Bibliographic record
Abstract
When problem based learning occurs in a social context it is open to a common social behaviour, scrounging. In the animal behaviour literature, scroungers do not attempt to find resources themselves but rather exploit resources found by other group members (referred to as producers). We know from studies of animal behaviour (including humans) that scrounging can be expected whenever animals exploit resources in groups. We also know that scrounging can have deleterious effects on the group. We can expect scrounging to occur during social learning because the exchange of information (which I will consider here as a resource) is essential to social learning. This exchange can be seen as each individual scrounging from the other members of the group whenever the individual learns from the work of others. However, there is a danger if some individuals learn mostly through their own efforts while others indulge in “social loafing” relying heavily on colleagues to provide knowledge. Here I propose that game theory models developed to analyse feeding in animal societies may also apply to social learning. We know from studies of birds feeding in groups that scrounging behaviour depends on the extent to which resources can be shared. Further, when scrounging is prevalent groups tend to obtain fewer resources. By contrast, in social learning we attempt to facilitate sharing of knowledge. We thus encourage scrounging and run the risk of reducing learning within study groups. Here I analyse the role of scrounging in problem based learning. I argue that scrounging is inherent and necessary to any social learning process. However, it can have perverse effects if the acquisition of facts rather than understanding comes to dominate learning objectives. Further, disparities among individuals within a group can lead certain individuals to specialise in scrounging thus undermining the functioning of the group. I suggest that motivation, problem structure, discussion group dynamics, attention to results expected from students and careful evaluation can be used to encourage scrounging as a cooperative tactic while minimising its negative impacts on group performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".