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Utility-Value Score: A Case Study in System Generalization for Writing Analytics.

Beata Beigman Klebanov1, Stacy Priniski2, Jill Burstein1

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Summary

Automated writing analysis shows that context variations in student essays challenge consistent scoring. System generalization varies, impacting the reliability of writing analytics across diverse student populations and institutions.

Keywords:
STEM motivation. student writingautomated writing evaluationdata variabilityfirst-year STEMmodel evaluationmodel generalizationutility valuewriting analytics

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Area of Science:

  • Writing analytics
  • Educational technology
  • Computational linguistics

Background:

  • Large-scale analysis of student writing is crucial for understanding writing characteristics.
  • Variability in writing contexts (institution, purpose, demographics) poses challenges for automated analysis.
  • The emerging field of writing analytics aims to provide insights into student writing through automated methods.

Purpose of the Study:

  • To investigate the system generalization of automated writing analysis tools.
  • To assess the impact of data parameter variations on the validity and meaningfulness of automated writing indices.
  • To examine the challenges in creating consistent automated scores for constructs like utility value across diverse student essays.

Main Methods:

  • Developed an automated system to assess the expression of utility value in first-year biology student essays.
  • Conducted a case study involving variation of data parameters to observe system performance.
  • Analyzed the generalization capabilities of different components within the automated system.

Main Results:

  • Findings indicate that not all contextual variations in student writing are equally manageable for automated systems.
  • Some components of the automated writing analysis system demonstrate better generalization than others.
  • The automated utility-value score's consistency is affected by variations in essay production contexts.

Conclusions:

  • Automated writing analytics face challenges in maintaining validity and meaningfulness due to contextual variations in student writing.
  • System generalization in writing analytics is component-dependent and influenced by data parameter shifts.
  • Further research is needed to address the implications of contextual variability for robust and reliable writing analytics.