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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Psychol. | doi: 10.3389/fpsyg.2019.02461

Predictive Feature Generation and Selection Using Process Data from PISA Interactive Problem-Solving Items: An Application of Random Forests

 Zhuangzhuang Han1*,  Qiwei He2* and Matthias von Davier3*
  • 1Teachers College, Columbia University, United States
  • 2Educational Testing Service, United States
  • 3National Board of Medical Examiners, United States

The Programme for International Student Assessment (PISA) introduced the measurement of problem-solving skills in the 2012 cycle. The items in this new domain employ scenario-based environments in terms of students interacting with computers. Process data collected from log files are a record of students’ interactions with the testing platform. This study suggests a two-stage approach for generating features from process data and selecting the features that predict students’ responses using a released problem-solving item from PISA 2012. The primary objectives of the study are (1) introducing an approach for generating features from the process data and using them to predict the response to this item, and (2) finding out which features have the most predictive value. To achieve these goals, a tree-based ensemble method, the random forest algorithm, is used to explore the association between response data and predictive features. Also, features can be ranked by importance in terms of predictive performance. This study can be considered as providing an alternative way to analyze process data having a pedagogical purpose.

Keywords: Process data, Feature generation, Feature Selection, PISA, interactive items, Random forests, Problem solving

Received: 11 Jan 2019; Accepted: 17 Oct 2019.

Copyright: © 2019 Han, He and von Davier. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Mr. Zhuangzhuang Han, Teachers College, Columbia University, New York City, 10027, New York, United States,
Dr. Qiwei He, Educational Testing Service, Princeton, United States,
Dr. Matthias von Davier, National Board of Medical Examiners, Philadelphia, Pennsylvania, United States,