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Record W754137402 · doi:10.1177/153660061503600205

E-Portfolios in Music and other Performing Arts Education: History through a Critique of Literature

2015· article· en· W754137402 on OpenAlexaboutno aff
Peter Dunbar‐Hall, Jennifer Rowley, W Brooks, Hugh Cotton, Athena Lill

Bibliographic record

VenueJournal of Historical Research in Music Education · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsnot available
Fundersnot available
KeywordsThe artsMusic technologyMusic educationRelevance (law)MultimediaComputer scienceElectronic musicVisual artsSociologyPedagogyPsychologyArt

Abstract

fetched live from OpenAlex

The introduction and use of various forms of technology have been noted by historians of music as significant influences on ways in which music has developed. For example, George N. Heller notes that education in general and music in particular have felt impact of sound recording, film, television, videotape, computers, laser disk technology and a host of other innovations, while published materials and conferences on music regularly include research into applications and implications of types of electronic technology, as Peter R. Webster indicates. (1) This reflects technologizing of both in general and of music production and dissemination specifically. (2) In contrast to general computer-based technology, specific types of computer-based technology have strong relevance to learning and teaching of music and other performing arts, owing to their ability to store and present sound and filmed events, to represent multiple identities of performance educators (as creators, designers, performers, producers, researchers, teachers, technologists) through multimedia, and to provide sites of music creation, manipulation, and dissemination. As Renee Crawford puts it, the importance of technology in music has meant its necessary inclusion in teaching and (3) In discussion that follows, we focus on one such technological innovation, introduction and utilization of student e-portfolios in music and other performing arts education. We show history of their use, ways in which they have been and are currently implemented, and their influence on learning and teaching in these discipline areas. Since their introduction into university learning and teaching in early 1990s, e-portfolios have become standard artifacts through which students collate, archive, reflect on, and present outcomes of their studies. (4) For teaching staff they have numerous uses, including a means for assessment, an influence on curriculum design, a component of course content delivery, and an institutional capstone object in form of a final, summative object through which a student collates work completed in a degree program and thus demonstrates both knowledge gained and skills developed. The scope of their use extends from single task assignments to representation of student progression throughout degree programs. They have entered management systems of universities. (5) In some universities, e-portfolios have been mandated for both students and staff, and their use is widespread; in others, their use remains voluntary and piecemeal. (6) Research on e-portfolios is strongly represented from writers in Britain and United States but with regular contributions from researchers in Australia, Canada, Europe, and Scandinavia, all locations where educational technology is well established and economically supported and where its use is an expectation of systems. (7) Alongside generic research publications for and music and performing arts education, dedicated journals, professional associations, conferences, and websites have also examined e-portfolios. (8) In that timeframe, publication on issues surrounding use of e-portfolios has increased. Specifically in performing arts, a small amount of research in late 1990s by L. Castiglione, P. Moss, and Susan McGreevy-Nichols has been followed by widening exposure through discussions of their applications, roles, and significance. (9) At first, writers on e-portfolios discussed their usefulness alongside, or as replacements for, paper-based portfolios, which had been standard means of collating and demonstrating outcomes of student learning. As technology required for e-portfolio construction developed, especially with introduction of Web 2.0 and associated applications, a second stage of research tended to explain how e-portfolios were being used by students and components that were included. …

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.

How this classification was reachedexpand

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)0.008
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.289
GPT teacher head0.516
Teacher spread0.228 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2015
Admission routes1
Has abstractyes

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