Board 128: An Automated Management Process for Digital Correction
Notice bibliographique
Résumé
Abstract When students complete an assessment activity, the teacher's corrective work begins. It is done under the pressure of time. However, correcting in a relatively short time may affect the quantity and quality of feedback. It is therefore not surprising that the teacher is thinking of time-saving solutions to provide and further enrich their feedback while offering rapid correction. Traditionally, in mechanical engineering courses featuring multiple groups and a large population of students, the grading is organized so that each teacher corrects an open-ended engineering problem on an exam for all groups in the same course. Correcting this way is fairer and faster than each teacher correcting all problems for a subgroup of students. This method also blurs the Pygmalion effect. The downside is that the teacher must wait for a colleague's papers to return before they can correct their question. To recover this lost time, we developed an automated digital correction management tool (a Python script). The tool automatically splits scanned student copies by question into pdf format and then assembles, for each question, all student copies into a single file. A customized exam booklet, with known allotted number of pages for each question, was produced to ease the process. The assembled files are uploaded to a cloud storage platform, where each teacher can access their assigned file for handwritten correction on a tablet using a digital pencil. Thus, the teacher corrects at his own pace, avoiding conflicts with other correctors. The anonymity of copies is guaranteed since the identification page of the exam booklet is absent from the assembled files, and the equality of chances is reinforced. When the correction is complete, a digital mark recognition algorithm is used to extract the marks assigned to each question by the correctors. The copies are reassembled and the mark is reported automatically for each question on the main page of the exam booklet. The integration of the tool into the correction process has been seamless, since only the professor in charge has to interact with the tool and the correctors grade the copies in the same manner as usual – albeit on a tablet. Digital correction allows both the student and the teacher to benefit from the advantages of digital technology. It saves the teacher the need to carry a large number of copies and reduces the risk of loss of copies. Electronic delivery of copies to students also frees up the classroom time usually reserved for handing in copies. This time is now used more efficiently by the teacher to give feedback to the class.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».