La valutazione formativa come strategia di apprendimento
Bibliographic record
Abstract
In this period, the higher education systems are committed to a thorough reform to optimize the quality of learning and to identify functional skills to increase employability and to participate actively in community (Ministerial Conference Yerevan, 2015; Felisatti, Serbati, 2017; Boffo, Fedeli, 2018). In this context it is essential to offer students the opportunity to build learning ability together to allow the acquisition of an increased degree of understanding, thanks to the support of the teacher (Venet, Correa Molina, Saussez, 2016; Cinque, 2013; Vygotski, 1931/2014; Chauvigné, Coulet, 2010). In this perspective, the paper presents a case study that concerns a group of students attending the course of pedagogy and special didactics for professional education at the University of Cagliari. The analysis of the context has led to the following reflections: What activities should the teacher present to help students achieve the programmed learning objectives? How can the teacher assess the expectation of learning goals? How can you implement the feeling of competence in learners? How can you give students the possibility of controlling their learning? (Magnoler, 2018; Coggi, 2016; European Commission, 2008; Semeraro, 2006). The hypothesis proposes to put in coherence the objectives of the course with the methods and the evaluation process (Biggs, 1996; Peretti, Tore 2016). In this regard, the teacher of the course shared with the students the teaching form elaborated according to the objectives indicated in the Dublin Descriptors (Tore R., 2017; Ciavaldini-Cartaut 2016). It also provided the methods (Bonaiuti, 2017; Ramsden, 2002) for the study work and shared with the students the use of the formative assessment which favoured and positively anticipated the results of the final exam (Coggi, Ricchiardi, 2018; Poumay, 2014; Cajola, Domenici, 2005; Romainville, Coggi, 2011; Rapport du Canada, 2005; Leone, Moretti, 2010).
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".