SUMMARY OF PROCEEDINGS OF THE “LINKING THE MIDDLE AGES” WORKSHOP (MAY 11-12, 2015) at the University of Texas at Austin

Summary of Proceedings of Workshop on the possible application(s) of Linked Open Data to Medieval Studies.In May 2015 the CLIR/Mellon Foundation Postdoctoral Fellows in Medieval Data Curation convened a two-day workshop on the sharing and publishing of linked open data (LOD). Funded by a CLIR/Mellon microgrant, the workshop brought together librarians, technologists, and scholars to exchange ideas on the challenges posed to medievalists in sharing data on digital platforms. More than thirty experts took part in the workshop, which was held at The University of Texas at Austin on May 11-12, 2015. Participants presented their work in linked open data operational sites and took part in discussions about the obstacles and opportunities of LOD in three areas: research, teaching, and publication. This paper documents the possibilities and problems of applying LOD in medieval studies. It focuses specifically on its application to the field’s data, which requires medievalists to work together with librarians and technologists, and considers a technical infrastructure that can maintain and proliferate this type of collaborative work.Council on Library and Information Resources, The Andrew W. Mellon Foundation, University of Texas Libraries, University of Texas at AustinUT Librarie

Community College Online

In this report we find that the majority of Americans enrolling in higher education today do not match the mainstream image of recent high school graduates leaving home for the first time to settle into dorm life at a residential university campus. In 2012, only 12 percent of college students lived on campus. In fact, over four in ten college students in this country attend community colleges. In the fall of 2012, the public two-year sector enrolled 6.8 million undergraduates at over 1,000 institutions nationwide, more than any other higher education sector.This report indicates that often overlooked in conversations about college that tend to focus on elite, residential, four-year schools, community colleges occupy a critical space in higher education. Community college students not only make up a greater proportion of the college-going population than typically recognized, but they differ markedly in their demographic composition compared to the public four-year and private nonprofit sectors of higher education. Community college students are more likely to be older, commute to school, and care for dependents. They are also much more likely to attend part time and need remediation. In terms of racial and socioeconomic demographics, community college students are more diverse and lower-income than their four-year counterparts

Measuring Learning, Not Time: Competency-Based Education and Visions of a More Efficient Credentialing Model

Competency-based education is intended to benefit working non-traditional students who have knowledge and skills from prior work experiences, but it also enables self-motivated students to accelerate their time to degree, thereby increasing affordability and efficiency. Competency-based education clarifies what a credentialed student will be able to do and makes assessment more transparent and relevant to those outside of higher education. Competency-based education has arisen in response to the problem defined by the national reform discourses of accountability and affordability. In the first manuscript, History & Objections Repeated: Re-Innovating Competency-Based Education, I review the history of social efficiency reform efforts in American education in order to re-contextualize the “innovation” of competency-based education as a repackage of older ideas to fit the public’s current view of what needs to be fixed in higher education. I discuss the concept of “efficiency” and how it has been interpreted in the past and today with regard to competency-based education and its rejection of an earlier attempt at increasing efficiency in education: the Carnegie credit hour. For the second manuscript, Framing Competency-Based Education in the Discourse of Reform, I analyzed four years of news articles and white papers on competency-based education to reveal the national discourses around competency-based education. I used thematic discourse analysis to identify diagnostic and prognostic narrative frames (Snow & Benford, 1988) that argue for and against competency-based education. These frames were put in the context of the politicized conversation around the current main issues in higher education: access, attainment, accountability, and affordability. Each of these issues provided a foundation of coding the discourse which was then shaped by the context of competency-based education, particularly its positioning as a solution to the Iron Triangle dilemma of decreasing cost while increasing access and quality. The third manuscript, Idea and Implementation: A Case Study of KCTCS’s CBE Learn on Demand, involves an institutional case study of a competency-based education program, Learn on Demand (LOD), within the Kentucky Community and Technical College System (KCTCS). Eleven semi-structured interviews were conducted with student success coaches, faculty, and staff who are directly involved with the program across seven different colleges, and documents such as marketing materials, presentations, and administrator-written articles were also analyzed as a representation of the official discourse of the program. As institutions start to explore and develop competency-based education programs, the faculty and administrators at those institutions are likely influenced by the intersection of pre-existing organizational and subgroup culture, societal beliefs about the definition and purpose of education, and how innovations may shape the experiences of individuals. Through interviewing individuals, I was able to parse out the impacts of both institutional politics and innovation-related concerns on the success of implementation

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

PERICLES Deliverable 4.3:Content Semantics and Use Context Analysis Techniques

The current deliverable summarises the work conducted within task T4.3 of WP4, focusing on the extraction and the subsequent analysis of semantic information from digital content, which is imperative for its preservability. More specifically, the deliverable defines content semantic information from a visual and textual perspective, explains how this information can be exploited in long-term digital preservation and proposes novel approaches for extracting this information in a scalable manner. Additionally, the deliverable discusses novel techniques for retrieving and analysing the context of use of digital objects. Although this topic has not been extensively studied by existing literature, we believe use context is vital in augmenting the semantic information and maintaining the usability and preservability of the digital objects, as well as their ability to be accurately interpreted as initially intended.PERICLE

Recommender Systems based on Linked Data

Backgrounds: The increase in the amount of structured data published using the principles of Linked Data, means that now it is more likely to find resources in the Web of Data that describe real life concepts. However, discovering resources related to any given resource is still an open research area. This thesis studies Recommender Systems (RS) that use Linked Data as a source for generating recommendations exploiting the large amount of available resources and the relationships among them. Aims: The main objective of this study was to propose a recommendation tech- nique for resources considering semantic relationships between concepts from Linked Data. The specific objectives were: (i) Define semantic relationships derived from resources taking into account the knowledge found in Linked Data datasets. (ii) Determine semantic similarity measures based on the semantic relationships derived from resources. (iii) Propose an algorithm to dynami- cally generate automatic rankings of resources according to defined similarity measures. Methodology: It was based on the recommendations of the Project management Institute and the Integral Model for Engineering Professionals (Universidad del Cauca). The first one for managing the project, and the second one for developing the experimental prototype. Accordingly, the main phases were: (i) Conceptual base generation for identifying the main problems, objectives and the project scope. A Systematic Literature Review was conducted for this phase, which highlighted the relationships and similarity measures among resources in Linked Data, and the main issues, features, and types of RS based on Linked Data. (ii) Solution development is about designing and developing the experimental prototype for testing the algorithms studied in this thesis. Results: The main results obtained were: (i) The first Systematic Literature Re- view on RS based on Linked Data. (ii) A framework to execute and an- alyze recommendation algorithms based on Linked Data. (iii) A dynamic algorithm for resource recommendation based on on the knowledge of Linked Data relationships. (iv) A comparative study of algorithms for RS based on Linked Data. (v) Two implementations of the proposed framework. One with graph-based algorithms and other with machine learning algorithms. (vi) The application of the framework to various scenarios to demonstrate its feasibility within the context of real applications. Conclusions: (i) The proposed framework demonstrated to be useful for develop- ing and evaluating different configurations of algorithms to create novel RS based on Linked Data suitable to users’ requirements, applications, domains and contexts. (ii) The layered architecture of the proposed framework is also useful towards the reproducibility of the results for the research community. (iii) Linked data based RS are useful to present explanations of the recommen- dations, because of the graph structure of the datasets. (iv) Graph-based algo- rithms take advantage of intrinsic relationships among resources from Linked Data. Nevertheless, their execution time is still an open issue. Machine Learn- ing algorithms are also suitable, they provide functions useful to deal with large amounts of data, so they can help to improve the performance (execution time) of the RS. However most of them need a training phase that require to know a priory the application domain in order to obtain reliable results. (v) A log- ical evolution of RS based on Linked Data is the combination of graph-based with machine learning algorithms to obtain accurate results while keeping low execution times. However, research and experimentation is still needed to ex- plore more techniques from the vast amount of machine learning algorithms to determine the most suitable ones to deal with Linked Data