A Summary and Response
In his Scientific American article, How Big Data is Taking Teachers Out of the Lecturing Business” Seth Fletcher describes the power of data-driven adaptive learning for increasing the efficacy of education while also cutting the costs associated with hiring teachers. Looking specifically at the case of Arizona State University, where computer-assisted learning has been adopted as an efficient way to facilitate the completion of general education requirements (math in particular), Fletcher describes a situation in which outcomes for students scores increase, teacher satisfaction improves (as teachers shift from lecturing to mediating), and profit is to be made by teams of data-scientists for hire.
There are, of course, concerns about computer-assisted adaptive learning, including those surrounding issues of privacy and the question of whether such a data-driven approach to education doesn’t tacitly favor STEM (training in which can be easily tested and performance quantified) over the humanities (which demands an artfulness not easily captured by even the most elaborate of algorithms). In spite of these concerns, however, Fletcher concludes with the claim that “sufficiently advanced testing is indistinguishable from instruction.” This may very well be the case, but his conception of ‘instruction’ needs to be clarified here. If by instruction Fletcher means to say teaching in general, then the implication of his statement is that teachers are becoming passé, and will at some point become entirely unnecessary. If, on the other hand, instruction refers only to a subset of activities that take place under the broader rubric of education, then there remains an unquantifiable space for teachers to practice pedagogy as an art, the space of criticism and imagination…the space of the humanities, perhaps?
As the title of Fletcher’s piece suggests, Big Data may very well be taking teachers out of the lecturing business, but it is not taking teachers out of the teaching business. In fact, one could argue that lecturing has NEVER been the business of teaching. In illustrating the aspects of traditional teaching that CAN be taken over by machines, big data initiatives are providing us with the impetus to return to questions about what teaching is, to clarify the space of teaching as distinct from instruction, and with respect to which instruction is of a lower-order even as it is necessary. Once a competence has been acquired and demonstrated, the next step is not only to put that competency to use in messy, real-world situations–situations in which it is WE who must swiftly adapt–but also to take a step back in order to criticize the assumptions of our training. Provisionally (ALWAYS provisionally), I would like to argue that it is here, where technê ends and phronesis begins, that the art of teaching begins as well.