Category Archive : Ielts Reading Learning App

Abbreviations Can Time During Your OET Listening Test

Abbreviations Can Time During Your OET Listening Test

One of the commonest problems most of the candidates who take OET Listening Sub-Test come across is that they find it almost difficult to manage the time.

The recordings are played only once. There is no going back. What if you miss listening to some very important information

Experts suggest that time can be managed only when the OET test takers focus on taking the notes while listening to the recordings. Of course, this is a skill and it needs to be improved before the test. If you are good at taking the notes from the recorded scripts, then it will be easy for you to answer the questions.Abbreviations Can Time During Your OET Listening Test

Utilize the time that you get before the recordings are played for you. Take a look at the questions. Understand more about what type of information you may have to listen more closely to in order to extract the answers.

Use of Abbreviations

Experienced OET masters always suggest the use of the abbreviations in order to cope up with the problems that may arise at the time of the OET listening sub-test.

Use abbreviations or symbols to record the information that you listen to. And later you can use this information for the answers.

These abbreviations or symbols are something that healthcare professionals also make use of in their workplace.

It should be said that there is certainly no standard list of the symbols or the abbreviations that are accepted by the OET examining committee. The OET assessors will, however, not mark negatively if you use abbreviations or the symbols that are common and globally accepted in the medical world.

Some of the common examples include the following

Generally, In General (gen.)
Blood Pressure (BP)
cap: Capsule.
Nausea and Vomiting (N+V)
Information (info.)
ARF: Acute renal (kidney) failure
CC: Chief complaint. Main concerns of the patient
a/g ratio: Albumin to globulin ratio.
b.i.d.: Twice daily.
BKA: Below the knee amputation.
C/O: Complaint of.
ADHD: Attention deficit hyperactivity disorder
NBCCS: Nevoid basal cell carcinoma syndrome
PE: Pulmonary embolism.

If you can practice a little, then you will be able to jot down important information while listening making use of the right abbreviations and symbols.

Apart from standard abbreviations and symbols, you also have the freedom to use any kind of symbol or abbreviation in order to remember the information. Of course, you can’t write down any kind of abbreviation or symbol as an answer in your OET listening subtest but, you can use it your way to arrive at the right answers.

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Challenges in OET Reading Test

Challenges in OET Reading Test

Any English test of international standard will have its own challenges. OET is no exception. But, here, in OET, the challenges are too big.

Being a professional, you may think it is easy at one glance as you will find the content of the test to be relevant to your profession. But, in reality, it will be difficult to pass this test. So, what are the challenges in the OET reading sub-test? Let us discuss a few of them.

Part A of the Reading Sub-Test 

Part A of the reading sub-test is enough to make anyone confused. There will be 15 minutes to complete this part but it will require expeditious reading skills.  How good are you at locating the specific information? Can you read different texts and come to one common and acceptable solution?

You will be given four short texts. These four texts will talk about one particular subject.  And there will be 20 questions to answer. Challenges in OET Reading Test

All different texts may point out to one particular subject but, superficially all these texts may look different to the test takers. The test taker may get tricked into considering these four texts as four different topics. So, be careful and read twice if you find it difficult to see direct relevance among these texts. 

Different types of questions & the limited time for it makes this task difficult.  Most of the candidates spend more than the required time on it and because of this, they do not manage to complete the whole test on time. So, it is recommended that you should try to complete this first part of the test within or less than 15 minutes. 

Part B and Part C of the reading sub-test There will be 45 minutes to complete these two parts of the test. But, you will have to practice hard to answer part B completely well within 12-15 minutes of time. Generally, part C of the test is more challenging and may take more time. 
 
What is analyzed? Are you good at comprehending the lengthy and complex text? Are you good at analyzing and finding the answers from the given text? Are you good at identifying the gist of the content? 

Part B will comprise six short texts. The total word length of all the six texts may go up to 600-900 words. Each text may have about 100-150 words. Part C will have two different texts of about 800 words each.  

Difficulty level

You will have very little time to complete the test. 45 minutes may really be not enough if you are not good at smart reading and comprehension. The questions may seem more complex to you. Almost all of the candidates, even the smartest of the candidates, feel some level of difficulty in answering all the questions. Most of the candidates complain of not being able to manage the time because of the difficulty in comprehending and finding the answers to the questions. 

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How to Listening Test and Spoken BEC

How to Listening Test and Spoken BEC

A user can scan through a text easily, but it is not the case for spoken content, because they cannot be directly displayed on-screen. As a result, accessing large collections of spoken con-tent is much more difficult and time-consuming than doing so for the text content. It would therefore be helpful to develop machines that understand spoken content. In this paper, we propose two new tasks for machine comprehension of spoken content. The first is a listening comprehension test for BEC, a challenging academic English examination for English learners who are not the native English speakers. How to Listening Test and Spoken BEC

Group of young college students using laptop in a cafe.

We show that the proposed model out performs the naive approaches and other neural network based models by exploiting the hierarchical structures of natural languages and the selective power of attention mechanism. For the second listening comprehension task – spoken squad we find that speech recognition errors severely impair machine comprehension; we propose the use of sub word units to mitigate the impact of these errors.

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How to PTE Scores to Predict in SPEAK Test

How to PTE Scores to Predict in SPEAK Test

The PTE english test , produced by the Educational Testing Service (ETS), has been in use in institutions of higher American education since the 1960s as a means of measuring incoming international students’ English proficiency. But like any test, the PTE is imperfect. For instance, whereas a high PTE score may be sufficient to admit an international student to an American graduate school, many colleges and universities require more rigorous proof of a student’s English proficiency—often in the form of a passing score on a school-specific oral assessment—if he seeks employment as a Graduate Teaching Assistant (GTA). How to PTE Scores to Predict in SPEAK Test

To mitigate this risk, forecasting models which use the PTE sub-scores of Speaking, Listening, Writing, and Reading to forecast SPEAK test outcome are applied. A student’s sub-scores act as predictive inputs to each model, which outputs the posterior probability of his SPEAK test failure. Bayes Theorem provides the structure required to obtain this probability, and the multivariate meta-Gaussian distribution captures the stochastic dependence between the sub-scores. Therefore, these models are classified as Bayesian Meta-Gaussian Forecasters (BMGFs).

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TOEFL English test for the Graduate and School students

TOEFL English test for the Graduate and School students

To correspond to drastic change in international society such as “globalization”, graduate education plays a key role in development of human resources. The new trans-graduate-school education program called “Nitobe School” was launched in 2015 as one of the main education projects of “Top Global University Project” in Hokkaido University. This is a trial case report of the comparison of commercial English test for the Graduate School students. We employed about 50 students from the various graduate schools in Hokkaido University, and they took same commercial English tests (TOEIC and TOEFL), then its result is discussed in here. TOEFL English test for the Graduate and School students

The pre-post English speaking test: Describing the picture , Pre-English Speaking Test was used without teaching. Post-English Speaking Test was used after teaching.

The English speaking ability evaluation form. The evaluation criteria were as follows: Grammar and vocabulary ,Structures: Process of speaking , Fluency and pronunciation , Self confidence,Persuasiveness .

The collected data was analyzed using computer program. The t-test was employed to compare the subjects’ English speaking ability before and after using English speaking based on communicative approach. The mean and standard deviations of scores from English speaking evaluation form, the satisfaction questionnaire were used to measure at the end of the class.

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How to Test of Spoken English Competence Advanced Learners

How to Test of Spoken English Competence Advanced Learners

It is evident that English has become an essential part of everyone’s life. In higher education, especially graduate studies, English inevitably plays a crucial role in determining the success or failure of the students. In order to screen applicants for graduate studies, it is important to devise a standardized test which is reliable, valid, and practical. Conventionally, most English proficiency tests in Thailand will have three subtests in common. These sub tests include: Error identification, Multiple-choice cloze, and Reading comprehension. How to Test of Spoken English Competence Advanced Learners

Teaching speaking English has a crucial role in English instruction as a foreign language. That’s because teaching English based on communicative approach theory is worldwide. Therefore, Institute of Technology emphasizes communicative approach teaching. Thus the students need to have competence in listening and speaking. [9] stated that the principle of communicative training involves the use of complex communicative situations, aimed at developing the pupil’s speech that promotes “overcoming a sharp transition from education to the natural conditions of communication due to the formation of students’ strong associative links”. The teacher becomes free to choose a variety of instructional techniques and incentives that can maintain motivation and mental activity of students during the entire study period.

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Information System for Game TOEFL like App

Information System for Game TOEFL like App

Educative games are trending among students. Games can be used to support student learning.Difficulties in completing a TOEFL (Test of English as a Foreign Language) can be helped by doing game exercises that are similar to the actual test conditions. The TOEFL learning method while playing the game becomes an interesting project, to see how students can use the game experience to master the skills needed for doing a TOEFL. Information System for Game TOEFL like App

One of the criteria of an English student‟s mastery in the language is to show how proficient that student is in doing a TOEFL. For this reason, a TOEFL-Like App game has been created by the researchers, who are a team made up from the English Department and Information Systems Department that specifically deals with Game Technology. The researchers see that managing the results of a TOEFL test that goes on for almost an hour, needs to be assisted with some kind of system to ease the burden of the English teachers. Henceforth, the TOEFL-Like Game App is built with an information system to help teachers access the final results online.

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Relationship between Perception and Production of English Vowels by Chinese English Learners

Relationship between Perception and Production of English Vowels by Chinese English Learners

In previous studies, no consensus has been reached on the existence of significant correlation between perception and production. A large number of empirical studies have been done upon first and second languages from different language families. However, few studies were carried out on the perception-production relation of Chinese English learners. Therefore, in the current study, under the theoretical framework of PAM-L2, 40 subjects with even numbers in two genders, who differ in language proficiency, are invited to participate in the perception experiment and the production experiment, in which discrimination, identification and pronunciation of /ɪ/-/ε/, /ε/-/æ/, /ʊ/-/ʌ/, and /ʌ/-/ɒ/ contrasts are observed. Results reveal that vowel perception of Chinese English learners is neither statistically correlated nor spectrally related to vowel production. Relationship between Perception and Production of English Vowels by Chinese English Learners

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As foreign language teaching develops worldwide, scholars in second language (L2) learning and acquisition unanimously found a vague link between “listening” and “speaking”. Spoken proficiency of L2 learners was improved even though they didn’t receive training in pronunciation but increased exposure to native production [1]. Scholars began to consider whether there was a close bond between perception and production. If there was, in L2 education, teachers could not only concentrate their training on production, but also add some training to the perception of L1 sounds. Training of perception could also be applied as a supplementary method in phoniatric training for those who failed to adjust their pronunciation merely by articulation training such as imitation.

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TWO-LAYER APPROACH FOR SPOKEN LANGUAGE TRANSLATION

TWO-LAYER APPROACH FOR SPOKEN LANGUAGE TRANSLATION

This study proposes a new two-layer approach for spoken language translation. First, we develop translated examples and transform them into speech signals. Second, to properly retrieve a translated example by analyzing speech signals, we expand the translated example into two layers: an intention layer and an object layer. The intention layer is used to examine intention similarity between the speech input and the translated example. The object layer is used to identify the objective components of the examined intention. Experiments were conducted with the languages of Chinese and English. The results revealed that our proposed approach achieves about 86% and 76% understandable translation rate for Chinese-to English and English-to-Chinese translations, respectively. TWO-LAYER APPROACH FOR SPOKEN LANGUAGE TRANSLATION

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With the growing of globalization, people now often meet and do business with those who speak different languages, on-demand spoken language translation (SLT) has become increasingly important (See JANUS 111 [I], Verbmobil [2], EUTRANS [3], and ATR-MATRIX [4]). Recently, an integrated architecture based on stochastic finite-state transducer (SFST) has been presented for SLT [3,5]. The SFST approach integrated three models in a single network where the search process takes place. The three models are Hidden Markov Models for the acoustic part, language models for the source language and finite state transducers for the transfer between the source and target language. The output of this search process is the target word sequence associated to the optimal path. Fig. 1 shows an example of the SFST approach. The source sentence ‘‘una habitacidn doble” can he translated to either “a double room” or “a room with two beds”. The most probable translation is the first one with probability of 0.09.

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TEACHER-STUDENT TRAINING OF ACOUSTIC MODELS FOR AUTOMATIC FREE SPEAKING LANGUAGE ASSESSMENT

TEACHER-STUDENT TRAINING OF ACOUSTIC MODELS FOR AUTOMATIC FREE SPEAKING LANGUAGE ASSESSMENT

A high performance automatic speech recognition (ASR) system is an important constituent component of an automatic language assessment system for free speaking language tests. The ASR system is required to be capable of recognising non-native spontaneous English speech and to be deployable under real-time conditions. The performance of ASR systems can often be significantly improved by leveraging upon multiple systems that are complementary, such as an ensemble. Ensemble methods, however, can be computationally expensive, often requiring multiple decoding runs, which makes them impractical for deployment. In this paper, a lattice-free implementation of sequence-level teacher-student training is used to reduce this computational cost, thereby allowing for real-time applications. This method allows a single student model to emulate the performance of an ensemble of teachers, but without the need for multiple decoding runs. Adaptations of the student model to speakers from different first languages (L1s) and grades are also explored. TEACHER-STUDENT TRAINING OF ACOUSTIC MODELS FOR AUTOMATIC FREE SPEAKING LANGUAGE ASSESSMENT

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There is a high demand around the world for the learning of English as a second language. Assessment of a learner’s language proficiency is a key part of learning both in measuring progress made and for formal qualifications required e.g. for entrance to university or to obtain a job. Given the high demand from English learners, it will be very difficult to train sufficient examiners and the introduction of automatic markers will be beneficial especially for practice situations.

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