A manual for blended mobility

This manual has been designed for teachers, researchers, and course administrators involved in developing online programmes. For that reason, it has been written in accessible language and avoids being overly prescriptive.Section 1 introduces the EUCERMAT project, which is concerned with the promotion of the field of ceramic and material science and the development of educational programmes in that area.Section 2 deals with the setting-up and recognition of international common modules in the field of ceramic and material science, but some of the guidelines will be useful to faculty members in other disciplines who are thinking of developing international modules.Section 3 has been written for teachers in any discipline and describes how best to design and develop online materials. Some of the guidelines can be used in on-campus, blended, and fully online courses.Section 3 also provides some practical guidelines for EUCERMAT teachers, to ensure a consistent look-and-feel for their online courses, but the guidelines may of use to teachers in other disciplines also.The appendices comprise links to useful resources for teaching and learning online

Consultation approaches for large scale systems adoption in higher education

In January 2021, the University of Limerick embarked on a project to identify a new Learning Management system (LMS) or Virtual Learning Environment (VLE) platform for the institution. Recent surveys, both internally and nationally, had found that staff and students were looking for greater LMS functionality, a more user-friendly learning experience, and greater integrations with other learning tools. Furthermore, the pandemic had increased the demands on the institution’s existing LMSs. This paper will outline the approach taken by the author (also the Project Lead for the first phase of the LMS Review) to establish working groups and consult with the various stakeholders across the campus community and external parties. It will conclude with an overview of some lessons learned from the consultation phase, which ended in June 2021, and how that phase has fed into subsequent phases.</p

Using information and communication technologies to support deep learning in a third-level on-campus programme: A case study of the taught Master of Arts in E-Learning Design and Development at the University of Limerick

This paper explores how Information and Communication Technologies (ICTs) were used to support deep learning in some of the modules on the Master of Arts in E- Learning Design and Development at the University of Limerick. This paper describes how the author used ICTs, as well as online teaching and learning theories, to improve the learning experience for on-campus students undertaking certain Project-Based Learning (PBL) activities. Examples of students' online Computer-Mediated Communication (CMC) postings, as well as extracts from online reflective journals and written reports, show how a sample of former students benefited from using ICTs.</p

Using financial event phrases and keywords to classify form 8-K disclosures by likely share price response

It is generally agreed that there are three different types of financial information: information in past stock prices, information that is available to all the public, and information that is both available to the public and available privately to insiders (Fama 1970; Haugen 1990; Hellstrom and Holmstrom 1998; Elton et al 2003). There is considerable debate about the possible impact that different kinds of information can have on the value of financial instruments. On the one hand, the efficient markets hypothesis (EMH) states that the price of a financial instrument properly reflects all available information immediately (Fama 1970). If security prices respond to all available information quickly, then the market is deemed efficient and no excess profits or returns can be made. On the other hand, fundamental and technical analysts argue that the market is inefficient because information disseminates slowly through the market and prices under- or over-react to the information (Haugen 1990). A number of different data sources, features, goals, and methods have been used to automatically analyse content in financial documents. However, there has been very little research undertaken in the area of automatic event phrase recognition and classification of online disclosures. Our research study focuses on content contained in Form 8-K disclosures filed on EDGAR, a system maintained by the Securities and Exchange Commission (SEC). In our research study, we developed a prototype automatic financial event phrase (FEP) recogniser and we automatically classified a small sample of 8-Ks by likely share price response, using the automatically recognised FEPs and hand-chosen keywords as features. In four comparative classification experiments, we used the C4.5 suite of programs and the SVM-Light support vector machine program. Our datasets comprised 8-Ks filed by 50 randomlychosen S&P 500 companies from 1997 to 2000 and 2005 to 2008. Our research experiments yielded some interesting findings. In an experiment on the 2005 to 2008 dataset comprising 280 8-Ks, C4.5 was able to correctly classify 63.2% of the ‘ups’1 (as against 58.2% at chance), when using FEPs and keywords. We also found that C4.5 appears to be better at identifying patterns in the training cases than SVM-Light, regardless of whether they were ‘ups’ or ‘downs’. When we compared the results from our FEP experiments with the results from two baseline approaches—n-gram classification and Naïve Bayes bag-of-words classification—we found that C4.5 using FEPs and keywords yielded marginally higher overall classification accuracy than C4.5 using n-grams or Naïve Bayes bag-of-words. A detailed description of the classification experiments is provided in the thesis, along with a discussion of the strengths and limitations of the research study. Recommendations for future work include further refinement of the FEPs and keywords, classification of larger datasets, and incorporation of additional classification variables beyond financial event phrases and hand-chosen keywords

Using financial event phrases and keywords to classify form 8-K disclosures by likely share price response

It is generally agreed that there are three different types of financial information: information in past stock prices, information that is available to all the public, and information that is both available to the public and available privately to insiders (Fama 1970; Haugen 1990; Hellstrom and Holmstrom 1998; Elton et al 2003). There is considerable debate about the possible impact that different kinds of information can have on the value of financial instruments. On the one hand, the efficient markets hypothesis (EMH) states that the price of a financial instrument properly reflects all available information immediately (Fama 1970). If security prices respond to all available information quickly, then the market is deemed efficient and no excess profits or returns can be made. On the other hand, fundamental and technical analysts argue that the market is inefficient because information disseminates slowly through the market and prices under- or over-react to the information (Haugen 1990). A number of different data sources, features, goals, and methods have been used to automatically analyse content in financial documents. However, there has been very little research undertaken in the area of automatic event phrase recognition and classification of online disclosures. Our research study focuses on content contained in Form 8-K disclosures filed on EDGAR, a system maintained by the Securities and Exchange Commission (SEC). In our research study, we developed a prototype automatic financial event phrase (FEP) recogniser and we automatically classified a small sample of 8-Ks by likely share price response, using the automatically recognised FEPs and hand-chosen keywords as features. In four comparative classification experiments, we used the C4.5 suite of programs and the SVM-Light support vector machine program. Our datasets comprised 8-Ks filed by 50 randomlychosen S&P 500 companies from 1997 to 2000 and 2005 to 2008. Our research experiments yielded some interesting findings. In an experiment on the 2005 to 2008 dataset comprising 280 8-Ks, C4.5 was able to correctly classify 63.2% of the ‘ups’1 (as against 58.2% at chance), when using FEPs and keywords. We also found that C4.5 appears to be better at identifying patterns in the training cases than SVM-Light, regardless of whether they were ‘ups’ or ‘downs’. When we compared the results from our FEP experiments with the results from two baseline approaches—n-gram classification and Naïve Bayes bag-of-words classification—we found that C4.5 using FEPs and keywords yielded marginally higher overall classification accuracy than C4.5 using n-grams or Naïve Bayes bag-of-words. A detailed description of the classification experiments is provided in the thesis, along with a discussion of the strengths and limitations of the research study. Recommendations for future work include further refinement of the FEPs and keywords, classification of larger datasets, and incorporation of additional classification variables beyond financial event phrases and hand-chosen keywords

Learning analytics as a tool for evaluating engagement in technical communication discussion forums

Learning analytics (LA) refers to the collection and analysis of learners’ interactions with virtual learning environments (VLEs) and other information systems, with the aim of improving the overall teaching and learning experience. Most VLEs, including Blackboard, Moodle, and Sakai, collect data about how students interact with VLE resources and with one another. In terms of forum-based interaction on VLEs, LA can measure which students posted, when they posted, how many words they posted, and which resources they accessed around that time. This paper uses a case study to demonstrate how LA data can be used to evaluate engagement in technical communication discussion forums. The paper also outlines some techniques for measuring participation in forum-based activities, using LA data available in most VLEs.</p