Product as Praxis: How Learning Analytics tools are ACTUALLY Differentiated

I’ve been thinking a lot recently about product as praxis. Without putting too much conceptual weight behind the term ‘praxis,’ what I mean is merely that educational technologies are not just developed in order to change behavior. Ed tech embodies values and beliefs (often latent) about what humans are and should be, about what teaching and learning are, and about the role that institutions should play in guiding the development of subjectivity. As valued, educational technology also has the power to shape, not just our practices, but also how we think.

When thought of as praxis, product development carries with it a huge burden. Acknowledging that technology has the power (and the intention) to shape thought and action, the task of creating an academic technology becomes a fundamentally ethical exercise.

Vendors are not merely responsible for meeting the demands of the market. ‘The market’ is famously bad at understanding what is best for it. Instead, vendors are responsible for meeting the needs of educators. It is important for vendors to think carefully about their own pedagogical assumptions. It is important for them to be explicit about how those assumptions shape product development. The product team at Blackboard (of which I am a part), for example, is committed to values like transparency and interoperability. We are committed to an approach to learning analytics that seeks to amplify the power existing human capabilities rather than exclude them from the process (the value of augmentation over automation). These values are not shared by everyone in educational technology. They are audacious in that they fly in the face of some taken-for-granted assumptions about what constitute good business models in higher education.

Business models should not determine pedagogy. It is the task of vendors in the educational technology space to begin with strong commitments to a set of well-defined values about education, and to ensure that business models are consistent with those fundamental beliefs. It will always be a challenge to develop sustainable business models that do not conflict with core values. But that’s not a bad thing.

When it comes to the market for data in eduction, let’s face it: analytics are a commodity. Every analytics vendor is applying the same basic set of proven techniques to the same kinds of data. In this, it is silly (and even dangerous) to talk about proprietary algorithms. Data science is not a market differentiator.

What DOES differentiate products are the ways in which information is exposed. It is easy to forget that analytics is a rhetorical activity. The visual display of information is an important interpretive layer. The decisions that product designers make about WHAT and HOW information is displayed prompt different ranges of interpretation and nudge people to take different types of action. Dashboards are the front line between information and practice. It is here where values become most apparent, and it is here where products are truly differentiated.


Also published on Medium.