The importance of critical thinking in STEM subjects (Science, Technology, Engineering and Mathematics) does not show through the way they are taught in universities.
Teaching heavily relies on lectures. These scripted sessions however rarely provide students opportunities to think, due to the limited time available and the audience scale; students usually end up listening passively to the instructors, oftentimes taking notes to stay alert. Their format also did not dramatically varied over the years despite being more technologically-integrated: it is still learning through listening.
Active reading of textbooks and problem solving take up most of the remaining study time, and should give students chances to think. They would ideally try to discuss and assimilate the knowledge before applying it to a few scenarios. In practice, this sequence is often reversed: the set exercises become the base material, relegating the course notes to a reference guide. In this case, thinking becomes secondary, and learning is just a matter of memorising and following instructions.
Reflecting pool is a learning method for STEM subjects in universities, powered and directed by students’ critical thinking.
Instead of relying on a pre-written scenario, Reflecting pool uses students’ own questionings, doubts and thoughts as the primary material for transmitting knowledge.
As they actively read the course notes, students are encouraged to suggest open-ended questions 1 to the class, in an anonymous and shame-free setting.
The most popular interrogations are promoted, signalling that they can now receive answers. Students are then given a short period to make up their own mind, and write detailed answers to these questions, even if they believe that their solution is flawed.
The optimisation phase begins once enough responses have been collected. In this stage, students consecutively review and grade 2 a few answers from the pool, thereby confronting their own thinking to a likely different opinion. They are then given the chance, after each review, to re-consider, and possibly to amend their own answer (without penalty).
Student answers are expected to change throughout the optimisation phase, converging, in the end, to what they confidently think is their most thoughtful and convincing proposition.
Reflecting pool gathers, analyses and compiles 3 these answers and grades into a map which shows how the class, as a unit, answered each of these questions.
Answers, represented as small pointers, are positioned according to their similarity: two similar answers are shown close to each other, and vice versa.
By looking at the map, instructors can therefore identify if a consensus or a dispute existed among the students, and judge, after reviewing a few answers, whether the underlying concepts were properly assimilated 4. This requires just a few minutes, and do not rely on long examination and correction sessions.
From there, any misunderstood concept could be re-explained, in more details, using the gathered data, in dedicated lectures.
Questions are contextualised: they can refer to a specific chapter, paragraph, or figure. They may even include runtime parameters for an explorable explanation, or annotations for a figure.↩
To account for the fact that each student only grades a limited sample of answers, the platform uses a low-rank matrix completion algorithm to extrapolate the missing grades based on an initial group of reviews. This is a well-known approach similar to recommendation systems (Netflix ratings…).↩
Reflecting pool encourages, by design, students to think critically as they learn new concepts.
This process provides time and space to think. It does not expect initial answers to remain untouched: students have many opportunities to change their minds, to make mistakes, to re-write their answers as they are being confronted to other students’ viewpoints.
Answers are anonymous. This frees students from any premature judgement when reviewing: they have to look at each solution with the same fresh eye and attention. All opinions are legitimate.
This anonymity also gives the teaching staff the possibility to suggest answers without being detected. They would typically write persuasive, convincing yet wrong explanations, to see the class reaction. This way, students must remain constantly alert and skeptical; no opinion, as cogent as they might sound, should be accepted as granted.
A proof of concept was successfully developed. Implementation and extensive testing within the Imperial College’s Department of Mechanical Engineering was planned, but it has been recently postponed due to time constraints.
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