Academic achievement critical factors and the bias and variance decomposition: evidence from high school students’ grades

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

Abstract

This study is centered on the sources of machine learning bias in the prediction of students’ grades.
The dataset comprises 29,788 Portuguese high school teacher final grades corresponding to 10,364 public high school students’ academic paths (from the 10th to the 11th grades). We use an artificial neural network to perform the tasks. In the experimental phase, we undertake a bias and variance decomposition when predicting the 11th year students’ grades. Two different implementations are used, a critical implementation that comprises only academic achievement critical factors and a lagged implementation where the
preceding teacher grade is appended. The critical implementation has a higher machine learning bias, notwithstanding the higher critical factors’ contribution. The lagged implementation, on the other hand, has a smaller bias, but a smaller critical factors’ contribution. It is possible for a machine learning model
to have a reduced bias and simultaneously a little critical factors’ contribution, simply by accessing information about the historical value of the target variable. The education stakeholders should therefore be aware of the critical quality of the model in use. In defining policies and choosing the variables to influence, predictive models with low biases and built upon the critical factors information are indispensable. A machine learning model based on the critical factors produces more consistent estimates of their effects on AA. They are therefore suitable models to assist in policymaking. On the other hand, if the goal is to obtain a simple set of predictions, the use of target variable historical values is appropriate.
Original languageEnglish
Title of host publicationPapers of 6th Canadian International Conference on Advances in Education, Teaching & Technology 2022
Subtitle of host publicationPapers proceedings
Place of PublicationToronto, Canada
PublisherUnique Conferences Canada
Pages54-62
Number of pages9
ISBN (Electronic)978-1-988652-51-1
Publication statusPublished - 1 Sep 2022
Event6th Canadian International Conference on Advances in Education, Teaching & Technology 2022
- University of Toronto, Toronto, Canada
Duration: 25 Jun 202226 Jun 2022
Conference number: 6t
https://educationconference.info/conference-history/

Publication series

NameInternational Multidisciplinary Research Journal
PublisherUCC
NumberConferences - Proceedings
VolumeSpecial Issue
ISSN (Electronic)2424-7073

Conference

Conference6th Canadian International Conference on Advances in Education, Teaching & Technology 2022
Abbreviated titleEduTeach2022
Country/TerritoryCanada
CityToronto
Period25/06/2226/06/22
Internet address

Keywords

  • Bias and variance decomposition
  • Education policy
  • Academic achievement

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