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

Prior Learning Assessment

2023· article· en· W4387267204 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings of the ALISE Annual Conference · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsDalhousie University
Fundersnot available
KeywordsExperiential learningCredentialHigher educationPortfolioFlexibility (engineering)Value (mathematics)Medical educationPsychologyPedagogyComputer scienceBusinessPolitical scienceMedicineManagement

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.386
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it