Theoretical Evolution of Metaphor

This article brings together and discusses long-persisting theoretical perspectives that differ in their approaches to the nature and functions of metaphor—starting from Aristotle and his traditional view on metaphor and continuing up to contemporary metaphor theorists, such as Lakoff and Johnson. The aim is to offer insight into how metaphor has evolved from a mere figure of speech residing in literary works alone to a pervasive conceptual phenomenon permeating a wide spectrum of discourse domains

On Recommendation of Learning Objects using Felder-Silverman Learning Style Model

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation

A Novel Adaptation Model for E-Learning Recommender Systems Based on Student’s Learning Style

In recent years, a substantial increase has been witnessed in the use of online learning resources by learn- ers. However, owing to an information overload, many find it difficult to retrieve appropriate learning resources for meeting learning requirements. Most of the existing systems for e-learning make use of a “one-size-fits-all” approach, thus providing all learners with the same content. Whilst recommender systems have scored notable success in the e-commerce domain, they still suffer from drawbacks in terms of making the right recommendations for learning resources. This can be attributed to the differences among learners’ preferences such as varying learning styles, knowledge levels and sequential learning patterns. Hence, to identify the needs of an individual student, e-learning systems that can build profiles of student preferences are required. In addition, changing students’ preferences and multidimensional attributes of the course content are not fully considered simultaneously. It is by failing to review these issues that existing recommendation algorithms often give inaccurate recommendations. This thesis focuses on student learning styles, with the aim of dynamically tailoring the learning process and course content to meet individual needs. The proposed Ubiquitous LEARNing (ULEARN) system is an adaptive e-learning recommender system geared towards providing a personalised learning environ- ment, which ensures that course learning objects are in line with the learner’s adaptive profile. This thesis delivers four main contributions: First, an innovative algorithm which dynamically reduces the number of questions in the Felder-Silverman Learning Styles (FSLSM) questionnaire for the purpose of initialising student profiles has been proposed. The second contribution comprises examining the accuracy of various similarity metrics so as to select the most suitable similarity measurements for learning objects recommendation algorithm. The third contribution includes an Enhanced Collaboration Filtering (ECF) algorithm and an Enhanced Content-Based Filtering (ECBF) algorithm, which solves the issues of cold-start and data sparsity in- herent to the traditional Collaborative Filtering (CF) and the traditional Content-based Filtering (CBF), respectively. Moreover, these two new algorithms have been combined to create a new Enhanced Hybrid Filtering (EHF) algorithm that recommends highly accurate personalised learning objects on the basis of the stu- dents’ learning styles. The fourth contribution is a new algorithm that tracks patterns of student learning behaviours and dynam- ically adapts the student learning style accordingly. The ULEARN recommendation system was implemented with Visual Studio in C++ and Windows Pre- sentation Foundation (WPF) for the development of the Graphical User Interface (GUI). The experimental results revealed that the proposed algorithms have achieved significant improvements in student’s profile adaptation and learning objects recommendation in contrast with strong benchmark models. Further find- ings from experiments indicated that ULEARN can provide relevant learning object recommendations based on students’ learning styles with the overall students’ satisfaction at almost 90%. Furthermore, the results showed that the proposed system is capable of mitigating the problems data sparsity and cold-start, thereby improving the accuracy and reliability of recommendation of the learning object. All in all, the ULEARN system is competent enough to support educational institutions in recommending personalised course content, improving students’ performance as well as promoting student engagement.Arab academy for science technology & maritime transpor

Antibacterial activity of Commiphora molmol (Myrrha) against the periodontal pathogen, Aggregatibacter actinomycetemcomitans

The exogenous pathogen Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans) is most frequently involved in periodontitis and other systemic diseases. The treatment of these infections involves antibiotic therapy with Amoxicillin being the most popular antibiotic against A. actinomycetemcomitans but now, the drug is not 100% effective due to the development of 0-84% antibiotic resistance. The present study aimed to determine antibacterial activity of Commiphora molmol (Myrrha) against the periodontal pathogen, Aggregatibacter actinomycetemcomitans. In detail, the strain LIU1239 of Aggregatibacter actinomycetemcomitans was used where the plasmid pVJT128 is used for transposon mutagenesis and the Escherichia coli strain MV10Nal (stock # LIU4). The slow growing mutant, LIU 1380 strain was obtained by transposon mutagenesis and was confirmed to be slow growing by sequential streaking on AAGM plates for three passages. The experiment was mainly designed to show the antimicrobial activity of 20% Myrrha oil in AAGM and phosphate buffer. In nutrient rich AAGM, the oil took twenty hours to kill most cells of both strains, and at 5 hours, both strains were killed to the same extent. However, in nutrient-free phosphate buffer Myrrha killed the small colony mutant much faster than the wild type cells. For the very first time, the current study reported a marvelous antibacterial activity of Myrrha oil against A. actinomycetemcomitans in the nutrient-free medium. The findings showed that Myrrha oil extract can kill both growing and non-growing bacteria effectively. This antibacterial activity increases with the increasing concentration of oil up to 0.3 % against LIU1142, LIU 1239, and 1380 mutant. The use of Myrrha against A. actinomycetemcomitans could be a promising treatment to combat periodontitis and other bacterial infections

Machine Learning in Educational Technology

Machine learning is a subset of artificial intelligence (AI) that helps computers or teaching machines learn from all previous data and make intelligent decisions. The machine-learning framework entails capturing and maintaining a rich set of information and transforming it into a structured knowledge base for different uses in various fields. In the field of education, teachers can save time in their non-classroom activities by adopting machine learning. For example, teachers can use virtual assistants who work remotely from the home for their students. This kind of assistance helps to enhance students’ learning experience and can improve progression and student achievement. Machine learning fosters personalized learning in the context of disseminating education. Advances in AI are enabling teachers to gain a better understanding of how their students are progressing with learning. This enables teachers to create customized curriculum that suits the specific needs of the learners. When employed in the context of education, AI can foster intelligence moderation. It is through this platform that the analysis of data by human tutors and moderators is made possible

Researching Conceptual Metaphor in a Parallel Corpus

This article will explore the translational treatment of conceptual metaphors in a parallel corpus of American self-help texts on marriage relationships and their Arabic translations. The focus here on conceptual metaphors is primarily motivated by the need for a definitive account of the challenges posed by such metaphors in translation, the sorts of procedures used to handle them, and the actual factors contributing to the ease or difficulty of their translation. These issues have not been adequately addressed in previous analyses, which have concentrated largely on individual metaphorical expressions rather than on concepts that give rise to them. Little information was therefore available on the translation of different kinds of conceptual metaphors that characterize a particular discourse. This study introduces a detailed and replicable methodology for researching conceptual metaphor within the context of a parallel corpus from a descriptive perspective.&nbsp