Presentation Title

A cross-cultural comparison of adult skills: The ensemble approach

Start Date

November 2016

End Date

November 2016

Location

HUB 302-106

Type of Presentation

Poster

Abstract

In a world of increasing global competition, many nations devote tremendous effort to improve the education and skill level of their citizens. However, Programme for the International Assessment of Adult Competencies (PIAAC), conducted by Organization for Economic and Cooperation and Development (OECD), recently indicated that US adults are far behind their international peers in all three test categories: literacy, numeracy, and technology-based problem solving. This study utilized data mining to explore the possible association between PIAAC scores and several constructs from PIAAC questionnaires.

Since the US, Canada, and New Zealand were considered as culturally similar nations, patterns between PIAAC scores and selected constructs were analyzed by a variety of big data analytical methods, including cluster analysis, bootstrap forest, boosted tree, and data visualization. Given that PIAAC used multiple computerized adaptive testing, the consequential plausible values were randomly selected when the ensemble approach was used. Thus, model comparison was utilized to decide between bagging and boosting to select the optimal models.

In these three samples, cultural engagement, readiness to learn, and social trust emerged as strong predictors on learning outcomes. Social trust was shown to be the strongest predictor in the New Zealand sample, while in the US and Canadian sample, cultural engagement was identified as the most compelling factor for learning outcomes.

With respect to large scale assessments, it is suggested that the goal of big data analytics should center on pattern recognition, which include data visualization tools such as median smoothing, rather than dichotomous decisions yielded from hypothesis testing.

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Nov 12th, 1:00 PM Nov 12th, 2:00 PM

A cross-cultural comparison of adult skills: The ensemble approach

HUB 302-106

In a world of increasing global competition, many nations devote tremendous effort to improve the education and skill level of their citizens. However, Programme for the International Assessment of Adult Competencies (PIAAC), conducted by Organization for Economic and Cooperation and Development (OECD), recently indicated that US adults are far behind their international peers in all three test categories: literacy, numeracy, and technology-based problem solving. This study utilized data mining to explore the possible association between PIAAC scores and several constructs from PIAAC questionnaires.

Since the US, Canada, and New Zealand were considered as culturally similar nations, patterns between PIAAC scores and selected constructs were analyzed by a variety of big data analytical methods, including cluster analysis, bootstrap forest, boosted tree, and data visualization. Given that PIAAC used multiple computerized adaptive testing, the consequential plausible values were randomly selected when the ensemble approach was used. Thus, model comparison was utilized to decide between bagging and boosting to select the optimal models.

In these three samples, cultural engagement, readiness to learn, and social trust emerged as strong predictors on learning outcomes. Social trust was shown to be the strongest predictor in the New Zealand sample, while in the US and Canadian sample, cultural engagement was identified as the most compelling factor for learning outcomes.

With respect to large scale assessments, it is suggested that the goal of big data analytics should center on pattern recognition, which include data visualization tools such as median smoothing, rather than dichotomous decisions yielded from hypothesis testing.