Application of Bloom's taxonomy to PSI.
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Notice bibliographique
Résumé
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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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle