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Record W2564993172

Call for papers: Inclusive design for e-learning and distance education

2016· paratext· en· W2564993172 on OpenAlexaff
David Porter, Irwin DeVries

Bibliographic record

Venuenot available
Typeparatext
Languageen
FieldComputer Science
TopicE-Learning and Knowledge Management
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsDistance educationLifelong learningOpen learningComputer sciencePedagogySociologyPsychologyMathematics educationCooperative learningTeaching method
DOInot available

Abstract

fetched live from OpenAlex

Call for papers – Special Issue IJEDE International Journal of E-learning and Distance Education Inclusive design for e-learning and distance education Special issue guest editors: • Jutta Treviranus, Professor, Inclusive Design; Founder/Director, Inclusive Design Research Centre, OCAD University (lead) • Lizbeth Goodman, Professor Inclusive Design for Education, Founder/Director, SMARTLab, University College Dublin • Sambhavi Chandrashekar, Faculty, Inclusive Design, OCAD University Overview The International Journal of E-learning and Distance Education, an open access journal, is inviting submission of original manuscripts on inclusive design for e-learning and distance education, for publication in late Fall 2017. Most formal education systems are not designed to recognize that all learners learn differently, or that our transforming society requires a diversification of learning outcomes. Diverse factors that affect learning can include but are not limited to: • sensory, motor, cognitive, emotional and social requirements, • individual learning approaches, • linguistic or cultural perspectives, • technical, financial or environmental constraints. Thus, all learners potentially face barriers to learning. Barriers can be seen as a product of a mismatch between the needs of the learner and the learning experience and environment offered. Some learners are more constrained than others and are therefore less able to adapt to a mismatch. This adds education disparity to the compounding disparities our society is currently facing. Education disparity, and barriers to learning can be seen as wicked problems, or problems that are “difficult to solve because of incomplete, contradictory, and changing requirements that are often difficult to recognize.” Our systems of education globally can be characterized as complex adaptive systems within the larger complex adaptive systems of our society. Formal education systems were built to withstand change (and pressures from political forces or transient ideologies) but are caught in inevitable and unprecedented disruptive technical, economic and social changes. Successful interventions in these systems cannot be simple or static; to effect and sustain the desired change requires responsive, multi-faceted interventions. The combination of: • the move to digitally mediated education or e-learning, • adoption of Open Education Resources (OER) and open education, • explorations in personalization, • a focus on deeper learning, • personalized data analytics, • and connected communities and classrooms, offers a convergence of factors that can be catalyzed to remove barriers to education for students who are marginalized. With opportunities also come challenges including digital disparity, reactionary promotion of exclusive education, and top-down interpretations of quality that are intolerant of diversity and remove critical thinking and self-determination from the teacher and the student. Like economic disparity, education disparity is intensified by complex vicious cycles that reinforce inequity and disadvantage for excluded learners. In addition to the barriers listed above learners often face attitudinal and economic barriers as well. It can cost as much as ten times or more for someone with a disability to get online and use a computer through required alternative access systems. Disability and poverty are often co-occurring, with a disproportionate number of people with disabilities well below the poverty line globally. Marginalized learners are the first to feel the effects of threats to education or educational design failures. With this special issue we hope to bring together the multi-perspectival insights, lessons learned and proposals needed to advance inclusive, equitable education. We especially welcome submissions that combine multiple disciplines, and manuscripts that demonstrate inclusive research practices (co-design and participatory research) and collaborative approaches. Topics Topics of interest for this special issue may include, but are not limited to, areas such as the following: • inclusively designed personalized learning, • deeper learning and learner diversity, • metacognition, learning-to-learn and student exploration of learning needs with respect to students with learning differences, • potential of Open Education Resources, learner choice and inclusive education • insights from adoption of Universal Design for Learning (UDL) or Differentiated Learning, • inclusive design of interactive, spatial, immersive and experiential e-learning, • inclusive pedagogy and androgogy in e-learning • learning analytics and students with learning differences • economics of inclusive e-learning • inclusive learning communities • inclusive life-long learning • the impact of regulation in inclusive e-learning • diversity-supportive assessment • peer-to-peer learning and inclusion • learning metrics and inclusive education practices and policies Key dates • Manuscript submission deadline: May 30, 2017-11-27 • Notification of acceptance: July 15, 2017 • Submission of final revised papers: September 15, 2017 • Publication of special issue (tentative): November 2017 Submission procedures Authors must follow the research articles submission procedures and guidelines available at the following link: http://www.ijede.ca/index.php/jde/about In order to submit a research article, authors must register as authors on the journal site to make an online submission: http://www.ijede.ca/index.php/jde/user/register

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)0.006
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.523
Threshold uncertainty score0.681

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0120.009
Open science0.0030.005
Research integrity0.0100.006
Insufficient payload (model declined to judge)0.5230.376

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 head0.015
GPT teacher head0.291
Teacher spread0.276 · 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.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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