Session Information
16 SES 03 A, Digital Literacy and Problem Solving Competences
Paper Session
Contribution
In today's era of rapid technological advancement, technology has a profound impact on our daily lives and society. Technology-rich environments (TRE) not only offer tertiary students collaborative opportunities that foster teamwork and communication skills, but also present unique challenges requiring critical thinking and problem-solving abilities. These challenges include diagnosing and resolving technical issues, designing new hardware or software solutions, and analyzing complex data sets (Mishra et al., 2013; Verdonck et al., 2019). Strong problem-solving abilities enable students to adapt quickly to novel challenges and solve complex problems, making problem-solving in TRE an essential skill for success in the modern workforce and future careers (Hämäläinen et al., 2015). Previous research has explored the relationship between socioeconomic status (SES) and students' problem-solving skills to address educational disparities (e.g., Martin et al., 2012). To gain a better understanding of this relationship, it is crucial to examine the potential mediation mechanisms. Previous studies have shown positive connections between SES and students' behavioral engagement (e.g., Guo et al., 2015) and between behavioral engagement and problem-solving (e.g., Guo et al., 2016). Therefore, it is expected that behavioral engagement may serve as a mediator between SES and problem-solving. However, there is limited knowledge about whether behavioral engagement truly mediates the association between SES and problem-solving, particularly in TRE. Thus, this study aimed to investigate the mediating role of behavioral engagement in the relationship between SES and problem-solving in TRE.
Research consistently demonstrates that individuals who employ strategic learning approaches tend to exhibit higher levels of problem-solving (Tan, 2019). Effective learning strategies, such as goal setting, self-regulation, metacognitive monitoring, and the use of problem-solving techniques, play a crucial role in facilitating the acquisition of problem-solving skills (Hoffman & Spatariu, 2008). In addition, previous studies have indicated that students from high-SES families are more likely to possess high levels of problem-solving skills (e.g., Martin et al., 2012). Hence, the strength of the association between SES and problem-solving may vary depending on learning strategies. However, there is currently limited knowledge about the effect of the interaction between SES and learning strategies on problem-solving in TRE. Building upon established research on the relationships between SES and problem-solving (e.g., Martin et al., 2012) and between learning strategies and problem-solving (e.g., Hoffman & Spatariu, 2008), it can be hypothesized that learning strategies moderate the association between SES and problem-solving in TRE. In other words, learning strategies may weaken the strength of the association between SES and problem-solving in TRE. Furthermore, the indirect association, where the relationship between SES and problem-solving in TRE is mediated by behavioral engagement, may also vary depending on learning strategies. To the best of our knowledge, no studies have explored the moderating role of learning strategies in the indirect pathways from SES to problem-solving in TRE through behavioral engagement. Based on emerging evidence concerning the relationships between SES and behavioral engagement (e.g., Guo et al., 2015) and between learning strategies and behavioral engagement (e.g., Hospel et al., 2016), it is plausible to hypothesize the existence of a moderation mechanism involved in the indirect association between SES and problem-solving in TRE. Therefore, this study aimed to investigate whether learning strategies moderate both the direct and indirect associations between SES and problem-solving in TRE, mediated by behavioral engagement.
Method
The current study utilized data from the Programme for International Assessment of Adult Competencies (PIAAC), an assessment framework initiated by the Organization for Economic Cooperation and Development (OECD). PIAAC aims to measure and compare the proficiency levels of adults in various domains, including problem-solving, across participating countries. By employing standardized tests and surveys, PIAAC evaluates adults' cognitive abilities and workplace skills, with a specific focus on their problem-solving capabilities in real-life situations. In this study, a sample of 12,148 tertiary students (Mage = 25.68 years, 55% female) was analyzed. The variables examined in the study included SES, behavioral engagement in reading, writing, numeracy, and information and communication technology (ICT), learning strategies, and problem-solving in TRE. The initial analysis focused on exploring the mediating role of behavioral engagement in the relationship between SES and problem-solving in TRE. First, the study examined the direct effects of SES on students' problem-solving in TRE. Next, behavioral engagement was introduced as a mediator to investigate the direct effects of SES on problem-solving in TRE. The subsequent analysis aimed to explore the moderating effect of learning strategies on the associations between SES, behavioral engagement, and problem-solving in TRE. To achieve this, a moderated mediation model was estimated, incorporating an interaction term between SES and learning strategies. The interaction term was used to assess the effects of SES on problem-solving in TRE at different levels of learning strategies. If the interaction between SES and learning strategies was found to be significant, a simple slope analysis was conducted to evaluate the conditional direct and indirect effects of SES on students' problem-solving in TRE at low (-1 SD) and high (+1 SD) levels of learning strategies (Preacher, Curran, & Bauer, 2006). The study calculated 95% confidence intervals (95% CI) for the conditional direct and indirect effects. To handle missing data, the study employed the full information maximum likelihood (FIML) approach (Enders, 2010). All main analyses were conducted using Mplus 8 (Muthen & Muthen, 1998–2018).
Expected Outcomes
The current study employed a moderated mediation model to investigate the mechanisms underlying the relationship between SES and students' problem-solving in TRE. The findings provided support for the mediating role of behavioral engagement in the association between SES and problem-solving in TRE. First, in line with previous research indicating a positive link between SES and problem-solving (e.g., Martin et al., 2012), the present study established a significant contribution of SES to students' problem-solving in TRE. Second, the study demonstrated that behavioral engagement mediated the relationship between SES and problem-solving in TRE. Third, the study explored the significance of learning strategies in relation to students' behavioral engagement and problem-solving in TRE. However, the results indicated that learning strategies did not moderate the direct effect of SES on students' problem-solving in TRE. Learning strategies were also found to not moderate the indirect association between SES and problem-solving in TRE through behavioral engagement.
References
Enders, C. K. (2010). Applied missing data analysis. Guilford Press. Guo, F., Yao, M., Wang, C., Yan, W., & Zong, X. (2016). The effects of service learning on student problem solving: The mediating role of classroom engagement. Teaching of Psychology, 43(1), 16-21. Guo, Y., Sun, S., Breit-Smith, A., Morrison, F. J., & Connor, C. M. (2015). Behavioral engagement and reading achievement in elementary-school-age children: A longitudinal cross-lagged analysis. Journal of Educational Psychology, 107(2), 332-347. Hoffman, B., & Spatariu, A. (2008). The influence of self-efficacy and metacognitive prompting on math problem-solving efficiency. Contemporary educational psychology, 33(4), 875-893. Hospel, V., Galand, B., & Janosz, M. (2016). Multidimensionality of behavioural engagement: Empirical support and implications. International Journal of Educational Research, 77, 37-49. Martin, A. J., Liem, G. A., Mok, M., & Xu, J. (2012). Problem solving and immigrant student mathematics and science achievement: Multination findings from the Programme for International Student Assessment (PISA). Journal of educational psychology, 104(4), 1054-1073. Mishra, P., Fahnoe, C., Henriksen, D., & Deep-Play Research Group. (2013). Creativity, self-directed learning and the architecture of technology rich environments. TechTrends, 57(1), 10–13. Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics, 31(4), 437–448. Tan, R. E. (2019). Academic self-concept, learning strategies and problem solving achievement of university students. European Journal of Education Studies, 2, 287-303 Verdonck, M., Greenaway, R., Kennedy-Behr, A., & Askew, E. (2019). Student experiences of learning in a technology-enabled learning space. Innovations in Education and Teaching International, 56(3), 270–281.
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