In this age of information technology, it has become possible for people all over the world to communicate in different languages through social media platforms with the help of machine translation (MT) systems. As far as the Arabic-English language pair is concerned, most studies have been conducted on evaluating the MT output for the standard varieties of Arabic, with fewer studies focusing on the vernacular or colloquial varieties. This study attempts to address this gap through presenting an evaluation of the performance of MT output for vernacular or colloquial Arabic in the social media domain. As it is currently the most widely used MT system, Google Translate (GT) has been chosen for evaluating the reliability of its output in the context of translating the Arabic colloquial language (i.e., Egyptian/Cairene Arabic variety) used in social media into English. With this goal in mind, a corpus consisting of Egyptian dialectal Arabic sentences were collected from social media networks, i.e., Facebook and Twitter, and then fed into GT system. The GT output was then evaluated by three human translators to assess their accuracy of translation in terms of adequacy and fluency. The results of the study show that several translation problems have been spotted for GT output. These problems are mainly concerned with wrong equivalents, inappropriate additions and deletions, and transliteration for out-of-vocabulary (OOV) words, which are mostly due to the literal translation of the Arabic vernacular sentences into English. This can be due to the fact that Arabic vernacular varieties are different from the standard language for which MT systems have been basically developed. This, consequently, necessitates the need to upgrade such MT systems to deal with the vernacular varieties.

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Reading is not synonymous to comprehension; rather it is a prerequisite that doesn't, by itself, guarantee comprehension. This is to say that being efficient in decoding letters, syllables and whole words and recognizing vocabulary does not ensure natural r automatic comprehension. Fluency seems to be the bridge between the mastery of the mechanics of reading and the dynamics of comprehension. Abundant research exists that explores how to improve reading skills of EFL learners at Saudi universities. However little, if any, of this array of research sought to discern the potential effects of educational technology on the fluency of struggling readers in continuous learning programs. To fill this gap, this study seeks to probe the multi dimensions of the problem and suggest ways to solve it. For this purpose, 24 EFL lecturers from three Saudi universities were selected and interviewed. A suggested computer-assisted collaborative reading model was put forth to be applied in the three universities. Students were diagnosed by their instructors as gaining relatively enough grasp of decoding skills at the multilevels of orthographic knowledge, mono and polysyllabic words, but exhibit slow and inaccurate reading indicating reprehensive symptoms for a fluency problem. The lecturers explained that the disappointment resulting from learners' inability to reach comprehension despite mastering decoding skills influences their attitudes towards reading and language learning, bringing about reading apathy and low self-esteem. The proposed model is designed to enhance reading fluency which is perceived as the underlying problem that makes the reader struggle. It is to be delivered partly individually and partly collaboratively online. Collaboration also is operated via face to face instruction especially in teaching the reading strategy. In doing so, the procedures followed are in line with the blended learning. The findings indicate clearly that the proposed model was successfully used to improve reading fluency through accelerating the different reading subskills for decoding and create positive attitudes toward reading. The results highlight the importance of establishing a level of automaticity that gives rise to the higher skills of comprehension.