Grammarly as AI-powered English Writing Assistant: Students’ Alternative for Writing English

The presence of ‘Grammarly’ as one of the online grammar checkers as the impact of technology development.  This paper aims to reveal an overview of ‘Grammarly’ as an AI-powered English Writing Assistant for EFL students in Writing English. This research applies descriptive qualitative research. Based on the analysis, using Grammarly software shows the performance increased. Before using Grammarly, the performance of the test score is 34 out of 100. After using Grammarly, the performance text score is 77 out of 100. This score shows the quality of writing in this text increased. The performance can be increased based on Grammarly's suggestions in a Premium account. The researcher recommends the students to use Grammarly. Grammarly is a web tool to perform grammar checks well, starting from the spelling of words, sentence structure to standard grammar. Grammarly is free, so it is recommended for students who want to check various documents or articles in English. Grammarly helps check the grammatical rule, the spelling rule in English structure, also correct errors in writing such as punctuation and capitalization. Grammarly runs on an Artificial Intelligence (AI) system, which is built to analyze English sentences relying on a set of rules. Grammarly takes context when showing corrections or suggestions, and inform the students quickly but still precisely. For accuracy, two service options available both free and paid features. Of course, the Grammarly free version still has limitations and in-service features, unlike the paid version (premium) which has full advantages and benefits, many features, and complete

The investigation and evaluation of the support mechanisms offered to adults with a diagnosis of dyslexia in higher education study

This research had two aims. The first aim was to investigate the support mechanisms available to learners with dyslexia on programmes of higher education (HE), and the second aim was to assess the effectiveness of any inventions used to improve access to learning for this group of learners. Since the introduction of the National Student Survey (NSS) in 2005, efforts have been ongoing to improve the quality of the educational experience for students on HE programmes. Although the general trend in satisfaction scores is an improving one, this is not the case for learners experiencing disabilities. Around 43% of these learners will have dyslexia. The research consists of two distinct parts. The first is a cross-sectional website survey and documentary analysis, and the second is a systematic review. The cross-sectional website survey and documentary analysis located and extracted data that detailed the learning support available to learners with dyslexia, from a representative number of higher education institution (HEI) websites in England. The systematic review analysed 10 single studies of experimental or quasi-experimental design and one literature review. These studies focused upon interventions provided to learners with dyslexia in higher education (and its international equivalents). The combined findings suggest that support for learners with dyslexia in these settings is fragmented and inconsistent, and that there are many areas of existing practice that could be modified to improve opportunities for learning. There is an absence of any model of good organisational practice. There are examples of ‘in-class’ curricular adaptations and ‘outside-class’ additional learning and study skills support, including the use of information communications technology and assistive technology, which have shown some success in supporting the learning of those with dyslexia, but they are not implemented consistently or widely

Multilingual Children with Dyslexia: A Further Study of the Multi-sensory Approach using IT

This study is about the difficulties that multilingual dyslexic children face and whether the enhancement of the multi-sensory teaching techniques using the Orton-Gillingham (O-G) Method could increase the effectiveness of Information Technology in helping these dysiexics children. This project was conducted to overcome this problem since most software is designed for monolingual children and to This done by conducting a study on improving on the multi-sensory level by further adding and manipulating the senses to the courseware which already uses ttte Orton-Giiingaham method as a baseline and testing it to dyslexic children. The result overall will shows that the OGmethod does help a lot in teachingdysiexics,but prove to be less effective with dysiexics~with auditory skills

Intralingual translation and cascading crises: evaluating the impact of semi-automation on the readability and comprehensibility of health content

During crises, intralingual translation (or simplification) of medical content can facilitate comprehension among lay readers and foster their compliance with instructions aimed to avoid or mitigate the cascading effects of crises. The onus of simplifying health-related texts often falls on medical experts, and the task of intralingual translation tends to be nonautomated. Medical authors are asked to check and remember different sets of plain language guidelines, while also relying on their interpretation of how and when to implement these guidelines. Accordingly, even simplified health-related texts present characteristics that make them difficult to read and comprehend, particularly for an audience with low (health) literacy. Against this background, this chapter describes an experimental study aimed at testing the impact that using a controlled language (CL) checker to semi-automate intralingual translation has on the readability and comprehensibility of medical content. The study focused on the plain language summaries and abstracts produced by the non-profit organisation Cochrane. Using Coh-Metrix and recall, this investigation found that the introduction of a CL checker influenced some readability features, but not lay readers’ comprehension, regardless of their native language. Finally, strategies to enhance the comprehensibility of health content and reduce the vulnerability of readers in crises are discussed

Is A Single Or Multicomponent Reading Intervention Program More Effective At Enhancing Outcomes For Struggling Readers In Intermediate Grades?

In 2015-2016, the selected school-site realized that reading instruction needed to change for the bottom quartile of readers. Struggling readers in grades five through eight were not making significant gains in reading. On the annual state assessment, students who scored a one the previous year remained at the same level the following year. In addition, students reading two or more grade levels behind their peers made the smallest gains throughout the school. This research project addressed the question, “Is a single or multicomponent reading intervention program more effective at enhancing outcomes for struggling readers in intermediate grades?” The purpose of the project was identify the most effective intervention to improve the school’s reading intervention programing and increase academic gains for developing readers

Framework to manage labels for e-assessment of diagrams

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Automatic marking of coursework has many advantages in terms of resource benefits and consistency. Diagrams are quite common in many domains including computer science but marking them automatically is a challenging task. There has been previous research to accomplish this, but results to date have been limited. Much of the meaning of a diagram is contained in the labels and in order to automatically mark the diagrams the labels need to be understood. However the choice of labels used by students in a diagram is largely unrestricted and diversity of labels can be a problem while matching. This thesis has measured the extent of the diagram label matching problem and proposed and evaluated a configurable extensible framework to solve it. A new hybrid syntax matching algorithm has also been proposed and evaluated. This hybrid approach is based on the multiple existing syntax algorithms. Experiments were conducted on a corpus of coursework which was large scale, realistic and representative of UK HEI students. The results show that the diagram label matching is a substantial problem and cannot be easily avoided for the e-assessment of diagrams. The results also show that the hybrid approach was better than the three existing syntax algorithms. The results also show that the framework has been effective but only to limited extent and needs to be further refined for the semantic stage. The framework proposed in this Thesis is configurable and extensible. It can be extended to include other algorithms and set of parameters. The framework uses configuration XML, dynamic loading of classes and two design patterns namely strategy design pattern and facade design pattern. A software prototype implementation of the framework has been developed in order to evaluate it. Finally this thesis also contributes the corpus of coursework and an open source software implementation of the proposed framework. Since the framework is configurable and extensible, its software implementation can be extended and used by the research community

A survey on sentiment analysis in Urdu: A resource-poor language

© 2020 Background/introduction: The dawn of the internet opened the doors to the easy and widespread sharing of information on subject matters such as products, services, events and political opinions. While the volume of studies conducted on sentiment analysis is rapidly expanding, these studies mostly address English language concerns. The primary goal of this study is to present state-of-art survey for identifying the progress and shortcomings saddling Urdu sentiment analysis and propose rectifications. Methods: We described the advancements made thus far in this area by categorising the studies along three dimensions, namely: text pre-processing lexical resources and sentiment classification. These pre-processing operations include word segmentation, text cleaning, spell checking and part-of-speech tagging. An evaluation of sophisticated lexical resources including corpuses and lexicons was carried out, and investigations were conducted on sentiment analysis constructs such as opinion words, modifiers, negations. Results and conclusions: Performance is reported for each of the reviewed study. Based on experimental results and proposals forwarded through this paper provides the groundwork for further studies on Urdu sentiment analysis

Advanced document data extraction techniques to improve supply chain performance

In this thesis, a novel machine learning technique to extract text-based information from scanned images has been developed. This information extraction is performed in the context of scanned invoices and bills used in financial transactions. These financial transactions contain a considerable amount of data that must be extracted, refined, and stored digitally before it can be used for analysis. Converting this data into a digital format is often a time-consuming process. Automation and data optimisation show promise as methods for reducing the time required and the cost of Supply Chain Management (SCM) processes, especially Supplier Invoice Management (SIM), Financial Supply Chain Management (FSCM) and Supply Chain procurement processes. This thesis uses a cross-disciplinary approach involving Computer Science and Operational Management to explore the benefit of automated invoice data extraction in business and its impact on SCM. The study adopts a multimethod approach based on empirical research, surveys, and interviews performed on selected companies.The expert system developed in this thesis focuses on two distinct areas of research: Text/Object Detection and Text Extraction. For Text/Object Detection, the Faster R-CNN model was analysed. While this model yields outstanding results in terms of object detection, it is limited by poor performance when image quality is low. The Generative Adversarial Network (GAN) model is proposed in response to this limitation. The GAN model is a generator network that is implemented with the help of the Faster R-CNN model and a discriminator that relies on PatchGAN. The output of the GAN model is text data with bonding boxes. For text extraction from the bounding box, a novel data extraction framework consisting of various processes including XML processing in case of existing OCR engine, bounding box pre-processing, text clean up, OCR error correction, spell check, type check, pattern-based matching, and finally, a learning mechanism for automatizing future data extraction was designed. Whichever fields the system can extract successfully are provided in key-value format.The efficiency of the proposed system was validated using existing datasets such as SROIE and VATI. Real-time data was validated using invoices that were collected by two companies that provide invoice automation services in various countries. Currently, these scanned invoices are sent to an OCR system such as OmniPage, Tesseract, or ABBYY FRE to extract text blocks and later, a rule-based engine is used to extract relevant data. While the system’s methodology is robust, the companies surveyed were not satisfied with its accuracy. Thus, they sought out new, optimized solutions. To confirm the results, the engines were used to return XML-based files with text and metadata identified. The output XML data was then fed into this new system for information extraction. This system uses the existing OCR engine and a novel, self-adaptive, learning-based OCR engine. This new engine is based on the GAN model for better text identification. Experiments were conducted on various invoice formats to further test and refine its extraction capabilities. For cost optimisation and the analysis of spend classification, additional data were provided by another company in London that holds expertise in reducing their clients' procurement costs. This data was fed into our system to get a deeper level of spend classification and categorisation. This helped the company to reduce its reliance on human effort and allowed for greater efficiency in comparison with the process of performing similar tasks manually using excel sheets and Business Intelligence (BI) tools.The intention behind the development of this novel methodology was twofold. First, to test and develop a novel solution that does not depend on any specific OCR technology. Second, to increase the information extraction accuracy factor over that of existing methodologies. Finally, it evaluates the real-world need for the system and the impact it would have on SCM. This newly developed method is generic and can extract text from any given invoice, making it a valuable tool for optimizing SCM. In addition, the system uses a template-matching approach to ensure the quality of the extracted information