Student Essays Research Articles

Can AI provide useful holistic essay scoring?

Researchers have sought for decades to automate holistic essay scoring. Over the years, these programs have improved significantly. However, accuracy requires significant amounts of training on human-scored texts—reducing the expediency and usefulness of such programs for routine uses by teachers across the nation on non-standardized prompts. This study analyzes the output of multiple versions of ChatGPT scoring of secondary student essays from three extant corpora and compares it to quality human ratings. We find that the current iteration of ChatGPT scoring is not statistically significantly different from human scoring; substantial agreement with humans is achievable and may be sufficient for low-stakes, formative assessment purposes. However, as large language models evolve additional research will be needed to continue to assess their aptitude for this task as well as determine whether their proximity to human scoring can be improved through prompting or training.

ОБРАЗ СТРАНЫ В ЯЗЫКОВОМ СОЗНАНИИ КАЗАХСТАНСКОЙ МОЛОДЕЖИ

The article attempts a psycholinguistic and cognitive-discursive assessment of the perception of the image of Kazakhstan by Kazakhstani youth. The relevance of the research is conditioned by the need to study the value orientation of young people, their ability to preserve national-cultural and personal self-identification in the context of expanding globalization and various internal and external processes taking place in the country. The novelty of the research lies in examining the country`s image through the prism of how the word “Kazakhstan” is perceived and through the analysis of essay texts. The results of the conducted research allowed for determining the attitude of Kazakhstani youth (with a focus on university students in this article) towards their country, assessing its significance in their lives, their readiness to associate their future with it, and to shape the trajectory of their personal and professional development. The data of the associative experiment and textual material in the form of student essays on a given topic are used in the work. During the data analysis process, MAXQDA software was used, which is designed for automated data analysis and provides qualitative, quantitative, and combined results of processed information. The research yielded cognitive models that reflect specific perceptions and evaluations of the country

Peningkatan Kemampuan Menulis Karangan Narasi dengan Menggunakan Model Pembelajaran Brain Based Learning Siswa Kelas VI SD Negeri pada Kecamatan Wangi-Wangi Kabupaten Wakatobi

The purpose of this research is to determine "Improving the Ability to Write Narrative Essays of Grade VI Students of Pada Pada State Elementary Schools Through the Application of the Brain Based Learning Model". The type of research used in this research is Classroom Action Research (PTK) which is carried out collaboratively between researchers and sixth grade teacher. The subjects in this research were 10 class VI students, with details of 3 boys and 7 girls. The data collection techniques in this research were tests of students' narrative essay writing abilities, observation and documentation. The data analysis technique uses quantitative analysis techniques and finds the average. The indicator of the success of this research is that if individual students get a KKM score of 70, then classically 80% of students achieve the KKM score out of all class VI students. The research results show that the ability to write narrative essays can be improved by using the Brain Based Learning learning model. This increase can be seen from the test results for each meeting carried out by researchers in cycle I. Meeting I was 53%, while at meeting II it increased to 62% and the test results in cycle II, meeting I obtained a score of 75% and at meeting II increased to 84%.

Utilizing large language models for EFL essay grading: An examination of reliability and validity in rubric‐based assessments

AbstractThis study investigates the validity and reliability of generative large language models (LLMs), specifically ChatGPT and Google's Bard, in grading student essays in higher education based on an analytical grading rubric. A total of 15 experienced English as a foreign language (EFL) instructors and two LLMs were asked to evaluate three student essays of varying quality. The grading scale comprised five domains: grammar, content, organization, style & expression and mechanics. The results revealed that fine‐tuned ChatGPT model demonstrated a very high level of reliability with an intraclass correlation (ICC) score of 0.972, Default ChatGPT model exhibited an ICC score of 0.947 and Bard showed a substantial level of reliability with an ICC score of 0.919. Additionally, a significant overlap was observed in certain domains when comparing the grades assigned by LLMs and human raters. In conclusion, the findings suggest that while LLMs demonstrated a notable consistency and potential for grading competency, further fine‐tuning and adjustment are needed for a more nuanced understanding of non‐objective essay criteria. The study not only offers insights into the potential use of LLMs in grading student essays but also highlights the need for continued development and research. Practitioner notesWhat is already known about this topic Large language models (LLMs), such as OpenAI's ChatGPT and Google's Bard, are known for their ability to generate text that mimics human‐like conversation and writing. LLMs can perform various tasks, including essay grading. Intraclass correlation (ICC) is a statistical measure used to assess the reliability of ratings given by different raters (in this case, EFL instructors and LLMs). What this paper adds The study makes a unique contribution by directly comparing the grading performance of expert EFL instructors with two LLMs—ChatGPT and Bard—using an analytical grading scale. It provides robust empirical evidence showing high reliability of LLMs in grading essays, supported by high ICC scores. It specifically highlights that the overall efficacy of LLMs extends to certain domains of essay grading. Implications for practice and/or policyThe findings open up potential new avenues for utilizing LLMs in academic settings, particularly for grading student essays, thereby possibly alleviating workload of educators.The paper's insistence on the need for further fine‐tuning of LLMs underlines the continual interplay between technological advancement and its practical applications.The results lay down a footprint for future research in advancing the use of AI in essay grading.

Cross-Cultural and Cross-Proficiency Analysis of Selected Cohesion Indices in Student Essays

Cross-Cultural and Cross-Proficiency Analysis of Selected Cohesion Indices in Student Essays

Exploring College Purpose

Background: Upon returning to in-person teaching at Kingsborough Community College after the pandemic, I realized something profound had shifted in my students. The well-documented drop in community college enrollment attributed to the pandemic seemed to reflect deeper disjunctures that had led students to an existential questioning of the purpose and value of a college degree. Purpose, Objective, Research Question, or Focus of Study: This study explores 30 community college students’ evolving perceptions of the purpose of college as they navigate through a first-year composition course with a curriculum that explicitly engages them in reading, writing, and researching various perspectives on the purpose, value, and challenges of pursuing a college education. Research Design: This qualitative, exploratory study involved redesigning my Composition I and II curricula to engage students in reading, writing, and thinking about the purpose of college; researching the challenges they and other community college students face in pursuing a degree; and proposing student-generated solutions to the community college enrollment and retention crisis. Data collected include two semesters of student essays, discussion boards, annotations on course readings, class activities, and notes from in-class discussions. Data analysis for this essay centered on student writing and followed a grounded theory approach with a focus on identifying salient themes related to student beliefs around college purpose and the impact of academic momentum policies on their college experience. Conclusions or Recommendations: The data from this study show that (1) community college students’ understanding of the purpose of college is diverse and malleable and (2) academic momentum policies that press students to attend college full time, despite their many life exigencies, are undermining community college students’ passions and purpose in pursuing a college education.

RESEARCH ON THE USE OF NON-INFLECTIVE FORMS OF THE VERB BY THE STUDENTS OF GJAKOVA

We know that the Albanian language has simple forms of the verb, such as: participle, transitive, past participle, negative, which are then divided into two groups. What piqued our interest about the non-elaborated forms, are the syntactic functional forms of the use of the infinitive in particular (among others), in the essays of students at the Faculty of Education in Gjakova.If we look and analyze the first documents written in Albanian, it is noticeable that the infinitive was very commonly used, even when looking at its phonetic and morphological aspect. Therefore, this formal structure of the infinitive is quite disputable considering its antiquity in our language. In addition to the infinitive, we also have the participle, to which the auxiliary verbs “be” and “have” have been added, we also see that in the essays of our students the syncope is used in most of their writings, without overlooking the apocope. We also have the conjunctive, which shows an action that accompanies or follows the action shown by using another verb, which is also quite utilized in our students' essays. As well as the negative infinitive form, which is formed by placing the word with a negative meaning –pa, before the participle of the verb, which can also be expressed with the negative participle -nuk or -s'. We will present all of this in our research, where we will then show the changes that the formal structure has undergone, be it phonetic, morphological or syntactic, which leaves a lot of room to discuss their linguistic antiquity.

The Impact of Teacher’s Feedback on Students’ Writing Ability

Teacher feedback on students' essay writing is often an alternative for teachers to support the improvement of students' writing quality. This study has the main objective to find out the relationship between teacher’s feedback and student essay writing by looking at the difference in student essay scores before and after feedback. Methodology used in this research is descriptive quantitative by looking at the data of students' project scores in one class. The results showed that there was a good improvement in students' ability to write essays, this was shown through the average student score which had a significant difference between before and after being given feedback from the lecturer. The conclusion obtained through this research is that feedback from teachers is effective in improving the quality of student writing, especially in this research essay writing.

LLMs in Automated Essay Evaluation: A Case Study

This study delves into the application of large language models (LLMs), such as ChatGPT-4, for the automated evaluation of student essays, with a focus on a case study conducted at the Swiss Institute of Business Administration. It explores the effectiveness of LLMs in assessing German-language student transfer assignments, and contrasts their performance with traditional evaluations by human lecturers. The primary findings highlight the challenges faced by LLMs in terms of accurately grading complex texts according to predefined categories and providing detailed feedback. This research illuminates the gap between the capabilities of LLMs and the nuanced requirements of student essay evaluation. The conclusion emphasizes the necessity for ongoing research and development in the area of LLM technology to improve the accuracy, reliability, and consistency of automated essay assessments in educational contexts.

The Implementation of PAKEM Learning Method to Improve the Competence of the Students in Using Proverbs in Writing Among the Students of Grade V of Public Elementary School 104189 Datar City

The issues in this study is: How to improve the ability to use proverbs in the essays of fifth grade students of SD Negeri 104189 Kota Datar? The purpose of this study is to describe the improvement of the ability to use proverbs in the essays of the students of the fifth grade of SD Negeri 104189 Kota Datar. The type of research used is Classroom Action Research (PTK) using 3 cycles, namely cycle I, cycle II and cycle III, broadly speaking, there are four stages that are passed in carrying out Classroom Action Research (PTK), namely planning, implementing, observing and reflecting. The subjects in this study were fifth grade students of SD Negeri 104189 Kota Datar, a total of 39 people. The findings showed that the application of PAKEM method with proverbial teaching aids by the teachers of Grade V students of SD Negeri 104189 Kota Datar, as evidenced by the increase in student activity for each index. In the first indicator, the number of very active pupils rose from 4 in the first cycle to 10 in the second, and again to 21 in the third. The other indicators of student activity continued to increase in each cycle. The increase in the ability to use proverbs in the essays of fifth grade students of SD Negeri 104189 Kota Datar occurred significantly, this can be seen from the average value of the ability to use proverbs in cycles I, II and III. From cycle I to cycle II, the average ability to use proverbs increased from 48.84 to 69.23, an increase of 20.39, likewise from cycle II to cycle III increased from 69.23 to 75. 46 again increased by 6.23. Logically, from Cycle I to Cycle III it increased by 26.62.

Common syntactic errors in the writing skills of the 3rd year students at Van Lang University

This study aimed to analyze common syntactic errors found in the argumentative essays of third-year English major students at the Faculty of Foreign Languages, Van Lang University, Vietnam. Quantitative and qualitative methods were used to obtain data in this study. The quantitative approach involved counting and calculating the frequency, percentage, and ranking of syntactic errors, while the qualitative method was adopted to explain these errors. The instrument for collecting data was argumentative essays written by 37 third-year English majors enrolling in Writing-5 at Van Lang University. Microsoft Word was used to collect and analyze syntactic errors and sentence structures, while Microsoft Excel was employed to calculate the frequency and rank of these errors and structures. The results found that the five most frequent errors in their essays were at the noun phrase and sentence levels. These errors included run-on sentences, preposition errors, article errors, sentence fragments, and pronoun errors. Through error analysis, it was indicated that the majority of errors made by students were due to the negative transfer from their first language and the literal translation. Based on the findings, language teachers and syllabus designers could use these results as guidance to design appropriate teaching methods and materials.

Enhancing students’ L2 writing skills online: a case study of an introductory English literature course for ESL students

Abstract This research paper is based on a case study of an introductory English literature course that is being offered at a community college in Hong Kong, China. The 2020–2021 cohort was unique as it had the entire course online using Zoom without any face-to-face or hybrid teaching, which was considered the “new normal” of higher education under the pandemic. This study employs narrative inquiry (Barkhuizen, G. (2014). Revisiting narrative frames: An instrument for investigating language teaching and learning. System, 47, 12–27; Clandinin, D. J. (2016). Engaging in narrative inquiry. Routledge), semi-structured interviews, and analysis of student essays to demonstrate that online essay consultations and collaborative writing are useful for enhancing the essay writing skills of second language learners. The paper specifically reports and highlights how individual Zoom essay consultations, together with collaborative writing on Padlet, scaffold and enhance students’ essay writing and critical thinking skills. Essentially, the paper argues that online technologies expand the dimension of literature education and empower it as a form of content-based ESL instruction for long-term use.

The Revival of Essay-Type Questions in Medical Education: Harnessing Artificial Intelligence and Machine Learning.

To analyse and compare the assessment and grading of human-written and machine-written formative essays. Quasi-experimental, qualitative cross-sectional study. Place and Duration of the Study: Department of Science of Dental Materials, Hamdard College of Medicine & Dentistry, Hamdard University, Karachi, from February to April 2023. Ten short formative essays of final-year dental students were manually assessed and graded. These essays were then graded using ChatGPT version 3.5. The chatbot responses and prompts were recorded and matched with manually graded essays. Qualitative analysis of the chatbot responses was then performed. Four different prompts were given to the artificial intelligence (AI) driven platform of ChatGPT to grade the summative essays. These were the chatbot's initial responses without grading, the chatbot's response to grading against criteria, the chatbot's response to criteria-wise grading, and the chatbot's response to questions for the difference in grading. Based on the results, four innovative ways of using AI and machine learning (ML) have been proposed for medical educators: Automated grading, content analysis, plagiarism detection, and formative assessment. ChatGPT provided a comprehensive report with feedback on writing skills, as opposed to manual grading of essays. The chatbot's responses were fascinating and thought-provoking. AI and ML technologies can potentially supplement human grading in the assessment of essays. Medical educators need to embrace AI and ML technology to enhance the standards and quality of medical education, particularly when assessing long and short essay-type questions. Further empirical research and evaluation are needed to confirm their effectiveness. Machine learning, Artificial intelligence, Essays, ChatGPT, Formative assessment.

“Anti-Texture” Agents in the Written English Essays of Students in a Ghanaian University

Texture proves to be a key indicator for measuring the quality of a text but establishing this text quality is a challenge for most English as a Second Language (ESL) students. This challenge has led to an array of studies to find the root cause(s) of this/these writing challenge(s) of ESL students. Despite these numerous studies, there is still paucity of research on “anti-texture” agents in the written English essays of students from the Ghanaian context, especially those at the tertiary level. In filling this gap, this study uses Halliday and Hasan’s (1976) cohesive theory as a framework to explore the types of cohesive errors that serve as “anti-texture” agents in 150 written English essays of students from a Ghanaian university. Because of the highly descriptive nature of this current investigation, it uses the qualitative content analysis as its design. The findings show that challenges like unclear pronoun references, wrong selection of cohesive devices, over usages of one cohesive device within the same textual environments, wider textual distances between references and their referents, the omission of cohesive devices, wrong insertions of cohesive devices and references lacking referents served as barriers to ESL students quest to use cohesive devices as means of creating texture in their texts. The findings further confirmed that the inappropriate use of cohesive devices in the students’ essays disrupted the free flow of information and as such, rendered the essays incoherent. The study has pedagogical implication for ESL academic writing instructors and curriculum developers.

Analysis of Information Literacy Skills in Student Essay Writing Using Mind Mapping

Analysis of Information Literacy Skills in Student Essay Writing Using Mind Mapping

“Just a pocket knife, not a machete”: Large language models in TEFL teacher education & digital text sovereignty

This paper approaches AI in TEFL teacher education from a perspective of digital text sovereignty (digitale Textsouveränität). Digital sovereignty (digitale Souveränität) is a concept that goes beyond media literacy and data literacy as a set of skills, to include personal competences in a more Humboldtian vision of education. Digital text sovereignty focusses on all those aspects of digital sovereignty that apply to the reception and production of texts, from the ability to produce and consume digital texts to the development of a critically-reflexive attitude to these texts (Frederking, 2022; Frederking & Krommer, 2022), making it especially useful in contexts of TEFL teacher education. This paper is based on an empirical study conducted within the EFL teacher education department of a German metropolitan university during the 2023 summer term. It analyses 21 student essays written after an interactive 90-minute training session on ChatGPT conducted in three intact TEFL teacher education seminars. Qualitative content analysis is used to identify domains of digital text sovereignty discussed in these essays, and to identify key themes related to four aspects of digital text sovereignty: Mediality, Source Code, Intentionality, and Veracity. The findings might be of interest for researchers and teachers educators interested in (a) modelling digital text sovereignty as it applies to AI, and (b) developing teacher education material and programs that target digital text sovereignty in an AI context, or that aim to support (future) teachers in developing an AI-informed pedagogy.

Latching Medical Students onto a Virtual Breastfeeding Elective During the COVID-19 Pandemic.

Objectives: To describe the implementation of a successful two-week virtual breastfeeding elective for medical students during the COVID-19 pandemic and characterize student demographics, objective knowledge, and perspectives on breastfeeding before and after the elective. Study Design: We adapted the Santa Rosa Kaiser Permanente Family Medicine breastfeeding residency curriculum to create a two-week virtual medical student elective using Kern's six steps of curriculum development and a competency-based education framework. Educational components included self-paced modules, shadowing experiences, and group didactics. Objective knowledge was assessed with multiple-choice tests before and after the elective compared using a paired t-test. Reflective writing pieces were qualitatively analyzed using the six phases of thematic analysis developed by Braun and Clarke. Results: From 2020 to 2023, 40 medical students completed the elective. Breastfeeding knowledge increased significantly from the pre-test 72% (95% CI: 52-92%) to post-test 91% (95% CI: 81-100%) (p < 0.001). Over 90% of students felt that learning objectives were met well or very well and agreed or strongly agreed that the elective increased their knowledge and confidence in providing anticipatory guidance to breastfeeding parents. Similar themes were shared across students' reflective writing pieces, with nearly 30% (n = 23) of the student essays addressing socio-cultural and racial differences in beliefs surrounding breastfeeding. Conclusion: A virtual breastfeeding curriculum for medical students is well-received by stakeholders (patients, lactation consultants, students, etc.) and improves breastfeeding knowledge and confidence. A virtual elective is an innovative and effective way to deliver breastfeeding education and can be used even when institutional breastfeeding or lactation support is unavailable.

Student Essay Competition – 2024

Student Essay Competition – 2024

Student Essay Competition

Student Essay Competition

Accuracy pecking order – How 30 AI detectors stack up in detecting generative artificial intelligence content in university English L1 and English L2 student essays

Accuracy pecking order – How 30 AI detectors stack up in detecting generative artificial intelligence content in university English L1 and English L2 student essays