The aim of the “Journal Club” is to present a summary of a journal article and discuss it in the comments below or on social meeja. The emphasis is not on discussing the paper itself (e.g. methodology etc) but more what the observations or outcomes reported can tell us about our own practice. Get involved! It’s friendly. Be nice. And if you wish to submit your own summary of an article you like, please do. If you can’t access the paper in question, emailing the corresponding author usually works (CERP is free to access).
Comments on this article are open until Friday 27th September.
#2 T Overton, N Potter, C Lang, A study of approaches to solving open-ended problems in chemistry, Chemistry Education Research and Practice, 2013, doi: 10.1039/C3RP00028A
There is a lot of literature that promotes the use of contextualization in our teaching, to generate interest and promote engagement in a topic. This is often coupled with more open-ended activities, reflecting real-life situations. There is also a lot of literature that advocates that teachers should be cognisant of working memory by providing structure to student learning and by reducing “noise” as much as possible. I see these as conflicting viewpoints, and have struggled with over the last few years in thinking where the balance between providing enough of the “carrot” of context and the “stick” of structure lies.
Tina Overton’s paper on student approaches to open ended problems is useful (and unusual) in this regard; the opening section presents a synopsis of several studies on approaches to problem solving when the problem is structured or algorithmic. But what happens when students are given a problem where the data is incomplete, or the method is unfamiliar, or the outcomes may be open (Johnstone, 1993)? Three examples of such problems are given. One of these is:
Many commercial hair-restorers claim to stimulate hair growth. If human hair is composed mainly of the protein α-keratin, estimate the rate of incorporation of amino acid per follicle per second.
I have to be honest and say that this question scares the hair out of me, made my hair stand on end, and other hair-related puns, but as an “expert” in the discipline, I think I would have an idea where to start. The research involved listening to students talk out how they approached the problem, and these approaches were coded. They ranged from approaches that could lead to a feasible solution (makes estimations, knowing what approach to take, having a logical reasoning or approach, sensible assumptions) to approaches that are unlikely to succeed (seeking an algorithmic approach, distracted by context, lack of knowledge). When participants were grouped by common approaches, three distinct groupings emerged.
- The first were those who used predominantly positive (scientific) approaches to the problem. They could clarify the aims and identify the data needed and assumptions that could be made. (10/27)
- The second where those who couldn’t attempt the problem, they didn’t know where to start in tackling the problem and didn’t have the basic knowledge required to call upon. They weren’t able to take a scientific approach to solving the problem. (10/27).
- Finally were students who got caught out with their prior knowledge confusing them (e.g. writing out rate equations), and although they tried to tackle the problem, were unsuccessful (7/27)
The study participants were from all years, but the authors state that there groups identified above did not correlate with stage in degree.
This study interests me a lot. The headline is that of this (small) sample of students, a majority had difficulty with open ended, highly contextualised problems. I am making a sweeping statement, but I would hazard a guess that students in this institution get exposed to a lot more open ended problems than average. However, some students displayed significant expertise in approaching problems, and frankly their responses recorded are very impressive. Questions I think worth teasing out are:
- Do you use open-ended problems of this nature in teaching? Why/why not?
- There is clearly a gap in approaches. The middle group are caught in between, making good efforts to solve the problem, but calling on irrelevant prior knowledge. If you were planning to incorporate open-ended problem solving, how could this gap be addressed in curriculum design?
In regard to the second question, I’m trying to think of an approach around the worked example/fading approach used for simple algorithmic problems. Would something similar have a role, or does that approach necessitate a change in categorisation of the problem, back to closed, defined…?
Love to hear your thoughts. Remember the “Be nice” part…