The relationship between differences in students’ computer and information literacy and response times: an analysis of IEA-ICILS data

<jats:title>Abstract</jats:title><jats:sec> <jats:title>Background</jats:title> <jats:p>Due to the increasing use of information and communication technology, computer-related skills are important for all students in order to participate in the digital age (Fraillon, J., Ainley, J., Schulz, W., Friedman, T. &amp; Duckworth, D. (2019). <jats:italic>Preparing for life in a digital world: IEA International Computer and Information Literacy Study 2018 International Report.</jats:italic> Amsterdam: International Association for the Evaluation of Educational Achievement (IEA). Retrieved from <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://www.iea.nl/sites/default/files/2019-11/ICILS%202019%20Digital%20final%2004112019.pdf">https://www.iea.nl/sites/default/files/2019-11/ICILS%202019%20Digital%20final%2004112019.pdf</jats:ext-link>). Educational systems play a key role in the mediation of these skills (Eickelmann. Second Handbook of Information Technology in Primary and Secondary Education. Cham: Springer, 2018). However, previous studies have shown differences in students’ computer and information literacy (CIL). Although various approaches have been used to explain these differences, process data, such as response times, have never been taken into consideration. Based on data from the IEA-study ICILS 2013 of the Czech Republic, Denmark and Germany, this secondary analysis examines to what extent response times can be used as an explanatory approach for differences in CIL also within different groups of students according to student background characteristics (gender, socioeconomic background and immigrant background).</jats:p> </jats:sec><jats:sec> <jats:title>Methods</jats:title> <jats:p>First, two processing profiles using a latent profile analysis (Oberski, D. (2016). Mixture Models: Latent Profile and Latent Class Analysis. In J. Robertson &amp; M. Kaptein (Eds.), <jats:italic>Modern Statistical Methods for HCI</jats:italic> (pp. 275–287). Switzerland: Springer. <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" xlink:href="https://doi.org/10.1007/978-3-319-26633-6">10.1007/978-3-319-26633-6</jats:ext-link>) based on response times are determined—a fast and a slow processing profile. To detect how these profiles are related to students’ CIL, also in conjunction with students’ background characteristics (socioeconomic and immigrant background), descriptive statistics are used.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>The results show that in the Czech Republic and Germany, students belonging to the fast processing profile have on average significantly higher CIL than students allocated to the slow processing profile. In Denmark, there are no significant differences. Concerning the student background characteristics in the Czech Republic, there are significant negative time-on-task effects for all groups except for students with an immigrant background and students with a high parental occupational status. There are no significant differences in Denmark. For Germany, a significant negative time-on-task effect can be found among girls. However, the other examined indicators for Germany are ambiguous.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusions</jats:title> <jats:p>The results show that process data can be used to explain differences in students’ CIL: In the Czech Republic and Germany, there is a correlation between response times and CIL (significant negative time-on-task effect). Further analysis should also consider other aspects of CIL (e.g. reading literacy). What becomes clear, however, is that when interpreting and explaining differences in competence, data should also be included that relates to the completion process during testing.</jats:p> </jats:sec>