Most trainers are "conditioned" to expect improvements in the performance and also the "happiness" of their trainees. Instructors don't see how their students perform after a course, but during the course, it is important for them, as well as for the students, to see that progress is being made with measurable improvements. However, most training methods produce impressive short term improvements with no long term benefits. As an example, practice drilling exercises may give the impression that the students have actually acquired a set of knowledge and skills through increasing familiarity of the material. However, it has been shown that the more familiar the learners believe they are with a subject, their actual level of comprehension is actually inversely related (as least in certain domains like physics or music). As another example, if answers are given readily, learners adopt a mentality that they "knew it all along". Hence a well polished lecture where the listeners can follow easily may give the illusion that the learners have already learned or known the material, which in fact, may not be the case when they are called upon the task to actually solving a problem based on the material presented.
Bjork gives five examples of training that may produce durable transfer of knowledge and skills in post-training environments. This implies long term retention and transfer of knowledge and skills to new situations. The key is to introduce meaningful and desirable difficulties in the training. Here are the five examples:
- Varying the conditions of practice such as the (un)predictability of the training environment, scheduling practice exercises in variety and in random fashion rather than a block of training on one specific task, etc.
- Providing contextual interference such as designing and interleaving materials to be learned, rearranging the material presentation that is inconsistent with an outline, or adding to the complexity of the tasks to be performed.
- Distributing practice on a given task over time rather than "cramming" the material in a short session.
- Reducing feedback to the learner (mainly applicable to motorized skill).
- Using tests as learning events rather than providing more study opportunities.
In Computer Science, the lab exercises and problem sets do help students in their learning, especially when the complexity and context of the problems are varied. By mixing the type of problems to be solved, changing the duration between similar types problems to be solved, and the use of tests to continue monitor student progress, not just within a course, but over several courses, we may begin to get a clearer picture of our student learning.
Bjork, R. (1994). Memory and metamemory considerations in the training of human beings. In J. Metcalfe and A. Shimamura (Eds.), Metacognition: Knowing About Knowing (pp. 185-205). Cambridge, MA: MIT Press.