Deep Learning vs. Surface Learning
It occurred to me while I was practicing various coin tricks that there is no possible way that these movements could be learned by studying theory alone. For some skills, surface learning is not always possible. As an example, to pass a coin magic test, a bare bones demonstration of the manipulations based purely on memory would not suffice. Even if your test were just to demonstrate deliberate movements to a master magician, but not at a level of proficiency to fool even an inexperienced observer of magic, but rather to show that you had been practicing (doing the thing), and all you had done was cram the theory in the night before, you would fail miserably. The slow, deliberate, mechanical movements that might be required for such a test would not even be possible by knowing just the theory by memorization alone. Some skills require doing, plain and simple.
Then I started to think about other learning experiences that do not involve physical manipulation, yet still require the same level of dedication to doing in order to pass a test. Something that I learned very early on in my academic career was that some skills require doing in order to even pass the class, let alone do well in any sense. For example, in my Math 101 class (Calculus 2), the failure rate was 50%. Half of the students taking the class were taking it for a second time. Yes, there are gifted mathematically minded students who think that math is a breeze, and they never really had to try very hard. However, for the average person, the only possible way to pass classes like that are to do problems ad nauseam; to spend literally four hours per day, nearly every day for the whole semester was the only way to get a good mark at this class (unless you were one of the gifted few). So, learning by doing isn’t a requirement to learn only physical manipulation skills. Some subjects require a deep understanding in order to even pass the class.
Many teachers of these subjects simply present the material, expecting that their students will pass the course or they won’t, and that’s the students responsibility. In a course similar to Math 101, Linear Algebra was another tough course (at least for me). I remember going to office hours where my professor refused to show me an example of how to go through a sample question, stating that I would just memorize the steps in order to complete similar questions in the future. I got the distinct impression that this professor did not care about how his students did. According to John Biggs, who created the SOLO (Structure of the Observed Learning Outcome) Taxonomy, the highest “level” of teacher are the ones who care about how students do during and after the lecture material, and establish outcomes of learning for the course, and encourage engagement with the material. Clearly, this was an example of poor teaching practice.
If I was to be a a level 3 teacher, one who cared about how students did during and prior to a lecture, and I was my own student, how could I teach myself to be a better coin magician? First off, I would establish a set of learning goals: by the end of this learning experience I would like to be able to demonstrate a convincing display of five magic tricks: the standard vanish, the tunnel vanish, the back clip vanish, the smart vanish, and the King Midas. Next, I would assess myself at the end of a certain interval. This assessment might be well served if it was defined in terms of the SOLO taxonomy.
How might a SOLO taxonomy look for a coin magician marking rubric? Lets take a stab at it. The SOLO has five levels of understanding and, according to Wikipedia, the five levels are defined follows.
- Pre-structural: The task is not attacked appropriately; the student hasn’t really understood the point and uses too simple a way of going about it.
- Uni-structural: The student’s response only focuses on one relevant aspect.
- Multi-structural: The student’s response focuses on several relevant aspects but they are treated independently and additively. Assessment of this level is primarily quantitative.
- Relational: The different aspects have become integrated into a coherent whole. This level is what is normally meant by an adequate understanding of some topic.
- Extended abstract: The previous integrated whole may be conceptualized at a higher level of abstraction and generalized to a new topic or area.
Assume the following are potential statements from the perspective of a teacher of coin magic making an assessment on one of their students demonstrations of learned material:
- Pre-structrual: The student simply holds the coin between his right hand pointer and index fingers, grasps the coin with the left hand and makes no effort to hide the palming of the coin in the right, then opens the left hand showing that the coin is not there, but clearly there is no illusion.
- Uni-structural: The student demonstrates all of the steps of the required illusions poorly except for the palming aspect in each illusion. It is clear that the student has focused on only one aspect.
- Multi-structural: The student demonstrates proficiency in each of the components of the illusions, but flow between each is poorly done, mechanical, and obvious.
- Relational: The student demonstrates the required illusions in a convincing way, each step flows naturally to the next. The student shows promise.
- Extended Abstract: The student demonstrates the required illusions in a natural, convincing way, but goes beyond the requirements of the assessment, narrating each step while they go through the demonstration, simultaneously discussing how each movement component could be utilized for other illusions, improved upon, or which ones are weaker.
To be good teacher to myself, by establishing learning goals in terms of the SOLO taxonomy, I am demonstrating constructive alignment. Good teaching gets students to use their higher level cognitive processing (evaluating, assessing, critiquing) instead of simply memorizing (like in surface learning). The goal is to make a surface learner behave like a deep learner by engaging with their material. To learn we must do the thing, whatever that might be.
This weeks progress
I established that each trick would be practiced 50 times over the course of a week. Completing the practice this week took dedication, and made my hands sore. Not only that, but I was forced to endure what seemed like endless dropping and retrieval of coins. This was certainly an exercise in patience. It would seem that self-assessment by utilizing the SOLO taxonomy in a way that I outlined above could be an exercise in futility, since I am biased of my own skills, and I am not an authority in the area of coin tricks. However, even if I cannot assess myself according to SOLO, I can employ the other aspects of deep learning simply by establishing learning goals in terms of SOLO, and keeping myself engaged with the learning task at hand.
Featured image by Krika99 at Flikr