Bibliographic record
Abstract
A modified form of taxonomy from the cognitive shows promise as a way to behaviorally define and develop higher-order thinking in college level courses taught using computer-aided personalized system of instruction (CAPSI). In system, levels of material mastery are assessed behaviorally at the knowledge (or rote memorization), comprehension, application, analysis, synthesis, and evaluation levels. Here we explore their usefulness in specifying educational objectives for CAPSI courses. Research currently in progress focuses on moving students from the lower to the higher levels in our CAPSI-taught courses at the University of Manitoba. ********** The prescription for teaching a course using the personalized system of instruction (PSI) developed by Keller (1968) is straightforward, and follows the behaviorist formula: First define the behavior you want to teach; then arrange the contingencies that will establish, reinforce, and maintain that behavior. In PSI, the behavior you want to teach is defined by study questions on the course material. The contingencies are specified by the units the material is divided into, the way in which the learner's answers to the questions are evaluated, and the reinforcement that is provided for correct answers to the questions. Various ways of arranging the contingencies have been described in great detail, and validated in numerous experiments in which variables are manipulated (Born, Gledhill & Davis, 1972; Brooke & Ruthven, 1984; Buerkel-Rothfuss, Grey & Yerby, 1993; Caldwell, Bissonnettee, Klishis, Ripley, Farudi, Hochstetter, & Radiker, 1978; Glick, Moore, Roberts & Born, 1982; see Kulik, Kulik, & Bangert-Drowns [1990] for a meta-analysis showing the effectiveness of PSI.) In contrast, there is very little information on how to specify the educational objectives in a PSI-taught course. A modified form of taxonomy (Bloom, 1956; Crone-Todd, Pear, & Read, 2000; Pear, Crone-Todd, Wirth, & Simister, in press) from the cognitive shows promise as way to behaviorally define and develop such objectives. What kinds of study questions should the instructor write? Presumably, in keeping with typical behaviorally defined goals, one should write the kinds of questions that occasion responses capable of wide application or generality. But what kinds of questions would those be? Likely they would not be questions that ask for isolated facts or describe contexts having little relevance to situations in which the student would likely find him or herself in later years. These would be questions asking the student to apply what he or she has learned, either practically or verbally. Also they would probably be questions about situations that are novel and largely unpredictable, especially given that the effects of learning ideally are supposed to last for years and even decades. Early on, factual knowledge questions would be rather specific and produce discrete responses under tight control. Later, questions that evoke a wider range of applications in the world are used to help develop more creative responses that involve combining of elements. The latter type of questions is emphasized by educators (even if, for practical reasons, they are not always true to this goal), since knowledge that goes beyond the merely factual is considered the hallmark of education. Knowledge that goes beyond the factual is often called higher-level thinking. But what is it, and how do we teach it? In computer-aided PSI (CAPSI) courses at the University of Manitoba (Kinsner & Pear, 1990; Pear & Crone-Todd, 1999; Pear & Kinsner, 1988; Pear & Novak, 1996), rather than re-invent the wheel we are researching a question-level classification scheme called Bloom's taxonomy in the cognitive domain (Bloom, 1956; Crone-Todd et al., 2000; Pear et al, 2001). This classification scheme is a good starting point for behavior analysts studying higher-order thinking because it has face validity and its terms can be behaviorally defined. …
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".