GBS aka CBR aka PBL

While Schank’s work in scenario-based learning is well-known, this article expands his approach to encompass a broader design theory he calls goal-based scenarios and a learning theory he terms case-based reasoning; both seem derived from problem-based learning. CBR is postulated as the way in which experts solve problems and is essentially learning from prior experience via analogy: a case is a memory, and experts have large libraries upon which they draw. CBR enables reasoning across contexts, and while it seems obvious that experts organize their libraries through indexing (labeling and filing), little practical design advice is offered. The general suggestion to design roles and goals to create motivational and sensible contexts is tied to Schank’s narrative approach. More generally applicable are the ideas that goals produce expectations, and that expectation failures demand explanations; the necessity of failure as a primer for learning (reminiscent of Wiggins’ misperceptions) suggests building learning experiences with high probability of non-optimal solutions. The most useful aspect of the article came in the editorial comment (not from Schank) that teaching is the transition from learning theory to design theory.

The 7 components of GBS include:

  1. Goal – process knowledge or content knowledge learning goal
  2. Mission – performance goal provided for initial motivation
  3. Story – narrative for immersion and context leading to motivation
  4. Role
  5. Operations – activities with decision points leading to consequences
  6. Resources – well-organized stories to compare with cases in memory
  7. Feedback – consequences, coaching, and domain expert stories
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Case-Based Problem Solving

Uribe, Daniel and Klein, James. “The Effect of Case-Based Versus Systematic Problem Solving in a Computer-Mediated Collaborative Environment.” The Quarterly Review of Distance Education 4.4 (2003): 417-433.

The article is a few years old but the premise was interesting: in a collaborative online environment, is case-based or systematic problem-solving more effective. Uribe and Klein begin by citing research that argues for case-based learning: more effective (than lecture) because it more accurately reflects (real world) ill-defined problems; allows learners to develop strategies characteristic of experts. However, I was surprised to learn that case-based learning is at the bottom rung of a scaffolded 4-level tier: cases are product-oriented because they provide a given state, a goal, and a solution. I’ll need to read the citation to Van Merrienboer (2002) to learn about the top 3 rungs (I’m guessing that the top rung is real life).

Cases were juxtaposed with systematic problem-solving which provides learners with rules of thumb (or a process) for solving a class of problems; Gagné couched problem-solving as an extension of both rule learning and schema learning, so the provision of rules and a schema should allow for problem-solving transfer. The authors mention the vast amount of information available on the Web as an advantage to constructing a problem-solving environment; however, they fail to expand on this idea to discuss that this advantage is also the characteristic that demands a problem-solving approach.

The research itself surprised me, mostly because the authors were actually more interested in testing the size of groups than the effectiveness of case-based over systematic problem-solving. Nevertheless, the study showed:

  • NSD between the two approaches in terms of mean performance scores (although systematic learners scored higher than case learners)
  • dyads scored higher than quads, especially among systematic learners
  • more time spent on the tutorial by case learners, especially in quads (this makes sense because a case approach is less didactic)
  • less time spent solving the problem by case learners, especially in quads

The conclusions focused on the size of groups, presumably because the data indicated differences due to group size but not to method (this may suggest that research should always entertain multiple hypotheses in case the data is unclear about one). The authors attribute the difference to increased and higher-quality interaction in dyads than in quads, although they admit the interaction technology may have played a role (text-based chat-indeed all synchronous virtual communication – is more complex when more than two “speakers” are involved because of coordination). And case-based dyads had significantly more communication than any other combination and solved the problem in the smallest amount of time (with only a slightly lower score than systematic-based dyads). The intriguing claim is that 65% of case-based learners used a 3-step heuristic (as opposed to the 4-step heuristic explicitly taught to systematic learners) which could explain the additional time spent on the tutorial by case learners; the intrigue comes from this question: since case learners derived their own heuristic, could they more quickly and more accurately (in terms of performance) solve a related problem?