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Enregistrement W4387267204 · doi:10.21900/j.alise.2023.1254

Prior Learning Assessment

2023· article· en· W4387267204 sur OpenAlex
Tomas A. Lipinski, Sarah Beth Nelson, Louise F. Spiteri, Dietmar Wolfram, Chad Zahrt

Pourquoi ce travail est dans la base

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Notice bibliographique

RevueProceedings of the ALISE Annual Conference · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueHigher Education Learning Practices
Établissements canadiensDalhousie University
Organismes subventionnairesnon disponible
Mots-clésExperiential learningCredentialHigher educationPortfolioFlexibility (engineering)Value (mathematics)Medical educationPsychologyPedagogyComputer scienceBusinessPolitical scienceMedicineManagement

Résumé

récupéré en direct d'OpenAlex

Higher education continues to experience challenges arising from changing demographics and perceptions of the value of higher education. Prospective students want flexibility in how they pursue higher education. Programs must adapt to meet the changing needs and expectations of the audiences they serve. This is equally true for programs offered by library and information science schools. In an increasingly competitive educational environment where traditional student populations are shrinking, higher education institutions must be agile and proactive (Grawe, 2021).
 Prior Learning Assessment (PLA) or Credit for Prior Learning (CPL) recognize the students may bring knowledge and skills that are not documented as credit-bearing learning experiences from higher education institutions. The idea of PLA and CPL is not new. The Council for Adult and Experiential Learning (CAEL) (https://www.cael.org/) has advocated for PLA and CPL for more than 40 years. Learning can take place through nontraditional instructional outlets. Work-related and military experiences also provide learning opportunities for which evidence may be available. Central to PLA and CPL is rigorous assessment. This assessment can take multiple forms, including portfolio evaluation, examinations for learning achievement, or equivalency determination of non-credit learning experiences. A recent study on PLA for adult learners concluded that PLA is associated with better student outcomes, including higher credential completion, cost savings and time savings (Klein-Collins et al., 2020). The panel topic addresses the conference theme by focusing on learning, practice and competencies.
 Many students applying to undergraduate or graduate programs at LIS schools bring work or non-credit experiences in areas such as information technology, librarianship or archival studies that are germane to the areas they plan to study. Should these applicants be provided the opportunity to demonstrate the knowledge they bring for admission consideration, course exemptions or credit recognition?
 The objective of this juried panel is to begin a dialogue on the place of PLA in library and information science programs. After a brief introduction to PLA concepts, the panelists will discuss PLA initiatives in their schools. This will be followed by a discussion with the audience during the second half of the panel on questions that arise when considering the adoption of PLA. These questions include:
 
 What are the potential benefits of, and concerns with, offering PLA options to prospective undergraduate and graduate students?
 Which programs offered by LIS schools lend themselves to PLA?
 How much credit should be provided to students for demonstrating prior learning?
 What are the most effective approaches for assessing prior learning?
 Can PLA make LIS programs more inclusive, and can it serve as an effective recruitment tool?
 
 The following presenters will participate on the panel:
 Dietmar Wolfram (Moderator), Head of School and Associate Dean, University of Wisconsin-Milwaukee’s School of Information Studies (SOIS) will provide a brief overview of PLA and methods of assessment.
 Louise Spiteri, Professor, School of Information Management, Dalhousie University will discuss how Dalhousie’s School of Information Management (SIM) moved from an ad hoc process to an official PLA process for admission assessment for their Master of Information (MI) program, and how this has allowed SIM to broaden the students entering the program. The presentation will also discuss the experience of students who have entered this way.
 Sarah Beth Nelson, Assistant Professor, School of Information Studies, University of Wisconsin-Milwaukee, has led the University of Wisconsin System School Library Education Consortium where students can submit a portfolio to demonstrate prior learning in order to be exempted from three of the courses required for school library licensure.
 Chad Zahrt, Assistant Dean, School of Information Studies, University of Wisconsin-Milwaukee will outline how SOIS has been providing elective credit for non-credit workshops in relevant information technology areas to incoming students in the Bachelor of Science in Information Science and Technology program.
 Tomas A. Lipinski, Professor, School of Information Studies, University of Wisconsin-Milwaukee, will describe how SOIS is addressing the challenge of recognizing the knowledge of incoming MLIS students who bring substantial experience working in the field and how this may be assessed.

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.

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)0,002
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,671
Score d'incertitude au seuil0,514

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,042
Tête enseignante GPT0,386
Écart entre enseignants0,344 · 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