Towards understanding how to assess help-seeking behavior across cultures

Amy Ogan, Erin Walker, Ryan Baker, Ma Mercedes T Rodrigo, Jose Carlo Soriano, Maynor Jimenez Castro

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

In recent years, there has been increasing interest in automatically assessing help seeking, the process of referring to resources outside of oneself to accomplish a task or solve a problem. Research in the United States has shown that specific help-seeking behaviors led to better learning within intelligent tutoring systems. However, intelligent tutors are used differently by students in different countries, raising the question of whether the same help-seeking behaviors are effective and desirable in different cultural settings. To investigate this question, models connecting help-seeking behaviors with learning were generated from datasets from students in three countries - Costa Rica, the Philippines, and the United States, as well as a combined dataset from all three sites. Each model was tested on data from the other countries. This study found that models of effective help seeking transfer to some degree between the United States and Philippines, but not between those countries and Costa Rica. Differences may be explained by variations in classroom practices between the sites; for example, greater collaboration observed in the Costa Rican site indicates that much help seeking occurred outside of the technology. Findings indicate that greater care should be taken when assuming that the models underlying AIED systems generalize across cultures and contexts.

Original languageEnglish (US)
Pages (from-to)229-248
Number of pages20
JournalInternational Journal of Artificial Intelligence in Education
Volume25
Issue number2
DOIs
StatePublished - Mar 31 2015

Keywords

  • Assessment
  • Cross-curricular skills
  • Help seeking
  • Intelligent tutoring system

ASJC Scopus subject areas

  • Education
  • Computational Theory and Mathematics

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