Using Neural Network and Logistic Regression Analysis to Predict Prospective Mathematics Teachers’ Academic Success upon Entering Graduate Education

Solomon, Susan; Portmann, R. W.; García, Rolando R.; Thomason, L. W.; Poole, L. R.; McCormick, M. P.

doi:10.12738/estp.2016.3.0214

Cited by 15 publications

(15 citation statements)

References 24 publications

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“…Algorithms have been used to predict the probability of a student failing an assignment or dropping out of a course with high levels of accuracy (e.g. Bahadır, 2016).…”

Section: Ai In Education (Aied)mentioning

confidence: 99%

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

Zawacki‐Richter

Marín

Bond

et al. 2019

Int J Educ Technol High Educ

1,626

468

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According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.

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“…Algorithms have been used to predict the probability of a student failing an assignment or dropping out of a course with high levels of accuracy (e.g. Bahadır, 2016).…”

Section: Ai In Education (Aied)mentioning

confidence: 99%

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

Zawacki‐Richter

Marín

Bond

et al. 2019

Int J Educ Technol High Educ

1,626

468

View full text Add to dashboard Cite

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“…It is seen that decision trees are used in the studies for estimating student achievement (Aydin, 2007), identifying the variables affecting the attitude towards a specific purpose (Idil et al, 2016) and classifying students according to certain characteristics (Guruler et al, 2010;Yelegin, 2012). Other techniques commonly used in EDM research include clustering techniques (Avsar & Yalcin, 2015;Baran & Kilic, 2015;Dogan & Camurcu, 2010), regression (Bahadir, 2016;Coskun, 2013;Turhan et al, 2013), association rules techniques (Hark, 2013;Onan et al, 2016;Yucel, 2012), bayes classifiers (Cebi, 2016;Cifci et al, 2018;Sahin, 2018;Uysal, 2015), and support vector machines (Kentli & Sahin, 2011;Sohsah et al, 2015;Tekin, 2014). In addition to these studies, fuzzy logic (Uysal, 2015), path analysis (Cebi, 2016) and genetic algorithm (Yildiz, 2014) techniques were used.…”

Section: Resultsmentioning

confidence: 99%

“…In the EDM studies, data mining techniques are applied for the specified purpose. In the literature, according to the purpose of the EDM; decision trees (Lin, Yeh, Hung, & Chang, 2013), bayes classifiers (Bhardwaj & Pal, 2012), regression (Bahadir, 2016), artificial neural networks (Yang & Li, 2018) and support vector machines (Al-Shehri et al, 2017) techniques are widely used.…”

Section: Introductionmentioning

confidence: 99%

Educational Data Mining Studies in Turkey: A Systematic Review

Aydoğdu¹

2020

Turkish Online Journal of Distance Education

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The purpose of this research is a comprehensive review of studies towards educational data mining (EDM) in Turkey. For the purpose of this study, graduate theses and articles conducted in Turkey were examined in detail. As a result of the literature review, 48 studies were analyzed in the context of the data mining purpose, the technique used in the study, the purpose of the study, the sample, the data sources and the analysis tool used in the study. In addition, the input variables used in the studies for estimating student achievement in EDM were examined. As a result of the research, it was found that EDM studies were mostly aimed at prediction from data mining tasks. One of the results of this study is that artificial neural networks are the most commonly used technique in EDM studies. When the distributions of EDM studies according to their objectives are examined, it is seen that studies are predominantly aimed at predicting student achievement. It is seen that university students are preferred as the sample in EDM studies, achievement scores are used as data source and SPSS application is used more as an analysis tool. In the last part of the research, recommendations were made based on the results to the researchers.

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“…Logistic regression is an analysis method in which the dependent variable consists of a two-level or multiple-level categorical data. With logistic regression, the causality relationship between the dependent variable and independent variables can be determined (Bahadir, 2016). The dependent variable must be a categorical data, while the independent variable could be either continuous or categorical data (Işığıçok, 2003).…”

Section: Data and Methodsologymentioning

confidence: 99%

Landscaping Pre-Service Teachers’ Stress Caused by Lack of Financial Management

Ramesh¹,

Sharmini²,

Assunta³

et al. 2018

BER

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Age plays an important role in the financial behavior of people. Financial matters are usually more seriously perceived at later stages of life. Nevertheless, the role of age in financial behavior is inconclusive. This study intends to add to the literature on financial behavior by focusing on the relationship between expenditure planning, gender and stress. The distinctive feature of this study is its scope and the sample of people participating in it. Besides being young, the participants are pre-service teachers. Hence, studying their financial behavior also augurs well for the nation, for these are the shapers of its future. Using an online survey, 127 pre-service teachers responded to 17 questions, dwelling mainly on their financial behavior. Logistic regression was then used to identify whether expenditure planning is related to stress and gender. The findings reveal that the failure to plan one’s expenditures could cause stress and in which gender could be the determinant factor. Thus, it is recommended to take actions on higher rate of financial literacy among pre-service teachers.

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Using Neural Network and Logistic Regression Analysis to Predict Prospective Mathematics Teachers’ Academic Success upon Entering Graduate Education

Cited by 15 publications

References 24 publications

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

Educational Data Mining Studies in Turkey: A Systematic Review

Landscaping Pre-Service Teachers’ Stress Caused by Lack of Financial Management

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