grammatical analysis thesis

• Title & authors

Abdurahman, Nur H., et al. "Grammatical Cohesion Analysis Of Students Thesis Writing." Jurnal Pendidikan dan Pembelajaran Untan , vol. 2, no. 11, 2013.

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Thisresearchaimedtofindouttypesof grammaticalcohesive devicesstudentsmostly usedintheirthesiswritingandhowthese devices create cohesive discourse.Inthisstudy, researcher applieddescriptive case study asformofresearch.Theobjectsofthisresearchwereresearch backgroundpartof10theseswriting writtenby studentsofEnglish EducationStudy Program.Thedatarevealedthatstudentscouldproduce varioustypesofgrammaticaldevices.Themajority ofdevicesusedbythe studentswerereferentialcohesive devicesandconjunctive cohesive devices with 82.25% and 17.12% respectively. Other types-substitution and ellipsis-contributedonly0.24%and0.39%,respectively.Inmany cases, studentstendedtomisuse singular pronounwhilereferencingpluralobjects or vice versa.Fromthe total1273grammaticalcohesive devicesused, studentssomehowadequatelymasterusinggrammaticaldevicesby only usinginappropriately74(5.81%) ofthem.

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An Analysis of Grammatical Errors in EFL College Students " Proposal Thesis Writing

The study is aimed at describing the dominant grammatical errors made by English Education students at STKIP YDB in writing their thesis proposal. The objectives of the study were formulated as (1) to find out the most common grammatical errors made by the students in writing their thesis proposal and (2) to find out the source of the grammatical errors made by the students in writing thesis proposal. The method of the study was qualitative descriptive. Documentation was used as instrument of the study from which data was collected through 15 thesis proposals of fourth semester English education students in the academic year from August 2016 to February 2017 year. Technique of data analysis that was used as proposed by Ellis (1996) is through collecting samples, identification of errors, classification of errors, explanation of error and evaluation of the errors. The classification of the grammatical errors are based on Linguistic and Surface Taxonomy category , while source of the errors are based on theories of interlingual and intralingual transfer errors. The research findings reveal that the most common errors made by the students in linguistic taxonomy fall into the error use of passive voice, subject-verb agreement, tense verb, part of speech which are comprised 79.38 % of total grammatical error, while in the surface taxonomy falls into the omission and misordering errors which are comprised 71.73 % of total errors. Dealing with the source of the errors, it was found that intralingual transfer such as overgeneralization and incomplete application of rules which comprise 65,64 % of total of errors, are the major source of grammatical errors that the students made in writing their thesis proposal. The research findings come to a conclusion to the need of discussing its impacts on pedagogical implication in teaching and learning grammar in the classroom.

Related Papers

SMP PAB 18 MEDAN

Vision: Journal for Language and Foreign Language Learning

This paper makes a study of grammatical error on paragraph writings of English department students. The students were asked to write the paragraph of writing as their final assignment for paragraph based writing Class. The errors found in the students' compositions are then classified based on the surface strategy taxonomy on errors of omission, addition, misformation, and misordering. Based on the result of the research findings whole have been analyzed and the discussions which have been presented, the writer generally concluded that there are four kinds of errors which are omission, addition, misinformation and misordering. The writer found that mostly the students had grammatical error on their writing in misinformation error 43%. They consist of misinformation of adverb, V2, subject-verb agreement, article, modal, passive and word choices. It also can be concluded that most students of intermediate level made Grammatical Error in Omission and Misinformation of their paragraph writing as Dulaay at.all (1982) said that Omission of to be is common error made by students. Even tough those sentences appear without to be, article or preposition and still have meaning, student must use the Grammar correctly or accurately in writing. Most students made grammatical error in using modal, for example of error in misinformatiom " We can learning all about English, after modal must be followed by main verb. It mostly happened on students' writing. The most common types of errors made by students are misinformation with the number of errors is 15 or 43% and omission with the number of error is 11 or 31%. The type of errors with the lowest frequency is misordering errors (9%). They consist of misordering of noun phrase and misordering of adverb. These errors seem to be caused by interlingual and intralingual interference.

Sisilia Endah

Lestari, Sisilia Endah. (2014). An Analysis on Grammatical Errors Made by ELESP Students in Writing the Research Proposal. Yogyakarta: English Language Education Study Program, Sanata Dharma University. Teachers are the role model for the students in class. The students will imitate and learn what their teachers do in class. Therefore, in order to give the correct example for the students, the teachers candidates of English Language Education Study Program should fulfill many requirements such as having a good mastery of writing skill. However, based on the researcher’s observation, the students did some errors so that the writings did not meet the minimum requirements of writing such as Simple Present Tense, articles, concord, finite verbs, verb groups, punctuation, and spelling. In fact, the students should produce grammatically correct pattern of writings to the students. In this study, the researcher made two research problems: (1) What types of grammatical errors are made by ELESP students in writing the research proposals? (2) What are the causes of making the grammatical errors in students’ research proposals? Based on the two research problems, this study aimed to find the types of grammatical errors made by ELESP students in writing the research proposals. In addition, this study also aimed to find the causes of making the grammatical errors. Considering the two objectives, this study could give contributions to the lecturers and the students. This study could accommodate the students to revise and comprehend their writings appropriately. For the lecturers, this study could help them to find the effective teaching strategy especially in teaching writing. To support this study, the researcher used some theories from books. In order to find out the types of grammatical errors, the researcher used the theory from Ellis and Barkhuizen (2005) and Dulay, Burt, and Krashen (1982). For the causes of errors, the theories were obtained from a book written by Norrish (1983). The researcher used the students’ research proposals as the instrument to obtain the data. Therefore, this study used document analysis method. The findings showed that there were 33 errors in omission or 32.35 %, 12 errors in addition or 11.76 %, and 57 errors in misinformation errors or 55.89 %. For the causes of errors, the students admitted that Bahasa influenced their writings. Moreover, carelessness was one of the causes which frequently appeared in the research proposals. Keywords: grammatical errors, minimum requirements, research proposals

Instabright Book of Abstracts

Crisanto Daing

ABSTRACT This study used a one-group quasi experimental method and a descriptive-comparative as the research design. The focus of the study is to identify and analyse the common errors committed by the Grade 11 of Humanities and Social Sciences (HUMSS) students from a public school in Quezon City. The gathered data were taken from the written compositions of 176 students. Data were from two different phases called: initial-writing assessment with a title, “My Journey as HUMSS Student” and final-writing assessment with a title of, “How I Met My Best Friend”. Between the two writing tasks, an intervention was given to the participants in three (3) sessions of lesson on the dominant errors. Results showed that the highest number of students committed errors in the initial-writing assessment were the incorrect use of punctuations 128, placed in rank 1 and capitalization 116, placed in rank 2, spelling 64, placed in rank 3, subject-verb agreement 59, placed in rank 4, and dangling modifiers 55, placed in rank 5. Other errors were verb tense 48, placed in rank 6, misplaced modifiers 29, placed in rank 7, preposition 17, placed in rank 8, and parallelism 15, placed in rank 9. Consequently, after the intervention/treatment was given to the students, the number of errors found in the final-writing assessment were reduced except in the use of verb tense. Furthermore, from the four types of Surface Strategy of Taxonomy (SST), the category of Misformation 712, found to have the highest number of frequency of errors, followed by the Omission 453, Addition 53, and Misordering 50 from the initial-writing assessment of the students. Though the number of frequency of errors was reduced in the final-writing assessment, the category of Misformation 689, remained to have the highest number of errors, followed by Omission 235, then Misordering 27, and lastly, the category of Addition has obtained 24 errors. Subsequently, a significant difference occurred after the intervention was given which interpreted that the intervention was able to address the issue in various types of errors, except to the misplaced and dangling modifiers.

Journal Of Advances In Linguistics

Rini Susanti , Adina Nikmawati , Indawan Syahri, University of Muhammadiyah Palembang

The thesis entitled Grammatical Errors in Debate Arguments Made by the Students of Global English Language Center. The objectives of this study were (1) to find out the kinds of errors made by the students of Global English Language Center in their debate arguments and (2) to find out the causes of errors made by the students of Global English Language Center in their debate arguments. The writer used descriptive-qualitative method of research. The population of this study was all the students of Global English Language Center. The sample was chosen by using purposive sampling. The samples were 12 students which were grouped into two debate sessions. The data were calculated and analyzed based on surface strategy taxonomy and linguistic category taxonomy to know the errors. Samples' personal reasons and personal judgement were used to know the causes of the errors. The findings showed that there were four types of errors that the students made which are omission, addition, misformation, and misordering. Misformation is the major error made by the students. Nervousness, time limit, native language interference, and the insufficient grammar learning were the causes why the students made errors.

Faisal Mustafa , mulia kirana

Errors made by students in skills such as speaking and writing have been treated as important information to help teachers direct the foci of their teaching. Teachers need to pay attention to the most serious errors before addressing other errors. Hence, this research was aimed at finding the most serious errors produced by students at one of the junior high schools in Indonesia, i.e. State Junior High School No 7 in Banda Aceh. The errors were categorized into surface strategy taxonomy and linguistic category taxonomy errors. The data were collected from recount texts produced by 19 students who were known to make errors in writing. The data was analyzed by calculating the percentage of errors based on the total cumulative errors for surface strategy taxonomy, but based on the number of attempts in the same grammatical elements for linguistic category taxonomy. The results of data analysis showed that the dominant errors for surface strategy taxonomy were selection (72%) followed by omission (14.4%), and addition (10.6%). In the linguistic category taxonomy, the most dominant types were word forms (48.4%), followed by articles (35%), nonfinite verbs (34.9%), verb tenses (34.3%), plurals (33.3%), and prepositions (30%). It is suggested that teachers pay more serious attention to the most serious problems because solving these problems will hasten the students' progress in learning.

Suhono Suhono

This study deals with error analysis on composition written by EFL Students IAIM NU Metro. It aims at developing further analyzing of error analysis in second language learners. For this purpose, an empirical study was conducted, using Indonesian students learning English as the subject of research. To achieve this purpose, the researcher explored the type of grammatical errors made by students at different grade semesters: the second, the sixth, and the eighth. More specifically, this study was an attempt to describe the type of grammatical error which frequently exist in written composition, to describe frequency grammatical error among the grade semesters, and to describe the sources of errors. Students' writings were analyzed based on surface strategy taxonomy theory. The results of this research revealed that 268 sentences indicated errors. In all semesters, types of omission error was the highest one 131 (48.9%) sentences. Furthermore, grammatical error in the second semester was the highest one 124 (46.8%) sentences. The sources of errors of this research were mother-tongue influence (Interlingual errors). It was influenced by the native language which interferes with target language learning. The second was Intralingual errors, caused by the target 1 DosenTetap Prodi PendidikanBahasaInggris (PBI) FakultasTarbiyahInstitut Agama Islam Ma'arif (IAIM NU) Metro Lampung.

Lis Indriyani

Published article

Sahar M . Alzahrani

The aim of the study is to analyse the linguistic errors in the writings of EFL students in a Saudi university. For error analysis, the writing tasks of a group of students were collected and analysed. The results showed that the frequently committed errors were at the sentential level as well as the word level. The most frequent errors were the errors of spellings and the least were the errors of demonstratives. The marked errors were categorised as errors of omission, addition, misinformation and misordering. Further analysis of the collected data indicated interlingual interference, intralingual interference, inadequate practice in English writing and carelessness of students as the major sources of the errors. Hence, the study has pedagogical implications as it will help teachers to device appropriate measures for improving the students' writing proficiency.

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Nuvation Bio: Continuing To Justify A Buy Thesis With Catalysts In 2024

• Nuvation Bio transitioned from early-stage to late-stage biotech with a near-approval, sitting at a $700 million market cap.
• The main project is taletrectinib, a novel ROS1 inhibitor with high response rates observed in studies, aiming for NDA filing.
• Financially stable with cash runway of 12-13 quarters, potential for accelerated approval of taletrectinib, but uncertainty in market adoption.

ewg3D/E+ via Getty Images

Topline Summary and Update

Nuvation Bio ( NYSE: NUVB ) made a rare splash in oncology by propelling itself from a phase 1 developmental biotech to a late-stage entity in one fell swoop earlier this year. This led to a surge in market interest and valuation, as well as confidence from me about the investment thesis. Since then, they've experienced declines and gains but have generally held relatively steady since my first article. Today, they're a company with a near-approval, sitting at $700 million in market cap. Let's have another look to see how the story is shaping up.

Pipeline Updates

Taletrectinib.

NUVB's main project is pursuit of approval for a novel ROS1 inhibitor, taletrectinib, acquired as part of their purchase of AnHeart Therapeutics . As I mentioned in my previous article, this vaulted the company from an early-stage biotech to one on the precipice of filing an NDA, putting them tantalizingly close to commercial stage.

I detailed what we knew about taletrectinib pre-ASCO 2024 before, but as a reminder, ROS1 is an uncommon driver mutation in patients with non-small cell lung cancer (NSCLC), with three approved therapies in the frontline setting.

The promise of taletrectinib is its high response rates observed in the TRUST-II study, both in patients with no prior ROS1 inhibitor exposure, as well as in patients who had progressed on prior anti-ROS1 therapy. Given that oncologists tend to ask "when," not "if" progression will occur with targeted therapy, novel options that can help overcome resistance are a constant unmet need.

At ASCO 2024, the company presented findings from TRUST-I, which enrolled patients in China and showed high tumor shrinkage rates, consistent with the TRUST-II study. NUVB has guided that they will present more mature data from both TRUST-I and TRUST-II at the upcoming ESMO and World Lung meetings in September , and they intend that these data will be the basis of a New Drug Application with the FDA for ROS1-positive NSCLC.

Other pipeline projects

In my first outing on NUVB, I gave some details on the company's varied projects in other disease areas. These include the IDH1 inhibitor safusidenib, the BET inhibitor NUV-868, and a drug-drug conjugate called NUV-1511. I detailed how these are supposed to work and the disease areas of interest for each in the past, and NUVB's latest guidance has only been that the work continues with each of these in various stages of development.

Financial Overview

In the latest quarterly report , NUVB disclosed $34.3 million in cash and equivalents, as well as another $542.9 million in marketable securities. They recognized $1.4 million in revenue, but this was almost totally offset by costs incurred to fulfill customer contracts. They had operating expenses of $45.4 million for the quarter, up year over year from $26.1 million in Q2 2023.

They also recognized a one-time acquisition expense of $425.1 million related to acquiring taletrectinib. At this cash burn rate, the company's estimated cash runway is between 12 and 13 quarters.

Strengths and Risks

Strength - cash sufficient to support the company through an nda application.

With upwards of three years of cash before entering into some kind of crisis, NUVB is able to be completely focused on execution of their main goal: get taletrectinib approved. And with response rates like the ones we're seeing out of TRUST-I and II, I suspect that accelerated approval is very much within grasp, barring some kind of heretofore unseen toxicity. We have not yet entered a new generation of ROS1 inhibitors, and taletrectinib may just be the first entry in that area, giving NUVB the opportunity for an important niche.

Importantly, the cash to fund the acquisition did not come from the company's coffers, but rather the issuance of class A shares and non-voting convertible preferred stock and warrants . This means that the dilutive pain from the acquisition will not affect the cash runway, and it is not an ongoing risk for would-be new shareholders.

Risk - Taletrectinib is supposed to be a better ROS1 inhibitor, but we still don't know that

No matter how good the phase 2 data are, NUVB cannot demonstrate that taletrectinib should be used before other approved ROS1 inhibitors, which are now quite firmly established in the standard of care. This leaves them to focus, at least for the time being, on that post-progression setting, which limits the potential market size and makes it difficult to predict what the sales of taletrectinib could be.

Bottom-Line Summary

With a lot of momentum, a lot of cash, and a lot of upcoming data, NUVB is a highly de-risked, late-stage clinical entity that I don't feel is being valued as such. Of course, there's no predicting how the market would react to a drug approval for the company, but the likes of Turning Point Therapeutics were taken out for over $4 billion mainly on the basis of the promise of a single ROS1 inhibitor's success. It's not hard to imagine that NUVB could be worth a significant fraction of that market cap themselves upon approval, and the fact that ROS1 remains a relevant niche makes me continue to feel optimistic.

There are, of course, risks of delays and failure, so the current valuation does not say to me that this is a "red-hot" opportunity, per se. Rather, I think they've got a strong shot at success, and if successful that the ceiling is a good deal higher. To me, that is a continued "Buy" sentiment.

This article was written by

Analyst’s Disclosure: I/we have no stock, option or similar derivative position in any of the companies mentioned, and no plans to initiate any such positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

Seeking Alpha's Disclosure: Past performance is no guarantee of future results. No recommendation or advice is being given as to whether any investment is suitable for a particular investor. Any views or opinions expressed above may not reflect those of Seeking Alpha as a whole. Seeking Alpha is not a licensed securities dealer, broker or US investment adviser or investment bank. Our analysts are third party authors that include both professional investors and individual investors who may not be licensed or certified by any institute or regulatory body.

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• Corpus ID: 149391425

An Analysis Of Grammatical And Lexical Cohesion In Emma Watson’s Speech Text On Gender Equa;ity

• Dewi Mustika Arifiani
• Published 1 September 2016
• Linguistics

Figures and Tables from this paper

2 Citations

A study of pakistani english newspaper texts: an application of halliday and hasan’s model of cohesion: a discourse analysis, textual analysis in chimamanda’s speech : a critical discourse analysis thesis by: khanza prima andyrani larashayu reg. number: a73215050 inside cover page english department faculty of arts and humanities, 6 references, cohesion analysis on the jakarta post's editorial, language and culture, related papers.

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More From Forbes

Embracing ai to transform education.

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Sach Chaudhari, Business strategist and Technologist, CEO & Founder of Paradiso Software .

I've had the privilege of witnessing the impact AI can have across various sectors; however, one area where its potential truly excites me is education. AI is altering traditional college lecture settings and being incorporated into corporate training and continuing education. The application of AI for educational purposes holds great significance for business leaders and entrepreneurs because the implications extend far beyond the classroom.

For example, AI-powered corporate training programs can now adapt to each employee's learning pace, leading to more effective and engaging training experiences. In the EdTech space, startups are leveraging AI to develop solutions for language learning and accessibility, including applications for the visually impaired. Educational institutions also use AI to enhance classroom and online learning, providing content in multiple languages interactively and inclusively.

Let's ignore business metrics for now and focus specifically on what this AI integration in our education means for entrepreneurs and business leaders. I believe that today's students are tomorrow's innovators, high-performance employees and leaders. By backing these young talents and using technology that complements their knowledge, we can build a more proactive, adaptable and prepared workforce that is aligned with the buzzing AI business landscape.

Tech And Personalization

I think education should be available anywhere, to anyone and at any time. Integrating AI in education includes the ability to offer personalized learning experiences and provide round-the-clock student support.

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Plenty of tools already dominate traditional teaching methods, such as AI tutors that engage students in a customized session tailored to their interests or AI-assisted course authoring tools that can ease the workload of instructional designers. When educators have time to focus on teaching and helping students learn, real knowledge-sharing happens, which is not available everywhere.

The Applications Of AI In Education

1. ai can assist teachers..

AI tools can create comprehensive lesson plans, classroom materials, and even e-learning courses, which can be completed with assessments and quizzes. For example, a customized curriculum can include AI-generated interactive modules and engaging videos.

Assessment is another area where AI has made significant strides. Automated systems can evaluate student responses and provide immediate feedback, which can help create a more adaptive and responsive learning environment. For example, we implemented an AI assessment tool in a college that could evaluate essays for grammatical accuracy, coherence and creativity. This tool freed up teachers to focus on providing personalized instruction.

2. Streamline administrative support.

AI's impact isn't limited to the classroom; it also extends to administrative support. AI assistants can manage queries, evaluate student data and provide actionable insights, reducing the administrative burden. These systems can address more complex issues by alleviating the workload for administrative staff.

3. Automate routine tasks.

AI algorithms can automate routine administrative tasks such as attendance tracking, scheduling and grading. By leveraging natural language processing (NLP) and machine learning (ML), AI can streamline workflows, reduce administrative burdens and free up educators' time for more personalized student interactions.

4. Leverage predictive analytics for resource allocation and maintenance decisions.

Predictive analytics can forecast resource needs based on historical data trends. AI-powered algorithms , such as decision trees ( random forest ) and regression analysis, can analyze factors like student enrollment patterns, course demand and budgetary constraints to optimize resource allocation. This proactive approach ensures efficient resource management and enhances operational effectiveness.

AI algorithms can also help predict and schedule maintenance for educational facilities. By analyzing data from IoT sensors and historical maintenance records, AI can forecast equipment failures before they occur. This proactive approach, leveraging time series analysis (ARIMA) and machine learning models like support vector machines (SVM) can help minimize downtime, reduce maintenance costs and ensure a conducive learning environment for students and staff.

The Road Ahead: Integrating AI Into Education

From my experience, a few specific strategies have proven invaluable.

1. Refine your AI models.

Personalized education goes beyond adaptive learning algorithms; it involves creating a customized experience tailored to individual learning styles, preferences and goals. This pursuit of adaptivity requires sophisticated AI models to analyze vast amounts of data in real time to adapt content and learning pathways.

For example, if you made a "perfect" prompt for your conversational AI and aligned it with the LLM but are still not getting the desired output, then maybe it is time to consider a different AI model. Sometimes, the AI model itself may need adjustment. With careful model placement, you can personalize content and provide actionable insights into learner behavior and engagement patterns.

2. Balance innovation with educational pedagogy.

While AI offers innovation opportunities, aligning with established educational pedagogies is essential. Personalized learning solutions should combine technology with proven teaching methods to help students learn better and stay engaged.

I suggest tech leaders collaborate closely with educators and educational psychologists to integrate AI technologies in ways that enhance, rather than replace, human teaching expertise.

Teachers need ongoing training to use AI well, and building a community where they can share experiences and solutions is key. Starting with pilot programs allows for testing, feedback and adjustments before expanding.

3. Prioritize scalability and accessibility.

Scaling personalized education solutions requires careful planning to ensure accessibility for diverse student populations. Common pitfalls include outdated infrastructure, digital divide issues and the cultural adaptability of AI-driven content.

I think tech companies need to prioritize designing scalable AI architectures that can accommodate varying levels of infrastructure and connectivity. Implement accessibility features to support learners with disabilities and ensure equitable access to personalized learning opportunities.

4. Be prepared for the hurdles.

As AI enters education, we face both excitement and hurdles. Introducing AI into classrooms involves handling sensitive data, so transparency about data collection, use and storage is crucial for building trust. Strong security measures, like encryption and regular updates, are necessary to keep data safe.

AI governance frameworks should set clear rules and responsibilities, outlining processes to promptly address issues and maintain ethical standards. Regularly reviewing these systems ensures they perform as expected, and involving diverse people in creating guidelines ensures fairness and comprehensiveness.

The thoughtful use of AI in education could unlock its potential, creating a more personalized, efficient and engaging learning experience. The journey may be complex, but the rewards of a better educational experience can make it worthwhile.

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