An Investigation into Annual Faculty Evaluation Process of EFL Teachers in the Middle East: Challenges for Leadership & Management

This study examined the effectiveness of the Annual Faculty Evaluations Process at an English language centre in the Middle Eastern context. It sought to determine to what extent this evaluation process helped the English language teachers improve their teaching practice. A mixed-method design was used to collect quantitative and qualitative data. The data were collected via teacher questionnaire & teacher interviews and triangulated via cross examination of the documents pertaining to the teacher evaluation policy and procedures used by the language centre. The findings of this case study revealed that the teachers believe that the process of annual faculty evaluation (AFE) has had a very positive impact on their professional development. However, the AFE process is quite rigid and does not take cultural diversity of the teachers into account. The report makes various recommendation in terms of teacher autonomy as well as participation in the evaluation process and using the three-track evaluation system (Danielson and McGreal, 2000) by the administration

Sentiment Analysis of Textual Content in Social Networks. From Hand-Crafted to Deep Learning-Based Models

Aquesta tesi proposa diversos mètodes avançats per analitzar automàticament el contingut textual compartit a les xarxes socials i identificar les opinions, emocions i sentiments a diferents nivells d’anàlisi i en diferents idiomes. Comencem proposant un sistema d’anàlisi de sentiments, anomenat SentiRich, basat en un conjunt ric d’atributs, inclosa la informació extreta de lèxics de sentiments i models de word embedding pre-entrenats. A continuació, proposem un sistema basat en Xarxes Neurals Convolucionals i regressors XGboost per resoldre una sèrie de tasques d’anàlisi de sentiments i emocions a Twitter. Aquestes tasques van des de les tasques típiques d’anàlisi de sentiments fins a determinar automàticament la intensitat d’una emoció (com ara alegria, por, ira, etc.) i la intensitat del sentiment dels autors a partir dels seus tweets. També proposem un nou sistema basat en Deep Learning per solucionar el problema de classificació de les emocions múltiples a Twitter. A més, es va considerar el problema de l’anàlisi del sentiment depenent de l’objectiu. Per a aquest propòsit, proposem un sistema basat en Deep Learning que identifica i extreu l'objectiu dels tweets. Tot i que alguns idiomes, com l’anglès, disposen d’una àmplia gamma de recursos per permetre l’anàlisi del sentiment, a la majoria de llenguatges els hi manca. Per tant, utilitzem la tècnica d'anàlisi de sentiments entre idiomes per desenvolupar un sistema nou, multilingüe i basat en Deep Learning per a llenguatges amb pocs recursos lingüístics. Proposem combinar l’ajuda a la presa de decisions multi-criteri i anàlisis de sentiments per desenvolupar un sistema que permeti als usuaris la possibilitat d’explotar tant les opinions com les seves preferències en el procés de classificació d’alternatives. Finalment, vam aplicar els sistemes desenvolupats al camp de la comunicació de les marques de destinació a través de les xarxes socials. Amb aquesta finalitat, hem recollit tweets de persones locals, visitants i els gabinets oficials de Turisme de diferents destinacions turístiques i es van analitzar les opinions i les emocions compartides en ells. En general, els mètodes proposats en aquesta tesi milloren el rendiment dels enfocaments d’última generació i mostren troballes apassionants.Esta tesis propone varios métodos avanzados para analizar automáticamente el contenido textual compartido en las redes sociales e identificar opiniones, emociones y sentimientos, en diferentes niveles de análisis y en diferentes idiomas. Comenzamos proponiendo un sistema de análisis de sentimientos, llamado SentiRich, que está basado en un conjunto rico de características, que incluyen la información extraída de léxicos de sentimientos y modelos de word embedding previamente entrenados. Luego, proponemos un sistema basado en redes neuronales convolucionales y regresores XGboost para resolver una variedad de tareas de análisis de sentimientos y emociones en Twitter. Estas tareas van desde las típicas tareas de análisis de sentimientos hasta la determinación automática de la intensidad de una emoción (como alegría, miedo, ira, etc.) y la intensidad del sentimiento de los autores de los tweets. También proponemos un novedoso sistema basado en Deep Learning para abordar el problema de clasificación de emociones múltiples en Twitter. Además, consideramos el problema del análisis de sentimientos dependiente del objetivo. Para este propósito, proponemos un sistema basado en Deep Learning que identifica y extrae el objetivo de los tweets. Si bien algunos idiomas, como el inglés, tienen una amplia gama de recursos para permitir el análisis de sentimientos, la mayoría de los idiomas carecen de ellos. Por lo tanto, utilizamos la técnica de Análisis de Sentimiento Inter-lingual para desarrollar un sistema novedoso, multilingüe y basado en Deep Learning para los lenguajes con pocos recursos lingüísticos. Proponemos combinar la Ayuda a la Toma de Decisiones Multi-criterio y el análisis de sentimientos para desarrollar un sistema que brinde a los usuarios la capacidad de explotar las opiniones junto con sus preferencias en el proceso de clasificación de alternativas. Finalmente, aplicamos los sistemas desarrollados al campo de la comunicación de las marcas de destino a través de las redes sociales. Con este fin, recopilamos tweets de personas locales, visitantes, y gabinetes oficiales de Turismo de diferentes destinos turísticos y analizamos las opiniones y las emociones compartidas en ellos. En general, los métodos propuestos en esta tesis mejoran el rendimiento de los enfoques de vanguardia y muestran hallazgos interesa.This thesis proposes several advanced methods to automatically analyse textual content shared on social networks and identify people’ opinions, emotions and feelings at a different level of analysis and in different languages. We start by proposing a sentiment analysis system, called SentiRich, based on a set of rich features, including the information extracted from sentiment lexicons and pre-trained word embedding models. Then, we propose an ensemble system based on Convolutional Neural Networks and XGboost regressors to solve an array of sentiment and emotion analysis tasks on Twitter. These tasks range from the typical sentiment analysis tasks, to automatically determining the intensity of an emotion (such as joy, fear, anger, etc.) and the intensity of sentiment (aka valence) of the authors from their tweets. We also propose a novel Deep Learning-based system to address the multiple emotion classification problem on Twitter. Moreover, we considered the problem of target-dependent sentiment analysis. For this purpose, we propose a Deep Learning-based system that identifies and extracts the target of the tweets. While some languages, such as English, have a vast array of resources to enable sentiment analysis, most low-resource languages lack them. So, we utilise the Cross-lingual Sentiment Analysis technique to develop a novel, multi-lingual and Deep Learning-based system for low resource languages. We propose to combine Multi-Criteria Decision Aid and sentiment analysis to develop a system that gives users the ability to exploit reviews alongside their preferences in the process of alternatives ranking. Finally, we applied the developed systems to the field of communication of destination brands through social networks. To this end, we collected tweets of local people, visitors, and official brand destination offices from different tourist destinations and analysed the opinions and the emotions shared in these tweets

Critical investigation into a textbook for actual and potential uses in Pakistani higher secondary education

Morgan (1997:16) observes that any form of education aims to bring about changes in students. It must, therefore, have in view both what an educated person should be and the ideal society to whose relationship they will contribute. Such educated individuals will presumably contribute to the betterment of their society insofar as adjustments to their status quo are desirable. In line with Morgan, this study has suggested that disempowered learners in Pakistani higher secondary classroom, by taking the ownership of their learning, can emerge as independent critical thinker with a better perception of the world. This study has explored how conservative pedagogical treatment affects the learners’ understanding of texts by disempowering and having them either misperceived or incomplete information. The study has proposed an alternative route to learning which might ensure a more effective impact on the learning process and the learning outcome. For this purpose, the study critically analyses the texts of a Pakistani higher secondary English textbook to investigate how ineffective treatment of these texts influences the learners’ perception of the world and their learning outcome. The critical discourse analysis complements a questionnaire survey followed by interviews with the learners to gauge their level of understanding of the texts in line with the goals and objectives set by the national curriculum of Pakistan. Following a critical paradigmatic pattern, the study not only points out the problem but also comes up with a change agenda by advocating the case for critical pedagogy for these learners. The study proposes sample material to support how adding a critical dimension to the existing English syllabus may well achieve better results in term of academic accomplishments, in addition to broadening the learners’ vision, and preparing them to face the rapidly changing and growing world of the 21st century

CuisineNet: Food Attributes Classification using Multi-scale Convolution Network

Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models.Comment: 8 pages, Submitted in CCIA 201

Teacher Development: An Overview of the Concept and Approaches

Teaching is an on-going professional activity rather than something that can be mastered once and for all through the acquisition of a restricted set of skills. It needs to be refreshed and developed with the passage of time as new ideas and approaches towards teaching and learning are discovered. This emphasises the need of development activities for staff to update and enhance their professional skills. This paper explores the concept of teacher- development giving a comprehensive account of the topic and discusses the teacher appraisal system as its integral part. A review of various approaches is also given to enhance the concept of teacher development. The paper also makes suggestions to implement a teacher development plans taking into account the phases in the successful implementation of change. DOI: 10.5901/jesr.2014.v4n6p14

Thou Shalt Not Think: Editors’ Voice in an English Textbook to Propagate Vested Agendas

Textbooks, particularly in developing countries, are used as a tool to propagate the agendas of state and other groups in power. This paper informs the reader on the issue of how a tertiary level English textbook used editors’ voice to form the opinion of its readers by shaping facts and perspectives depicted in the texts. The editors of the textbook not only censored the information to block autonomous learning, but also attempted to misrepresent the themes of various texts to meet the censorship guidelines set by the textbook-board and/or the state. The paper aims to raise the question of learner autonomy and learners’ right to access information in its original form to be interpreted independently in the schematic background of each individual. By pointing out the issue and initiating the discussion, the paper hopes to bring awareness in the less explored area of the use of language power in the Pakistani educational context