BEYOND BLOOM’S: USEFUL CONSTRUCTS FOR DEVELOPING GRADUATE ATTRIBUTE INDICATORS
Bibliographic record
Abstract
What do we want our students to learn from an experience? This is the central question that underpins learning objectives. Learning objectives attempt to describe the manifestations of learning that we would like to see by the end of a learning experience (e.g. a course or a learning module). Traditionally areas of knowledge that are the target of learning objectives are described as domains. Typically knowledge is described as cognitive, affective, or psychomotor and there are other domains such as interpersonal1-4. The domain describes the nature of the learning. Has the student learned a new cognitive process, or learned to care about something new? The organization of learning into these domains helps us to make sense of the types of knowledge that our students are learning. A domain is like a country, it defines a piece of the knowledge landscape. A taxonomy of learning attempts to map that landscape. It creates categories that describe ways of knowing. Just as a map describes the landscape using categories (e.g. roads, parks, towns), a taxonomy categorizes ways of knowing so that we can better define the manifestation of learning that we want our students to achieve. Most taxonomies are meant to be thorough maps of one domain. For example Bloom’s taxonomy describes ways of knowing within the cognitive process domain1. It attempts to categorize all of the different levels of learning in this domain. When Anderson and Krathwohl later updated Bloom’s taxonomy they added a second dimension, the knowledge dimension, which breaks apart the domain into 4 parts: factual knowledge, conceptual knowledge, procedural knowledge, and metacognitive knowledge5. Their taxonomy applies the same levels of learning (i.e. cognitive processes) to each of these four pieces of the domain. Bloom’s (or Anderson’s) do not describe everything that a student should learn. They are only meant to describe one type of learning: cognitive process. Other taxonomies map other domains and some taxonomies cut across domains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".