HELIN Data Analytics Task Force Final Report

The main task undertaken by the HELIN Data Analytics Task Force was to conduct a proof-of-concept usability test of HELIN OneSearch, which is the Consortium’s brand name for the Encore Duet discovery service. After the initial meeting in November 2014, the Task Force met 6 times in 2015 to plan and execute a prototype test. Staff members from EBSCO Information Services’ User Research group acted as usability test advisers and coordinators and attended all meetings, either onsite or via WebEx. Task Force members collaborated to come up with specific scenarios and personas which would best emphasize patron likes, dislikes and general understanding of OneSearch. Using a small sample of volunteer student test subjects from 3 different HELIN institutions, testing took place in mid-April. The results were analyzed by EBSCO and presented at the final meeting of the Task Force on April 28. Based on this limited testing, general findings were as follows: Students who don’t receive prior information instruction are generally not aware of OneSearch. Students who do know about OneSearch do not necessarily understand the difference between OneSearch and the HELIN Catalog. Most students still continue to do their research by searching database lists, LibGuides, the Journal A to Z list, and the HELIN catalog (although not necessarily in that order). When features and operation of OneSearch are explained to students, they recognize its usefulness (especially facets, which many referred to as “filters”). Lack of clarity on how to get directly to full text items causes frustration. A larger and more comprehensive usability test would be needed to draw out more specific conclusions. Secondary tasks undertaken by the Task Force included trials and reviews of 5 data analysis tools, as well as a review of EBSCO User Research, which is quantitative data on the use of OneSearch available directly from EBSCO. The remainder of this document is a detailed account of the proceedings of the HELIN Data Analytics Task Force

Using Google Analytics Data to Expand Discovery and Use of Digital Archival Content

This article presents opportunities for the use of Google Analytics, a popular and freely available web analytics tool, to inform decision making for digital archivists managing online digital archives content. Emphasis is placed on the analysis of Google Analytics data to increase the visibility and discoverability of content. The article describes the use of Google Analytics to support fruitful digital outreach programs, to guide metadata creation for enhancing access, and to measure user demand to aid selection for digitization. Valuable reports, features, and tools in Google Analytics are identified and the use of these tools to gather meaningful data is explained

Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains. Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin. Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed. Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Broadening the Scope and Increasing the Usefulness of Learning Analytics: The Case for Assessment Analytics

This paper argues that the role that assessment could play within a learning analytics strategy is both significant and, as yet, underdeveloped and underexplored. It proposes that assessment analytics has the potential to make a valuable contribution to the field of learning and academic analytics by both broadening its scope and increasing its usefulness. In doing so it considers issues of operationalization and then moves on to define what we might understand as assessment analytics

Analytics and complexity: learning and leading for the future

There is growing interest in the application of learning analytics to manage, inform and improve learning and teaching within higher education. In particular, learning analytics is seen as enabling data-driven decision making as universities are seeking to respond a range of significant challenges that are reshaping the higher education landscape. Experience over four years with a project exploring the use of learning analytics to improve learning and teaching at a particular university has, however, revealed a much more complex reality that potentially limits the value of some analytics-based strategies. This paper uses this experience with over 80,000 students across three learning management systems, combined with literature from complex adaptive systems and learning analytics to identify the source and nature of these limitations along with a suggested path forward

Learning at Scale: Using an Evidence Hub To Make Sense of What We Know

The large datasets produced by learning at scale, and the need for ways of dealing with high learner/educator ratios, mean that MOOCs and related environments are frequently used for the deployment and development of learning analytics. Despite the current proliferation of analytics, there is as yet relatively little hard evidence of their effectiveness. The Evidence Hub developed by the Learning Analytics Community Exchange (LACE) provides a way of collating and filtering the available evidence in order to support the use of analytics and to target future studies to fill the gaps in our knowledge

What Types of Predictive Analytics are Being Used in Talent Management Organizations?

[Excerpt] Talent management organizations are increasingly deriving insights from data to make better decisions. Their use of data analytics is advancing from descriptive to predictive and prescriptive analytics. Descriptive analytics is the most basic form, providing the hindsight view of what happened and laying the foundation for turning data into information. More advanced uses are predictive (advanced forecasts and the ability to model future results) and prescriptive (“the top-tier of analytics that leverage machine learning techniques … to both interpret data and recommend actions”) analytics (1). Appendix A illustrates these differences. This report summarizes our most relevant findings about how both academic researchers and HR practitioners are successfully using data analytics to inform decision-making in workforce issues, with a focus on executive assessment and selection