A Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WIKIS

Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences. Traditionally, most personalized learning software systems focused on formal learning. However, learning personalization is not only desirable for formal learning, it is also required for informal learning, which is self-directed, does not follow a specified curriculum, and does not lead to formal qualifications. Wikis among other informal learning platforms are found to attract an increasing attention for informal learning, especially Wikipedia. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. Nevertheless, navigation on information wikis suffer from several limitations. To support informal learning on Wikipedia and similar environments, it is important to provide easy and fast access to relevant content. Recommendation systems (RSs) have long been used to effectively provide useful recommendations in different technology enhanced learning (TEL) contexts. However, the massive diversity of unstructured content as well as user base on such information oriented websites poses major challenges when designing recommendation models for similar environments. In addition to these challenges, evaluation of TEL recommender systems for informal learning is rather a challenging activity due to the inherent difficulty in measuring the impact of recommendations on informal learning with the absence of formal assessment and commonly used learning analytics. In this research, a personalized content recommendation framework (PCRF) for information wikis as well as an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis are proposed. The presented recommendation framework models learners’ interests by continuously extrapolating topical navigation graphs from learners’ free navigation and applying graph structural analysis algorithms to extract interesting topics for individual users. Then, it integrates learners’ interest models with fuzzy thesauri for personalized content recommendations. Our evaluation approach encompasses two main activities. First, the impact of personalized recommendations on informal learning is evaluated by assessing conceptual knowledge in users’ feedback. Second, web analytics data is analyzed to get an insight into users’ progress and focus throughout the test session. Our evaluation revealed that PCRF generates highly relevant recommendations that are adaptive to changes in user’s interest using the HARD model with rank-based mean average precision (MAP@k) scores ranging between 100% and 86.4%. In addition, evaluation of informal learning revealed that users who used Wikipedia with personalized support could achieve higher scores on conceptual knowledge assessment with average score of 14.9 compared to 10.0 for the students who used the encyclopedia without any recommendations. The analysis of web analytics data show that users who used Wikipedia with personalized recommendations visited larger number of relevant pages compared to the control group, 644 vs 226 respectively. In addition, they were also able to make use of a larger number of concepts and were able to make comparisons and state relations between concepts

Poverty, Growth and Income Distribution in Lebanon

This Country Study is based on a full national report that is the first to draw a profile of poverty in Lebanon based on money-metric poverty measurements of household expenditures. The report provides a comprehensive overview of the characteristics of the poor and estimates the extent of poverty and the degree of inequality in the country. It finds that nearly 28 per cent of the Lebanese population can be considered poor and eight per cent can be considered extremely poor. However, the most important finding of the report is that regional disparities are striking. For example, whereas poverty rates are insignificant in the capitol, Beirut, they are very high in the Northern city of Akkar. In general, the North governorate has been lagging behind the rest of the country and thus its poverty rate has become high. Levels of poverty are above-average in the South but are not as severe as expected. There are three other major results that have notable implications for a poverty-reduction programme in Lebanon. First, with few exceptions, measures of human deprivation, such as that provided by an Unsatisfied Basic Needs methodology, are generally commensurate with those for money-metric measures based on household expenditures. Second, the projected cost of halving extreme poverty is very modest, namely, a mere fraction of the cost of the country?s large external debt obligations. However, such a cost would rise dramatically if inequality were to worsen (i.e., if future growth were anti-poor). Also, the cost of reducing overall poverty would be substantially higher. Third, the poor are heavily concentrated among the unemployed and among unskilled workers, with the latter concentrated in sectors such as agriculture and construction. This places a priority on a broad-based, inclusive pattern of economic growth that could stimulate employment in such sectors. Based on such findings, the report concentrates on providing general policy recommendations on issues of directing public expenditures to poor households. One of its major recommendations is to concentrate on channelling resources to poor regions below the governorate level, such as to four ?strata? where two-thirds of the poor in Lebanon are concentrated. However, the report notes that macroeconomic policies, particularly fiscal policies, will have to be redesigned to mobilize the reources necessary to finance the increases in public expenditures on the social safety nets and public investment in social services that should be part of a major poverty-reduction programme.Poverty, Growth and Income Distribution in Lebanon

Overexpression of S6 Kinase 1 in Brain Tumours Is Associated with Induction of Hypoxia-Responsive Genes and Predicts Patients' Survival

mTOR/S6K pathway is a crucial regulator of cell growth and metabolism. Deregulated signalling via S6K has been linked to various human pathologies, including metabolic disorders and cancer. Many of the molecules signalling upstream of S6K have been shown to be either mutated or overexpressed in tumours, leading to S6K activation. The role of S6K1 in brain tumours is not fully investigated. In this study, we investigated the gene expression profile of S6 kinases in brain and CNS tumours using the publically available Cancer Microarray Database. We found that S6K1 but not S6K2 gene is overexpressed in brain tumours and this upregulation is associated with patients' poor survival. Furthermore, we interrogated Oncomine database for the expression profile of hypoxia-induced genes using a literature-defined concept. This gene list included HIF1A, VEGFA, SOX4, SOX9, MMP2, and NEDD9. We show that those genes are upregulated in all brain tumour studies investigated. Additionally, we analysed the coexpression profile of S6K1 and hypoxia responsive genes. The analysis was done across 4 different brain studies and showed that S6K1 is co-overexpressed with several hypoxia responsive genes. This study highlights the possible role of S6K1 in brain tumour progression and prediction of patients' survival. However, new epidemiological studies should be conducted in order to confirm these associations and to refine the role of S6K1 in brain tumours as a useful marker for patients' survival

Algorithm for solving fractional partial differential equations using homotopy analysis method with Pade approximation

In recent years nonlinear problems have several methods to be solved and utilize a well-known analytic tools such as homotopy analysis method. In general, homotopy analysis method had gain a wide focus and improvement especially in typical nonlinear problem. The aim of this paper is to use homotopy method of analysis to solve partial differential equation in addition to improve method’s efficiency. The method in this paper is to apply approximation to Pade’ approach to obtain sufficient efficiency. As a result, the improvement has been verified by solving two cases beside a mean value comparison of the homotopy analysis method’s squared error with the improved form

Survey of Personalized Learning Software Systems: A Taxonomy of Environments, Learning Content, and User Models

This paper presents a comprehensive systematic review of personalized learning software systems. All the systems under review are designed to aid educational stakeholders by personalizing one or more facets of the learning process. This is achieved by exploring and analyzing the common architectural attributes among personalized learning software systems. A literature-driven taxonomy is recognized and built to categorize and analyze the reviewed literature. Relevant papers are filtered to produce a final set of full systems to be reviewed and analyzed. In this meta-review, a set of 72 selected personalized learning software systems have been reviewed and categorized based on the proposed personalized learning taxonomy. The proposed taxonomy outlines the three main architectural components of any personalized learning software system: learning environment, learner model, and content. It further defines the different realizations and attributions of each component. Surveyed systems have been analyzed under the proposed taxonomy according to their architectural components, usage, strengths, and weaknesses. Then, the role of these systems in the development of the field of personalized learning systems is discussed. This review sheds light on the field’s current challenges that need to be resolved in the upcoming years

The Effect of Using Inductive and Deductive Methods on 7th Grade Students’ Achievement in Grammar in Bethlehem District and their Attitudes toward EFL

This study aims to investigate the effect of using the inductive and deductive methods on 7th grade students’ achievement in grammar in Bethlehem District and their attitudes toward EFL. To achieve the purpose of the study, the researchers applied the instruments to a purposeful sample from 7th grade students at two schools, one for boys and the other for girls. Two groups, the experimental and the control ones were used in this study. The experimental group was taught by the inductive and deductive methods, and the control group was taught by the traditional method. To answer the main research question whether the inductive and deductive methods are more effective in teaching grammar rather than the traditional way, the researchers used a pre and post-tests to measure students' achievement in grammar. The researchers also designed a questionnaire to measure students' attitudes toward English as foreign language (EFL). In this study, the researchers used the quasi-experimental design and the used Analysis of Covariance (ANCOVA) for measuring the contrast between the experimental and control groups. The research concluded that there are statistical significant differences between the mean scores of 7th grade students' achievement in grammar, due to interaction between teaching method and gender and the differences were in favor of the male students in the experimental group. Also, the results showed that there are no statistical significant differences between attitudes towards EFL, due to interaction between teaching method and gender

Triggers And Tweets: Implicit Aspect-Based Sentiment And Emotion Analysis Of Community Chatter Relevant To Education Post-Covid-19

This research proposes a well-being analytical framework using social media chatter data. The proposed framework infers analytics and provides insights into the public\u27s well-being relevant to education throughout and post the COVID-19 pandemic through a comprehensive Emotion and Aspect-based Sentiment Analysis (ABSA). Moreover, this research aims to examine the variability in emotions of students, parents, and faculty toward the e-learning process over time and across different locations. The proposed framework curates Twitter chatter data relevant to the education sector, identifies tweets with the sentiment, and then identifies the exact emotion and emotional triggers associated with those feelings through implicit ABSA. The produced analytics are then factored by location and time to provide more comprehensive insights that aim to assist the decision-makers and personnel in the educational sector enhance and adapt the educational process during and following the pandemic and looking toward the future. The experimental results for emotion classification show that the Linear Support Vector Classifier (SVC) outperformed other classifiers in terms of overall accuracy, precision, recall, and F-measure of 91%. Moreover, the Logistic Regression classifier outperformed all other classifiers in terms of overall accuracy, recall, an F-measure of 81%, and precision of 83% for aspect classification. In online experiments using UAE COVID-19 education-related data, the analytics show high relevance with the public concerns around the education process that were reported during the experiment\u27s timeframe

FORMULATION AND OPTIMIZATION OF SOLID SELF-NANOEMULSIFYING SYSTEM USING POROUS CARRIERS FOR ORAL DELIVERY OF CINNARIZINE

Objective: The present study aims to utilize the nanotechnology technique to formulate the Cinnarizine (CNZ) in the form of solid self-nano emulsifying system to enhance the dissolution and hence the bioavailability.Methods: Screening study for solubility of CNZ in different vehicles was carried out. The selected system was optimized for saturated solubility, globule size, zeta potential, polydispersity index (PDI) and self-emulsification time. The solidified nanoemulsion was prepared using; Aeroperl 300, Aerosil 200, hydrophilic nanosilica and Neusilin US2 as porous carrier materials. The compressed CNZ tablets were evaluated regarding their physicochemical characteristics, in-vitro release, and bioavailability study.Results: Self nano-emulsifying system composed of Labrafil (oil), tween 80 (surfactant), and transcutol (cosurfactant) was successfully developed with a droplet size range of 11.37-92.58 nm. The in-vitro release results revealed that the developed formulation improved the release of CNZ and enhanced the bioavailability in the rabbits (190%) more than the commercial product (Stugeron® tablets).Conclusion: Solid self-nano-emulsifying system of CNZ was successfully developed by different ratios of Labrafil (oil), tween 80 (surfactant), transcutol (cosurfactant) and solidified by the adsorption on hydrophilic nano silica and the optimized formula could be expected to increase and improve the bioavailability of CNZ.Â