How do gender, Internet activity and learning beliefs predict sixth-grade students’ self-efficacy beliefs in and attitudes towards online inquiry?

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ How do gender, Internet activity and learning beliefs predict sixth-grade students’ selfefficacy beliefs in and attitudes towards online inquiry? © The Author(s) 2021 Published version Sormunen, Eero; Erdmann, Norbert; Otieno, Suzanne C.S.A.; Mikkilä-Erdmann, Mirjamaija; Laakkonen, Eero; Mikkonen, Teemu; Hossain, Md Arman; González- Ibáñez, Roberto; Quintanilla-Gatica, Mario; Leppänen, Paavo H.T.; Vauras, Marja Sormunen, E., Erdmann, N., Otieno, S. C., Mikkilä-Erdmann, M., Laakkonen, E., Mikkonen, T., Hossain, M. A., González-Ibáñez, R., Quintanilla-Gatica, M., Leppänen, P. H., & Vauras, M. (2023). How do gender, Internet activity and learning beliefs predict sixth-grade students’ selfefficacy beliefs in and attitudes towards online inquiry?. Journal of Information Science, 49(5), 1246-1261. https://doi.org/10.1177/01655515211043708 2023 Research Paper Journal of Information Science 1–16 ÓThe Author(s) 2021 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/01655515211043708 journals.sagepub.com/home/jis How do gender, Internet activity and learning beliefs predict sixth-grade students’ self-efficacy beliefs in and attitudes towards online inquiry? Eero Sormunen Faculty of Information Technology and Communication Sciences, Tampere University, Finland Norbert Erdmann Department of Teacher Education, University of Turku, Finland Suzanne CSA Otieno Department of Psychology, Faculty of Education and Psychology, University of Jyva ¨skyla ¨, Finland Mirjamaija Mikkila ¨-Erdmann Department of Teacher Education, University of Turku, Finland Eero Laakkonen Department of Teacher Education, University of Turku, Finland Teemu Mikkonen Faculty of Information Technology and Communication Sciences, Tampere University, Finland Md Arman Hossain Faculty of Information Technology and Communication Sciences, Tampere University, Finland Roberto Gonza ´lez-Iba ´n ˜ez Departamento de Ingenierı ´a Informa ´tica, Universidad de Santiago de Chile, Chile Mario Quintanilla-Gatica Facultad de Educacio ´n, Pontificia Universidad Cato ´lica de Chile, Chile Paavo HT Leppa ¨nen Department of Psychology, Faculty of Education and Psychology, University of Jyva ¨skyla ¨, Finland Marja Vauras Department of Teacher Education, University of Turku, Finland Corresponding author: Eero Sormunen, Faculty of Information Technology and Communication Sciences, Tampere University, 33014 Tampere, Finland. Email: [email protected] Abstract Today’s students search, evaluate and actively use Web information in their school assignments, that is, they conduct an online inquiry. This current survey study addresses sixth-grade students’ self-efficacy beliefs in and attitudes towards online inquiry, and to what extent free-time and school-related Internet activity, gender and learning beliefs explain these. The questionnaire was administered in 10 schools to 340 sixth-graders in Finland. Exploratory and confirmatory factor analyses revealed three elements of self-efficacy beliefs: self-efficacy in Web searching, the evaluation of sources and synthesising information. Furthermore, attitudes towards online inquiry loaded into two factors: a positive and a negative attitude towards online inquiry. A structural equation model was used to analyse the effects of the explanatory variables on the factors. The results of this work suggest that gender and free-time Internet use predict most sixth-graders’ self-efficacy beliefs in and attitudes towards online inquiry. Keywords Information and reference skills; online searching; primary school students; sixth-grade students; World Wide Web 1. Introduction The purpose of this study is to investigate six-graders’ self-efficacy beliefs and attitudes towards online inquiry. By online inquiry, we mean the process of searching for information on the Internet, evaluating the reliability of retrieved sources and writing a synthesis based on the sources. Teachers are increasingly assigning online inquiry projects in schools [1,2]. Current European curricula emphasise online inquiry as transversal, cross-subject competence even in primary education [3]. The Internet has also become an important source of information for primary school children who are often expected to find more than one relevant source and synthesise the main ideas of the sources to solve a school task [4,5]. Compared with traditional inquiry-based learning [6], online inquiry requires an additional set of skills. Kuhlthau’s [7] seminal research on the information search process (ISP) characterises the affective, cognitive and behavioural uncertainty faced by students in a genuine inquiry process. Early research into online inquiry showed that students need skills to plan their online inquiry process, monitor the information obtained and synthesise that information [8,9]. However, recent research convincingly reveals that students’ online inquiry skills are insufficient [10,11] and that teachers are experiencing serious difficulties when developing their pedagogical practices to overcome the challenge of missing skills in online inquiry (e.g. [10]). Certain student characteristics, including attitudes, learning beliefs, self-efficacy and Internet use determine students’ effective interaction with the online environment. Previous studies concerning adolescents and adults indicate that students’ attitudes, self-beliefs and self-efficacy appear to predict online reading performance [12,13]. However, demands for using online sources already at an early age has substantially increased during the few past years, and hence, a better understanding of the role of these factors at the primary school level is urgently needed. As we acknowledge, the empirical evidence on the relationship of attitudes and self-efficacy beliefs in online inquiry (as discussed below), the majority of studies has concentrated on older adolescents and students in higher education, which still leaves scarce knowledge of primary school children. In addition, this study aims to contribute to the knowledge of online inquiry, and we further explore how these factors relate to other key student characteristics in this context, such as gender, learning beliefs and activity as Internet users. Finally, since measures adapted particularly for this purpose are still at the stage of development (see Putman [21]), novel self-report survey tools were developed and applied to assess students’ self-efficacy beliefs and attitudes towards online inquiry in primary education. 1.1. Online inquiry, attitudes and self-efficacy beliefs The key concept of this study is online inquiry, as defined above, which is the process of searching for information on the Web, evaluating the reliability of retrieved sources and writing a synthesis based on the sources. Our participants were sixth-grade primary school students who more or less frequently work on project assignments requiring online inquiry skills. Thus, we operationalise online inquiry in terms of subtasks needed to complete the assignments by applying the theory of task-based information interaction [15]. The concept of online inquiry overlaps with online research used by some scholars [16,17]. The main difference is that we excluded lower-level subtasks included in the latter construct, such as the operation of Web browsers or email systems. Instead, we emphasise the link of online inquiry to the framework of inquiry-based learning (see, e.g., Lonka et al. [18]) and operationalise the concept of online inquiry accordingly. Attitude is a ‘psychological tendency that is expressed by evaluating a particular entity with some degree of favour or disfavour’, as defined by Eagly and Chaiken [19]. In more recent conceptions, attitude has been characterised as a Sormunen et al. 2 Journal of Information Science, 2021, pp. 1–16 ÓThe Author(s), DOI: 10.1177/01655515211043708 multi-component construct [20,21]. In this study, following Joyce and Kirakowski’s [20] model, we adopted the threecomponent framework, which defines the construct of attitudes more explicitly: affect (an individual’s feelings, likes or dislikes about the attitude object), cognition (an individual’s ideas and beliefs about the attitude object) and behavioural intention (an individual’s intention to act in a certain way regarding the attitude object). Putman [14] operationalised attitudes towards online inquiry as a component of the Survey of Online Reading Attitudes and Behaviours (SORAB) and validated it among fifth- and sixth-grade students. Previous studies have indicated that attitudes contribute to online inquiry. Students with positive attitudes towards the Internet tend to be engaged in more activities within the Internet [22]. Attitudes also contribute to online inquiry skills. A positive attitude supports self-efficacy beliefs when using the Internet. Furthermore, students’ self-efficacy beliefs contribute to positive learning tendencies on the Internet. Student attitude is also viewed as an important indicator of student self-efficacy [13,20,23,24]. Yet today, direct evidence is scarce concerning attitudes towards online inquiry, especially at the primary level. Bandura [25] defined self-efficacy beliefs as an individual’s confidence in their capabilities to organise and execute the course of actions required to perform a task or attain a goal. Bandura’s [26] seminal work showed how individuals with low self-efficacy avoid challenging activities and develop their competences to a lesser degree than individuals with high self-efficacy. Self-efficacy is also a significant predictor of students’ satisfaction and achievement in the contexts of online learning [27]. It contributes to online inquiry skills [28,29], but it is important to notice that selfefficacy beliefs are task- or domain-specific. The level of a person’s perceived competences may vary from task to task [30]. Actual information and communication technology (ICT) competences and previous ICT experience are related to the accuracy of students’ perceptions of their ICT self-efficacy [30,31]. Overall, students with high self-efficacy tend to exhibit higher online and digital competences [32], greater abilities to complete online tasks and courses [29] and better information search strategies on the Web [33]. However, the role of self-efficacy beliefs as a performance indicator is problematic because students having the least experience with a learning task tend to overestimate their skills in performing the task [30]. 1.2. Learning beliefs Hofer and Pintrich [34] define learning beliefs as a construct referring to students inherent knowledge about their learning. Learning beliefs play an important role in influencing actual learning [35]. Chan and Sachs [36] elaborate on learning beliefs along constructivist learning theories by differentiating between ‘constructivist’ and ‘reproductive’ conceptions of learning. In this framework, ‘constructivist’ students view learning as meaning-oriented to develop understanding and transformation of knowledge, while ‘reproductive’ students regard learning as accumulation of knowledge and memorisation [35]. Studies done by Chan and Sachs [36] and Law et al. [35] indicate that students with constructivist beliefs perform better in academic tasks than those with reproductive beliefs. This indicates that learning beliefs can facilitate or hinder the actual online inquiry performance. In both studies by Chan and Sachs [36] and by Law et al. [35], students’ learning beliefs were observed to become more constructivist with age, and their learning beliefs were seen as probably to vary in different learning environments. Furthermore, students who held constructivist beliefs performed better in text processing and in understanding tasks than students with reproductive beliefs. The constructivist students reported their interest in seeking the meaning of the issue in contrast to the reproductive students, who believed that memorising was a better way to achieve a good performance [35]. Hence, it is plausible to assume that students’ learning beliefs are closely related to their online inquiry skills and processes. 1.3. Gender, ICT and the Internet Different conditions and concomitant factors of attitudes, self-efficacy and learning beliefs must be considered to properly understand their role in inquiry learning. Some of these have been explicitly included in this study. First, several studies have shown that the use of ICT/Internet in free time outside of school supports the development of Internet skills [37,38] and is related, for example, to positive attitudes towards the Internet and Internet self-efficacy [24,39]. Second, gender differences have been reported, but they seem somewhat contradictory. Although a few studies suggest gender equivalence between boys and girls regarding Internet interest, use and skill levels [22,40], others have reported gender differences in the use of ICT and related activities [41–43]. Nonetheless, there seems to be evidence supporting gender differences in several variables concerning Internet usage and social interaction. Boys and girls approach computer usage in distinct ways and differ, for example, in their learning Sormunen et al. 3 Journal of Information Science, 2021, pp. 1–16 ÓThe Author(s), DOI: 10.1177/01655515211043708 beliefs, attitudes, self-efficacy and online communication [24,29,40,41,44]. A meta-analysis of 50 articles about gender difference determined that boys still hold more favourable attitudes and learning beliefs towards technology use than girls [45]. In this study, both gender and Internet activity are included in the multifactor model for explaining self-efficacy beliefs in and attitudes towards online inquiry. 2. Research questions The overall goals of this study were to develop a survey tool to measure students’ self-efficacy beliefs and attitudes regarding online inquiry in primary education. The empirical objective was to explore how students’ self-efficacy beliefs and attitudes relate to other student characteristics such as gender, learning beliefs and activity as users of the Internet. The study aimed to contribute to the existing literature by deepening the understanding of the complex relationships of these multiple concepts and by supporting the development of pedagogical tools and practices for teachers in online inquiry instruction. The following research questions were formulated to achieve the goals of this study: RQ1. What factors comprise sixth-graders’ self-efficacy beliefs in and attitudes towards online inquiry, and how are they related? RQ2. What is the relationship between the sixth-graders’ (1) gender, ICT and Internet activity and learning beliefs and (2) their self-efficacy in and attitudes towards online inquiry? 3. Methods 3.1. Participants The questionnaire was administered in 10 primary schools located in three medium-sized cities in Finland. The convenience sample of 340 sixth-grade students (n= 340; 164 girls and 176 boys) completed the questionnaire. Each participant had returned a signed parent’s consent. The students were informed that they were free to stop their participation whenever they liked. Most students were 12 (81.5%) or 13 (14.7%) years old. The everyday life of sixth-graders in the sample is characterised by a rich presence of ICT and the Internet. Most participants could use a computer at home (96.5%), and nearly all (98.8%) had a smartphone of their own. Most students reported that they could access the Internet using various options: 99.4% used a computer or tablet at home, 97.3% used their smartphones and all used computers at school. In Finland, practically all children go to their local primary school. We expect that our sample is balanced in terms of students’ socio-economic backgrounds since 10 schools were selected in different types of districts in three cities. 3.2. Instrument development The survey instrument was developed in a cross-cultural research consortium of three universities in Finland and two universities in Chile. To establish a solid basis for cross-cultural comparative studies, the instrument was first constructed in the English language. When a consensus was reached about the contents in English, the survey was translated into Finnish and Spanish by national research teams. One independent expert translated the Finnish version into Spanish, and another independent expert translated the Spanish version into Finnish. The national research teams checked the translations to correct any anomalies or inconsistencies. The problems were negotiated and resolved collaboratively by the Finnish and Chilean teams. Thus, quite a large group of researchers and pedagogical experts contributed to the content validity evaluation of the instrument. Only data collected by the Finnish version were included in this study. The self-report survey instrument consisted of four sections: •Information technology and Internet at home and school (adopted from Hautala et al. [46]). •Learning beliefs (adopted from Chan and Sachs [36]). •Self-efficacy beliefs in online inquiry (composed in the study, some items from Putman [14]). •Attitudes towards online inquiry (adopted mainly from Putman [14]). The first section was intended to collect background data about the availability of ICT devices and the Internet to students and about the profiles in using ICT and the Internet for various purposes. The selected scales depended on the items’ nature. The availability of ICT devices or access to the Internet was inquired about with a binary scale (yes/no). The frequency of ICT/Internet use for defined purposes was asked on a 7-point scale (from ‘Never’ to ‘Daily, more than Sormunen et al. 4 Journal of Information Science, 2021, pp. 1–16 ÓThe Author(s), DOI: 10.1177/01655515211043708 2 h’) or on a 6-point scale (from ‘Never’ to ‘At least once a day’). The questionnaire could be used as such because it was originally created in Finnish and successfully applied in a previous study, see Hautala et al. [46]. The second section on learning beliefs was originally designed to measure differences in children’s implicit notions of learning [36]. Each item offers three fixed-choice answers to the student. One option is expected to represent more constructivist views than others. The sum of ‘correct’ answers is the measure of how strongly the student identified his or her role as the constructor of knowledge [36]. The point of departure in self-efficacy beliefs was factor F5 (Efficacy for Online Reading) in the SORAB instrument developed by Putman [14]. However, we found that most items dealt with technical Internet skills (e.g. use of the Web browser) and were not built on an explicit model of subtasks of online inquiry (or online research). Thus, a new design was made by modelling online inquiry into three subtasks: Web searching, the evaluation of sources and the use of Web sources (see, e.g., Leu et al. [47]). We subsequently designed new items for each subtask. The resulting scale included one generic item and four specific items for each online inquiry subtask. Each item comprises an affirmation that starts with the phrase, ‘I feel confident ...’ and a 5-point Likert-type scale from ‘Strongly disagree’ to ‘Strongly agree’. Attitude items were adopted more directly from the SORAB’s factors F3 (Anxiety) and F4 (Value/Interest). The former probes the individual’s negative and reserved feelings about using the Internet or doing online research. Eight items of the highest loadings were adopted from the Anxiety factor and one new item (no. 19) was composed. In the latter, the items are statements about positive feelings and beliefs related to use of the Internet in the learning context. Seven items were adopted from the Value/Interest factor and six new items (nos 9, 14, 15, 17, 20 and 21) were composed to cover the behavioural intention dimension in attitudes. A 5-point Likert-type scale, from ‘Strongly disagree’ to ‘Strongly agree’, was also used here. The quality of the questionnaire draft was assessed in a pilot before the main data collection. The questionnaire was administered in three fifth-grade and three sixth-grade classes (n= 133, 63 boys and 70 girls). Ten pairs of student volunteers (n= 20) participated in post-interviews to help with the cognitive validation of the items (cf. Karabenick et al. [48]). Students’ comments or explanations were organised into item-specific chunks and analysed to identify needs for item-level edits. The logic and scales of the learning beliefs section were so different from the other sections that it confused the students. It was decided that the section would be introduced to students separately from the other sections. Exploratory factor analysis (EFA) revealed some weakly loaded items. Four items, both in self-efficacy and attitude scales, were reformulated. In addition, one attitude item was removed. The self-efficacy and attitude parts of the instrument developed for the main study are presented in a supplementary file assigned to the paper (supplementary material). 3.3. Data analysis 3.3.1. Data preprocessing. After creating a data file, the responses of students for whom the team did not have parental consents were removed, and the remaining data were anonymised. The data were checked both across subjects and items for non-numeric and out-of-range entries, multiple selections and missing data. The total number of acceptable responses was 340, which included some incomplete but still useful responses. First, the EFA was used to explore the factor structure (number of the factors, factor structure and the level of the loadings). After that, using the results from the EFA analyses, an empirically and theoretically sound structure was formed. The two-factor confirmatory factor analysis (CFA) for attitudes towards and self-efficacy in online inquiry supports that structure. The CFA was conducted on the same sample as the EFA to elaborate any methodological discrepancies (cf. van Prooijen and van der Kloot [49]). No discrepancies were found. It can be assumed that the factor structure is ready for further processing and follow-up study on different data. Then, a Structural Equation Model (SEM) was conducted with the measurement model from CFA and four Explanatory variables like gender, students’ learning beliefs and their ICT use in school and in free time. 3.3.2. EFA. The EFA was conducted using software IBM SPSS 24.0 separately for the attitude and self-efficacy data to achieve an acceptable subjects/items ratio (min >10). The maximum likelihood extraction method with Oblimin rotation was used to explore the factor structure. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.91 for the self-efficacy (SE) data. This indicates that the EFA should yield distinct, reliable factors for self-efficacy beliefs (KMO >0.90 superb; Field [50]). The initial result indicated three factors with eigenvalues greater than 1 that explained 58.2% of the total sample variation. Furthermore, the Scree plot for self-efficacy indicates three underlying factors. The final EFA resulted in three selfefficacy factors of online inquiry (Table 1). Each factor was associated with one online inquiry subtask. Items related to Web searching were loaded into factor 1 SE Search, which encompassed four items (SE1 to SE4, Cronbach’s α= 0.75). Sormunen et al. 5 Journal of Information Science, 2021, pp. 1–16 ÓThe Author(s), DOI: 10.1177/01655515211043708 The items related to the evaluation of Web sources loaded into factor 2 SE Evaluate (SE5 to SE9, α= 0.71). Sourcebased synthesis writing as factor 3 was labelled as SE Write and encompassed four items (SE10 to SE13, α= 0.84). The three factors accounted for 46.25% of the total variance. The correlation table and descriptive statistics for the selfefficacy items are presented in Table 5 (in Appendix 1). The KMO value for the attitude data is 0.81 and can be considered excellent (cf. [50], 647). The attitudes section contained 20 items. The results indicated five factors with eigenvalues greater than 1 that explained 53.3% of the total sample variation. The Scree plot for the attitude data indicates two underlying factors. Furthermore, items with low loadings and no clear allocation were excluded. In the final EFA, seven items loaded into a positive attitude factor labelled ATT Positive (ATT2, ATT3, ATT5, ATT6, ATT8, ATT11 and ATT19, α= 0.80). Five items loaded into a negative attitude labelled ATT Negative (ATT10, ATT13, ATT16, ATT17 and ATT18, α= 0.63) (see Table 2). The model explains 46.3% of the total variance. The correlation table and descriptive statistics for the attitude items are presented in Table 6 (in Appendix 2). 3.3.3. CFA. CFA with the five factors was performed using the software Mplus 8.0. The full information maximum likelihood estimation method with robust standard errors was chosen (cf. Muthe ´n and Muthe ´n [51]). This method has desirable statistical properties especially for not normally distributed items and for applied research for this kind of sample size with minor missing at random data [52]. A goodness-of-fit test was performed respecting the fit indices to be considered [53]. First, the minimum fit function χ 2 was used. Due to the highly sensitive nature of the χ 2 [54], the ratio of the χ 2 Table 1. Self-efficacy factor structure. Item Factor Communality 1 SE Evaluate 2 SE Search 3 SE Write SE1. I feel confident that I can gather information for my school assignments using the Internet. 0.015 0.720 −0.095 0.607 SE2. I feel confident that I can locate information on the Internet using a search engine (e.g. Google). 0.169 0.448 0.050 0.293 SE3. I feel confident that I can choose good search terms to search for information on the Internet. 0.269 0.305 −0.136 0.373 SE4. I feel confident that I can find information on Wikipedia. −0.058 0.684 −0.039 0.445 SE5. I feel confident that if I see a new word on a Web page, I can find out what it means. 0.442 0.163 0.008 0.305 SE6. I feel confident that I can identify the best search results. 0.775 −0.063 −0.086 0.635 SE7. I feel confident that I can find useful information on an open Web page. 0.358 0.155 −0.156 0.342 SE8. I feel confident that I can determine if information on a Web page is trustworthy. 0.607 −0.079 −0.062 0.364 SE9. I feel confident that I can check the author of a Web page. 0.611 0.085 0.049 0.404 SE10. I feel confident that I can write in my own words about what was said on the Web page. −0.007 0.029 −0.693 0.493 SE11. I feel confident that I can make a summary of the main points of several Web pages. 0.126 −0.014 −0.681 0.583 SE12. I feel confident that I can combine information from more than one Web page in a way that makes sense to other people. 0.010 −0.075 −0.821 0.632 SE13. I feel confident that I can compare information presented on more than one Web page. −0.037 0.112 −0.698 0.535 M 3.87 4.44 4.01 SD 0.60 0.48 0.73 Cronbach’s α0.75 0.71 0.84 SD: standard deviation; SE Evaluate: self-efficacy beliefs in evaluation; SE Search: self-efficacy beliefs in searching; SE Write: self-efficacy beliefs in synthesising information. Bold indicates the highest factor loadings. Sormunen et al. 6 Journal of Information Science, 2021, pp. 1–16 ÓThe Author(s), DOI: 10.1177/01655515211043708 Table 2. Attitudes towards online inquiry factor structure. Item Factors Communality 1 ATT Positive 2 ATT Negative ATT2: I would rather complete research on the Internet than use a book or magazine. 0.565 −0.026 0.324 ATT3: I believe using the Internet for school assignments makes learning more interesting. 0.653 0.110 0.421 ATT5: Being able to use the Internet is important to me. 0.632 −0.047 0.409 ATT6: I believe that using the Internet is beneficial because it saves time. 0.701 0.002 0.491 ATT8: I believe it is very important to learn how to use the Internet for finding information. 0.492 −0.085 0.260 ATT11: I learn a lot when I search for information on the Internet. 0.657 0.103 0.426 ATT19: I like the Internet because I find various opinions about questions interesting to me. 0.495 −0.042 0.251 ATT10: I feel helpless when I am asked to research information on the Internet. 0.021 0.403 0.161 ATT13: I feel intimidated when I am researching information on the Internet. −0.014 0.485 0.237 ATT16: I sometimes worry that other kids do not think I can read on the Internet as well as they can. 0.063 0.573 0.323 ATT17: I believe it is easy to get lost when I am using the Internet for research. −0.115 0.481 0.258 ATT18: I often feel disoriented due to the huge amount of information on the Internet. 0.021 0.651 0.632 M 3.99 1.95 SD 0.63 0.62 Cronbach’s α0.80 0.63 SD: standard deviation; ATT Positive: positive attitudes towards online inquiry; ATT Negative: negative attitudes towards online inquiry. Bold indicates the highest factor loadings. Table 3. Information concerning convergent and discriminant validity of the constructs: correlations between CFA factors, Composite Reliability (CR) and Average Variance Extracted (AVE). Factors 12345 1 SE Search 1 2 SE Evaluate 0.75*** 1 3 SE Write 0.60*** 0.75*** 1 4 ATT Positive 0.37*** 0.42*** 0.29*** 1 5 ATT Negative −0.21*** −0.19*** −0.11*−0.14** 1 Average Variance Extracted 0.43 0.40 0.56 0.36 0.28 Composite Reliability 0.75 0.76 0.84 0.87 0.65 SE Evaluate: self-efficacy beliefs in evaluation; SE Search: self-efficacy beliefs in searching; SE Write: self-efficacy beliefs in writing (synthesising); CFA: confirmatory factor analysis. * Correlation is significant at the 0.05 level. ** Correlation is significant at the 0.01 level. *** Correlation is significant at the 0.001 level. Sormunen et al. 7 Journal of Information Science, 2021, pp. 1–16 ÓThe Author(s), DOI: 10.1177/01655515211043708 statistic to its degree of freedom (χ 2 /df, χ 2 = 365.90 and df = 265) was used and indicates an acceptable fit with a value of less than 2 [55]. Furthermore, these indices were evaluated and considered to be satisfactory [56]: Comparative Fit Index (CFI) = 0.95, Tucker–Lewis Index (TLI) = 0.94, standardised root mean square residual (SRMR) = 0.052, the Root Mean Square Error of Approximation (RMSEA) = 0.033 and the lower limit (LL) = 0.025 and upper limit (UL) = 0.042 of the 90% confidence interval of RMSEA. The Composite Reliability (CR) has an acceptable value, greater than 0.70 for all factors of the construct [57] whereas the Average Variance Extracted (AVE) values as presented in Table 3 are mainly less than 0.50, which indicates that the convergent validity of the instrument is not especially high. Discriminant validity of the constructs was evaluated using the AVE estimates of the factors and the correlations between the factors. One proposed criteria for discriminant validity is that the AVE values of the two constructs should be larger than the squared correlation between those [58,59]. This condition was met for the other factors, except for the latent variables SE SEARCH and SE EVALUATE (squared correlation 0.56 was larger than AVE 0.43 and 0.40) and SE EVALUATE and SE WRITE (squared correlation 0.56 was larger or equal than AVE 0.43 and 0.56). However, all correlations between the factors were less than r= 0.85, which has been used as another cut-off criteria of acceptable discriminant validity (see, e.g., [52]). The correlations of the self-efficacy belief and attitude factors are presented in Table 3. The factors of self-efficacy beliefs correlate quite strongly with each other (r= 0.60–0.75), which indicates that the components of self-efficacy beliefs represent mutually dependent constructs. Positive attitudes towards online inquiry seem to correlate moderately with self-efficacy beliefs (r= 0.29–0.42), but negative attitudes only have weak negative (r=−0.11 to −0.21) associations with other components of the CFA model. 3.3.4. Explanatory variables. The model is processed according to four explanatory variables: gender, use of ICT and Internet for free-time and school purposes, and learning beliefs. Exploratory factor analyses were conducted to examine the dimensionality of the nine items of the ICT activity questionnaire. Maximum likelihood estimation was used with the Oblimin rotation. The questionnaire consists of 9 Likert-type scale items, labelled here as ICT9 to ICT19 according to the order in the questionnaire. The KMO value is moderate (KMO = 0.70) and indicates that the data were suitable for a factory analysis. Two factors explained 35.56% of the total variance. The ICT Free factor consists of five items (ICT9, ICT11, ICT12, ICT13 and ICT20; α= 0.61) and illustrates the use of ICT for leisure. The second factor, ICT School, comprised of four items (ICT15, ICT16, ICT18 and ICT19; α= 0.74) and refers to the use of Internet services for school purposes (Table 4). Item ICT12 loaded rather equally into both factors. However, it was included in the ICT Free factor. Although the item’s content is generic, it is a sequence of items related to leisure activities. Furthermore, correlations between the items support this allocation (see Table 7 in Appendix 3). Learning beliefs were measured by a count variable. The questionnaire includes nine main statements about learning beliefs, each with three possible answers, for example, (Item 1): The most important thing in learning is: (a) To remember what the teacher has taught you; (b) To practice lots of problems; (c) To understand the problems you work on. The answers were scored with 1 indicating a deep constructivist orientation and with 0 for a reproductivist view. The correlation between all nine items showed correlations below 0.2 for three items. These items were excluded. The final scale was composed as the sum of the five learning belief items (translated as items 1, 2, 3, 4, 7 and 9 from Chan and Sachs [36]) with Cronbach’s α= 0.52, M = 3.36 and SD = 0.24. The effects of the explanatory variables on the Self-Efficacy and Attitude factors were examined using SEM. SEM is a powerful and flexible tool for multivariate analysis, in which it is possible to have several dependent and independent variables in the same model. SEM also allows latent variable modelling, in which the measurement error variance is taken into account in the analyses. Model results were evaluated, especially the goodness-of-fit and the parameter estimates (regression coefficients) of the model. 4. Results RQ1. What factors comprise sixth-graders’ self-efficacy beliefs in and attitudes towards online inquiry, and how are they related? Sormunen et al. 8 Journal of Information Science, 2021, pp. 1–16 ÓThe Author(s), DOI: 10.1177/01655515211043708 [52] Brown TA. Confirmatory factor analysis for applied research. New York: Guilford Press, 2014. [53] Kline RB. Principles and practice of structural equation modeling. 3rd ed. New York: Guilford Press, 2011. [54] Hu LT and Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling 1999; 6: 1–55. [55] McIver JP and Carmines EG. Unidimensional scaling. Thousand Oaks, CA: SAGE, 1981. [56] Little TD. Longitudinal structural equation modeling. New York: Guilford Press, 2013. [57] Hair JF, Black WC, Babin BJ et al. Multivariate data analysis. Upper Saddle River, NJ: Pearson Education, 2006. [58] Fornell C and Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Marketing Res 1981; 18: 39–50. [59] Voorhees CM, Brady MK, Calantone R et al. Discriminant validity testing in marketing: an analysis, causes for concern, and proposed remedies. J Acad Market Sci 2016; 44: 119–134. [60] Torppa M, Eklund K, Sulkunen S et al. Why do boys and girls perform differently on PISA reading in Finland? The effects of reading fluency, achievement behaviour, leisure reading and homework activity. J Res Read 2018; 41: 122–139. [61] Hargittai E and Shafer S. Differences in actual and perceived online skills: the role of gender. Soc Sci Quart 2006; 87: 432–448. [62] Gwizdka J and Bilal D. Analysis of children’s queries and click behavior on ranked results and their thought processes in Google search. In: Proceedings of the 2017 conference on conference human information interaction and retrieval (CHIIR’17), Oslo, 7–11 March 2017, pp. 377–380. New York: ACM. [63] Bilal D. Children’s use of the Yahooligans! Web search engine. III. Cognitive and physical behaviors on fully self-generated search tasks. J Am Soc Inf Sci Tec 2002; 53: 1170–1183. [64] Coiro JL, Kiili C, Ha ¨ma ¨la ¨inen J et al. Digital scaffolds for reading multiple online sources and writing an argumentative text. Paper presented at the 64th annual conference of the literacy research association, Marco Island, FL, 3–6 December 2014, pp. 1–19. https://www.academia.edu/download/37050883/Coiro___Kiili_et_al_LRA_2014_Digital_Tool_Study-libre.pdf (accessed 30 August 2021). [65] Muis KR and Duffy MC. Epistemic climate and epistemic change: instruction designed to change students’ beliefs and learning strategies and improve achievement. J Educ Psychol 2013; 105: 213–225. [66] Graafland JH. New technologies and 21st century children: recent trends and outcomes. Organisation for Economic Cooperation and Development (OECD) Education working paper no. 179, 12 September 2018. Paris: OECD. [67] Mikkila ¨-Erdmann M, Warinowski A and Iiskala T. Teacher education in Finland and future directions. In: Oxford research encyclopedia of education. Oxford: Oxford University Press, 2019. https://oxfordre.com/education/view/10.1093/acrefore/ 9780190264093.001.0001/acrefore-9780190264093-e-286 (accessed 30 August 2021). Table 5. Correlation table of the items of three self-efficiency factors: SE Search, SE Evaluate and SE Write. SE Search SE Evaluate SE Write 12345678910111213 1 SE1 – 2 SE2 0.45** – 3 SE3 0.45** 0.38** – 4 SE4 0.48** 0.37** 0.32** – 5 SE5 0.37** 0.32** 0.38** 0.28** – 6 SE6 0.38** 0.33** 0.45** 0.29** 0.42** – 7 SE7 0.38** 0.27** 0.33** 0.33** 0.33** 0.46** – 8 SE8 0.23** 0.23** 0.29** 0.20** 0.29** 0.48** 0.31** – 9 SE9 0.35** 0.21** 0.34** 0.24** 0.36** 0.48** 0.35** 0.39** – 10 SE10 0.28** 0.19** 0.36** 0.27** 0.29** 0.37** 0.33** 0.30** 0.33** – 11 SE11 0.36** 0.20** 0.37** 0.27** 0.28** 0.50** 0.38** 0.37** 0.36** 0.58** – 12 SE12 0.27** 0.22** 0.33** 0.23** 0.30** 0.47** 0.37** 0.34** 0.28** 0.56** 0.59** – 13 SE13 0.38** 0.30** 0.38** 0.26** 0.29** 0.43** 0.32** 0.34** 0.32** 0.50** 0.56** 0.61** – M 4.43 4.68 4.21 4.42 4.24 3.88 4.10 3.94 3.20 4.06 3.91 4.04 4.02 SD 0.66 0.55 0.70 0.74 0.82 0.78 0.76 0.79 1.04 0.92 0.89 0.84 0.89 Skewness −1.36 −2.07 −0.78 −1.44 −0.87 −0.31 −0.75 −0.51 −0.06 −1.12 −0.60 −0.74 −0.80 Kurtosis 3.79 6.73 1.26 2.80 0.25 −0.28 0.86 0.19 −0.65 1.43 −0.03 0.63 0.39 SE: self-efficacy; SD: standard deviation. * Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed). Appendix 1 Sormunen et al. 15 Journal of Information Science, 2021, pp. 1–16 ÓThe Author(s), DOI: 10.1177/01655515211043708 Table 6. Correlation table of items concerning two observed online inquiry attitude factors, ATT Positive and ATT Negative. Item ATT Positive ATT Negative 123456789101112 1 ATT2 – 2 ATT3 0.40** – 3 ATT5 0.32** 0.32** – 4 ATT6 0.46** 0.43** 0.52** – 5 ATT8 0.28** 0.30** 0.35** 0.33** – 6 ATT11 0.30** 0.45** 0.42** 0.43** 0.36** – 7 ATT19 0.24** 0.33** 0.36** 0.30** 0.30** 0.36** – 8 ATT10 0.02 −0.01 −0.07 −0.12*−0.03 0.06 −0.06 – 9 ATT13 −0.06 0.02 −0.13*−0.06 −0.14*0.00 −0.08 0.25** – 10 ATT16 −0.01 0.06 −0.01 −0.05 −0.10 0.01 −0.02 0.28** 0.27** – 11 ATT17 −0.14** −0.13*−0.06 −0.09 −0.08 −0.06 −0.06 0.17** 0.24** 0.25** – 12 ATT18 −0.08 0.05 −0.10 0.00 −0.07 0.00 −0.09 0.23** 0.30** 0.37** 0.36** – M 4.29 3.73 4.14 3.94 4.28 3.74 3.82 2.62 1.23 1.77 2.16 1.96 SD 0.94 1.05 0.94 0.98 0.77 0.92 0.97 1.07 0.61 1.04 1.08 1.02 Skewness −1.24 −0.71 −0.99 −0.91 −0.88 −0.52 −0.66 0.19 3.30 1.24 0.71 0.91 Kurtosis 0.92 0.06 0.50 0.62 0.55 0.03 0.19 −0.61 12.49 0.78 −0.17 0.17 ATT: attitudes towards online inquiry; SD: standard deviation. * Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed). Table 7. Correlation table of items regarding the two ICT activity factors ICT Free and ICT School. Item ICT Free ICT School 123456789 1 ICT9 – 2 ICT11 0.24** – 3 ICT12 0.17** 0.18** – 4 ICT13 0.24** 0.42** 0.18** – 5 ICT20 0.07 0.22** 0.47** 0.27** – 6 ICT15 0.05 0.05 0.26** 0.12*0.13*– 7 ICT16 0.14*0.17** 0.28** 0.16** 0.21** 0.39** – 8 ICT18 0.04 −0.01 0.23** 0.02 0.18** 0.55** 0.26** – 9 ICT19 0.15*0.20** 0.26** 0.21** 0.37** 0.37** 0.51** 0.48** – M 4.96 4.55 3.25 2.55 2.87 2.38 2.59 2.06 2.14 SD 0.96 1.00 1.03 1.65 1.27 0.98 1.27 1.09 1.09 Skewness −0.44 −0.89 −0.08 0.24 −0.17 −0.37 0.13 0.03 0.21 Kurtosis −0.42 2.91 0.44 −0.63 −0.66 0.42 −0.45 −0.19 −0.21 ICT: information and communication technology; SD: standard deviation. Appendix 2 Appendix 3 Journal of Information Science, 2021, pp. 1–16 ÓThe Author(s), DOI: 10.1177/01655515211043708 Sormunen et al. 16