We live in an increasingly quantified world.
Advances in electronic database architectures, a rapid increase in computing power, and the development of sophisticated strategies for analyzing massive amounts of data have converged to produce analytics as a distinct approach to solving complex problems in a variety of fields. Sitting at the intersection of data warehousing and data mining, analytics has been used in sciences such as physics, biology, and climate science since the 1970’s. It is used extensively in business as a tool for optimizing processes, and by marketers as a way of targeting advertisements to particular audiences. We are living at the very beginning of an era of data-driven decision-making, in which sensors are capable of capturing data about nearly every part of our lives, in which massive data warehouses are capable of storing and making this data accessible, and in which computing power and sophisticated data mining techniques are capable of providing feedback to stakeholders in near real-time.
Analytics arrived late to the learning sciences. This first journal dedicated to the use of analytics in the learning sciences, the Journal of Educational Data Mining, only began publication in 2009 (Baker & Siemens, 2013). Since then, however, the field of education has been transformed, as institutions increasingly seek to leverage the power of their existing databases in order to improve efficiency and increase student success by optimizing their learning environments. EDUCAUSE has called Learning Analytics a ‘Gamer Changer’. The 2013 Higher Education Edition of the NMC Horizon Report lists data-driven learning and assessment as a mid-range trend likely to take three to five years to create substantial change.
What is Learning Analytics?
In its most common and general formulation, learning analytics is defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for the purpose of understanding and optimizing learning and the environments in which it occurs” (Long & Siemens, 2011). To those actively involved in the use of learning analytics, it represents a powerful set of tools and techniques that will increasingly allows administrators and instructors to identify at-risk students in order to develop strategies that increase their chances of success. In higher education, the field of learning analytics consists primarily of individuals representing two different perspectives. On the one hand, administrators and university information technology (IT) professionals are interested in leveraging their existing IT infrastructure in order to track student activity and increase student success, usually defined either in terms of retention or grade performance. The primary publication venue for this group is EDUCAUSE and its various publications. On the other hand, there are data scientists who are interested in optimizing learning within particular learning environments. Although there is a growing number of organizations and journals dedicated to learning analytics from this perspective, the most established and prominent venues for scholarly communication in this field are the Journal of Educational Data Mining and the International Conference on Learning Analytics Knowledge. The orientation of these two communities is symptomatic of the history of analytics more generally, which comes out of IT and business intelligence on the one hand (the orientation of EDUCAUSE), and out of data science on the other (the educational data mining perspective). What is currently largely lacking from the field of learning analytics, however, is a humanistic viewpoint, a perspective that would first ask deeper questions about the task of education, and only afterward inquire after how best to accomplish that task.
What is Humane Education?
Humanists of the early Italian Renaissance — specifically Vergerio, Bruni, Piccolomini, Guarino, and Vegio — are remarkable because they were first and foremost teachers, and pursued philosophy only as a secondary activity. For these thinkers, philosophy was not an important activity in and of itself, but rather as served an important supporting function in the service of the larger task of education. Looking to this Humanist tradition, I will focus on three main themes that address the ‘what,’ the ‘how,’ and the ‘why’ of education. In answer to the ‘what’ question, humane education is concerned with cultivating those habits and sensibilities required in order to be responsive to particular situations. For the Humanists of the Italian Renaissance, education involved cultivating ingenium, or the capacity to rally disparate and apparently unrelated elements and put them together in such a way to address the demands of a particular here and now. The ‘what’ of humane education is not a content or a method, but rather an openness to the world, a practice of embracing information from multiple and diverse sources in order to be prudent at a moment that calls for action. Second, the ‘how’ of education involves eloquence. Through eloquence, the student is not taught, but rather seduced into a personal relationship with a world of knowledge. It is a mistake to think that the teacher is in any way responsible for the student’s learning, for learning only ever takes place in the learner. The teacher-student relationship is not one in which some datum is effectively transferred from one mind to another. Instead, the teacher functions as a liaison between the student and knowledge with the task of showing, modeling, and inspiring. Lastly, for the Humanists of the Italian Renaissance who were tremendously influenced by Cicero’s De Oratore, the general aim of education is truth, by which they meant knowledge of the whole. On the other hand, and more specifically, they gave a special place to self-knowledge as necessary in order to arrive at an understanding of the whole. Giambattista Vico, for example, insisted that “knowledge of oneself is for everyone the greatest incentive to acquire the universe of learning in the shortest period of time.” From the humane perspective, the end of education as a relationship between teacher and student is merely to bring students into an understanding of themselves and in such a way as to make learning possible. In order to maximize students’ potential for learning, students must first come to an understanding of themselves as ingenious agents responsible for their own learning activity.
Why Humane Education?
The question of the compatibility of learning analytics and humane education is important for three reasons. First, as mentioned above, in spite of the interdisciplinary aspirations of the emerging field of learning analytics, the humanities are currently almost entirely unrepresented. In fact, within some circles (particularly within the educational data mining community), the kind of non-experimental research that is characteristic of studies in the humanities is treated with relative disdain. If the field of learning analytics seeks to make prescriptive judgments about learning in general, and learning is something that takes place within the sciences and humanities alike, then the field is doing itself a disservice by excluding the humanities from the conversation. Furthermore, since institutional decisions with consequences for the future of higher education are increasingly data-driven, a failure on the part of humanists to take a critical interest is to give up their place at the table.
Second, the role of the humanities is to ask, not just how best to accomplish a particular end, but rather to interrogate the end itself. The language of optimization pervades the field of learning analytics, but there is not a clear consensus within the field about what an optimal state might look like (except, perhaps, the two most common definitions of success: (1) retention through to degree, and (2) a grade of C- or higher). In other words, there is a strong sense that there is a standard of success, but little reflection on what that standard is, and why it should be adopted as the end of education.
Lastly, and most importantly, in the absence of a humanistic perspective, the assumptions underlying the field of learning analytics put it at odds with the demands of education in the twenty-first century. Bauman, Thomas and Brown, Davidson, and a growing contingent of others observe that the social landscape has seen a radical shift since the 1950s, that technological advancement and globalization have thrust us into a world of constant change. In this new social and technological milieu, what is called for on the part of individuals is exactly the kind of ingenious activity demanded by the Humanists of the Italian Renaissance. With respect both to the conception of human nature put forth by these thinkers, and to the skills necessary to survive and thrive in the 21st century, education must be humane in the sense described above. The problem with learning analytics, however, and with the data-driven approach to problem solving in general, is that it tends to undermine the very creativity that education needs to cultivate. In a recent article in Wired Magazine, Felix Salmon notes that in business and politics, with respect to systems of people, ‘quants’ can arrive at highly efficacious insights that have a tremendous amount of predictive power, but that algorithm-powered systems have a way of encouraging people to change their behaviors in perverse ways, in order to provide a system with more of what it is designed to measure. In other words, proxy variables quickly become mistaken for the concepts they are meant to represent. As a consequence, predictive systems end up rewarding conformity and discouraging innovative behaviors that actually produce enduring value.
The question, ‘is learning analytics compatible with humane education,’ is not a question about the use of learning analytics in the humanities. It is rather a question about the use of learning analytics in general. If learning analytics is incompatible with humane education, then it ought to be severely restricted in scope, if not jettisoned entirely, as a technique that can only undermine self-knowledge and human flourishing. What I would like to suggest, however, is that learning analytics and humane education are not incompatible at all. The problem is not with analytics itself, but rather emerges only with a lack of reflection upon what it might mean to incorporate analytics as a meaningful part of a complete philosophy of teaching and learning.