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Record W2762421432 · doi:10.19173/irrodl.v19i2.3469

Reflection on MOOC Design in Palestine

2018· article· en· W2762421432 on OpenAlexvenueno aff
Saida Affouneh, Katherine Wimpenny, Ahmed Ra'fat Ghodieh, Loay Abu Alsaud, Arij Abu Obaid

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2018
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsnot available
Fundersnot available
KeywordsPalestineThematic analysisMassive open online coursePedagogySociologyPolitical sciencePublic relationsQualitative researchHistorySocial science

Abstract

fetched live from OpenAlex

This paper will share Discover Palestine, an interdisciplinary Massive Online Open Course (MOOC) and the first MOOC to be created in Palestine, by the E-Learning Centre, Faculty from the Department of Geography, and Department of Tourism and Archaeology from An-Najah National University in Palestine. The paper traces the process of development of the Discover Palestine MOOC from its early inception as a cross institutional online course, to its current delivery and engagement with a global and diverse group of learners. Using a descriptive case study design and thematic analysis, the reflective experiences of four course team members involved as facilitators/designers in the design and delivery of the MOOC are shared. Three key themes, namely, “Informing pedagogies including delivery methods,” “A commitment to a national cause,” and “Teacher presence,” are presented and contextualized with data evidence. The findings share not only the hurdles the Discover Palestine team had to navigate during the MOOC development, but more importantly, how academic collaborations promoting open education practices offer powerful tools for the reciprocal exchange of knowledge, not least in shifting mindsets, and offering opportunities for shared fields of understanding to be realized in revealing creative, cultural practices, as well as lost histories.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.809
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.186
GPT teacher head0.495
Teacher spread0.309 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations12
Published2018
Admission routes1
Has abstractyes

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