“WE HEAR YOU, WE UNDERSTAND YOU” - USING VARK SURVEY TO UNDERSTAND FIRST YEAR LAW LEARNERS
نویسندگان
چکیده
منابع مشابه
Tag as you like..., we can understand you!
Nowadays, the approaches that combine semantic web ontologies and web 2.0 technologies constitute a significant search field that attracted the interest of a best part of searchers. We will present in this paper an original approach concerning a technology that has recognized a great popularity in these recent years, we talk about folksonomies. Our aim in this contribution is to give birth to a...
متن کاملWe hear you
95.7% underwent only one or two rounds of external review. No papers endured more than three external review cycles. Our belief is that if we invest additional editorial effort at the point of decision, we can avoid protracted cycles of review and re-review. For example, we neither expect nor require all referees to agree on the merits or deficiencies of every manuscript. When referees disagree...
متن کاملWhat you seize is what you get: do we yet understand epilepsy in rett syndrome?
Commentary Mutations in methyl–CpG-binding protein-2 (MeCP2, a transcription factor binding methylated DNA) most often cause Rett Syndrome (RTT). The incidence of RTT is approximately 1:10,000 female births. Clinically, female patients with RTT have apparently normal infant development followed by a plateau and then regression; males succumb in utero or develop a severe, lethal epileptic enceph...
متن کاملTo Understand Your Understanding, You Must Understand What Understanding Means
Although critical for the regulation of many reading and studying behaviors, metacomprehension accuracy is generally observed to be quite low. The present research examined how metacomprehension accuracy would be affected by practice tests designed to give readers expectations about the kind of tests they would be given, and self-explanation instructions to give readers access to valid cues for...
متن کاملTo understand deep learning we need to understand kernel learning
Generalization performance of classifiers in deep learning has recently become a subject of intense study. Deep models, which are typically heavily over-parametrized, tend to fit the training data exactly. Despite this overfitting, they perform well on test data, a phenomenon not yet fully understood. The first point of our paper is that strong performance of overfitted classifiers is not a uni...
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ژورنال
عنوان ژورنال: International Journal of Modern Education
سال: 2019
ISSN: 2637-0905
DOI: 10.35631/ijmoe.11007