Critical Perspectives on the Scholarship of Assessment and Learning in Law: Volume 1: England
Bibliographic record
Abstract
It is a well-accepted practice in higher education that the knowledge of law is assessed by written exams and coursework. But do these types of assessment are most suitable to develop professional skills, such as the ability to communicate effectively or the ability to gather and integrate information from various legal sources? An argument can be made that “the traditional exam is not the best way of assessing these skills because it is limited both by time and by the resources students are able to consult [and] ... in a traditional exam it is difficult to assess if professional skills have been acquired in depth” (López et al, 2011). \n \nThe universities in Australia, Canada, Sweden and the US set take-home exams to assess the knowledge of law on a regular basis. However, this type of assessment in the UK universities is not that common. This chapter argues in favour of take-home exam for assessing law students, in order to develop the skills that fall under what can be defined as ‘professionalism’. These skills include personal and professional integrity and ethics, time management, work/life balance, research, the ability to express the ideas in a logical manner and to find solutions to problems, and the ability to predict and to deal with IT and technological challenges. \n \nThe chapter draws on the limited academic literature on the topic and the related topics, the discussions in blogosphere, as well as the authors' own experience of take-home exams. The results of preliminary literature searches reveal lack of discussion of the advantages and the disadvantages of take-home exams to assess legal knowledge, especially bearing in mind the task of educating the future generations of professionals, who may choose legal or non-legal career paths.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".