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Register a new userAutonomy in Distance English Language Learning
التعلم الذاتي في مجال تعلم اللغة الانجليزية عن بعد
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Damascus University ورقة بحثية
Publication date
2009
fields
English
and research's language is
العربية
Authors
هالة دلباني( باحث )
Created by
Shamra Editor
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يشهد التعليم المفتوح و التعليم عن بعد في سورية اليوم نمواًً كبيرا كما هو الحال في أنحاء العالم كّله. مع حلول القرن الحادي و العشرين، أطلقت سورية مؤسستين حكوميتين للتعليم عن بعد: مركز التعليم المفتوح الذي تأسس في عام 2001 و الجامعة الافتراضية السورية التي تأسست عام 2002 . تتفرد الجامعة الافتراضية السورية بكونها الأولى من نوعها في الوطن العربي تمنح متعلميها فرصة الحصول على المعارف عبر الشبكة الالكترونية مستخدمة أحدث التقنيات. و بما أن تعليم اللغة الانجليزية أصبح مطلباً تعليمياً مهماً في سورية، فقد انخرط تعليم اللغة الانجليزية لغير الناطقين بها في مجال التعليم عن بعد.
Open and distance learning is experiencing a rapid growth throughout the world and Syria is no exception. With the turn of the new millennium, Syria launched two state institutes for distance learning: The Open Learning Centre (opened in 2001) and the Syrian Virtual University (opened in 2002). The Syrian Virtual University (SVU), one of its kind in the whole Arab region, offers students the opportunity to gain education through an online learning environment based on the latest technology. Since Syria is a country where English has become an important educational requirement, the teaching of English as a foreign language has therefore entered the arena of distance learning.
References used
Barnett L., (1993). ‘Teacher off: computer technology, guidance and self-access.’ System, 21 (3). 295-304
Benson, P. (2001). Teaching and researching autonomy in language learning. Harlow, Pearson Education Limited
Benson, P. (2002). Learner autonomy 7: challenges to research and practice. Dublin, Authentik
rate research
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This research seeks to extend our current knowledge in the area of
distanct English language learning by exploring the language learning
strategies used by students at the Syrian Virtual University (SVU).
Due to complex cognitive and inferential efforts involved in the manual generation of one caption per image/video input, the human annotation resources are very limited for captioning tasks. We define language resource efficient as reaching the same
performance with fewer annotated captions per input. We first study the performance degradation of caption models in different language resource settings. Our analysis of caption models with SC loss shows that the performance degradation is caused by the increasingly noisy estimation of reward and baseline with fewer language resources. To mitigate this issue, we propose to reduce the variance of noise in the baseline by generalizing the single pairwise comparison in SC loss and using multiple generalized pairwise comparisons. The generalized pairwise comparison (GPC) measures the difference between the evaluation scores of two captions with respect to an input. Empirically, we show that the model trained with the proposed GPC loss is efficient on language resource and achieves similar performance with the state-of-the-art models on MSCOCO by using only half of the language resources. Furthermore, our model significantly outperforms the state-of-the-art models on a video caption dataset that has only one labeled caption per input in the training set.
Worldwide, Information and communication technologies (ICTs)
have gained a vital importance in satisfying the requirements of both the
new Education System and the revised Curriculum for English Language
Teaching (ELT). The experience of introduci
ng different ICTs into the
classroom and other educational settings worldwide suggests that
teachers' effective integration of technology into their curricula largely
depends on their perceptions and attitudes to the value of these tools to
achieving their instructional purposes. This research seeks to evaluate the
readiness to integrate ICTs into the current teaching practices of the
faculty at the department of English Language and Literature at
Damascus University. In a context of large classes where resources are
minimal and staff members are in short supply, using technology assisted
learning at Damascus University may help enhance education by giving
learners more flexibility and helping them evolve into more autonomous
learners. Investigating the current faculty’s reading of the value of
technology as a means of overcoming some of the restrictions imposed
by their context will uncover potential limitations on the use of ICTs in
the existing context and suggest workable solutions.
The present study focuses on investigating the problems and
difficulties facing EFL learners regarding English collocations. It
reports the various factors influencing the process of reception and
production of English collocations. To achieve the
purposes of the
study, two tests were given to 25 students majoring in English
Language and Literature at the Department of English at Al-Baath
University: a gap-filling productive test and a multiple-choice
receptive test. There was also a translation productive test (from
Arabic into English), and it was given to 25 students majoring in
translation in the open learning program at the same department.
The three tests were analyzed and then results and findings were
presented. The results showed that the participants had poor
collocational knowledge and their reception and production of
collocations was unsatisfactory, but they performed better in the
receptive test. The study also concluded that they sometimes
resorted to their first language or to paraphrasing the collocational
expression. They made other types of error like general errors
(completely irrelevant answers) and leaving blank answers.
Moreover, it was found that the students majoring in translation
performed slightly better than the students majoring in English
Language and Literature. Finally, the study concluded with
implications for pedagogy.
We analyse how a transformer-based language model learns the rules of chess from text data of recorded games. We show how it is possible to investigate how the model capacity and the available number of training data influence the learning success of
a language model with the help of chess-specific metrics. With these metrics, we show that more games used for training in the studied range offers significantly better results for the same training time. However, model size does not show such a clear influence. It is also interesting to observe that the usual evaluation metrics for language models, predictive accuracy and perplexity, give no indication of this here. Further examination of trained models reveals how they store information about board state in the activations of neuron groups, and how the overall sequence of previous moves influences the newly-generated moves.
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