Smartphones have become increasingly important learning tools for remote and distance education. With the onset of COVID-19 and subsequent school closures, many institutions struggled to provide education to lower-income populations due to unequal access to technology. In my post in “Managing Change in Digital Learning,” I proposed Smartphones as a potential solution to combat this complex problem, as they are the most cost-effective and most widely distributed technology for those of a lower-socioeconomic status. This initiative aimed to encourage those working in digital learning environments (DLE) to develop learning environments that can be effectively accessed and navigated using Smartphone technology.

How can data help inform this perspective?

According to Sclater et al. (2016), data can have ‘wider benefits’ beyond the immediate aims of a project, and can influence the way an institution makes decisions (p.8). But, as Zettelmeyer (2015) contends, the collection of data must be done with purpose. Harvesting and analyzing student data may generate increased awareness and discussion around socioeconomic issues that restrict students’ access to technology. Being sufficiently informed by data, institutions can make strategic decisions to exploit opportunities to make education available to all and society more equitable.

Considering above, the following is a checklist for those working in DLE that can be used to ensure they conduct good data analytics:

– Create an organizational culture that values openness and collaboration to foster data use (Marsh et al., 2006)

– Promote high stakes and long-standing accountability for student achievement (Marsh et al., 2006)

– Allocate adequate time to collect, analyze, synthesize, and interpret data (Marsh et al., 2006)

– Possess strong data literacy skills (Marsh et al., 2006; Zettlemeyer, 2015)

– Select a specific problem to be solved (Zettlemeyer, 2015)

– Have the intrinsic desire to evaluate and improve that problem using data (Marsh et al., 2006)

– Know where the data will come from (Zettlemeyer, 2015). For instance, will you be using virtual learning environment (VLE) activity data or student information systems data in your collection process, or both (Sclater et al., 2016)

– Conduct random samples (Zettlemeyer, 2015)

– Examine and reflect on data and think about other causes for a result (Marsh et al., 2006; Zettlemeyer, 2015)

– Aim to restrict the time between decision-making and the time involved in receiving the results (Marsh et al., 2006)

In sum, to address the ‘invisible triage tag’ (Prinsloo & Slade, 2014, p. 2) of restricted access to technology which is attached to students from low socioeconomic populations, good data practices (like the ones mentioned in the checklist above) can help those working in DLE make informed decisions to satisfy the varying needs of all students.

References:

Marsh, J., Pane, J., & Hamilton, L. (2006). Making Sense of Data-Driven Decision Making in Education: Evidence from Recent RAND Research. RAND Corporation.

Prinsloo, P., & Slade, S. (2014). Educational triage in open distance learning: Walking a moral tightrope. The International Review of Research In Open And Distributed Learning, 15(4), 306-331.

Sclater, N., Peasgood, A, & Mullan, J. (2016). Learning analytics in higher education: A review of UK and international practice. Jisc. 

Zettelmeyer, F. (2015). A leader’s guide to data analytics. KelloggInsight.