Category Archive : Ielts Preparation Course Online

Developing E-learning Skills Within English Language Training

Developing E-learning Skills Within English Language Training

Foreign language training at the Slovak University of Technology (hereinafter referred as STU), the Faculty of Materials Science and Technology (MTF), Institute of Engineering Pedagogy and Humanities (ÚIPH), Department of Professional Language Training (KOJP) is being carried out according to the syllabus based on a thorough analysis of the Faculty’s students and graduates’ needs on one hand, and the demands of practice and employers on the other hand. The syllabus has been continuously innovated to improve the graduates’ skills and their competitiveness in the regional and international job markets, while satisfying also the demands of industrial practice. Developing E-learning Skills Within English Language Training

The innovations reflect the challenges of the Lisbon strategy, Sorbonne declaration and Bologna declaration: to improve foreign language competence of the students of technical branches, to increase their readiness to enter the European job market, and to harmonise the European university sectors in order to prepare graduates for mobility, global job market and global and international research and scientific environment.

Since the integration into the European Union, the STU students have encounter the challenges to participate in student mobility in foreign universities in the countries, including Germany, Russia, Spain, Finland, Belgium and Japan, where e-learning practices are a prerequisite for mastering the university study. At the same time, the Slovak Republic has attracted foreign investors and multinationals. In wider context, it can be considered as forming positive attitudes e.g. to entrepreneurship as well as building necessary entrepreneurial skills, e.g. initiative, creativity, willingness to risk, reliability, etc. [1]. This consequently calls for developing creative and challenging e-education environment and developing e-learning skills within English for Science and Technology (EST) language training.

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Training College Students’ Comprehensive Qualities by English Class Lectures

Training College Students’ Comprehensive Qualities by English Class Lectures

College English Curriculum Requirements points out that English teaching objective is to develop students’ English language proficiency, especially listening and speaking skills so that they can communicate in English effectively in their future study, work and social interactions, meanwhile to enhance their autonomy ability, improve their overall cultural quality to meet needs of China’s social development and international communication Training College Students’ Comprehensive Qualities by English Class Lectures

College students have learned English for at least six years before they enter the university, but generally their listening and speaking abilities are weak, which requires a proper environment for them to practice in the limited class time. The research group introduces English speech activities into the class based on Output Hypothesis and College English Curriculum Requirements. The students practice their oral English by lectures in classes.

In order to improve students’ English proficiency, since 2008 our reach group has carried out one two-year experiment. For the experimental class, in the first ten minutes students are asked to give short English lectures in the different stages, first prepared speech, and later semiprepared speech, impromptu speech last. Presentation includes related questions from teachers and other students who make recommendations based on the performance of the speaker. After two years of experiments, questionnaire result in the experimental class shows that among 36 students 34 felt that their English proficiency improved, 27 of them had a more substantial increase. Only 2 did remain at the same level. About 94% students in the experimental class improved their English application abilities. In the experimental class, 31 students improved their learning ability, 34 improved language application capabilities, 33 improved their ability to analyze problems, 35 improved adaptability, 32 improved innovation ability. The research group made the sub-trace analysis over the 2 years scores of the experimental class, from which we drew a conclusion that there are significantly differences between the experimental classes scores and control classes scores compared to their similar entrance scores after 2 years’ experiments. That is to say, after 2 years of class lecture experiment, students’ English scores improved significantly.

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AUTOMATIC LABELING OF CONTRASTIVE WORD PAIRS FROM SPONTANEOUSSPOKEN ENGLISH

AUTOMATIC LABELING OF CONTRASTIVE WORD PAIRS FROM SPONTANEOUSSPOKEN ENGLISH

The concept of contrast plays an important role in many spo-ken language technologies, ranging from spoken language un-derstanding to speech synthesis. According to the observationpoint one looks at it, contrast can be seen as: a) a discourserelation that ties discourse elements; b) a concept of infor-mation structure that makes a word (or a phrase) salient bycomparing it with other word(s) available from the discoursecontext; c) a linguistic concept often prosodically marked.Given the broad meaning of contrast, the different dis-course scenarios invoking it, the poor availability of corporaannotated with categories of contrast, and our main researchinterest of investigating the role of contrast in prosodic promi-nence modeling for text-to-speech applications, we decided tofocus on one aspect/category of contrast only: an informationstructure relation that links two semantically related wordsthat explicitly contrast with each other.

Before merging the syntactic and the information structureannotations we converted the constituent format in the PennTreebank into dependency trees using the Penn2Malt con-verter ([6]). Since the PennTreebank constituent annotationfor Switchboard uses slightly different (and not yet standardlyheld) conventions from whose presupposed by the Penn2Maltconverter we had to support the converter with some addi-tional scripts. However, because of problems we encounteredin the conversion process we had to remove 54 (out of 146)dialogues. For each remaining dialogue all the word senses(according to the WordNet senses set) were disambiguatedusing the WordNet::SenseRelate Perl module ([7]).

All syntactic features are POS, dependency relations (subjectof, object of, etc…) and features derived from both of them.Examples of features derived from POS are the features indi-cating if W1 is the only word in the sentence having the samebroad POS of W2, and the feature indicating if W1 is the clos-est (in term of words between them) word preceding W2 andhaving the same broad POS. The use of deeper than POS syntactic information suchas syntactic dependencies (and information related to them)is motivated by the need of identifying syntactic patterns ofcontrastiveness that can not be identified using POS and lex-ical features alone. For example knowing that W1 and W2have the same type of dependency with their heads as in ex-ample (3) (both “you” and the first “I” have a “subject of”dependency with “take” and “do” respectively) or that theirheads refer to the same item as in example (6), seems to bea necessary (but often not sufficient) information to identifycontrast. https://speakinenglish.in/

 AUTOMATIC LABELING OF CONTRASTIVE WORD PAIRS FROM SPONTANEOUSSPOKEN ENGLISH
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Programs in Non-Native English Speaking Nations

Programs in Non-Native English Speaking Nations

The essential element of globalization is communications in English. Technology and social science will influence the non-native English speaking nations (non-NESN) through the English communication. These kinds of communications are very important to these countries, because some information will make the non-NESN a great deal progress. In Taiwan, the globalizing policy has been proposed and stressing for a long time. Because Mandarin and some dialects are widely used in daily life, the general English level in Taiwan isn’t as good as that in some native English speaking nations (NESN). From the viewpoints of globalization, the English level in Taiwan should be elevated to approach the level of the NESN. The English teaching effectiveness directly affects the globalizing pace. The school-based curricula (SBC) in the technological and vocational education systems are developed by the Ministry of Education to cope with the rapid changes in the Age of Knowledge-based Economy [1-2]. These curricula are divided into 17 occupational families (OFs) are shown in Appendix [3-4]. This new SBC is going to work in practice in 2005. The 18 year-old students at the moment will work on the 2-year technological and vocational college/university (TVCU) program, and 16 year-old students will work on the 4-year TVCU program. Since they study English at the junior high school (about 12 years old), their English level could not be good enough to handle all globalizing things related to English after they graduate from 2-year or 4-year TVCU program. When they study in SBC, instructors can’t teach them in the same way as those in NESN. English is one of the core courses of the 17 OFs. It is needless to say that English plays the very important role in the Age of Knowledge-based Economy [5]. Therefore, English courses should be systematically designed. The purpose of this study is to systematically design SBC for the students in TVCU programs. After they graduate, they can cooperate with several OFs to communicate with the professionals in NESN smoothly, and speed up the globalizing pace. https://speakinenglish.in/

The essential element of globalization is communications in English. We proposed the spectrum analysis of English vocabulary for the professionals to communicate between NESN and non-NESN. The English curriculum overview of TVCU program is developed from the spectrum in Fig. 1. This method is helpful to the curriculum design of TVCU programs which will be put into practice in 2005. Due to the analysis and the curriculum design, the globalizing pace will be accelerated

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GROUND TRUTH ESTIMATION OF SPOKEN ENGLISH FLUENCY SCORE

GROUND TRUTH ESTIMATION OF SPOKEN ENGLISH FLUENCY SCORE

In this paper, we propose ground truth estimation of spo-ken English fluency scores using decorrelation penalized low-rank matrix factorization. Automatic spoken English fluencyscoring is a general classification problem. The model param-eters are trained to map input fluency features to correspond-ing ground truth scores, and then used to predict a score for aninput utterance. Therefore, in order to estimate the model pa-rameters to predict scores reliably, correct ground truth scoresmust be provided as target outputs. However, it is not simpleto determine correct ground truth scores from human raters’scores, as these include subjective biases. Therefore, groundtruth scores are usually estimated from human raters’ scores,and two of the most common methods are averaging and vot-ing.Although these methods are used successfully, ques-tions remain about whether the methods effectively estimateground truth scores by considering human raters’ subjectivebiases and performance metric. Therefore, to address theseissues, we propose an approach based on low-rank matrix fac-torization penalized by decorrelation. The proposed methoddecomposes human raters’ scores to biases andlatentscoresmaximizing Pearson’s correlation. The effectiveness of theproposed approach was evaluated using human ratings of theKorean-Spoken English Corpus.https://speakinenglish.in/

Recently, Computer Aided Language Learning (CALL) hasreceived considerable attention as a method for improving theEnglish speaking skills of non-native students. In order forCALL systems to provide useful tutoring feedback, an auto-mated scoring system is required for evaluating pronunciationquality, fluency, and specific mistakes made by non-nativestudents.In general, the fluency scoring system is composed of au-tomatic speech recognition, fluency feature extraction, anda scoring model. In fluency feature extraction, features as-sumed to be highly correlated to spoken English fluency arecomputed [1, 2, 3]. For example, long silence duration, num-ber of words per second, and phone duration are some of themost common fluency features [1, 4]. The scoring model is aclassifier in which the model parameters are trained to map in-put fluency features to corresponding ground truth scores, andthen used to predict a score for an input utterance. The mostcommon algorithms for scoring models are linear regression[2], support vector machine (SVM) [5], or Gaussian process[3].Score modelling is a general supervised learning problem.Therefore, in order for the model to be trained reliably, correctground truth scores must be provided as target outputs. How-ever, it is not simple to obtain correct ground truth scores fromhuman raters’ scores as these include variability due to humanraters’ subjective biases. For example, each human expertmight assign different scores to the same utterances. Con-sequently, ground truth scores are estimated by neutralizinghuman raters’ subjective biases. The most common methodis averaging, which estimates ground truth scores by averag-ing the biased scores [6, 7, 8, 9]; Another is voting, which isbased on majority opinions [10].Although averaging and voting are successfully usedin practice, questions remain about whether they producereliable ground truth scores by considering human raters’biases and scoring model metric such as Pearson’s correla-tion. Therefore, we propose an estimation approach based ondecorrelation penalized low-rank matrix factorization to takeaccount of both human raters’ subjective bias and Pearson’scorrelation.

GROUND TRUTH ESTIMATION OF SPOKEN ENGLISH FLUENCY SCORE

In spoken English fluency scoring modelling, the ground truthestimation problem is sometimes overlooked because a scor-ing rubrics are designed and human raters are trained to main-tain high correlation among their scores. Nevertheless, thereexists disagreement among raters’ scores and a single scoremust be determined for an each input feature to train the com-putational scoring model such as DNN.

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