The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Analyzing Process Data from Problem-Solving Items with N-Grams: Insights from a Computer-Based Large-Scale Assessment
Abstract
This chapter draws on process data recorded in a computer-based large-scale program, the Programme for International Assessment of Adult Competencies (PIAAC), to address how sequences of actions recorded in problem-solving tasks are related to task performance. The purpose of this study is twofold: first, to extract and detect robust sequential action patterns that are associated with success or failure on a problem-solving item, and second, to compare the extracted sequence patterns among selected countries. Motivated by the methodologies of natural language processing and text mining, we utilized feature selection models in analyzing the process data at a variety of aggregate levels and evaluated the different methodologies in terms of predictive power of the evidence extracted from process data. It was found that action sequence patterns significantly differed by performance groups and were consistent across countries. This study also demonstrated that the process data were useful in detecting missing data and potential mistakes in item development.
Related Content
|
Agah Tugrul Korucu, Handan Atun.
© 2017.
18 pages.
|
|
Larisa Olesova, Jieun Lim.
© 2017.
21 pages.
|
|
JoAnne Dalton Scott.
© 2017.
20 pages.
|
|
Geraldine E Stirtz.
© 2017.
25 pages.
|
|
Enilda Romero-Hall, Cristiane Rocha Vicentini.
© 2017.
21 pages.
|
|
Beth Allred Oyarzun, Sheri Anderson Conklin, Daisyane Barreto.
© 2017.
21 pages.
|
|
Nikolina Tsvetkova, Albena Antonova, Plama Hristova.
© 2017.
24 pages.
|
|
|