The current HE classroom: Promoting new types of learning, executive function processes and strategies to foster students’ motivation and academic success
Bibliographic record
Abstract
Students' academic success in this digital, globalized era requires their mastery of processes such as goal setting, planning, prioritization, organization, flexibility to change, storage/manipulation of information in working memory, and self-monitoring. These processes are called executive function (EF) processes. It is important to integrate strategies that systematically address these processes in the classroom to help students understand how they think and how they learn. This paper provides a paradigm for understanding/helping students integrate strategies involving EF processes; it describes how strategic, systematic instruction and adaptations to classroom-work and tasks may benefit all students, while effectively addressing the needs of students who exhibit significant weaknesses in these processes. Furthermore, individuals and organizations involved in HE express the need for other important types of learning that do not readily emerge from Bloom's taxonomy: e.g., learning to learn, leadership, interpersonal skills, ethics. This paper introduces Dee-Fink’s proposal for a broader taxonomy of significant learning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".