Digital Writing and Labor-based Grading: An Equitable and Inclusive Approach to Undergraduate Writing Instruction
Notice bibliographique
Résumé
The tradition of digital writing instructional practices is nearly 50 years old in the US (Handa, 2004). Sometimes, students fully online digital writing courses may fail to engage with and finish the course because they do not feel competent in handling the technologies in the academic context; thus, it is important to find out what equitable practices and what factors influence student success in these courses. Therefore, this study aims to examine digital writing assignments requested in a writing course, with the goal of demonstrating an inclusive and equitable practice: the Labor-based grading contract, in a way that promotes equal and fair grades. This practice is proposed as a solution to the shortcomings detected, since it has been observed that students inexperienced in pre-college education in the delivery of online writing assignments persist in their difficulties with technological educational platforms in college. The creation of a contract between the teacher and the students -at first-, fosters knowledge, motivation, involvement, or engagement with the task; thus, digital writing assignments go from being an unattainable goal to being a feasible task to engage in. Also, the cooperative creation of this online writing with an easy-to-use platform (Eli Peer Review) stimulates them to persist in the following tasks, as they have already reflected on them and have already found out among their peers what they consist of and how to deal with such tasks. In our research we present a case study of a course based on online writing instruction. Therefore, this study aims to examine a particular course in the United States in which multimedia writing assignments and labor-based grading allowed for deep student engagement and success (Dickson,1974; Inoue, 2019). The data comes from the use of mixed methods that combine qualitative information collected through document analysis (teacher materials: syllabi, guidelines, instructions; student materials: personal research, blog entries, and final letter); classroom observation field diaries and the semi-structured teacher interview, with the quantitative methods of a student survey. The results show that there is a high degree of alignment between the course and the best practices of online instruction, and that the nature of the assignments and the Labor-based Grading Contract (Dickson,1974, Inoue, 2019) appear to play key roles in student engagement and success in the course. Likewise, the most highlighted aspect by the students has been the emotional factor, since the tasks have allowed them to get involved and enjoy writing in the digital support. The systematic observation of this writing course aims to deeply understand its provenance, objectives, taxonomy and functionality, with the final purpose of highlighting the capabilities of this methodology in order to offer it as a model in other contexts to promote a fairer and more equitable education.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,008 |
| Communication savante | 0,009 | 0,005 |
| Science ouverte | 0,002 | 0,011 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».