Unveiling goods and bads: A critical analysis of machine learning predictions of standardized test performance in early childhood education

Lin Li, Namrata Srivastava, Jia Rong, Gina Pianta, Raju Varanasi, Dragan Gašević, Guanliang Chen

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


Learning analytics (LA) holds a promise to transform education by utilizing data for evidence-based decision-making. Yet, its application in early childhood education (ECE) remains relatively under-explored. ECE plays a crucial role in fostering fundamental numeracy and literacy skills. While standardized tests was intended to be used to monitor student progress, they have been increasingly assumed summative and high-stake due to the substantial impact. The pressures in succeeding in such standardized tests have been well-documented to negatively affect both students and teachers. Attempting to ease such stress and better support students and teachers, the current study delved into the LA potential for predicting standardized test performance using formative assessments. Beyond predictive accuracy, the study addressed ethical considerations related to fairness to uncover potential risks associated with LA adoption. Our findings revealed a promising opportunity to empower teachers and schools with more time and room to help students better prepared based on predictions obtained earlier before standardized tests. Notably, bias can be significantly observed in predictions for students with disabilities even they have same actual competence compared to students without disabilities. In addition, we noticed that inclusion of demographic attribute had no significant impact on the predictive accuracy, and not necessarily exacerbate the overall predictive bias, but may significantly affect the predictions received by certain demographic subgroups (e.g., students with different types of disability).

Original languageEnglish
Title of host publicationLAK 2024 Conference Proceedings - The Fourteenth International Conference on Learning Analytics & Knowledge
EditorsSrecko Joksimovic, Andrew Zamecnik
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages12
ISBN (Electronic)9798400716188
Publication statusPublished - 2024
EventInternational Learning Analytics & Knowledge Conference 2024 - Kyoto, Japan
Duration: 18 Mar 202422 Mar 2024
Conference number: 14th
https://dl.acm.org/doi/proceedings/10.1145/3636555 (Conference Proceedings)
https://ceur-ws.org/Vol-3667/ (LAK 2024 Workshop Proceedings)


ConferenceInternational Learning Analytics & Knowledge Conference 2024
Abbreviated titleLAK 2024
Internet address


  • bias
  • early childhood education
  • fairness
  • learning analytics
  • machine learning
  • standardized tests

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