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Record W4382540781 · doi:10.1002/curj.216

Editorial

2023· editorial· es· W4382540781 on OpenAlexaff
Kathryn Hibbert, Daniel Alvunger

Bibliographic record

VenueThe Curriculum Journal · 2023
Typeeditorial
Languagees
FieldSocial Sciences
TopicEducational Theory and Curriculum Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologySociology

Abstract

fetched live from OpenAlex

This issue of The Curriculum Journal brings together 10 articles that address a number of topics that have been ongoing sources of dialogue in curriculum and education for many years now: teachers as evolving professionals-and the necessary preparation and assessment that will prepare them for classrooms growing in complexity, and the influence of globalization and interdisciplinarity on the scholarly conversations that are taking place in research on curriculum.In many ways, the articles in this issue underscore Schwab's (1973) attention to the four commonplaces: the learners, the teacher, the subject matter and the milieus.As we brought the articles together as a collective, we could trace a focus on the commonplaces as the authors grapple with their expanded understandings of curriculum, content knowledge, the roles of teacher and learner and the varying contexts within which we teach and learn.At the time of this writing, the future of education in an era of augmented human intelligence is unclear.We are in many ways experiencing a convergence between the virtual world and the physical world in ways that promise to significantly change teaching, learning and education in ways we have not yet imagined.In this context, the need for teachers to express their professional agency to address anticipated concerns about, for instance, ethics, privacy, or unconscious bias grows even more relevant.It is with this in mind, that we introduce the articles in this issue to you.In the first article Sílvia de Almeida and Joana Viana explore Teachers as Curriculum Designers: What Knowledge is Needed.In the context of Portugal, they address the loss of professional autonomy felt by teachers with the introduction of the 'Essential Learning' curriculum benchmark in 2016.Their paper articulates a hard-won theoretical model of the knowledge and skills required for curriculum design through studying the participation and collaboration of teachers engaged in the process.Their conclusions speak to a need for clarification of expectations concerning teachers' level of knowledge of curriculum theory, and teachers' experience writing pedagogical strategies to engage in a project like this.It also drew attention to the need for leadership, modelling and support from curriculum design experts to achieve the desired internal consistency in the resulting design.The paper is a reminder that engaging professionals in collaborative curriculum work is a meaning making process that can be richly educative, but must begin with a well-articulated design for the work to occur, free of assumptions about the knowledge and experience each bring to the discussion.In the next article, Ashley Bough and Gabriela Martinez Sainz present Digital Learning Experiences and Spaces: Learning from the past to design better pedagogical futures.Using Computer Sciences as their focus, the authors document several historical conceptualizations through a period in which digital learning experiences (DLEs) have been incorporated into the curriculum in response to learner needs.Their systematic literature review of educational policies and practices in Ireland really highlights the role the milieu has played in shaping the changing policies.They call for teachers to engage as 'evolving agents' and recognize the variation in the digital competencies of teachers and learners alike.Their call for "individual ownership and creative autonomy" positions teachers as agentive in ways that

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.218
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0080.004
Open science0.0020.002
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.2180.126

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 head0.016
GPT teacher head0.347
Teacher spread0.331 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2023
Admission routes1
Has abstractyes

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