A machine learning approach to investigating the effects of mathematics dispositions on mathematical literacy

Gabriel, Florence; Signolet, Jason; Westwell, Martin S.

doi:10.1080/1743727x.2017.1301916

Cited by 46 publications

(24 citation statements)

References 36 publications

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“…Next, another advantage all ML methods possess, is the capability to capture both the linearities and non-linearities present in the relationships between variables. Gabriel et al (2018) analysed Australian students' mathematics literacy performance in the PISA 2012 using boosted regression trees with seven mathematical dispositions variables and five demographics variables, to predict mathematical literacy scores. They found the strongest linear relationship between mathematics self-efficacy and mathematical literacy, and the second strongest relationship between students' SES and mathematical literacy that was non-linear and fairly complex.…”

Section: Overview and Advantages Of Machine Learning (Ml) Methodsmentioning

confidence: 99%

Predicting Students’ Academic Performance: A Comparison between Traditional MLR and Machine Learning Methods with PISA 2015

Puah¹

2021

Preprint

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Predicting students’ academic performance has long been an important area of research in education. Most existing literature have made use of traditional statistical methods that run into the problems of overfitted models, inability to effectively handle large numbers of participants and predictors, and inability to pick out non-linearities that may be present. Regression-based ML methods that can produce highly interpretable yet accurate models for new predictions, are able to provide some solutions to the aforementioned problems. The present study is the first study that develops and compares between traditional MLR methods and regression-based ML methods (i.e. ridge regression, LASSO regression, elastic net, and regression trees) to predict students’ science performance in the PISA 2015. A total of 198,712 students from 60 countries, and 66 student- and school-related predictors were used to develop the predictive models. Predictive accuracy of the various models built were not that different, however, there were significant differences in the predictors identified as most important by the different methods. Although regression-based ML techniques did not outperform traditional MLR, significant advantages for using ML methods were noted and discussed. Moving forward, we strongly believe that there is merit for using such regression-based ML methods in educational research. Educational research can benefit from adopting ML practices and methods to produce models that can not only be used for explaining factors that influence academic performance prediction, but also for making more accurate predictions on unseen data.

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Section: Overview and Advantages Of Machine Learning (Ml) Methodsmentioning

confidence: 99%

Predicting Students’ Academic Performance: A Comparison between Traditional MLR and Machine Learning Methods with PISA 2015

Puah¹

2021

Preprint

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“…The past decade has seen the publication of many research works that use EDM techniques for performance prediction. Although there is some diversity in terms of the particular techniques used, the most popular seem to be decision trees and their different algorithms, such as Classification and Regression Trees (CART) (Asensio-Muñoz et al, 2018;Gabriel et al, 2018;She et al, 2019), Chi-squared Automatic Interaction Detection (CHAID) (Aksu and Güzeller, 2016;Asensio-Muñoz et al, 2018;Tourón et al, 2018) or other algorithms like C4.5 (Liu and Ruiz, 2008;Oskouei and Askari, 2014;Martínez-Abad, 2019) or J48, which is another form of the C4.5 algorithm (Aksu and Güzeller, 2016;Martínez-Abad and Chaparro-Caso-López, 2016;Kılıç Depren et al, 2017;Martínez-Abad et al, 2020). Some of these studies aggregate student variables to school level (e.g., Martínez-Abad, 2019), however, there are not, to our knowledge, any basic studies on the effects of the aggregation bias on the computation of data mining models.…”

Section: Edm and Large-scale Assessmentsmentioning

confidence: 99%

An Exploration of Factors Linked to Academic Performance in PISA 2018 Through Data Mining Techniques

Gamazo

Martínez‐Abad

2020

Front. Psychol.

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International large-scale assessments, such as PISA, provide structured and static data. However, due to its extensive databases, several researchers place it as a reference in Big Data in Education. With the goal of exploring which factors at country, school and student level have a higher relevance in predicting student performance, this paper proposes an Educational Data Mining approach to detect and analyze factors linked to academic performance. To this end, we conducted a secondary data analysis and built decision trees (C4.5 algorithm) to obtain a predictive model of school performance. Specifically, we selected as predictor variables a set of socioeconomic, process and outcome variables from PISA 2018 and other sources (World Bank, 2020). Since the unit of analysis were schools from all the countries included in PISA 2018 (n = 21,903), student and teacher predictor variables were imputed to the school database. Based on the available student performance scores in Reading, Math, and Science, we applied k-means clustering to obtain a categorized (three categories) target variable of global school performance. Results show the existence of two main branches in the decision tree, split according to the schools’ mean socioeconomic status (SES). While performance in high-SES schools is influenced by educational factors such as metacognitive strategies or achievement motivation, performance in low-SES schools is affected in greater measure by country-level socioeconomic indicators such as GDP, and individual educational indicators are relegated to a secondary level. Since these evidences are in line and delve into previous research, this work concludes by analyzing its potential contribution to support the decision making processes regarding educational policies.

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“…PISA'da matematik okuryazarlığı kavramını inceleyen pek çok çalışma yapılmıştır. Bu araştırmalardan bazıları PISA'da matematik okuryazarlık düzeyini ve düzeylere etki eden faktörleri belirlemeye yöneliktir (Stacey, 2011;Azapağası İlbağı, 2012;Gürsakal, 2012;Çam, 2014;Türkan, Üner, Alcı, 2015;Karabay, Yıldırım ve Güler, 2015;Andrés, 2017;Gabriel, Signolet & Westwell, 2017 PISA 2012 bağlamında 9. sınıf öğrencilerinin matematik okuryazarlık düzeyi nedir? PISA 2012 kapsamında 9. sınıf öğrencilerinin matematik okuryazarlık düzeyleri; annebaba eğitimlerine, anne-baba çalışma durumuna, ailenin aylık gelir durumuna, öğrenci devamsızlık durumuna, matematiğe yönelik duyuşsal özelliklerine, matematiğe yönelik kaygı ve endişe durumlarına göre farklılaşmakta mıdır?…”

Section: <35777unclassified

PISA 2012 Bağlamında 9.Sınıf Öğrencilerinin Matematik Okuryazarlığının İncelenmesi

Çoban¹

2019

rssstudies

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PISA 2012 Bağlamında 9.Sınıf Öğrencilerinin Matematik Okuryazarlığının İncelenmesi 1 Merve ÇOBAN(KOCAOĞLU) 2 , Sevinç MERT UYANGÖR 3 Özet Kısa adı OECD olan "Ekonomik İşbirliği ve Kalkınma Örgütü" nün bir eğitim araştırması olan Uluslararası Öğrenci Başarılarını Değerlendirme Programı (PISA); 15 yaş grubundaki öğrencilerin bilgi ve becerilerini değerlendiren uluslararası bir araştırmadır. PISA araştırmasına katılacak olan okul ve öğrencilerin seçim işlemi, OECD tarafından tesadüfi (seçkisiz) yöntemle belirlenmektedir. Bu çalışmada; ülkemizde örneklem dışında kalan bir ilçede öğrenim gören tüm 9. sınıf öğrencilerinin matematik okuryazarlık düzeylerini ve bu düzeylere etki eden faktörleri belirlemek ve elde edilen sonuçları Türkiye ortalamaları ile karşılaştırmak amaçlanmıştır. Araştırmada nicel araştırma yaklaşımı benimsenmiştir. Araştırmanın çalışma grubu; 2016-2017 öğretim yılında Balıkesir ili Savaştepe ilçesinde öğrenim gören 256 dokuzuncu sınıf öğrencisinden oluşturulmuştur. Öğrencilerin matematik okuryazarlığı yeterlilik düzeylerini ve bunu etkileyen değişkenleri belirlemek için PISA 2012 uygulamasında kullanılan "öğrenci anketi" ve "matematik okuryazarlığı testi" ile çalışmanın verilerine ulaşılmıştır. Çalışmanın sonuçlara göre; uygulamaya katılan öğrencilerin matematik okuryazarlığı yeterlilik düzeyi 2. düzey olarak bulunmuş olup bu sonuç PISA 2012 sonuçları ile tutarlıdır. Öğrencilerin sahip olduğu matematik okuryazarlık düzeyi ile seçilen bağımsız değişkenler arası anlamlı ilişkiler bulunmamakla birlikte; bağımsız değişkenlerin kendi aralarında ilişkilerine rastlanmıştır.

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A machine learning approach to investigating the effects of mathematics dispositions on mathematical literacy

Cited by 46 publications

References 36 publications

Predicting Students’ Academic Performance: A Comparison between Traditional MLR and Machine Learning Methods with PISA 2015

Predicting Students’ Academic Performance: A Comparison between Traditional MLR and Machine Learning Methods with PISA 2015

An Exploration of Factors Linked to Academic Performance in PISA 2018 Through Data Mining Techniques

PISA 2012 Bağlamında 9.Sınıf Öğrencilerinin Matematik Okuryazarlığının İncelenmesi

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