships between educational data as well as predicting how well students do in school has been made possible through educational data mining. Presented is a novel model utilizing machine learning computational methods to forecast the EdTech for the students by using their midterm exam results. Various machine learning algorithms were assessed and providing the EdTech for improving their performance in final exam. This study comprehensively examines different EdTech technologies and recommend a unified model that could serve as a solid framework for classroom teaching.

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Predicting Student Success: A Naïve Bayesian Application to Community College Data

Cited by 18 publications

References 9 publications

Educational data mining: prediction of students' academic performance using machine learning algorithms

Educational data mining: prediction of students' academic performance using machine learning algorithms

Systematic Literature Review of Predictive Analysis Tools in Higher Education

Exploring Software Infrastructures for Enhanced Learning Environments to Empowering Education

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