Orienting Teaching Toward the Learning Process
Bibliographic record
Abstract
Based on developments in educational psychology from the late 1980s, the authors present a model of an approach to teaching. Students' learning processes were analyzed to determine teacher functions. The learning-oriented teaching (LOT) model aims at following and guiding the learning process. The main characteristics of the model are (1) the components of learning: cognition (what to learn), affect (why learn), and metacognition (how to learn); and (2) the amount of guidance students need. If education aims at fostering one's ability to function independently in society, an important general objective should be that one learns how to fully and independently regulate his or her own learning; i.e., the ability to pursue one's professional life independently. This implies a transition from external guidance (from the teacher) through shared guidance (by the student together with the teacher) to internal guidance (by the student alone). This transition pertains not only to the cognitive component of learning (content) but also to the affective component (motives) and the metacognitive component (learning strategies). This model reflects a philosophy of internalization of the teacher's functions in a way that allows optimal independent learning after graduation. The model can be shown as a two-dimensional chart of learning components versus levels of guidance. It is further elaborated from learners' and teachers' perspectives. Examples of curriculum structure and teachers' activities are given to illustrate the model. Implications for curriculum development, course development, individual teaching moments, and educational research are discussed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".