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A machine learning-based procedure for leveraging clickstream data to investigate early predictability of failure on interactive tasks
Early detection of risk of failure on interactive tasks comes with great potential for better understanding how examinees differ in their initial behavior as well as for adaptively tailoring interactive tasks to examinees’ competence levels. Drawing on procedures originating in shopper intent predic...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10125949/ https://www.ncbi.nlm.nih.gov/pubmed/35650385 http://dx.doi.org/10.3758/s13428-022-01844-1 |
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author | Ulitzsch, Esther Ulitzsch, Vincent He, Qiwei Lüdtke, Oliver |
author_facet | Ulitzsch, Esther Ulitzsch, Vincent He, Qiwei Lüdtke, Oliver |
author_sort | Ulitzsch, Esther |
collection | PubMed |
description | Early detection of risk of failure on interactive tasks comes with great potential for better understanding how examinees differ in their initial behavior as well as for adaptively tailoring interactive tasks to examinees’ competence levels. Drawing on procedures originating in shopper intent prediction on e-commerce platforms, we introduce and showcase a machine learning-based procedure that leverages early-window clickstream data for systematically investigating early predictability of behavioral outcomes on interactive tasks. We derive features related to the occurrence, frequency, sequentiality, and timing of performed actions from early-window clickstreams and use extreme gradient boosting for classification. Multiple measures are suggested to evaluate the quality and utility of early predictions. The procedure is outlined by investigating early predictability of failure on two PIAAC 2012 Problem Solving in Technology Rich Environments (PSTRE) tasks. We investigated early windows of varying size in terms of time and in terms of actions. We achieved good prediction performance at stages where examinees had, on average, at least two thirds of their solution process ahead of them, and the vast majority of examinees who failed could potentially be detected to be at risk before completing the task. In-depth analyses revealed different features to be indicative of success and failure at different stages of the solution process, thereby highlighting the potential of the applied procedure for gaining a finer-grained understanding of the trajectories of behavioral patterns on interactive tasks. |
format | Online Article Text |
id | pubmed-10125949 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-101259492023-04-26 A machine learning-based procedure for leveraging clickstream data to investigate early predictability of failure on interactive tasks Ulitzsch, Esther Ulitzsch, Vincent He, Qiwei Lüdtke, Oliver Behav Res Methods Article Early detection of risk of failure on interactive tasks comes with great potential for better understanding how examinees differ in their initial behavior as well as for adaptively tailoring interactive tasks to examinees’ competence levels. Drawing on procedures originating in shopper intent prediction on e-commerce platforms, we introduce and showcase a machine learning-based procedure that leverages early-window clickstream data for systematically investigating early predictability of behavioral outcomes on interactive tasks. We derive features related to the occurrence, frequency, sequentiality, and timing of performed actions from early-window clickstreams and use extreme gradient boosting for classification. Multiple measures are suggested to evaluate the quality and utility of early predictions. The procedure is outlined by investigating early predictability of failure on two PIAAC 2012 Problem Solving in Technology Rich Environments (PSTRE) tasks. We investigated early windows of varying size in terms of time and in terms of actions. We achieved good prediction performance at stages where examinees had, on average, at least two thirds of their solution process ahead of them, and the vast majority of examinees who failed could potentially be detected to be at risk before completing the task. In-depth analyses revealed different features to be indicative of success and failure at different stages of the solution process, thereby highlighting the potential of the applied procedure for gaining a finer-grained understanding of the trajectories of behavioral patterns on interactive tasks. Springer US 2022-06-01 2023 /pmc/articles/PMC10125949/ /pubmed/35650385 http://dx.doi.org/10.3758/s13428-022-01844-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ulitzsch, Esther Ulitzsch, Vincent He, Qiwei Lüdtke, Oliver A machine learning-based procedure for leveraging clickstream data to investigate early predictability of failure on interactive tasks |
title | A machine learning-based procedure for leveraging clickstream data to investigate early predictability of failure on interactive tasks |
title_full | A machine learning-based procedure for leveraging clickstream data to investigate early predictability of failure on interactive tasks |
title_fullStr | A machine learning-based procedure for leveraging clickstream data to investigate early predictability of failure on interactive tasks |
title_full_unstemmed | A machine learning-based procedure for leveraging clickstream data to investigate early predictability of failure on interactive tasks |
title_short | A machine learning-based procedure for leveraging clickstream data to investigate early predictability of failure on interactive tasks |
title_sort | machine learning-based procedure for leveraging clickstream data to investigate early predictability of failure on interactive tasks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10125949/ https://www.ncbi.nlm.nih.gov/pubmed/35650385 http://dx.doi.org/10.3758/s13428-022-01844-1 |
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