Identifying patterns of students' performance on simulated inquiry tasks using PISA 2015 log‐file data.

Read the abstract here.

Picture of Ronny Scherer.

Professor Ronny Scherer, CEMO.

Photo: Shane Colvin, UiO

Previous research has demonstrated the potential of examining log‐file data from computer‐based assessments to understand student interactions with complex inquiry tasks. Rather than solely providing information about what has been achieved or the accuracy of student responses (product data), students' log files offer additional insights into how the responses were produced (process data). In this study, we examined students' log files to detect patterns of students' interactions with computer‐based assessment and to determine whether unique characteristics of these interactions emerge as distinct profiles of inquiry performance. Knowledge about the characteristics of these profiles can shed light on why some students are more successful at solving simulated inquiry tasks than others and how to support student understanding of scientific inquiry through computer‐based environments. We analyzed the Norwegian PISA 2015 log‐file data, science performance as well as background questionnaire (N = 1,222 students) by focusing on two inquiry tasks, which required scientific reasoning skills: coordinating the effects of multiple variables and coordinating theory and evidence. Using a mixture modeling approach, we identified three distinct profiles of students' inquiry performance: strategic, emergent, and disengaged. These profiles revealed different characteristics of students' exploration behavior, inquiry strategy, time‐on‐task, and item accuracy. Further analyses showed that students' assignment to these profiles varied according to their demographic characteristics (gender, socio‐economic status, and language at home), attitudes (enjoyment in science, self‐efficacy, and test anxiety), and science achievement. Although students' profiles on the two inquiry tasks were significantly related, we also found some variations in the proportion of students' transitions between profiles. Our study contributes to understanding how students interact with complex simulated inquiry tasks and showcases how log‐file data from PISA 2015 can aid this understanding.


Teig, Nani; Scherer, Ronny & Kjærnsli, Marit (2020). Identifying patterns of students' performance on simulated inquiry tasks using PISA 2015 log‐file data. Journal of Research in Science Teaching.  ISSN 0022-4308.  s 1- 30 . doi:

Published Aug. 18, 2020 3:18 PM - Last modified Aug. 18, 2020 3:25 PM