The combined emergence and popularity of digital technologies have substantially changed many classroom experiences and have created an upsurge of student data offering a raft of prospective evidence regarding how students learn (Polonetsky & Jerome, 2014).
Educators and educational institutions are now faced with such questions as: What do we do with these data? In what ways can we systematically collect and analyze student data? And in what ways can these data points be used to optimize student success and institutional effectiveness?
Given the hype and ethical concerns around predictive learning analytics, this session aims to examine the ways in which student data can be used to address various student academic concerns and institutional shortcomings (Selwyn, 2015).
To do this, participants will be asked to engage in a discussion around the Case of the Online Computer Programming Teaching Certificate. In this case, we will be working with a young online Program Director whose boss is adamant that she “measure everything that moves.” Participants will act as consultants, helping the Certificate’s Program Director think through how she can use student data and information to address the certificate’s low completion rates.
We will use elements of Ekowo & Palmer (2017) to guide our thinking around how to think about predictive analytics as a means for designing analytic models, learning from the results, and designing meaningful interventions. We will also conduct a brief yet important review of legal and ethical considerations (e.g., discrimination, privacy, security, and transparency).
Session Discussion Questions
Discussion questions for this session include:
What data can we legally collect? [And Why is this an important question to ask?]
What activity data offers the most insight into student knowledge, skills, behaviors?
In what ways can this data be used to predict student success?
In what ways can this data be used to optimize institutional efficiencies?
What legal and ethical considerations need to be taken into account regarding student data and information privacy laws?
3-5 min — Introduce the case
10-12 min — Discussion questions
3-5 min — Reflections and takeaways
The session will close with the identification of key takeaways gathered in this session.
By engaging in an interactive case discussion, participants will have an opportunity to gain a deeper understanding of learning analytics, how they can be used to optimize student and institutional success, and additional insights to consider when designing a learning analytics implementation plan.
Ekowo, M. & Palmer, I. (2017). Predictive analytics in higher education: Five guiding practices for ethical use. Retrieved from https://www.newamerica.org/education-policy/policy-papers/predictive-analytics-higher-education/#
Polonetsky, J., & Jerome, J. (2014). Student data: Trust, Transparency, and the role of consent. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2628877
Selwyn, N. (2015). Data entry: Towards the critical study of digital data and education. Learning, Media and Technology, 40(1), 64-82. doi:10.1080/17439884.2014.921628