Download full text
(1.824Mb)
Citation Suggestion
Please use the following Persistent Identifier (PID) to cite this document:
https://nbn-resolving.org/urn:nbn:de:0168-ssoar-91819-9
Exports for your reference manager
A machine learning-based procedure for leveraging clickstream data to investigate early predictability of failure on interactive tasks
[journal article]
Abstract 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... view more
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.... view less
Keywords
learning; interaction; error; learning assignment; competence
Classification
Curriculum, Teaching, Didactics
Interactive, electronic Media
Free Keywords
interactive tasks; early prediction; extreme gradient boosting; time-stamped action sequences; clickstreams · PIAAC
Document language
English
Publication Year
2023
Page/Pages
p. 1392-1412
Journal
Behavior Research Methods, 55 (2023) 3
DOI
https://doi.org/10.3758/s13428-022-01844-1
ISSN
1554-3528
Status
Published Version; peer reviewed