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Record W2755717395 · doi:10.5539/elt.v10n10p102

The Modern Trends and Applications in the Development of Academic Staff in the University of Maryland & George Mason University

2017· article· en· W2755717395 on OpenAlexvenueno aff
Alaa A. Asowayan, Sammar Y. Ashreef, Haya S. Aljasser

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsnot available
Fundersnot available
KeywordsCompetence (human resources)PsychologyMeaning (existential)Process (computing)Higher educationSociologyPublic relationsPedagogyMathematics educationComputer sciencePolitical scienceSocial psychologyLaw

Abstract

fetched live from OpenAlex

Several changes have occurred over the past century in the education system of Saudi Arabia. The changes have largely been associated with the fact that in the 21st century, information and communication technology is highly applied in the learning process, thereby leading to a major transformation of the process. The application of information and communication technology has also transformed interactions and rapidly changed the learning process, giving a new meaning to social interactions. Enterprises that operate in the information age enjoy information interchange, collaboration, and adoption and application of innovative tendencies and shared decision-making. Students’ demands have changed in that they no longer hope for middle-class success or application of routine skills, but they measure success in terms of ability to share, communicate and apply information to arrive at solutions to complex problems. The changing learning environment requires that the teaching staff learns new tendencies and skills that they can apply to cope with the ever-changing learner and general society expectations. Teachers’ competence at work is measured in terms of their ability to improve the power of technology in enhancing creation of new knowledge. Therefore, leaders of teacher education programs are responsible for developing sustainable programs that allow for teacher education. Training has become part of the ethics of the teaching profession, and members of the teaching staff must be ready for training throughout their profession. This paper will shed light on the training program of faculty members in two well-known universities in the United States: the University of Maryland & George Mason University, as an attempt to compare the above educational establishments with the conditions of training of faculty members of King Saud University in Saudi Arabia to suggest a training plan to develop training programs in KSU. It is time when leaders in educator preparation should critically reexamine their roles in the 21st century knowledge and skills whose landscape has largely changed.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.332
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.

Study designQualitative
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

Citations1
Published2017
Admission routes1
Has abstractyes

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