Representation of interactive objects in knowledge organization systems

Description and representation of non-linguistic characteristics of interactive information objects in digital libraries are underdeveloped for all forms of manipulation. These types of objects range from that of a tool (hammer) to that of sophisticated multimedia (video games). There is not standard subject description method on how describe object manipulation or interaction. For many information objects their primary purpose is to be manipulated by the user and this description needs to be clear and follow common guidelines so knowledge organization system users can understand the description without being an expert in the field. It is important to make sure these descriptions are accessible to a wide range of users and do not rely on images and videos alone. This project will look at multiple digital libraries connected with the Library of Congress to compare how these types of objects are being described and propose a standard for one type of object

Teaching the teachers: What's missing in LIS doctoral teacher education?

This panel presentation will discuss the results of a study that examines the status of teacher education in United States-based Library and Information Studies (LIS) doctoral degree programs. The study integrates analysis of program information, student perspectives, and institutional expectations to assess whether current approaches in developing discipline-specific educators are adequate for the immediate professional needs of doctoral students and the long-term academic viability of LIS programs. The analysis focuses on a subset of ALA-accredited LIS programs that hold membership in ALISE and/or the iSchool Organization. It assumes that the majority of the LIS degreed faculty personnel are drawn from these programs and thus are part of an overall network of doctoral teacher education and training that is ostensibly informed by shared policy frameworks. Yet, the notion of teaching doctoral students to be teachers is largely absent from professional discourse in LIS, where most discussions of education focus solely on training librarians, archivists, and other information professionals in information literacy instruction. In other words, there is not now, nor does there appear to have ever been, a clear consensus approach to training the people who ultimately become responsible for teaching LIS. Recent research and reporting demonstrate that across academe, PhD programs generally do not provide sufficient teacher training for doctoral students, mostly because academic faculty and department agendas are focused on research that attracts outside funding, facilitates industry partnerships, and adds notoriety and prestige for institutions in an increasingly competitive education marketplace. A 2018 study by the American Association of University Professors (AAUP) found that the scant training offered by the ‘professional apprenticeship’ system, defined mostly by teaching assistantships, may actually stunt doctoral students’ progress toward degree completion. The report indicates that “while teaching a few courses can be a valuable learning experience, many teaching assistants instead operate as a source of cheap labor for the academy,” producing a harmful “casualization” of academic labor that undermines traditional faculty roles and the tenure system. Further analysis by the AAUP shows that the proportion of teaching-intensive positions to research-intensive positions has risen sharply in recent years, representing a “seismic shift” with consequences for faculty and students due to the “lower levels of campus engagement across the board and a rising service burden for the shrinking core of tenurable faculty.” Discipline-specific studies of doctoral student teacher training in a variety of academic fields reveal an ambivalence among students toward their teaching responsibilities and opportunities, which often reflects a lack of confidence in and anxiety around their ability to teach effectively and leads to feelings of unpreparedness in assuming faculty positions. This is especially problematic for doctoral students in programs that promote the ideal of success as obtaining tenure-track positions in highly-ranked and research intensive academic institutions, while not adequately preparing doctoral students for alternative career paths. The trend of shrinking university budgets and diminishing opportunities for new PhDs to take on research-focused work has been accompanied by new expectations for education delivery by students, administrators, accreditors, employers, and other stakeholders, both of which contribute to the high attrition rate of doctoral students. Research shows that when combined with the firsthand experience gained through the apprentice systems, formal teacher training makes a positive difference in how new and aspiring faculty carry out their roles, manage their workloads, and build sustainable careers. Adequate teacher training also creates a ripple effect that benefits student learning outcomes and skills acquisition, which is especially important to LIS and other discipline areas built around a distinct but evolving set of practical professional pursuits. Very little scholarly research along these lines has been conducted in the LIS field and even a surface level scan of the status of doctoral student teacher training within LIS programs demonstrates that efforts are inconsistent, nonstandardized, and seemingly inadequate. This study attempts to dig deeper and address how teacher education and training is integrated into curricular offerings and requirements in American LIS doctoral programs. It incorporates perceptions from doctoral students about the teacher education and training they have received and it evaluates the education or training requirements included on faculty job position descriptions in these programs to see how they align with students’ experience and their own program expectations. The authors suggest that instruction needs to include and go beyond learning courseware, instructional design, educational theory, and ad hoc modelling of doctoral seminars to enable doctoral students to develop diverse but discipline-specific instructional approaches to LIS. In accord with the conference theme, this panel presentation is not limited to assessment and critique for its own sake, but rather seeks to propose possible solutions and recommendations for how teacher education and training might become more effective and more of a priority for LIS doctoral programs as they seek a more resilient future. The panel is composed of current doctoral students who will present on the various aspects of the research and discuss the findings in relation to their own experience with doctoral teacher training and education. Furthermore, the panelists intend to structure their delivery in a way that promotes interaction with faculty, students, administrators, and others in the audience and provides the basis for continuing conversations and research beyond the conference

Classifying the lod cloud: digging into the knowledge graph

Massive amounts of data from different contexts and producers are collected and connected relying often solely on statistical techniques. Problems to the acclaimed value of data lie in the precise definition of data and associated contexts as well as the problem that data are not always published in meaningful and open ways. The Linked Data paradigm offers a solution to the limitations of simple keywords by having unique, resolvable and shared identifiers instead of strings This paper reports on a three-year research project "Digging Into the Knowledge Graph," funded as part of the 2016 Round Four Digging Into Data Challenge (https://diggingintodata.org/awards/2016/project/digging-knowledge-graph). Our project involves comparing terminology employed within the LOD cloud with terminology employed within two general but different KOSs – Universal Decimal Classification and Basic Concepts Classification. We are exploring whether these classifications can encourage greater consistency in LOD terminology and linking the largely distinct scholarly literatures that address LOD and KOSs. Our project is an attempt to connect the Linked Open Data community, which has tended to be centered in computer science, and the KO community, with members from linguistics, metaphysics, library and information science. We focus on the shared challenges related to Big Data between both communities

Classifying the LOD Cloud: Digging into the Knowledge Graph

Massive amounts of data from different contexts and produc-ers are collected and connected relying often solely on statis-tical techniques. Problems to the acclaimed value of data lie in the precise definition of data and associated contexts as well as the problem that data are not always published in meaningful and open ways. The Linked Data paradigm offers a solution to the limitations of simple keywords by having unique, resolvable and shared identifiers instead of strings This paper reports on a three-year research project “Digging Into the Knowledge Graph,” funded as part of the 2016 Round Four Digging Into Data Challenge (https://diggingintodata.org/awards/2016/project/digging-knowledge-graph). Our project involves comparing terminol-ogy employed within the LOD cloud with terminology em-ployed within two general but different KOSs – Universal Decimal Classification and Basic Concepts Classification. We are exploring whether these classifications can encourage greater consistency in LOD terminology and linking the largely distinct scholarly literatures that address LOD and KOSs. Our project is an attempt to connect the Linked Open Data community, which has tended to be centered in comput-er science, and the KO community, with members from lin-guistics, metaphysics, library and information science. We focus on the shared challenges related to Big Data between both communiti