Proposing stochastic probability-based math model and algorithms utilizing social networking and academic data for good fit students prediction

Uddin, Muhammad Fahim; Lee, Jeong-Kyu

doi:10.1007/s13278-017-0448-z

Cited by 12 publications

(6 citation statements)

References 53 publications

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“…In our study, it is probable to have high-level verbal-reasoning abilities. The downside to using decision tree classifiers is their susceptibility to overfitting ( Uddin and Lee, 2017 ).…”

Section: Methodsmentioning

confidence: 99%

Predicting verbal reasoning from virtual community membership in a sample of Russian young adults

Kiselev¹,

Matsuta

Feshchenko

et al. 2022

Heliyon

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“…In our study, it is probable to have high-level verbal-reasoning abilities. The downside to using decision tree classifiers is their susceptibility to overfitting ( Uddin and Lee, 2017 ).…”

Section: Methodsmentioning

confidence: 99%

Predicting verbal reasoning from virtual community membership in a sample of Russian young adults

Kiselev¹,

Matsuta

Feshchenko

et al. 2022

Heliyon

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“…A model was developed by Uddin and Lee (2017) to predict a good fit in major for students to decrease dropout risk. The research was developed in three stages using academic data and data from social networks.…”

Section: Literature Review Of Prediction Models For Student Retention...mentioning

confidence: 99%

Data Mining and Machine Learning Retention Models in Higher Education

Cardona

Cudney

Hoerl

et al. 2020

Journal of College Student Retention: Research, Theory & Pr

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This study presents a systematic review of the literature on the predicting student retention in higher education through machine learning algorithms based on measures such as dropout risk, attrition risk, and completion risk. A systematic review methodology was employed comprised of review protocol, requirements for study selection, and analysis of paper classification. The review aims to answer the following research questions: (1) what techniques are currently used to predict student retention rates, (2) which techniques have shown better performance under specific contexts?, (3) which factors influence the prediction of completion rates in higher education?, and (4) what are the challenges with predicting student retention? Increasing student retention in higher education is critical in order to increase graduation rates. Further, predicting student retention provides insight into opportunities for intentional student advising. The review provides a research perspective related to predicting student retention using machine learning through several key findings such as the identification of the factors utilized in past studies and methodologies used for prediction. These findings can be used to develop more comprehensive studies to further increase the prediction capability and; therefore, develop strategies to improve student retention.

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“…In addition to the use of conventional student demographic and academic background variables, Uddin and Lee (2017) analyzed social networking-based data such as Facebook and Twitter to extract student personality traits (i.e. openness, conscientiousness, extraversion, agreeableness and neuroticism) and other relevant features (e.g.…”

Section: Data Sources and Student Variablesmentioning

confidence: 99%

Predictive analytic models of student success in higher education

Cui

Shiri

et al. 2019

ILS

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Purpose Many higher education institutions are investigating the possibility of developing predictive student success models that use different sources of data available to identify students that might be at risk of failing a course or program. The purpose of this paper is to review the methodological components related to the predictive models that have been developed or currently implemented in learning analytics applications in higher education. Design/methodology/approach Literature review was completed in three stages. First, the authors conducted searches and collected related full-text documents using various search terms and keywords. Second, they developed inclusion and exclusion criteria to identify the most relevant citations for the purpose of the current review. Third, they reviewed each document from the final compiled bibliography and focused on identifying information that was needed to answer the research questions Findings In this review, the authors identify methodological strengths and weaknesses of current predictive learning analytics applications and provide the most up-to-date recommendations on predictive model development, use and evaluation. The review results can inform important future areas of research that could strengthen the development of predictive learning analytics for the purpose of generating valuable feedback to students to help them succeed in higher education. Originality/value This review provides an overview of the methodological considerations for researchers and practitioners who are planning to develop or currently in the process of developing predictive student success models in the context of higher education.

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Proposing stochastic probability-based math model and algorithms utilizing social networking and academic data for good fit students prediction

Cited by 12 publications

References 53 publications

Predicting verbal reasoning from virtual community membership in a sample of Russian young adults

Predicting verbal reasoning from virtual community membership in a sample of Russian young adults

Data Mining and Machine Learning Retention Models in Higher Education

Predictive analytic models of student success in higher education

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