Financial overview and grant giving statistics of National Writing Project

The study has IRB approval and participant consent was collected through writing. This data set contains an Excel spreadsheet with multiple tabs for each survey question. The data set includes responses from participants who had consented to participate in the study. Where applicable, the researcher codes are included. Our study addressed the following research questions: What prior experience with screen recordings did students in seven different sections of First-Year Writing courses report? How did students in seven different sections of First-Year Writing courses taught by two different instructors perceive the effects of screen recording assignments on understanding their writing process? How did those students feel about the screen recording assignments? We gave students a survey using Microsoft Forms in class during Week 14. We asked them to complete it as part of normal coursework, and if they did not complete the survey in class, then they were to finish it for homework. The survey included questions that helped us determine the levels of experience students reported with both creating and viewing screen recordings (the umbrella term we used during instruction to refer to both screen recordings and screencasts), as well as demographic questions that asked students to state which class they were in. Other close-ended questions asked students to rate their confidence using technology prior to and after completing the course and the number of essays they had revised prior to the class.

Likert-scale questions also ask students to rate the degree to which they agreed with statements related to whether the screen recording assignments helped them “better understand” their writing and research process, their perceptions of the difficulty of the assignments, and whether the assignment helped them “think about my revision practices.” Open-ended questions were included asking students to describe how they felt about the screen recording assignments, the factors that affected their feelings, and how the assignment affected their technology skills.

We argue that the screencast assignment was the most positively received by students because it required students to use transmediation in the writing process, cohesively blending speech, writing, and video components in the writing process. Overall, our research showed that student-led screencasts and screen recordings can offer students unique affordances to incorporate transmodality and multimodality in the writing process.

The SPSS data file (AI writing figshare.sav) contains all the variables used in the analyses for the final sample of (N=170).Readme file provides the list of the variables in the dataset, variable names, explanations, and coded values.

Descriptive statistics of students’ essay writing performance.

According to Cognitive Market Research, the global essay writing services market size was USD 1.8 billion in the year 2022 and will grow at a compound yearly growth rate (CAGR) of 9.20% from 2023 to 2030. What are the Key Drivers Affecting the Essay Writing Services Market?

Increased Academic Workload and Time Constraints to Provide Viable Market Output

A significant market driver for language services has been the increased academic workload with time constraints. Students today frequently face a heavy workload with many assignments, exams and projects to manage. The stress of excelling in academics when juggling other responsibilities is leading some students to attain assistance from essay writing services to make sure the timely submission of high-quality assignments.

The study found that the average college student now spends 15 hours per week on homework. The essay writing service market is most commonly utilized by students in the age of 18-24.

(Source:www.ed.ac.uk/files/atoms/files/aewpg_ismaterials.pdf)

Students are involved in different activities outside of their academic studies, like internships, part-time jobs, extracurricular activities, and family commitments. The time constraints with these responsibilities make it challenging for students to allocate sufficient time for researching and writing essays, encouraging them to choose writing services.

The increasing significance of written communication skills in academic and professional spheres is driving the market growth

The Factors Restraining the Growth of the Essay Writing Services Market

Academic Integrity Policies to Hinder Market Growth

The academic integrity policies constrain the essay writing services market. Several educational institutes have stringent policies against plagiarism and the utilization of external services to complete assignments. In some cases, the utilization of essay writing services makes students dependent on them, hindering the intellectual and academic development of students. Some countries and regions have initiated to implementation rules in order to curb the operations of essay writing services. Further, some educational systems and organizations are launching awareness campaigns in order to inform students about the risk related to the use of essay writing services.

Impact Of COVID-19 on the Essay Writing Services Market

The pandemic has had a positive impact on the essay writing service market as educational institutes were shifting to online learning, and there has been an increased demand for online services like essay writing assistance. The unavailability of physical resources on campuses during the lockdown made it challenging for students to conduct thorough research for their essays, leading to increased utilization of essay writing services. The implementation of stringent rules in order to detect and avoid plagiarism influenced the demand for essay services. The market of essay writing services was already witnessing growth and evolving trends before the pandemic What is essay writing services?

Essay writing services are the platforms or companies that offer custom written essays, and other academic or professional writing services. The essay writing service is growing fast to provide the vast requirements of customers across various segments. The increased demand for professional writing services from professionals and students, growing penetration of the internet and enhanced telecommunications, and surge in the enrolment to higher education courses. The key market participants are providing advanced features like writing assistance tools and plagiarism detection, chat support, and other customized services.

These factors empower businesses to offer better-tailored solutions and services, which, in turn, contribute to the growth of the essay-writing services industry.

For instance, the first online essay writing service established in 2005 made the market grow rapidly, and there are now multiple online essay writing services available due to the increasing demand for academic writing. These services charge students per essay, and the costs vary depending on the length and complexity of the essay.

(Source:elearningindustry.com/why-students-online-essay-writing-services

Dataset

This dataset was created by Olivia Harry

Contents

Dataset with data on text quality, keystroke logging process measures, and writer characteristics.

This dataset serves as a baseline for studies on synthesis writing.

This dataset comprises 10,000 artificially generated student essays using GPT4, accompanied by holistic scores ranging from 1 to 6. These essays were generated based on the data from the Automated Essay Scoring 2.0 competition.

The Method

My aim was to produce essays that closely resembled those in the original AES dataset, essentially creating paraphrases while ensuring they remained distinct compositions. Equally important was maintaining scores consistent with the original holistic scoring system used in the competition. To accomplish this, I followed the process outlined below:

Prompt Template

The basic prompt template looks like this:

prompt_template = ''''
You are a {AGE} year old German student writing an English test, but you're stuck! Luckily, your neighbour is doing well and so you take a glimpse at his sheet and you could catch the following text:

=========
"{TEXT}"
=========

But you cannot simply copy it, you need to change it a bit so the teacher doesn't notice that you copied it, 
hence you copy it with the following rules:
- Paraphrase the text just a bit
- Adhere to the style and level of the original text
- Sprinkle some errors into the text, akin to the original
- Remember your age and incroporate that into the essay so it's feasible for a {AGE} year old student who writes not in his native language!

Output only the essay
'''

The produced essay woud be scored the same score as the original essay passed into the {TEXT} variable.

This prompt tries to implement a couple of ideas:

• Through the incentive of being a student who copies another student in a test situation, but doesn't want to be noticed by the teacher, the model should produce a mixture of copied, paraphrased and own text chunks. This would also allow the essay to be of similar quality (hopefully meaning similar score).
• Through the {AGE} variable, I tried to enforce the score of the original essay by prompting essays with a lower score, a lower age (minimum 11, highest 14) and thus also lowering the quality of the produced essay. The formular for the age is defined as: \(age = 15 - (4 - (originalEssayScore // 2))\)
• I tried to replicate the spelling and grammar mistakes of the original essay into the newly produced essay, but language models are really hard to get them to add random mistakes into their outputs, hence: I counted the spelling mistakes in the original essay through python's spellchecker and added as much random mistakes into the newly generated essays to again, replicate the score as best as I can.
• Since we have a class imbalance (way more 3s and 4s than 1s and 6s), I purposely made the essay pool, from which a new essay was prompted from, imbalanced in favour of those lower-quantity classes. The dataset should therefore contain more of these.

Examples

Here are some examples:

Initially, the author addresses the perception that Mars is tooy hazardous to explore. Many people are deterred by Mars' reputation as a dangerous and inhospitable planet. The author acknowledges these challenges but demonstrates how safe travel can still be achieved. By detailing Jthe plan proposed by the National Aeronautics and Space Administration (NASA) for astronauts to float above the dangerous conditions, the writer assures the audience of the safety measures in place. Specific aspects of the plan, such as Earth-like air pressure and abundant solar power, are highlighted to emphasize the feasibility of human survival. Drawing a comparison to a blimp-like vehicle, the author simplifies the concept for better understanding. By dispelling the notion of Mars being too perilous, the writer strengthens the argument for explorRing the planet.

Furthermore, the writer emphasizes the educational potential that studying Mars offers. Beyond simple facts about Mars' proximity in size and density to Earth, the author delves into the possibility of Mars once resembling Earth. Describing Mars' current environment as Earth-like with rocky surfaces, valleys, mountains, and craters, the author suggests that Mars may have supported life in the past, similar to Earth. This parallel betwveen the two planets Hcaptivates the audienc...

Metadata and data derived from Victorian Jewish Writers Project. Welcome to the Victorian Jewish Writers Project, a digital collection focusing on Anglo-Jewish writing during the long nineteenth century. The site is a work-in-progress, so please contact katzir [at] vjwp [dot] org if you need one-on-one assistance.

Title: A review of educational dialogue strategies to improve academic writing skills. Methods Search strategy: In April 2017, we searched the following online databases: Web of Science, EMBASE, ERIC, CINAHL, PsycINFO and Google Scholar. At first, we searched on ‘feed up’, ‘feed back’ and ‘feed forward’ but this strategy did not produce enough suitable articles so we added the term ‘feedback’. To minimize the chance of missing relevant articles, the scope was broad and included the following string of keywords and Boolean operators: ‘dialogue OR discussion OR conversation’ AND ‘feedback’ AND ‘writing’. Inclusion and exclusion criteria: The electronic literature search was limited to English full-text studies published since 1990. Only articles that met the following inclusion criteria were selected: peer-reviewed, empirical studies with a particular focus on academic writing, published in the field of academic education, including all disciplines that discussed interventions employing face-to-face feedback dialogue. We excluded literature reviews and case studies, studies that did not focus on academic writing or studies that only addressed the online, digital or ICT aspects of the main topics. Data extraction: The first author performed the search, yielding 1508 records. After removal of duplicates, the titles and abstracts of the remaining records (N=1182) were screened on the inclusion and exclusion criteria. The resulting records (N=304) contained the topics ‘dialogue’, ‘feedback’ and ‘writing’. Further eligibility was subsequently assessed by reading the full articles on this list. After this phase, 102 articles remained for consideration. Of those, only articles that discussed a feedback intervention involving ‘face-to-face dialogue’ before submission of an academic writing assignment were included. As a result, the final review was based on 19 studies (Figure 1). Data analysis: We scrutinized each intervention for the presence of feed-up, feed-back and feed-forward information (Black and Wiliam, 2009; Hattie and Timperley, 2007; Jonsson, 2012; Nicol and Macfarlane-Dick, 2006; Price et al., 2010; Rae and Cochrane, 2008). For the purpose of this review, we considered educational strategies such as assessment criteria, exemplars, worked examples and training (e.g. instructions or workshops) as expressions of feed-up information; written lecturer feedback and written peer review/assessments as feed-back information; and instructions to revise draft products as feed-forward information. In the next step, we checked which and how many participants were involved in the dialogue (student-student, lecturer-student or a combination of both). Since the studies did not describe the content of the face-to-face dialogues, we did not categorize them in terms of feed up, feed back and/or feed forward. Third, we operationalized intervention outcomes in terms of students’ perceptions of the intervention, their marks and by text/dialogue analysis. Finally, in assessing the effectiveness of each intervention, we took into account the methodological characteristics of each study, including their study design, data sources and data collection methods (Creswell, 2014).

Financial overview and grant giving statistics of Hub City Writers Project

Connecting language classrooms with 21st-century skills could be the potential framework for enhancing EFL learners’ performance in writing classes. However, investigating whether project-based learning, as a new field within ELT with unique pedagogical affordances, can enhance learners’ writing skills still needs to be improved in the literature. Accordingly, this study aimed to investigate the impact of project-based learning on EFL learners’ writing performance. It sought to determine whether and to what extent project-based learning could enhance writing skills in an EFL context. The study employed a quasi-experimental design with an interrupted pre-test-post-test time series design with single group participants. Twenty-three third-year EFL undergraduate students enrolled in the Advanced Writing Skills I course were selected using a comprehensive sampling method. An essay writing test and interview were used to gather data. The participants of the study were given a series of three problem-solving essay writing tests before and after the intervention, which employed project-based essay writing instruction. In addition, to discover their attitudes toward the impacts of project-based learning and its applications on the ground, three randomly selected students were interviewed at the end of the intervention. The data collected through the tests were analyzed through a one-way repeated measure ANOVA; narration was also used to analyze the qualitative data gathered through interviews. Accordingly, the quantitative data suggested that project-based learning significantly enhances EFL learners’ writing performance. Moreover, interview data showed that students felt optimistic about the impact of project-based learning on their writing performance, idea generation, and cooperation among themselves. Therefore, project-based learning is suggested as another method in ELT writing classes because it enhances learners’ writing via idea generation, data collection, organization, cooperation, and general communication skills. As students work on worthwhile projects, its emphasis on real-world applicability and realistic activities can help them become better writers. Hence, teachers can reinforce the relationship between form and purpose by incorporating a variety of genres and collaborative writing to reflect real-world or professional situations.

The aim of the project was to assess if and how creating a video essay would impact the revision process for students working on an essay. We ended up with 13 participants. The biggest challenge was getting participants. We did not get enough in Sem 1, and so had to extend the IRB and data gathering into Sem 2. While 13 is a small number, we have some rich data. We have interviews with each participant, journal entries, and the actual first draft/final submission for each essay. This has allowed us to triangulate our findings. For example, if a student claimed in the interview and journal entries that doing the video essay helped her to edit out inessential details, we can then review her draft and final essay to see if she actually did this editing. We have completed the analysis of the data and are in the process of drafting an article.

Most undergraduate instructors would agree that learning to write about data in the style of a scientific research paper is a worthy goal for their students. However, many hesitate to tackle the challenge of teaching scientific writing in their laboratory courses. This hesitation is often due to dissatisfying outcomes from writing assignments, especially given the effort required to provide feedback on student writing. To make writing assignments more manageable and productive for both instructors and students, I have developed a “formula” for writing a standard scientific Results section: the “WHY, HOW, WHERE, WHAT, SO WHAT?” strategy. Introducing students to this formula, helping them to recognize it in published papers, and asking them to implement it in writing about their own experimental data provides a learning scaffold that significantly improves student writing outcomes. The Results section is the heart of any research article, and the quality of its execution provides a measure of students’ understanding of experimental questions and procedures as well as their ability to analyze data and draw logical conclusions. Learning to craft a well-written Results section develops general organizational and communication skills that students can apply to other forms of writing and to oral presentations. Thus, it is a valuable experience regardless of whether students may ever actually prepare a manuscript for publication, and it is beneficial when used alone or in combination with assignments that also teach the writing of other sections of a research article.

Financial overview and grant giving statistics of The Writers Block Project

The Great Isaiah Scroll (1QIsaa) data set for writer identification

This data set is collected for the ERC project: The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls PI: Mladen Popović Grant agreement ID: 640497

Project website: https://cordis.europa.eu/project/id/640497

Copyright (c) University of Groningen, 2021. All rights reserved. Disclaimer and copyright notice for all data contained on this .tar.gz file:

1) permission is hereby granted to use the data for research purposes. It is not allowed to distribute this data for commercial purposes.

2) provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed.

3) provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this data.

4) the user should refer to the first public article on this data set:

Popović, M., Dhali, M. A., & Schomaker, L. (2020). Artificial intelligence-based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa). arXiv preprint arXiv:2010.14476.

BibTeX:

@article{popovic2020artificial, title={Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)}, author={Popovi{\'c}, Mladen and Dhali, Maruf A and Schomaker, Lambert}, journal={arXiv preprint arXiv:2010.14476}, year={2020} }

5) the recipient should refrain from proliferating the data set to third parties external to his/her local research group. Please refer interested researchers to this site for obtaining their own copy.

Organisation of the data:

The .tar.gz file contains three directories: images, features, and plots. The included 'README' file contains all the instructions.

The 'images' directory contains NetPBM images of the columns of 1QIsaa. The NetPBM format is chosen because of its simplicity. Additionally, there is no doubt about lossy compression in the processing chain. There are two images for each of the Great Isaiah Scroll columns: one is the direct binarized output from the BiNet (arxiv.org/abs/1911.07930) system, and the other one is the manually cleaned version of the binarized output. The file names for the direct binarized output are of the format '1QIsaa_col.pbm', for example, '1QIsaa_col15.pbm'. And, for the cleaned version, the format is '1QIsaa_col_cleaned.pbm', for example, '1QIsaa_col15_cleaned.pbm'. Note: the image files are not in a separate directory; they will be extracted in the same place. However, due to the unique naming, there is no problem extracting them in one single directory.

The 'features' directory contains feature files computed for each of the column images. There are two types of feature files: Hinge and Adjoined. They are distinguishable by their extension, for example, '1QIsaa_col15_cleaned.hinge' and '1QIsaa_col15_cleaned.adjoined'. They are also arranged in separate directories for ease of use.

The 'plots' directory contains a simple python script to perform PCA on the feature files and then visualize them in a 3D plot. The file takes the location of feature files as an input. The 'README_plot' file contains examples of how-to-run in the terminal.

Brief description: According to ImageMagick's' identify' tool, the original images are in grayscale (.jpg) from Brill collection, in '8-bit Gray 256c'. These images pass through multiple preprocessing measures to become suitable for pattern recognition-based techniques. The first step in preprocessing is the image-binarization technique. In order to prevent any classification of the text-column images based on irrelevant background patterns, a specific binarization technique (BiNet) was applied, keeping the original ink traces intact. After performing the binarization, the images were cleaned further by removing the adjacent columns that partially appear on the target columns' images. Finally, few minor affine transformations and stretching corrections were performed in a restrictive manner. These corrections are also targeted for aligning the texts where the text lines get twisted due to the leather writing surface's degradation. Hence, the clean images are there in the directory along with the direct binarized images. No effort has been made to obtain a balanced set in any way.

Tools: Binarization: The BiNet tool is available for scientific use upon request (m.a.dhal(at)rug.nl)

Image Morphing: In the original article, data augmentation was performed using image morphing. The tool is available on GitHub: https://github.com/GrHound/imagemorph.c

Features for writer identification: Lambert Schomaker http://www.ai.rug.nl/~lambert/allographic-fraglet-codebooks/allographic-fraglet-codebooks.html http://www.ai.rug.nl/~lambert/hinge/hinge-transform.html 1. L. Schomaker & M. Bulacu (2004). Automatic writer identification using connected-component contours and edge-based features of upper-case Western script. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 26(6), June 2004, pp. 787 - 798. 2. Bulacu, M. & Schomaker, L.R.B. (2007). Text-independent Writer Identification and Verification Using Textural and Allographic Features, IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Special Issue - Biometrics: Progress and Directions, April, 29(4), p. 701-717.

The features (hinge, fraglets) have been combined in a single MS Windows application, GIWIS, which is available for scientific use upon request (l.r.b.schomaker(at)rug.nl)

If you have any question, please contact us: Maruf A. Dhali Lambert Schomaker Mladen Popović

Please cite our papers if you use this data set: 1. Popović, M., Dhali, M. A., & Schomaker, L. (2020). Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa). arXiv preprint arXiv:2010.14476. 2. Dhali, M. A., de Wit, J. W., & Schomaker, L. (2019). Binet: Degraded-manuscript binarization in diverse document textures and layouts using deep encoder-decoder networks. arXiv preprint arXiv:1911.07930.

Fork this notebook to get started on accessing data in the BigQuery dataset by writing SQL queries using the BQhelper module.

Context

The Office of the Chief Economist (OCE) is responsible for advising the Under Secretary of Commerce for Intellectual Property and Director of the USPTO on the economic implications of policies and programs affecting the U.S. intellectual property (IP) system. The office disseminates detailed patent and trademark data, undertakes research, and conducts economic analysis on a variety of IP issues. OCE works with policy makers, collaborates with academics, and engages the public more generally through conferences it organizes, the publicly accessible research datasets it provides, and its publications.

Content

The USPTO OCE Patent Assignment Dataset contains detailed data patent assignments and other transactions recorded at the USPTO since 1970.

Acknowledgements

"USPTO OCE Patent Assignment Data" by the USPTO, for public use. Marco, Alan C., Graham, Stuart J.H., Myers, Amanda F., D'Agostino, Paul A and Apple, Kirsten, "The USPTO Patent Assignment Dataset: Descriptions and Analysis" (July 27, 2015).

Data Origin: https://bigquery.cloud.google.com/dataset/patents-public-data:uspto_oce_assignment

Banner photo by Jeff Sheldon on Unsplash

Financial overview and grant giving statistics of Teen Writers And Artists Project Nfp

Complete metadata export for Victorian Jewish Writers Project objects.

In the context of a Capstone course for prospective secondary teachers (PSTs) of mathematics, this paper examines how a journal assignment was implemented and how it can help us understand PSTs’ views of mathematics and mathematics teaching. First, the course design and specifics of the assignment are described. Then, details about implementation of the assignment are included. Finally, affordances of incorporating this type of assignment are discussed with examples of PSTs’ responses. The identified benefits include PSTs: (a) connecting new mathematical ideas to their prior knowledge; (b) identifying what they do not know; and (c) articulating the importance of reasoning and proving in mathematics teaching and learning. These benefits support the notion that using a journal assignment in mathematics content courses designed for PSTs can help them develop a profound understanding of mathematics content and practices.