Teaching Students, Not Standards: The New ACRL Information Literacy Framework and Threshold Crossings for Instructors
Notice bibliographique
Résumé
AbstractThe new ACRL Framework for Information Literacy for Higher Education is an opportunity for IL instructors to ask themselves whether their current approaches to instruction are meeting the higher goals of IL education. Instructors might re-examine their pedagogical approaches by considering their own knowledge practices and dispositions in teaching IL. How might we best create a space in which the desired student knowledge practices and dispositions flourish? How can we approach IL education as fellow students - ones who just happen to be at a different point on the same path of lifelong learning?KeywordsACRL Framework for Information Literacy for Higher Education; information literacy; threshold concepts; pedagogyFollowing years of growing sentiment among instructional librarians that the Association of College and Research Libraries' (ACRL) Information Literacy Competency Standards for Higher Education (2000) contained outdated notions of both student learning and the information landscape in higher education, the ACRL Board of Directors in June of 2012 unanimously decided that the Standards should be revised, and authorized the creation of a Task Force charged with drafting a new information literacy framework (Boylston 1). Over several revision projects, webinars, and conference presentations, librarians were invited to weigh in on the shape that the new document would take and the Task Force responded to their suggestions and concerns. The result of those revision efforts is the newly published ACRL Framework for Information Literacy for Higher Education (ACRL 2015).As a former secondary school teacher turned academic librarian (and advocate of lifelong learning), I engaged with several of these revision feedback projects which, in turn, evolved into an ongoing collaboration with a group of colleagues representing the TRY University Libraries of Toronto (Toronto, Ryerson, and York). Together we've unpacked the Framework in two conference presentations and training sessions, as well as a wiki to expand the conversation with other academic librarians (TRY University Libraries of Toronto). Now, in a new role as a Research and Instructional Services Librarian at Western University, I am examining how the Framework has shifted my perspectives on the respective roles of the teacher and student in information literacy (IL) education.While the ACRL Task Force intends the new Framework and its component frames, knowledge practices, and dispositions to guide instructors in designing curricula for students, I read the document differently: I see in the new Framework an opportunity to ask of ourselves if our current approach to instruction can meet the higher goals of IL education. In this document, I see an opportunity for instructors to reexamine their pedagogical approaches by considering student-focused IL knowledge practices and dispositions, and thinking of them from the point of view of the instructor. In other words, what are my knowledge practices and dispositions in teaching IL? How might my teaching evolve in order to facilitate a space in which the desired student knowledge practices and dispositions can flourish? The Framework in this light is an opportunity for instructors to consider their own knowledge practices and dispositions by adopting a beginner's frame of mind, and to approach IL education as a discussion with fellow students-ones who just happen to be at a different point on the same path of lifelong learning.What I find most compelling about this new Framework is that it inspires a different approach to IL education for instructor and student alike. The instructor must cross a threshold, evolving instruction from a point-and-click database demo style to an engaged and interactive IL discussion with students. The instructor occupies the role of coach, animator, or advisor leading the discussion, while encouraging students to become active agents in their own learning. …
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 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 | 0,016 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,031 |
| Communication savante | 0,016 | 0,018 |
| Science ouverte | 0,002 | 0,012 |
| Intégrité de la recherche | 0,005 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».