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Enregistrement W4395040322 · doi:10.1353/vcr.2023.a925210

Teaching Textual Analysis Using Hypothes.is

2023· article· en· W4395040322 sur OpenAlexvenueno aff
Martin A. Danahay

Notice bibliographique

RevueVictorian review · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueEducational Assessment and Pedagogy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésComputer science

Résumé

récupéré en direct d'OpenAlex

Teaching Textual Analysis Using Hypothes.is Martin Danahay (bio) I project selections from the texts onto a screen for close textual analysis in my third-year course on Victorian Gothic literature when we meet in person. The sessions are designed to teach close reading of texts and [End Page 16] how to identify key words, symbols, or themes using quotations rather than paraphrasing—which elides the process of interpretation by substituting the student's language in place of that of the text. The texts that I assign are in the public domain and available on sites like Project Gutenberg, so it's easy to create a small file of key passages. Like Montgomery et al. in Ways of Reading, I guide my students through the process of analysis by asking them questions about the text to have them reflect on the process of reading, understanding, and then analyzing the language used by the author. I expect students to quote and discuss key terms, symbols, and images in their papers following the model of our class discussion. I emphasize in my grading rubric that I expect them to provide their own analysis of texts to receive a passing grade; I phrase this as the difference between "why" and "what" in a text and encourage them to avoid paraphrase and summary. In class, I ask: why does a writer use certain words or symbols, or why does a certain event happen in the text? I had to modify this approach when my course went completely online because of the COVID-19 pandemic and I had to find a way to teach close reading online. I held class discussions of general themes in the text via video conference for the first half of a course meeting, and I then switched to Hypothes.is annotation for the second half to conduct real-time online annotation (see web.hypothes.is). The Hypothes.is tool allowed me to incorporate textual analysis into synchronous online meetings. I first created a simple website using Google sites (sites.google.com/view/danahayannotation/home). I uploaded images and copied and pasted selections from the assigned texts for each synchronous online meeting. The assigned texts were Victorian Gothic horror stories that embodied, in their "monsters," deep-seated but largely unacknowledged fears of racial and class others. For the second half of the meeting, students accessed the Google site using the Hypothes.is tool via our learning management system (LMS). The students then started annotating the text, discussing their interpretations with other students and with me via text as they analyzed the author's language in detail. These annotation exercises were not graded but were part of their overall participation mark, which also included posting an analysis in the LMS forum following our class discussion and annotation exercise. I have found that students are very articulate in class discussion, but this does not always carry over into their writing. Annotating a text in Hypothes.is meant that they started the process of articulating their thoughts before writing. To begin annotation, students must select a section of text and then explain why they highlighted the passage. They were encouraged by me to use their insights from the textual analysis in their final papers. The annotations were therefore a prewriting exercise that meant the students would write more developed papers after initially working through their ideas using the Hypothes.is tool. Hypothes.is adds to my tool chest of prewriting exercises, such as brainstorming and free writing.1 [End Page 17] While I have written about the body and class in the text, especially the symbolism of hands in "Dr. Jekyll's Two Bodies," I do not assign the article nor do I initially raise the question of bodies, preferring to allow the students to raise the issue and then help them build on their insights. I am not aiming in this discussion to cover every possible interpretation of the text but to help students enlarge on their initial reactions, although I can always count on certain issues arising in response to language in the text about the contrast between Dr. Jekyll's "white" and "comely" hand and Mr. Hyde's being of a...

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,883
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,136
Tête enseignante GPT0,474
Écart entre enseignants0,338 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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