Scott (2019) highlights a crucial issue in her article on learning technology developers – the dangers of over-reliance on commercial educational technologies. If such technologies become successful, they tend to monopolize the market, leading to increased dependency on a single tool and hindering innovation. To address this challenge, Scott proposes investing in internal and open-source tools, as well as the skills of educators and learning technologists. This approach can drive innovation and reduce dependency on a few privately controlled digital learning environments.
However, implementing this strategy comes with its own set of challenges, such as security, time, and financial resources. For example, open-source software often lacks ownership or a single source of responsibility, making it difficult for institutions such as colleges – who are responsible for large amounts of student data – to ensure its security. On the other hand, closed-source software provides a clear entity to turn to in case of issues, although it may not be inherently more secure. Additionally, individuals who wish to share tools they have developed may also face similar risks.
A balanced approach may be most effective in this scenario, where institutions use large-scale closed-source software tools to establish the foundation or infrastructure, allowing learning technologists to build smaller, in-house tools that complement and enhance that infrastructure.
References
Scott, A. (2019, July 1). Why we need learning technology developers. https://ammienoot.com/brain-fluff/why-we-need-learning-technology-developers/
AI Disclosure: This post was revised by ChatGPT for clarity. The original paragraph topics and the general flow of the post were written by me.
Hi Michael – you highlight some big challenges when choosing the types of technologies to implement – and emphasize that security of student data can be an issue in either case. As you say a balanced approach might be a best-case scenario – what does this look like in your organization, and who is making these decisions? Is there a role for the ed tech users, including students to take more of a lead? Does ensuring data is secure come into conversations? Thanks!
Digital learning environments allow for collecting learning analytics data beyond general demographical data collected during enrollment. Sclater et al. (2016) noted several positive uses, including:
– Providing students with information on their learning progress
– Giving students suggestions for future learning
– Identification of students in need of additional support
The positives from all this data come at a cost. These include ethical concerns about data privacy, such as who has access to the data and what decisions are made based on this data (Papamitsiou & Econondes, 2014, as cited in Sclater et al., 2016). Other risks include the cost of maintaining and securing the data. In my previous post, I discussed the dangers of relying on a single digital learning tool, which can hamper innovations. Creating, storing, and analyzing student learning data generated by these proprietary digital learning tools makes an even more significant concern. Who controls this student data, and how is it secured? Data created by closed-source software, where institutes may not have complete control over the source code, can provide an opportunity, knowingly or unknowingly, purposely or accidentally, for data to leak out of the system and out of the institution’s control.
While data can often help organizations better understand the environment they are working in, for education, the relationship between learning analytics and student success has yet to be fully proven (Watters, 2013, as cited in Prinsloo & Slade, 2014). Because of this, institutions should tread cautiously when deciding to collect, store and analyze student learning data. At the very least, instituting a data policy that is clear, easy to understand and discover by students, communicates what data is being collected and why will help students understand what information they are giving up by enrolling in courses at an institution. A further step would be to provide students with mechanisms to opt out of data collection and delete any non-essential data related to them. This would align learning analytics data policies with general data privacy laws such as the EU’s GDPR, which gives users control of personal data (The European Union’s General Data Protection Regulation, n.d.).
Data can be beneficial, but with its use comes responsibility. Suppose educational institutions want to leverage the benefits of learning analytics data. In that case, they must also secure that data and ensure that students understand what data is being collected when learners use digital learning tools.
References
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). https://doi.org/10.19173/irrodl.v15i4.1881
Sclater, N., Peasgood, A., & Mullan, J. (2016). Learning Analytics in Higher Education – A review of UK and international practice. In Jisc. Jisc. Retrieved February 25, 2023, from https://www.jisc.ac.uk/sites/default/files/learning-analytics-in-he-v3.pdf
The European Union’s General Data Protection Regulation. (n.d.). GAC. https://www.tradecommissioner.gc.ca/guides/gdpr-eu-rgpd.aspx?lang=eng