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Record W3043532527 · doi:10.5539/jas.v12n8p265

Knowledge and Perception of Nanotechnology Among Students of Agricultural Faculties’ in Jordan

2020· article· en· W3043532527 on OpenAlexvenueno aff
Mohammad Altarawneh

Bibliographic record

VenueJournal of Agricultural Science · 2020
Typearticle
Languageen
FieldEngineering
TopicNanotechnology research and applications
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionCurriculumInclusion (mineral)NanotechnologyPsychologyMaterials sciencePedagogy

Abstract

fetched live from OpenAlex

This study investigated Knowledge and Perception of Nanotechnology among Students of Agricultural Faculties’ in Jordan. The research was based on distributing a questionnaire. This study collected data from 485 respondents, of which 410 were analyzed. The results revealed that a very significant finding that the majority of the investigated students (45%) have already heard the word ‘nanotechnology’, though (72%) of those (45%) do not know about nanotechnology very well. The results of the present study indicated that students have basic or no enough knowledge about nanotechnology. The results also showed that students were with a very superficial knowledge of Nanotechnology. Moreover, none of the examined variables has no significant effect on the perception toward nanotechnology. Even though it is expected that students with higher years of study could show more expertise and acquire more developed topics such as the Nanotechnology concept, the students showed similar knowledge of Nanotechnology regardless of their year in study. The study recommends that the Jordanian educational policymakers in higher education should consider the inclusion of the Nanotechnology concept in the curricula of the different academic courses.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score0.192

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.012
GPT teacher head0.271
Teacher spread0.258 · 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 designBench or experimental
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
Published2020
Admission routes1
Has abstractyes

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