While I indicated in my previous post that my next one (i.e. this one) would be related once again to the last OECD report, I finally decided to postpone it to share some thoughts that came after a recent reading.
In a recent blog post, Jean-Charles Caillez, a blogger of the Educpros network, described a very interesting experience called “Inverse MOOC”, led at Davidson College in the US. I won’t describe the experience here, since Jean-Charles does that perfectly for French-speaking readers, while English-speaking readers may read a detailed explanation of the experience there.
The way that this experience was led, as well as other, similar kinds of experiences, is often qualified “non-academic”, because it reverses the way that teachers teach and students learn in class (or online). Building on a general topic or on a specific question, students go “on the field”, learn from the field, discuss collectively about what they have collected and what they have discovered, confront their ideas, experiment and learn from their successes and failures throughout the process, with the help of their professor who guides them to make sure that they actually learn from what they do. However, this is all but “non-academic”!!! Indeed, this is a very well-known process of learning and discovery, called inductive learning, and that has been extensively described by science historians – see for instance the excellent book from Alan F. Chalmers entitled “What is this thing called science?” written in the late seventies.
To put it simply (I do not intend to develop an epistemological course in this post, and thus apologize in advance for some of the inevitable shortcuts that may follow, but they should not diminish the general argument :-)), inductive learning is a way of learning that starts from the analysis of facts gathered through observation, in order to develop universal laws and theories. These laws and theories can then be used to predict and explain behaviors, events, etc. through deductive reasoning. Of course, inductive learning does not come without flaws: for instance, observations are fallible because they reflect the perception of the observer, or may not offer other, maybe more interesting and fruitful conceptions.
Partly because of these flaws, scientific reasoning, procedures and methods have progressively favoured deduction over induction, which has translated in scientific publications as a predominance of quantitative studies over qualitative studies (again, this comes with all due apologies for the shortcut that assimilates quantitative studies with deduction, and qualitative studies with induction). At the same time, higher education has professionalized, with academics gaining more weight over practitioners. Followed that teaching methods have been progressively pervaded by the same deductive methods, with more importance granted to theory and less to observations.
Of course, just like inductive learning / teaching is not the best way to learn / teach, nor is deductive learning / teaching. A combination of both is necessary to make sure that theories and practice echo each other, to make sure that observations enrich extant theories or enable the development of new ones, and that extant theories explain the “reality” and propose predictions that may be useful for practitioners when they need to take decisions or to analyze and understand their environment. In the end, even though it may seem obvious – yet not so much when observing teaching principles –, a combination and well-thought mix of teaching and learning practices should be favoured over a so-called pedagogical one-best-way (whether inductive or deductive). For instance, as showed in this article, flipped classroom does not answer everything, despite all its advantages. Pedagogical design is key to great teaching and learning, and this means most often combining different methods (something that I had already underlined with a colleague in a paper we wrote some years ago, focused on multichannel teaching and learning). The aim of this post is to recall that, insofar as the odds are high that after a period that saw the domination of deductive learning, the curve gets the other way around towards a domination of inductive learning. As in many domains, balance is necessary, and imbalance leads to negative or sub-optimal results. This makes, by the way, a perfect link with my next post on the risks of fads and the need for research in Higher Education!