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

2016· paratext· en· W2564993172 sur OpenAlex

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Notice bibliographique

Revuenon disponible
Typeparatext
Langueen
DomaineComputer Science
ThématiqueE-Learning and Knowledge Management
Établissements canadiensThompson Rivers University
Organismes subventionnairesnon disponible
Mots-clésDistance educationLifelong learningOpen learningComputer sciencePedagogySociologyPsychologyMathematics educationCooperative learningTeaching method
DOInon disponible

Résumé

récupéré en direct d'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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,638
Score d'incertitude au seuil0,807

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,015
Tête enseignante GPT0,291
Écart entre enseignants0,276 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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