Student rules: Exploring patterns of students’ computer-efficacy and engagement with digital technologies in learning
Introduction
Teachers’ decisions about how to design learning, such as selecting teaching strategies, resources and assessments are, in part, mediated by what they think students will find engaging and how they believe students learn (Trigwell & Prosser, 2004). In regard to technology integration, two guiding beliefs have been that students are confident users of and engaged in using digital technologies; and, technology use will increase engagement in learning and improve learning outcomes (e.g. Selwyn, 2009, Thompson, 2013). However, research has shown that many students are not confident or engaged in using digital technology (e.g. Margaryan et al., 2011, Wang et al., 2014, Warschauer and Matuchniak, 2010). Disagreement on this point suggests a possible range of student experiences using technology, where some are engaged and others are not. It is important to understand variation in students’ technology-related experiences, as misalignment between teacher and student expectations of technology use may lead to students’ disengagement in learning. The purpose of this paper is to examine variations in students’ confidence and engagement with digital technologies in learning and consider possible implications for teachers’ learning design. A better understanding of these differences, and what they mean for learning, is needed to develop more effective and inclusive learning designs (Könings et al., 2014a, Li, 2007, Skryabin et al., 2015).
To do this, we first address teachers’ perceptions of students’ needs and experiences in the classroom, followed by a conceptual framework of key factors affecting students use of information and communication technologies (ICTs) in learning. Data mining techniques, association rules mining and fuzzy representation, are used in the analytic framework. Data mining techniques can provide new insight into relations among known factors of digital integration, which can build on existing knowledge (Baker, 2010). Our analysis broadly examines eight key factors of ICT use, and then focuses on ICT engagement, computer-efficacy and school engagement. Results show two distinctly different patterns among these factors, which suggest differences in students’ experiences in technologically integrated learning. Implications for learning design and student support when using ICTs are discussed, as well as directions for future research and model development.
Section snippets
Teachers’ perceptions of students’ needs
In education, there is still a strong belief that young people are able to confidently use digital technologies, and that they want to use these tools in learning. This belief has influenced how public and educational systems think about technology integration and learning (Margaryan et al., 2011, Selwyn, 2009, Thompson, 2013). It also affects how teachers select and integrate digital technologies in the classroom. However, assumptions about students’ knowledge of and engagement in digital
Factors affecting students’ use and experiences
Digital technologies used in learning include, but are not exclusively, the use of laptops, smartphones and tablets, various software packages, online resources, etc. (e.g. Inan and Lowther, 2010, Thompson, 2013). In regard to young people’s actual use of digital technologies, research has shown that it is generally low-level (Margaryan et al., 2011, Wang et al., 2014). Personal interests and entertainment dominate use, and as a result, young people are not necessarily confident or engaged with
Data mining approach
“Data mining is the process of automatically discovering useful information in large data repositories” (Tan, Steinbach, & Kumar, 2005). Data mining is an inductive process, which makes it different from more traditional statistical approaches, which seek to fit data to a hypothesized model or fit new data to an existing validated model (Breiman, 2001). The inductive process is referred to as “knowledge discovery” and does not assume a particular model. Rather, the aim of knowledge discovery is
Method
Data from a large-scale study of the Australian Digital Education Revolution in New South Wales (DER-NSW) was used to explore students’ perceptions of technology integration. The DER was a federally funded program aiming to provide all secondary (Years 9–12) students and teachers across Australia with ICTs (Department of Education Employment and Workplace Relations [DEEWR], 2012). Across the country, each state engaged with the program differently. In NSW, a one-to-one laptop program was
Results
The students participating in the Part B questionnaire represented 216 secondary schools from across the state. They were evenly divided between male (49%) and female (51%). Of this sample, only 8.5% identified as being of either or both Aboriginal and Torres Strait Islander. Both of these distributions were representative of the wider school population (Australian Bureau of Statistics, 2016). The majority of students reported having a computer at home (96%) and that the computer was connected
Discussion
This study aimed to understand variations in students’ confidence and engagement with digital technologies in learning and consider possible implications for teachers’ learning design. To do this, data mining approaches, association rules and fuzzy representations, were used to explore a student questionnaire dataset from a large Australian school one-to-one laptop program. To address the research question: “What are different patterns occurring among key factors relating to students’
Future research and conclusions
While our analysis was quite focused, results already show important variations between student experiences and suggest considerations for teachers’ learning designs. The immediate next step in this work will be to validate findings using a second student dataset. To do this we will use the Year 10 student questionnaire dataset collected as part of the NSW-DER study in 2013. Both data collections included Computer-Efficacy, ICT Engagement and School Engagement factors and the five other key
Acknowledgements
This research was funded in part by the New South Wales Department of Education and Communities.
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