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W. Reed Scull, Mark Andrew Perkins, Jonathan W. Carrier & Michael Barber. (2023) Community College Institutional Researchers’ Knowledge, Experience, and Perceptions of Machine Learning. Community College Journal of Research and Practice 47:5, pages 354-368.
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Stephanie Thomson & Maria Pampaka. (2021) Exploring associations with mathematics dispositions: a novel perspective combining measurement and set-theoretic analytical approaches. International Journal of Research & Method in Education 44:5, pages 474-492.
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Cristiano Mauro Assis Gomes & Enio Jelihovschi. (2020) Presenting the Regression Tree Method and its application in a large-scale educational dataset. International Journal of Research & Method in Education 43:2, pages 201-221.
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Articles from other publishers (20)
Rogério Luiz Cardoso Silva Filho, Anvit Garg, Kellyton Brito, Paulo Jorge Leitão Adeodato & Martin Carnoy. (2023) Beyond scores: A machine learning approach to comparing educational system effectiveness. PLOS ONE 18:10, pages e0289260.
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Kieran Brian & Massimo Stella. (2023) Introducing mindset streams to investigate stances towards STEM in high school students and experts. Physica A: Statistical Mechanics and its Applications 626, pages 129074.
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Joseph Y. Haw & Ronnel B. King. (2023) Understanding Filipino students’ achievement in PISA: The roles of personal characteristics, proximal processes, and social contexts. Social Psychology of Education 26:4, pages 1089-1126.
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Rogério Luiz Cardoso Silva Filho, Kellyton Brito & Paulo Jorge Leitão Adeodato. (2023) A data mining framework for reporting trends in the predictive contribution of factors related to educational achievement. Expert Systems with Applications 221, pages 119729.
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Yi Wang, Ronnel King & Shing On Leung. (2022) Understanding Chinese Students' Well-Being: A Machine Learning Study. Child Indicators Research 16:2, pages 581-616.
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Yujia Bu & Fu Chen. (2023) What key contextual factors contribute to students’ reading literacy among top-performing countries and economies? Statistical and machine learning analyses. International Journal of Educational Research 122, pages 102267.
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Miho Taguma, Kelly Makowiecki & Florence Gabriel. 2023. Mathematics Curriculum Reforms Around the World. Mathematics Curriculum Reforms Around the World
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Abel Rodriguez, Daniel Quispe & Lenis Wong. (2022) Model for the Monitoring of Competences of the PISA Test in Peru under a B-Learning Approach. Model for the Monitoring of Competences of the PISA Test in Peru under a B-Learning Approach.
Yi-Jhen Wu & Jihyun Lee. (2022) The most salient global predictors of adolescents’ subjective Well-Being: parental support, peer support, and anxiety. Child Indicators Research 15:5, pages 1601-1629.
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Shoko Yamada & Christian S. Otchia. (2021) Differential effects of schooling and cognitive and non‐cognitive skills on labour market outcomes: The case of the garment industry in Ethiopia. International Journal of Training and Development 26:1, pages 145-162.
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Jie Hu, Xin Dong & Yi Peng. (2021) Discovery of the key contextual factors relevant to the reading performance of elementary school students from 61 countries/regions: insight from a machine learning-based approach. Reading and Writing 35:1, pages 93-127.
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Aleksandar Pejic, Piroska Stanic Molcer & Kristian Gulaci. (2021) Math proficiency prediction in computer-based international large-scale assessments using a multi-class machine learning model. Math proficiency prediction in computer-based international large-scale assessments using a multi-class machine learning model.
Dongjo Shin & Jaekwoun Shim. (2020) A Systematic Review on Data Mining for Mathematics and Science Education. International Journal of Science and Mathematics Education 19:4, pages 639-659.
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Ali Nadaf, Sebas Eliëns & Xin Miao. (2021) Interpretable-Machine-Learning Evidence for Importance and Optimum of Learning Time. International Journal of Information and Education Technology 11:10, pages 444-449.
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Jiangping Chen, Yang Zhang & Jie Hu. (2020) Synergistic effects of instruction and affect factors on high- and low-ability disparities in elementary students’ reading literacy. Reading and Writing 34:1, pages 199-230.
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Adriana Gamazo & Fernando Martínez-Abad. (2020) An Exploration of Factors Linked to Academic Performance in PISA 2018 Through Data Mining Techniques. Frontiers in Psychology 11.
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Neha Sharma, Sunil Kumar Gautam, Azriel A. Henry & Abhimanyu Kumar. 2020. Machine Learning and Big Data. Machine Learning and Big Data
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N Nugraheni & EF Sari. (2020) Mathematical literacy ability of the elementary school teacher education program of the students’ of Universitas Negeri Semarang. Journal of Physics: Conference Series 1567:2, pages 022054.
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Aleksandar Pejic & Piroska Stanic Molcer. (2019) Predicting the Outcome of a PISA Problem Solving Task Using Strategic Behavior Data. Predicting the Outcome of a PISA Problem Solving Task Using Strategic Behavior Data.
Chiara Masci, Geraint Johnes & Tommaso Agasisti. (2018) Student and school performance across countries: A machine learning approach. European Journal of Operational Research 269:3, pages 1072-1085.
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