Designing First-year Sociology Curricula and Practice
Bibliographic record
Abstract
Many countries are now specifying standards for graduates in different disciplines, including sociology. In Australia, the Australian Sociological Association (TASA) has developed Threshold Learning Outcomes (TLOs) for sociology to provide the learning outcomes that students graduating with a bachelor’s degree in sociology should achieve. These TLOs have encouraged universities to think explicitly about their sociology curriculum in a holistic way. This paper reports on a project that investigated the skills and concepts sociology students need to learn in first year to meet the TLOs by the time they graduate. The project identified the needs of students as they transition from school or work into the study of sociology in first year through a study of literature of first-year pedagogy and a student survey. A workshop was held for sociology that involved 37 academics from 14 universities. The workshop was used to promote a rethink of teaching of sociology in the light of the new TLOs as well as to collect ideas from the participants. The student surveys, workshop ideas and relevant literature were analyzed and synthesized for each TLO to determine what skills and concepts first-year students needed to learn, identify what they might find difficult and propose strategies for teaching. The paper also provides practical ideas for engaging academics with thinking holistically about the sociology curriculum and for teaching and learning sociology in the first year of an undergraduate degree.
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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 | 0.046 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".