Background

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. & Duckworth, D. (2019). Preparing for life in a digital world: IEA International Computer and Information Literacy Study 2018 International Report. Amsterdam: International Association for the Evaluation of Educational Achievement (IEA). Retrieved from https://www.iea.nl/sites/default/files/2019-11/ICILS%202019%20Digital%20final%2004112019.pdf). 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).

Methods

First, two processing profiles using a latent profile analysis (Oberski, D. (2016). Mixture Models: Latent Profile and Latent Class Analysis. In J. Robertson & M. Kaptein (Eds.), Modern Statistical Methods for HCI (pp. 275–287). Switzerland: Springer. https://doi.org/10.1007/978-3-319-26633-6) 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.

Results

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.

Conclusions

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.

中文翻译：

---

学生计算机和信息素养差异与响应时间之间的关系：IEA-ICILS数据分析

背景

由于越来越多地使用信息和通信技术，因此与计算机相关的技能对于所有学生来说都很重要，以便参与数字时代（Fraillon，J.，Ainley，J.，Schulz，W.，Friedman，T.＆Duckworth ，D.（2019）。为数字世界中的生活做准备：IEA国际计算机和信息素养研究2018年国际报告。阿姆斯特丹：国际教育成就评估协会（IEA）。取自https://www.iea.nl/sites/default/files/2019-11/ICILS%202019%20Digital%20final%2004112019.pdf）。教育系统在这些技能的中介中起着关键作用（Eickelmann。中小学教育信息技术第二手册。Cham：Springer，2018）。但是，以前的研究表明学生的计算机和信息素养（CIL）存在差异。尽管已使用各种方法来解释这些差异，但从未考虑过过程数据（例如响应时间）。根据来自IEA研究的捷克共和国，丹麦和德国的ICILS 2013的数据，

方法

首先，使用潜在轮廓分析（Oberski，D.（2016）。混合模型：潜在轮廓和潜在类别分析）处理两个轮廓。在J.Robertson和M.Kaptein（编辑）的《 HCI的现代统计方法》（pp。 275–287），瑞士：施普林格（https://doi.org/10.1007/978-3-319-26633-6），根据响应时间来确定-快速和缓慢的处理过程。为了检测这些概况与学生的CIL有何关系，还结合学生的背景特征（社会经济和移民背景），使用描述性统计数据。

结果

结果表明，在捷克共和国和德国，属于快速处理配置文件的学生的CIL平均高于分配给慢速处理配置文件的学生。在丹麦，没有显着差异。关于捷克共和国的学生背景特征，除具有移民背景的学生和具有较高父母职业地位的学生外，所有群体的工作时间都有很大的负面影响。丹麦没有显着差异。对于德国，女孩中的工作时间负面影响很大。但是，针对德国检查的其他指标尚不明确。

结论

结果表明，过程数据可用于解释学生的CIL差异：在捷克共和国和德国，响应时间与CIL（显着的负任务时间效应）之间存在相关性。进一步的分析还应考虑CIL的其他方面（例如阅读素养）。然而，显而易见的是，在解释和解释能力差异时，还应包括与测试过程中完成过程有关的数据。