Operationalizing Jonassen’s Design Theory of Problem Solving: An Instrument to Characterize Educational Design Activities
Bibliographic record
Abstract
Abstract Jonassen’s Towards a Design Theory of Problem Solving (2000) outlines the ways in which problems differ in their structure, complexity, and domain. His influential taxonomy describes eleven problem types that vary on those scales. Design (and to some degree, case) problems are some of the “highest” types of problem-solving, presenting students with complex real-word problems that are presented in an ill-structured way. Jonassen also emphasizes the difference in individual problem solvers, who vary in their familiarity with problems, domain knowledge, information processing, and ability to reflect on the problem at hand. In Engineering programs, especially in junior and intermediate years, students have traditionally been exposed mostly to lower forms of problem-solving (e.g., algorithmic problems, rule-using problems, and troubleshooting problems), lacking significant exposure to real, ill-structured design problems until their third and fourth year. To address this problem, the Faculty of Engineering at the University of Waterloo has launched the IDEAs Clinic, which develops and delivers a multitude of authentic, ambiguous, hands-on design activities that are integrated into existing degree programs. The most ambitious types of activities have been Engineering Design Days (EDD), which are in-class, multi-day curricular activities, where students work in teams to design and build solutions to open-ended problems, using knowledge from multiple courses. Our overarching research objective is to understand the effectiveness of EDD in developing engineering students design problem solving skills. Our research framework includes the characterization of (1) the EDD activities, (2) the problem solvers (i.e., students), (3) the problem solvers’ problem solving (i.e. design) process, and (4) the solution (i.e. design). We aim to understand the interaction of (1) and (2) with the hopes of improving (3) and (4). A first step in this research – a work in progress, is the development of an instrument for characterizing the content and structure of EDD activities. In this paper, we describe the development of an instructor survey that seeks to operationalize Jonassen’s definitions of problem structure, complexity, and representation. The survey development process began with semi-structured interviews with four instructors of previous EDD activities. These formed the basis for creating the survey questions. Survey questions were validated using a think-aloud protocol with a fifth EDD instructor. The final stage of data collection will include the dissemination of the survey to instructors of 5 different EDD activities held in the Fall 2019 term (all different from the ones interviewed/surveyed in earlier stages). This work in progress paper summarizes our efforts in developing an objective measurement instrument capable of describing ill-structured in-class design activities, and reports on the survey’s effectiveness in capturing variations in the different EDD activities along the problem dimensions described by Jonassen.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".