In my previous post, I explored the role perspective of the student in D’Arcy’s post about implementing a new AV system at their institution. A few of the questions I mentioned included the impact of a new AV system on learning and socioeconomic barriers, access to technology at home, and personal device compatibility with the AV system (Chan, 2022). Do the learners need and benefit from this technology (all for one)? Or does our decision expect all of the students to fall in line (one for all), and what happens if they cannot? In order to better understand the user population and select the appropriate classroom technology, data analysis of student demographics and their use of technology may offer fundamental insight to begin investigating.
Prinsloo and Slade (2014) proposed that learning analytics can offer data to help optimize resources for students. They argued that institutional decisions about the distribution of resources should be in the student’s best interest, and that demographics alone is not sufficient information to base the decision. Additionally, Prinsloo and Slade (2014) stated that student demographics play an important role in the decision-making process, but should be considered with other contextual factors which may not be necessarily quantifiable. For example, understanding which students were surveyed for their demographic data, potential confounding factors, and the possibility of reverse causality influence data generation and analysis (KelloggInsight, 2015).
In the context of implementing an AV system, we should begin by asking whether student demographics were accounted for during the decision-making process. Basic demographic data such as their socioeconomic status and location of residence can provide hints about key information like whether the students are likely to have compatible devices or quality internet access at home. Examples of confounding factors include students who may be eligible to use loaner laptops from the school, or students who live on campus and can use the AV system at the building for longer periods of time compared to students who need to commute and may not be able to do the same quantity or quality of work at home. Learner analytics may also provide feedback about digital literacy and technical proficiency of the students based on current learning systems. Lastly, we should consider the potential case of reverse causality. For example, students may feel pressured to purchase personal technology that is compatible with the AV system in order to keep up with their class, rather than having an AV system that enhances their learning using existing technology on hand.
References
Chan, J. (2022, February 11). Mirroring the learning experience from school. [Online post]. https://untextbookdemo.opened.ca/voice/mirroring-the-learning-experience-from-school/
KelloggInsight. (2015, May 1). A leader’s guide to data analysis: A working knowledge of data science can help you lead with confidence. [Blog post]. https://insight.kellogg.northwestern.edu/article/a-leaders-guide-to-data-analytics/
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. https://doi.org/10.19173/irrodl.v15i4.1881
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