Education

The aim of this study is to investigate EFL Secondary School Teachers' Views on Blended Learning. It also aims to investigate (a) the teachers' views on blended learning content and process, and (b) how blended learning is effective in developing teachers' performance. The study sample included 35 EFL Saudi teachers in Tabuk City, KSA. In order to collect the data required, the researchers developed a questionnaire that consisted of two sections, namely, process and content. The results indicate that the teachers' views toward blended learning were generally positive and very promising in both sections. Moreover, it was interesting to see that EFL teachers were highly optimistic about how blended learning would help them in improving their performance and how it would motivate their students to learn English. Finally, there are no significant differences between teachers' responses to the content and process of blended learning, with regards to qualification, experience, and the amount of training done.

Accent recognition refers to the problem of inferring the native language of a speaker from his foreign-accented speech. Differences in accent are due to both articulation and prosodic characteristics. The automatic identification of foreign accents is valuable for different speech systems, such as speech recognition, speaker identification or voice conversion. This paper aims to identify the native languages of non-native English speakers from different countries in the Arabic region: the researchers choose Saudi Arabia to represent the eastern Arabic region in Asia, Egypt to represent the eastern Arabic region in Africa and Tunisia to represent the western Arabic region in Africa. In this research, reinforcement learning (RL), a sub-branch of machine learning, and artificial neural network will be used as an intelligent method to classify and predict the speech. The aim is to train a neural network to automatically detect speech accents. Then the researchers develop a hybrid multi-agent RL algorithm that takes advantage from the multiagent communication and cooperative agents on the language detection process. Hence, the aim is to help sociolinguists and discourse analysts. As for the Saudi context, this study will be very useful on resolving e-learning issues such as linguistic problems of students.

Accents, or changes in how different people speak the same word/sentence in the same language, pose substantial communication issues in most spoken languages. This is a well-known fact, but how does the accent of one language affect learning/speaking another? In this paper, we look at how Arab accents influence the English language. To that end, we built a deep machine-learning system for Arabic accent recognition that was learned from an in-house English speech database of four Arabic accents collected from Jordan, Iraq, Saudi Arabia, and Tunisia. The proposed system employs Mel spectrograms of an English-spoken paragraph to train an LSTM neural network to recognize the accent in each sound signal. Although the collected data was extremely difficult to learn due to the presence of both males and females and fluent speakers in each class, the proposed system could recognize speakers with various accents by up to 79%. This answers the study's main question, demonstrating that speakers with an Arabic accent have their way of speaking English, which varies by country. As a result, if trained on appropriate and adequate data, the proposed system can also be used to recognize accents in any language.

Drawing on recent developments in dialogic approaches to learning and teaching mathematics, my PhD study investigates how Saudi mathematics teachers develop their understanding of classroom dialogue through a professional development process in mathematics teaching. The nature of this study is qualitative. It involved an embedded case study focusing on a teacher development programme (TDP) for three Saudi primary mathematics teachers in relation to their use of dialogic teaching. This research draws upon the community of practice theory (Wenger, 1998). The analysis of data shows how the three math teachers’ identities have been developed through their participations within the emergent community of practice. This paper will show evidence of the emergence of a new professional identity for one teacher Zayed as one case study.

This study is concerned with Saudi Grade Five students‟ common errors and
misconceptions of multiplication and division in whole numbers. It has considered
students‟ understanding of multiplication and division concepts and properties. Students‟
errors in multiplying and dividing by multiples of 10, and mental strategies and
calculations, and standard algorithms are investigated.
The study used both qualitative and quantitative methods. A written test and semi-
structured interviews were used to collect the data. The target population of the written
test was 96 students in four urban schools. Only 10 students of the sample were chosen
to take part in interviews. Selected data of the test and the interviews were used in the
study in order to address the research questions.
The study findings show that some students hold common misconceptions of
multiplication and division. There are various common errors in understanding
multiplication and division symbols and properties. Furthermore, mental strategies are
not used by the majority of students. Finally, there are common errors in the use of
standard algorithms of multiplication and division with a lack of understanding of
algorithmic procedures.