# Fostering Students’ Resilience. Analyses Towards Factors of Individual Resilience in the Computer and Information Literacy Domain

Kerstin Drossel, Birgit Eickelmann, Mario Vennemann, Nadine Fröhlich

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Kerstin Drossel, Birgit Eickelmann, Mario Vennemann, Nadine Fröhlich. Fostering Students’ Resilience. Analyses Towards Factors of Individual Resilience in the Computer and Information Literacy Domain. IFIP World Conference on Computers in Education (WCCE), Aug 2022, Hiroshima, Japan. pp.39-50, 10.1007/978-3-031-43393-1_5 . hal-05533320

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# Fostering Students' Resilience. Analyses Towards Factors of Individual Resilience in the Computer and Information Literacy Domain

Kerstin Drossel✉, Birgit Eickelmann✉, Mario Vennemann✉, and Nadine Fröhlich✉

Paderborn University, Technologiepark 21, 33100 Paderborn, Germany
{kdrossel,birgit.eickelmann,mario.vennemann,nadine.froehlich}@mail.uni-paderborn.de

**Abstract.** In this contribution the resilience of students in the *computer and information literacy* (CIL) domain is focused. In this context, research towards individual student resilience is of relevance in order to examine characteristics from the student level that can be used as setscrews by educators and other educational stakeholders to minimize or overcome social issues in the CIL domain. Taking advantage of the representative cross-sections of students from the *International Computer and Information Literacy Study 2018* (ICILS 2018), the question of the prevalence of resilient students (research question 1), differences between educational systems in international comparison (research question 2), and students’ related antecedents and process factors from the ICILS 2018 contextual model (research question 3) have been focused via using a logistic regression approach. The sample consisted of 46,561 students aged 14 from 14 countries. Cross country analyses revealed that student’s sex and their cultural capital are the strongest predictors for resilience in the CIL domain. However, including family’s process characteristics shows that students’ self-efficacy toward the use of *information and communication technology* (ICT), their use of ICT for information-related activities itself and the use of ICT for basic and advanced purposes have been identified as significantly related to student resilience.

**Keywords:** Student Resilience · Computer and Information Literacy · Logistic Regression · UneS-ICILS 2018

## 1 Introduction and Theoretical Framework

In this part of the contribution, an introduction (Sect. 1.1) is complemented by an allocation of the research presented in this paper onto a relevant theoretical framework (Sect. 1.2). Further, insights into already existing research towards resilience in the context of the competent use of ICT (Sect. 1.3) are given and corresponding research questions are developed (Sect. 1.4).
## 1.1 Introduction

The use of information and communication technology (ICT) has become an important part of our lives, which not only contributes to personal private life but also has a tremendous relevance for professional carriers in the present and in the future. In the light of on-going developments toward knowledge and information societies, the relevance of being able to use ICT in a competent manner is increasing on a global scale [1]. In this context, international organizations and initiatives started to conduct empirical studies in order to evaluate and monitor students' achievement in that domain [2]. Aside from providing researchers and educational stakeholders with recent information about the functioning of their educational systems, mentioned studies also aim on indications of how educational systems could be improved on different levels and what prerequisites can support (or hinder) student achievement in the aforementioned domain. In this context, it has been shown that the competent use of ICT is affected by partly substantial social inequality; often referred to as the so-called digital divide [3].

Having a focus on the socio-economic status (SES), research has shown that students aged about 14 years old with a high SES significantly outperform those students, whose families are regarded to have a low socio-economic profile [2, 4]. However, laying a focus on the SES profile of entire schools, researchers were also able to identify schools, where exactly the opposite pattern emerges: Here, at schools that have on average a challenging SES-related student body composition (low SES profile) students showed a high level of ICT-related proficiency. These schools were regarded as being organizationally resilient and school factors contributing to the resilience of entire schools (such as principal's leadership or teachers' attitudes towards teaching and learning with ICT) were stressed [5].

Although the concept of resilience stems from the field of psychology, the topic of resilience has not yet been focused with regard to the question of which student characteristics make students resilient in their proficiency of using computers and other digital media in a competent manner. For other educational domains (such as reading, mathematics or science), research has already examined the phenomenon of students' individual resilience and was able to show that resilient students in those educational domains share common characteristics that could be used by teachers or other educational stakeholders as a setscrew to overcome educational inequalities [6]. Acknowledging the increasing relevance of the use of ICT for students' future careers and private life, the question of which individual characteristics contribute to or hinder students' resilience in the use of ICT has only been addressed by identifying resilient schools as a whole.

Therefore, this contribution aims to identify resilient students in the context of competent ICT use and further aims to examine student characteristics that contribute to or hinder individual resilience by applying secondary analyses to the latest database from international large-scale assessments in this domain: The *International Computer and Information Literacy Study* (ICILS 2018) of the *International Association for the Evaluation of Educational Achievement* (IEA). Because of their fully computer-based assessment methodology, representative cross-sections from multiple educational systems and benchmarking participants and because of a rich body of student background information gathered by the study, this database can be regarded highly relevant for addressing previously mentioned research questions.
However, the research presented in this contribution is the first analytical step in a larger project that aims to generate deeper insights into teaching and learning with ICT at those schools who were identified as being organizationally resilient in the aforementioned study: *Unexpected successful schools in the digital age* (UneS-ICILS 2018). Due to the quantitative methodological approach of ICILS 2018, findings towards the educational use of ICT and its relation to students' achievement can only be drawn from the *frequency* of ICT use for teaching and learning. Hence, qualitative insights into what exactly students and teachers do with ICT to support achievement in the CIL domain is not a desiderate of ICILS 2018 and is investigated with the national follow-up study UneS-ICILS 2018 in Germany. The study is supported by the German Federal Ministry of Education and Research for the duration of about three years (10/2020 to 09/2023) and utilizes a qualitative methodology (lesson observation, interviews with agents of teaching and learning with ICT, document analyses) to answer the overarching question on *what* is done with ICT in those schools that showed to be somehow special with regard to their challenging SES-related student body composition and high average CIL.

Because of the fact that individual characteristics can be regarded as tremendously important for student learning in the CIL domain, a first step of the UneS project is to identify resilient students in ICILS 2018 and to conduct secondary analyses in order to evaluate covariates of students’ resilience in the domain of CIL and to prepare an interpretative framework for those qualitative analyses previously mentioned.

## 1.2 Theoretical Framework

In order to theoretically allocate the research conducted in this contribution, the contextual framework of ICILS 2018 is used since it acknowledges for the fact that student learning is influenced not only by prerequisites of the family but also by factors on multiple levels of education (e.g., school and system level). Further, ICILS 2018 is the first international large-scale assessment which assesses the *computer and information literacy* (CIL) of eight graders utilizing a fully computer-based approach for outcome measurement in this domain. Both aspects lead the authors to believe that the contextual model (and the corresponding outcome) is suitable to answer previously mentioned research questions.

The contextual model in ICILS 2018 differentiates four educational levels and emphasizes so-called *antecedents and processes* as structural elements on each level [2, 7]. Antecedents are external factors that are regarded as central prerequisites for CIL learning [8]. They are contextual factors that are not directly influenced by learning-process variables or outcomes. Processes are in contrast those factors that directly influence CIL learning.

The *individual (student) level* comprises individual characteristics of the student, their learning processes, and their performance in CIL. While students’ age, their gender and aspirations are regarded as antecedents of learning in the CIL domain, their computer-related self-efficacy, the use of information and communication technologies (ICT) or students’ attitudes towards learning with ICT are modelled as processes that are regarded as being important to student learning in the CIL domain.

The *level of the home environment* comprises students’ background characteristics, especially in terms of the learning processes associated with family, home, and other
immediate out-of-schools contexts. Examples for home environment antecedents are the provision with and access to ICT equipment at home. At this point in the model, the social background, and the migration background (immigration background and family language) of the students and their families are also located as individual student characteristics. In these family and extracurricular contexts, the theoretical model of the study also identifies the use of digital media and the acquisition of knowledge and skills with and about digital media as process factors.

However, although there are other levels conceptualized in the ICILS 2018 contextual framework such as the school and classroom level (e.g. ICT equipment of the schools, digitalization-related cooperation or measures to promote digital skills for teachers) or the level of the wider community (remoteness and access to internet facilities), these are not directly relevant to the research presented in this contribution and interested readers are referred to the Assessment Framework of ICILS 2018 [7].

With regard to the research presented in this contribution, it is important that from a theoretical point of view, all levels refer to each other and student achievement is related to student variables as well as to other variables, e.g., on the school or wider community level.

## 1.3 Research Findings towards the Phenomenon of Resilience in the CIL Domain

As reported in the previous paragraphs, the phenomenon of educational resilience in the CIL domain has only been explored with whole schools as a focus. Content wise this means that researchers were interested in the prevalence of so called organizationally resilient schools and their common characteristics. Here, Eickelmann, Gerick and Vennemann [9] were able to show that the number of resilient schools among those countries and regions participating in the ICILS 2013 study is subject to considerable variation. On average 7.7 percent of the schools focused could be regarded as being organizationally resilient. While for example in Poland about a fifth of the participating schools could be identified as resilient the lowest proportion of resilient schools was observed in the Czech Republic (ibid.). However, the authors were not only interested in the sheer prevalence of resilient schools but also focused on what school characteristics may foster (or hinder) organizationally resilience in the CIL domain. As a result, authors conclude that principal's school leadership and especially a high priority on developing a shared vision of using ICT in teaching and learning among teachers can be regarded as a necessary prerequisite for the resilience of whole schools in the CIL domain.

Similar findings were also part of the latest cycle of the ICILS 2018 study. Here researchers also focused on organizationally resilient schools and conducted in depth analyses in order to identify shared characteristics of resilient schools. Results towards the prevalence of organizationally resilient schools in the second ICILS cycle showed now that about a twentieth of the focused schools in 14 participating countries and regions could be regarded as “overcoming the digital divide” [5] and that teachers’ positive attitudes towards ICT and their high self-efficacy towards teaching and learning with ICT can be regarded as the “motor” (ibid., p. 16) for organizationally resilience in the CIL domain. Although both cycles cannot be directly compared due to a different number of countries participating the studies these results show that school specific
resilience is a dynamic phenomenon and that there are also countries where none of the schools could be identified as being resilient.

In contrast to the CIL domain in other educational domains such as reading [10] or mathematics [11] the phenomenon of resilience including its predictors were focused on the student level and researchers were interested in factors that contribute or hinder achievement. In this context, positive attitudes towards the educational domain have been found to be the strongest predictor for student resilience. However, in the CIL domain research towards student resilience is not readily available and will therefore be focused in this contribution.

## 1.4 Research Questions

As proposed, empirical research findings toward factors contributing to student resilience in the CIL domain are not available. Therefore, in this contribution, the following three research objectives will be addressed:

1. What is the proportion of students that show high achievement in the CIL domain and have a low SES (resilient students)?

2. Are differences observable between educational systems when an international comparison is focused?

3. Which student-level factors (antecedents and process variables) are related to student resilience in the CIL domain?

# 2 Data Sources, Methods, and Statistical Techniques

In this section of the contribution the technical prerequisites and constraints of the analyses are reported. Hence, the subsequent paragraph focus on the data source of ICILS 2018 (Sect. 2.1), the approach utilized in order to identify resilient students in the CIL domain (Sect. 2.2), on the method of logistic regression to determine the relation of predictor variables and the probability of being resilient (Sect. 2.3) and on those instruments and materials used (Sect. 2.4).

## 2.1 Data Source: Representative Samples from the ICILS 2018 Database

In order to answer the previously stated research questions and following the methodological rationale of UneS-ICILS 2018 secondary analyses of the ICILS 2018 database were used. In ICILS 2018 – as well as in other IEA studies – a multi-staged clustered sampling approach was administered [7]. This means that in a first step, a representative randomly selected cross-section of schools was sampled within the respective educational system or benchmark participants. In a second step, students of grade eight are sampled within the sampled schools [12]. With this approach, some sources of statistical uncertainty can be found but can be addressed by weighting and jackknifing [13]. Both are considered in this paper in order to ensure proper estimates. For this contribution, all students of the ICILS 2018 study (N = 46,561) and schools (N = 2,226) from 14 educational systems and benchmarking participants were included.
## 2.2 Identifying Resilient Students

In order to identify the resilient students in the CIL domain, their scores in the computer-based CIL test and information regarding their SES were taken into account. The Highest International Socio-Economic Index of Occupational Status (HISEI; [14]) was divided by percentile splits resulting in variable identifying students with low, medium, and high SES. The same procedure has been conducted to students' test scores in the CIL domain (5 plausible values) resulting in a variable that distinguishes a low, a medium, and a high performing group of students. In this contribution, a student is regarded as being resilient in the CIL domain when she or he originates from the lowest 33.3 percent of the HISEI spectrum of their educational system and simultaneously belongs to the 33.3 percent of highest scoring students in their country. This procedure is in line with already existing research of resilience in other educational domains such as reading, mathematics and science [6].

## 2.3 Statistical Techniques Explaining the Probability of being Resilient in the CIL Domain: Logistic Regression

In educational research and especially in educational large-scale assessments mostly so-called ordinary least square (OLS) linear regression techniques are used to determine the relation between an outcome variable and one or more predictor variables [15]. This method, however, is only valid when the outcome variable is continuous and in educational research other non-continuous nominal variables are also of interest. Here, for example it could be of researcher's interest what influences the likelihood of being allocated to one group or another. In this case the outcome variable has a binary nominal scale and corresponding analyses have to acknowledge this. Therefore, in this contribution the determinants of being resilient in the CIL domain are analyzed using a so-called logistic regression approach which accounts for the binary dependent variable distinguishing between non-resilient (0) and resilient students (1) in the CIL domain. The underlying model of the logistic regression approach utilized in this contribution could be summarized as follows:

$$P(Y = 1 | X = x_i) = \frac{1}{1 + \exp(-x_i\beta)}$$

Another difference compared to linear regression analysis is the method of obtaining proper estimates for the best fitting regression curve since logistic regression models use the iterative algorithm of Maximum-Likelihood-Estimation (MLE, [16]). Further, the model-fit of logistic regression models is also based on R² coefficients. But unfortunately, they cannot be interpreted as R² from OLS regression approaches since these determine the proportion of the explained variance in the total variance of the dependent variable. The R² in the context of logistic regressions are not based on variance decomposition or the ratio of two variances, but on the ratio of two probabilities, the likelihood of a Null-model and that of the fully specified model. These often results in lower R² coefficients compared to OLS regression approaches.

Further, in the case of analyzing data from large-scale assessments further statistical pitfalls have to be taken into account. Because of the complex sampling approach in
ICILS 2018 the regression analyses have to account for weighting and the so-called Jackknife-Repeated Replication Technique (JRR; [13]) in order to correct standard errors of respective estimates for the clustered sampling in ICILS. Further, in ICILS 2018 – as in other studies of the IEA – student achievement in the CIL domain is operationalized by 5 plausible values. This means, that every regression model presented in this paper was calculated with each of the plausible values and have been averaged afterwards [17].

## 2.4 Instruments and Materials

In contrast to the dependent variable of the subsequent analysis, the variables used as predictors of educational resilience in the CIL domain could either have ordinal and continuous scales and were selected according to the ICILS theoretical framework (cf. Section 1.2). Hence, students' sex, an indicator of their family's cultural capital (both regarded to be antecedents), one indicator of students' self-efficacy towards using ICT and three dimensions of their use of ICT in teaching and learning with different focuses were selected for the analyses in this paper. Table 1 summarizes those predictor variables and gives example questions in case an internationally scaled index [12] has been included.

<table><caption>**Table 1.** Summary of predictor variables used in the logistic regression analyses of this contribution and examples of corresponding item wordings</caption><thead><tr><td>Variable</td><td>Description</td><td>Item example wording</td></tr></thead><tbody><tr><td>S_SEX<sup>1</sup></td><td>Student's sex</td><td>Are you a girl or a boy?</td></tr><tr><td>S BOOKS<sup>2</sup></td><td>Student family's cultural capital</td><td>About how many books are there in your home?</td></tr><tr><td>S_BASEFF<sup>3</sup></td><td>ICT self-efficacy regarding the use of general applications</td><td>How well can you do each of these tasks when using ICT? – Write or edit text for a school assignment</td></tr><tr><td>S_USEINF<sup>3</sup></td><td>Use of ICT for exchanging information</td><td>How often do you use ICT to do each of the following communication activities? – Ask questions on forums or question and answer websites</td></tr><tr><td>S ADVTOOL<sup>3</sup></td><td>Use of specialist applications for activities</td><td>When studying throughout this school year, how often did you use the following tools during class? – Multimedia production tools</td></tr><tr><td>S_BASTOOL<sup>3</sup></td><td>Use of general applications for activities</td><td>When studying throughout this school year, how often did you use the following tools during class? – Word-processing software</td></tr></tbody></table>

<footnote n="1">¹ 0 – girl; 1 – boy</footnote>

<footnote n="2">² 0 – maximum of 100 books; 1 – more than 100 books</footnote>

<footnote n="3">³ Internationally scaled index (M = 50; SD = 10; cf. [12])</footnote>
# 3 Results, Summary, and Conclusion

In the subsequent paragraphs of this paper the results towards the research questions are reported. While Sect. 3.1 focuses on the identification of resilient eight graders Sect. 3.2 is subdivided into cross-country analyses (Sect. 3.2.1) and within-country analyses (Sect. 3.2.2).

## 3.1 Results Towards the Proportion of Resilient Students in the CIL Domain
(Research Question 1)

As described in the previous section of this paper, one main aim of research question 1 is to identify resilient students in the CIL domain by considering their SES (HISEI) and CIL test scores in the participating countries (cf. Section 2.1). The results show that there are partly tremendous differences in the proportion of resilient students and that three country groups can be distinguished: Those countries where the proportion of resilient students in the CIL domain is significantly higher than the ICILS 2018 average, countries where the mentioned proportion of resilient students does not significantly differ from the ICILS 2018 average and countries where the proportion of resilient students is significantly lower than the ICILS 2018 average (Table 2). In this context, it could be observed that in the Republic of Korea (33.1%), in Kazakhstan (28.2%) and in Finland (26.3%) the amount of low SES students with high test scores in the CIL achievement test (so-called resilient students) is significantly higher than in all other participating countries of ICILS 2018. However, in most countries the focused proportion does not significantly differ from the ICILS 2018 average (23.3%). In this group of countries Italy (25.7%), Portugal (25.5%), Denmark (25.3%), Russian Federation (24.4%), Germany (24.3%), Uruguay (24.2%), United States (23.9%), North-Rhine Westphalia (21.8%), and France (21.2%) can be found. In Chile (17.1%) and in Luxembourg (5.7%), the proportion of resilient students is significantly lower than the respective ICILS average.

## 3.2 Results Towards Determinants of Students’ Resilience in the CIL Domain
(Research Question 2)

As stated in the previous section, logistic regression is used in order to identify antecedent and process factors that are related to the phenomenon of eight graders resilience in the domain of CIL. Table 3 summarizes the results for two different regression models. While in model 1 only the *antecedent* variables are included model 2 additionally accounts for *those process factors* regarded as relevant for student learning in the contextual model of ICILS 2018 (cf. Section 1.2). As can be obtained from model 1 in Table 3 students’ sex ($B = 0.34; p < .05$) and students’ family’s cultural capital ($B = 0.36; p < .05$) show a positive relation to the phenomenon of resilience in the CIL domain when other process variables are not controlled for. Without controlling for any other variable that means that boys and students with more than hundred books in their home (student families with a high cultural capital) have a better chance to be resilient than girls or students whose families possess a maximum of 100 books. Interestingly, when process factors from the student- and the home environment-level are included in the logistic model, the relation between students’ sex ($B = -0.27; p < .05$) and their family’s cultural capital ($B$
<table><caption>**Table 2.** Proportion of resilient students in those educational systems and benchmarking participants of ICILS 2018 (N = 14)</caption><thead><tr><th>Participating country</th><th>%</th><th>(SE)</th><th></th></tr></thead><tbody><tr><td>Republic of Korea</td><td>33.1</td><td>(1.7)</td><td>▲</td></tr><tr><td>Kazakhstan</td><td>28.2</td><td>(2.1)</td><td>▲</td></tr><tr><td>Finland</td><td>26.3</td><td>(1.9)</td><td>▲</td></tr><tr><td>Italy</td><td>25.7</td><td>(1.7)</td><td>■</td></tr><tr><td>Portugal</td><td>25.5</td><td>(2.0)</td><td>■</td></tr><tr><td>Denmark</td><td>25.3</td><td>(1.6)</td><td>■</td></tr><tr><td>Russian Federation</td><td>24.4</td><td>(2.5)</td><td>■</td></tr><tr><td>Germany</td><td>24.3</td><td>(2.4)</td><td>■</td></tr><tr><td>Uruguay</td><td>24.2</td><td>(1.9)</td><td>■</td></tr><tr><td>United States</td><td>23.9</td><td>(0.9)</td><td>■</td></tr><tr><td><strong>ICILS 2018 Average</strong></td><td><strong>23.3</strong></td><td><strong>(0.5)</strong></td><td>■</td></tr><tr><td>North-Rhine Westphalia</td><td>21.8</td><td>(1.8)</td><td>■</td></tr><tr><td>France</td><td>21.2</td><td>(1.3)</td><td>■</td></tr><tr><td>Chile</td><td>17.1</td><td>(1.5)</td><td>▼</td></tr><tr><td>Luxembourg</td><td>5.7</td><td>(2.0)</td><td>▼</td></tr></tbody></table>

▲ Proportion of resilient students from the lower third of the HISEI spectrum is significantly larger than the ICILS 2018 average (p < .05).

■ Proportion of resilient students from the lower third of the HISEI spectrum is not significantly larger than the ICILS 2018 average

▼ Proportion of resilient students from the lower third of the HISEI spectrum is significantly lower than the ICILS 2018 average (p < .05).

= -0.52; p < .05) are now significantly and negatively related to the probability of being a resilient student in the CIL domain. Further, students' self-efficacy regarding the use of basic ICT applications (0.04; p < .05) and the use of those basic tools (0.03; p < .05) are the only constructs that positively related to student resilience in the CIL domain.
<table><caption>**Table 3.** Results of the logistic regression analysis towards student resilience in the CIL domain in all participating countries and benchmark participants of ICILS 2018 (N = 14)</caption><thead><tr><th rowspan="2"></th><th colspan="4">Model 1</th><th colspan="4">Model 2</th></tr><tr><th>B</th><th>(SE)</th><th>Exp</th><th>(SE)</th><th>B</th><th>(SE)</th><th>Exp</th><th>(SE)</th></tr></thead><tbody><tr><td colspan="9"><em>Antecedent factors</em></td></tr><tr><td>S_SEX</td><td>0.34**</td><td>0.08</td><td>1.53</td><td>0.37</td><td>-0.27**</td><td>0.07</td><td>0.81</td><td>0.05</td></tr><tr><td>S BOOKS<sup>1</sup></td><td>0.36**</td><td>0.08</td><td>1.59</td><td>0.15</td><td>-0.52**</td><td>0.09</td><td>0.65</td><td>0.09</td></tr><tr><td colspan="9"><em>Process factors</em></td></tr><tr><td>S_BASEFF</td><td></td><td></td><td></td><td></td><td>0.04**</td><td>0.00</td><td>1.04</td><td>0.00</td></tr><tr><td>S_USEINF</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-0.01**</td><td>0.00</td><td>0.99</td><td>0.00</td></tr><tr><td>S_ADVTOOL</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-0.03**</td><td>0.00</td><td>0.97</td><td>0.00</td></tr><tr><td>S_BASTOOL</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.03**</td><td>0.00</td><td>1.03</td><td>0.00</td></tr><tr><td>Intercept</td><td>-2.93**</td><td>0.10</td><td>0.06</td><td>0.00</td><td>-3.63**</td><td>0.49</td><td>0.06</td><td>0.04</td></tr><tr><td>Nagelkerke R<sup>2</sup></td><td>.02</td><td></td><td></td><td></td><td>.06</td><td></td><td></td><td></td></tr></tbody></table>

Notes: ¹ 0 – girl; 1 – boy. ² 0 – maximum of 100 books; 1 – more than 100 books. ³ Internationally scaled index (M = 50; SD = 10) ** p < .05

# 4 Summary and Conclusion

In this contribution, the resilience of *students* in the CIL domain is focused since analyses towards student resilience and its covariates can be regarded as the first analytical step of the UneS-ICILS 2018 project in order to establish an interpretative framework for the qualitative in-depth analyses. In this context research towards individual student resilience is of relevance in order to examine characteristics from the student level that can be used as setscrews by educators and other educational stakeholder to minimize or overcome social in the CIL domain. Taking advantage of the representative cross-sections of students from the ICILS 2018 study the question of the prevalence of resilient students (research question 1) and their related antecedents and process factors from the ICILS 2018 contextual model (research question 2) have been focused via using a logistic regression approach.

Analyses toward the prevalence of resilient students in the CIL domain show that there is considerable variation in the proportion of resilient students among those 14 countries and benchmark participants focused in this paper. While for example in the Republic of Korea, Kazakhstan, Finland, and Italy more than a quarter of sampled eight graders were identified as resilient, in Chile and Luxembourg the smallest proportion of resilient students was observed. The rest of the ICILS-countries and the ICILS 2018 average can be located in between of those both poles. In order to identify characteristics that foster or hinder students of being educationally resilient in the CIL domain (in terms of those antecedents and process variables distinguished by the ICILS 2018 contextual model) logistic regression analyses have been utilized. Cross country analyses revealed that students' sex and their cultural capital are the strongest predictors for resilience in the CIL domain. However, including family's process characteristics shows that students'
*self-efficacy* toward the use of ICT, their *use of ICT* for information-related activities itself and the use of ICT for *basic and advanced purposes* have been identified as significantly related to student resilience.

Although student background characteristics are quite stable and cannot be altered by teacher initiatives or school measures, results towards student antecedents have practical relevance since they imply the implementation of supporting measures for disadvantaged students in respective educational systems (e.g., for students with low cultural capital). Further, from the authors’ perspective initiatives and measures supporting students’ resilience in the CIL domain should influence those process characteristics mentioned in the previous paragraph. For example, teachers could support students’ use of ICT for different purposes by applying individual tasks (for example in the context of homework) that need the use of ICT to be solved. However, this would also be a chance to foster students’ self-efficacy in using ICT since this factor has also shown to be positively related to resilience in the CIL domain.

However, having in mind the cross-sectional nature of ICILS 2018 and the quantitative approach of the study some limitations also apply. First, with the cross-sectional design of ICILS only interrelations between students’ resilience and predictor variables can be revealed. For causal inference, however, longitudinal studies are strongly recommended. Secondly, the study assesses e.g., the use of ICT for teaching and learning based on quantitative methodology. This means that the study can draw inference on quantitative measures but what is done in detail with ICT in teaching and learning is still a blind spot of the study. Hence, those qualitative approaches applied by the follow-up study UneS-ICILS 2018 are regarded to initiate new insights into the topic of students’ resilience in the CIL domain and in its consequence reveal further setscrews for educators to foster students CIL achievement and in turn encourage creative learning for all students.

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