Iraqi graduate students' perceptions of academic writing in the STEM fields

The purpose of this study is to examine Iraqi multilingual graduate PhD STEM students' experiences and perceptions of their academic writing. In this study I explore the following areas: a) Experiences that shaped multilingual STEM students' thinking about academic writing; b) Strategies students use to help improve their writing skills; and c) Factors influencing multilingual STEM students' identity as writers. Qualitative intrinsic case study is the methodology approach that guided this study. The study consists of multiple bounded cases of four participants studying in a doctoral program in the STEM fields at an R1 Midwest university during the 2019 fall and 2020 spring semesters. I used White and Marsh's (2006) procedures of qualitative content analysis to analyze the data from the four participants. The findings show: a) students' past and present experiences with academic writing instruction impact their writing development; b) students' academic writing improves when positive and constructive feedback is provided; c) writing across disciplines leads to unique challenges; d) students created their own writing style through reading and using model papers; e) being multilingual helps students think in their native language and write in English; and f) writing within an academic discipline influences how students think about themselves as writers. The study shows some of the writing challenges the Iraqi students faced during their program. This includes challenges with writing across disciplines and challenges with some writing elements such as vocabulary and development of ideas. The study also presents implications and recommendations for college advisors, graduate departments, and campus Intensive English programs, including creating a positive work atmosphere as crucial for students' learning success. In addition, providing graduate students with academic writing classes and writing workshops helps improve their writing skills.Includes bibliographical references

Mapping DDI2 to DDI4

Poster presented at the North American Data Description Conference (NADDI2019) in Ottawa, Canada April 2019.This poster describes the effort to add a DDI-Codebook (DDI-C) import function into the DDI4R R package. The DDI4 Codebook Group did a lot of the modeling of one section of DDI4 using a spreadsheet mapping DDI-C elements into DDI4 properties. This started with a list of elements used by CESSDA and was refined at the May 2016 Knutholmen Sprint. Unfortunately, these mappings were not always at the leaf node level. An R program also imported DDI-C XML from the European Social Survey and generated a list of unique XPaths of leaf elements used in that set of metadata. These elements, along with corresponding DDI4 leaf paths, were used to update the spreadsheet. This spreadsheet has been further refined to create an actionable table mapping DDI-C leaf values to leaf properties in DDI4. Writing code to import the DDI-C required additional information: • mapping from DDI-C sub-paths to DDI4 Identifiable classes (e.g. all the information for one DDI-C “var” maps to one DDI4 IdentifiableVariable), • mapping abstract target classes to specific extensions, • additional semantic property values like “typeOfMethodology”. Importing DDI-C into a lifecycle level version of DDI like DDI4 also involves identifying repeated metadata like reused value domains (e.g. reused Likert style codelists) that are repeated for multiple variables. An R function served to do this sort of matching using the R “all.equal” function excluding differences in agency, id, and version

Citation Literacy

Citation literacy is the ability to read and write citations.[1] That’s it. The rest of this article will unpack what’s in those ten words and why they matter

ICT and adult literacy, numeracy and ESOL

Mellar, H., Kambouri, M., Sanderson, M., and Pavlou, V. (2004) ICT and adult literacy, numeracy and ESOL. London: NRDC. Available at: http://www.nrdc.org.uk/uploads/documents/doc_258.pdfResearch report for NRDCThis project set out to obtain a picture of present teaching practice in the use of ICT in adult literacy, numeracy and ESOL within formal provision. (http://www.nrdc.org.uk/uploads/documents/doc_258.pdf

CEAI: CCM based Email Authorship Identification Model

In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some more interesting and effective features for email authorship identification (e.g. the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell). We also included Info Gain feature selection based content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5% accuracy has been achieved on authors' constructed real email dataset. The results on Enron dataset have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1, 2]

Embedded librarianship and problem-based learning in undergraduate mathematics courses

A pedagogical approach of problem-based learning with embedded librarianship in several undergraduate mathematics courses is implemented in this educational research. The students are assigned to work on several projects on various applications of mathematical topics in daily life and submit written reports. An embedded librarian collaborates together with the instructor and the students to improve the students' information literacy. Initial reaction and anecdotal evidence show that the students' information literacy and academic performance have improved throughout the semesters.Comment: 4 pages, 2 tables, International Congress of Women Mathematicians Presentation Book, Ewha Womans University, Seoul, South Korea, pp. 117-120, 201

Mining online diaries for blogger identification

In this paper, we present an investigation of authorship identification on personal blogs or diaries, which are different from other types of text such as essays, emails, or articles based on the text properties. The investigation utilizes couple of intuitive feature sets and studies various parameters that affect the identification performance. Many studies manipulated the problem of authorship identification in manually collected corpora, but only few utilized real data from existing blogs. The complexity of the language model in personal blogs is motivating to identify the correspondent author. The main contribution of this work is at least three folds. Firstly, we utilize the LIWC and MRC feature sets together, which have been developed with Psychology background, for the first time for authorship identification on personal blogs. Secondly, we analyze the effect of various parameters, and feature sets, on the identification performance. This includes the number of authors in the data corpus, the post size or the word count, and the number of posts for each author. Finally, we study applying authorship identification over a limited set of users that have a common personality attributes. This analysis is motivated by the lack of standard or solid recommendations in literature for such task, especially in the domain of personal blogs. The results and evaluation show that the utilized features are compact while their performance is highly comparable with other larger feature sets. The analysis also confirmed the most effective parameters, their ranges in the data corpus, and the usefulness of the common users classifier in improving the performance, for the author identification task