Mostafa Al-Emran

Learners and educators attitudes towards mobile learning in higher education: State of the art

In the last few years, the way we learn has been significantly changed from traditional classrooms that depend on printed papers into e-learning relying on electronic teaching material. Contemporary educational technologies attempt to facilitate the delivery of learning from instructors to students in a more flexible and comfortable way. Mobile learning (M-learning) is one of such pervasive technologies that has been evolved rapidly to deliver e-learning using personal electronic devices without posing any restrictions on time and location. Literature that sheds light on using M-learning in various institutions of learning is beginning to emerge. The work in this paper demonstrates the state of the art of the M-learning. It discusses learners and educators attitudes towards the use and adoption of M-learning. Advantages and disadvantages of M-learning were also presented. The integration and implementation of M-learning with other technological resources has been described. Factors affecting the students and faculty members attitudes towards the use of M-learning have been demonstrated. Moreover, the new trends and challenges, which are evolved while conducting this survey, are explained.

Parsing modern standard Arabic using Treebank resources

A Treebank is a linguistic resource that is composed of a large collection of manually annotated and verified syntactically analyzed sentences. Statistical Natural Language Processing (NLP) approaches have been successful in using these annotations for developing basic NLP tasks such as tokenization, diacritization, part-of-speech tagging, parsing, among others. In this paper, we address the problem of exploiting Treebank resources for statistical parsing of Modern Standard Arabic (MSA) sentences. Statistical parsing is significant for NLP tasks that use parsed text as an input such as Information Retrieval, and Machine Translation. We conducted an experiment on Pen Arabic Treebank (PATB) and the parsing performance obtained in terms of Precision, Recall, and F-measure was 82.4%, 86.6%, 84.4%, respectively.

Technology Acceptance Model in M-learning context: A systematic review

Abstract Various review studies were conducted to provide valuable insights into the current research trend of the Technology Acceptance Model (TAM). Nevertheless, this issue still needs to be investigated from further directions. It has been noticed that research overlooks the investigation of TAM with regard to Mobile learning (M-learning) studies from the standpoint of different perspectives. The present study systematically reviews and synthesizes the TAM studies related to M-learning aiming to provide a comprehensive analysis of 87 research articles from 2006 to 2018. The main findings include that most of the TAM studies involving M-learning focused on extending the TAM with external variables, followed by the studies that extended the model by factors from other theories/models. In addition, the main research problem that was frequently tackled among all the analyzed studies was to examine the acceptance of M-learning among students. Moreover, questionnaire surveys were the primarily relied research methods for data collection. Additionally, most of the analyzed studies were undertaken in Taiwan, this is followed by Spain, China, and Malaysia, respectively among the other countries. Besides, most of the analyzed studies were frequently conducted in humanities and educational context, followed by IT and computer science context, respectively among the other contexts. Most of the analyzed studies were carried out in the higher educational settings. To that end, the findings of this review study provide an insight into the current trend of TAM research involving M-learning studies and form an essential reference for scholars in the M-learning context.

Mining Social Media Text: Extracting Knowledge from Facebook

Social media websites allow users to communicate with each other through several tools like chats, discussion forums, comments etc. This results in learning and sharing of important information among the users. The nature of information on such social networking websites can be straight forward categorized as unstructured and fuzzy. In regular day-to-day discussions, spellings, grammar and sentence structure are usually neglected. This may prompt various sorts of ambiguities, for example, lexical, syntactic, and semantic, which makes it difficult to analyse and extract data patterns from such datasets. This study aims at analyzing textual data from Facebook and attempts to find interesting knowledge from such data and represent it in different forms. 33815 posts from 16 news channels pages over Facebook were extracted and analyzed. Different text mining techniques were applied on the collected data. Findings indicated that Fox news is the most news channel that share posts on Facebook, followed by CNN and ABC News respectively. Results revealed that the most frequent linked words are focused on the USA elections. Moreover, results revealed that most of the people are highly interested in sharing the news of Mohammed Ali Clay through all the news channels. Other implications and future perspectives are presented within the study.

Academics’ Awareness towards Mobile Learning in Oman

Mobile Learning (M-Learning) witnessed a booming evolution contributed to the evolvement of learning within the higher educational settings. M-Learning enhances the collaboration between the educators and their students on anytime anywhere settings. Attitudes towards the utilization of M-Learning help the decision makers in building the required infrastructure. Our literature review indicated that there are very few studies that address the issue of educators’ awareness towards M-Learning which is a very significant factor for the success of smart education. In this study, a questionnaire survey has been administrated at Al Buraimi University College, Oman in order to examine the educators’ awareness and attitudes towards the utilization of M-Learning. Findings revealed that female instructors were more positive than males towards the utilization of M-Learning. Moreover, results revealed that academics who indicated that mobile technology is an effective tool in education were highly optimistic towards the utilization of M-Learning than those who were not. Nevertheless, results were not pointed any significant differences with regard to the academics’ perceptions towards the utilization of M-Learning in relation to age and academic qualifications factors, showing that M-learning can be embraced by all academics irrespective of the age and qualifications factors.

Mining and Exploration of Credit Cards Data in UAE

Credit cards have become an essential element in the banking industry. Credit cards add a significant value for the banks. Mining credit cards can find interesting patterns among different variables that may be used in the future by the policy makers for building their future policy. In this study, we have investigated the credit card-holders behavior in order to predict the market segmentation. An online questionnaire survey regarding credit card usage has been used for data collection. Two techniques have been applied on the collected data, Decision Trees and K-means through the use of training and testing sets. Results indicated how people are grouped based on their income which in turn will help in building the appropriate decision on which region needs to be targeted. Moreover, results revealed different work sectors for the credit card-holders and which type of credit card is used with regard to their income.

Development of M-learning Application based on Knowledge Management Processes

The wide spectrum use of mobile devices has brought numerous opportunities to develop and design Mobile Learning (M-learning) applications that will assist learners in their studies. From the M-learning perspective, in order to improve the learners capabilities through the use of mobile devices, Knowledge Management (KM) processes (Knowledge Acquisition, Knowledge Sharing, Knowledge Application, and Knowledge Protection) should be incorporated in M-learning applications. The literature shows that several research studies were carried out with the aim of developing M-learning applications. Due to the lack of literature on examining the impact of KM processes on M-learning acceptance theoretically and practically, the main objective of this study is to develop a M-learning application that enables KM processes (Acquisition, Sharing, Application, and Protection). Moreover, the paper describes the M-learning application framework that was developed based on KM processes. In addition, the paper deliberates the implementation of the application in two different universities in two different regions, namely: Universiti Malaysia Pahang (UMP) in Malaysia, and Al Buraimi University College (BUC) in Oman. Other implications and discussions are also presented in the paper.

PLS-SEM in Information Systems Research: A Comprehensive Methodological Reference

The partial least squares-structural equation modelling (PLS-SEM) has become a key approach for validating the conceptual models across many disciplines in general, and the Information Systems (IS) in specific. This is guided through the assessment of the measurement and structural models. Several research articles were carried out to provide an extensive coverage of the usage and application of PLS-SEM. These articles were mainly concentrated on providing guidelines of how to use PLS-SEM in terms of reflective and formative measures, measurement and structural models, and the steps for analysing a particular conceptual model. Nevertheless, there are several steps and procedures that precede the evaluation of the measurement and structural models. The understanding of these steps and procedures is very important for many IS scholars, Ph.D. and Master students who are always struggling to find a comprehensive reference that could guide them through their research journey. Hence, the main contribution of this study is to a build a comprehensive methodological guideline of how the PLS-SEM approach can be employed in the context of IS adoption and acceptance, starting from the research design stage till the assessment of the measurement and structural models. This study may serve as a comprehensive reference for formulating the methodology in the IS adoption and acceptance related studies in the case of PLS-SEM employment.

A Survey of Arabic Text Mining

Recently, text mining has become an interesting research field due to the huge amount of existing text on the web. Text mining is an essential field in the context of data mining for discovering interesting patterns in textual data. Examining and extracting of such information patterns from huge datasets is considered as a crucial process. A lot of survey studies were conducted for the purpose of using various text mining methods for unstructured datasets. It has been noticed that comprehensive survey studies in the Arabic context were neglected. This study aims to give a broad review of various studies related to the Arabic text mining with more focus on the Holy Quran, sentiment analysis, and web documents. Furthermore, the synthesis of the research problems and methodologies of the surveyed studies will help the text mining scholars in pursuing their future studies.

Analyzing the Arab Gulf Newspapers Using Text Mining Techniques

Nowadays, the broadcasting of news via social media networks is almost provided in a textual format. The nature of the broadcasted text is considered as unstructured text. Text mining techniques play an essential role in converting the unstructured text into informative knowledge. It has been observed that there is no research has addressed the textual analysis of Arabic newspapers on social media. Accordingly, this paper attempts to bridge this gap through building on related studies and applying various text mining techniques on a new under-researched context. 62,327 posts were collected from 24 Arab Gulf newspapers pages on Facebook. Results indicated that most of the discussed issues in the Arab Gulf region newspapers are related to trade, petroleum, and development. In addition, results revealed that the United Arab of Emirates (UAE) newspapers represent the source that is highly discussing issues regarding trade and economy followed by the Kingdom of Saudi Arabia (KSA) newspapers. Furthermore, results indicated that the financial and health-care issues news in the Arab Gulf region were highly tackled by Alkhaleej (UAE) newspaper. Besides, results pointed out that KSA newspapers, is on the top in disseminating issues regarding education.