The Art of Questioning: Using Bloom’s Taxonomy in the Elementary School Classroom
Bibliographic record
Abstract
The stated goal of education is to help students acquire knowledge through comprehension. Because of its potential to promote comprehension and learning, questioning is one of the most influential teaching strategies. Academic research confirms that children develop critical thinking skills through teacher-facilitated questions (Ennis, 1996). Consequently, the purpose of this workshop is to provide pre-service teachers with an opportunity to reflect upon ways of using questioning techniques in the classroom to help challenge students' thinking. In this workshop, pre-service teachers will use a taxonomy for classifying educational objectives originally developed in 1956 by Benjamin Bloom and a group of educational psychologists. This taxonomy consists of six criteria: 1) knowledge, or the recall of information; 2) comprehension, or the understanding of concepts; 3) application, or problem solving; 4) analysis, in which students separate the material into its various components; 5) synthesis, in which students combine elements to form a new structure; and 6) evaluation, or judging the material. Using Bloom’s Taxonomy (1956) and a more recent revision (Anderson, 2006), this workshop will demonstrate the value that meaningful questions have in the development of children's cognitive and critical thinking abilities. Specifically, participants will: (a) develop questionnaires for lessons; (b) reflect upon the rationale for certain types of questions; and (c) generate developmentally appropriate questions.
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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 | 0.047 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".