Abstract
The relevance of open research data is already acknowledged in many disciplines. Demanded by publishers, funders, and research institutions, the number of published research data increases every day. In learning analytics though, it seems that data are not sufficiently published and re-used. This chapter discusses some of the progress that the learning analytics community has made in shifting towards open practices, and it addresses the barriers that researchers in this discipline have to face. As an introduction, the movement and the term open science is explained. The importance of its principles is demonstrated before the main focus is put on open data. The main emphasis though lies in the question, Why are the advantages of publishing research data not capitalized on in the field of learning analytics? What are the barriers? The authors evaluate them, investigate their causes, and consider some potential ways for development in the future in the form of a toolkit and guidelines.
TopIntroduction
The movement to publish datasets has been growing for some time now. Research institutions, funders, a growing number of publishers, and even the research communities themselves, promote the publication of research data (DCC (Digital Curation Centre); Deutsche Forschungsgemeinschaft, 2019; European Commission, 2016; L. Jones, Grant, & Hrynaszkiewicz, 2019; Kim, 2019). Although the benefits of sharing data are already known (Heather A. Piwowar & Vision, 2013), Learning Analytics data has still held back. One of the reasons for this could be the large amount of personal data collected by the Learning Analytics systems. The strict data protection regulations and the anonymization procedures seem to prevent scientists from sharing their data, or at least make it more difficult (Biernacka, & Pinkwart, 2020).
The Humboldt-Elsevier Advanced Data and Text Centre (HEADT Centre)1 has set itself the goal of exploring the various facets of research integrity. The EU General Data Protection Regulation (GDPR) plays an important role for research integrity, as do the legal regulations of other countries and regions. One of the central topics of the initiative is therefore to investigate the legal regulations as an aspect of research integrity. The answer varies across disciplines and it is especially relevant when research data includes personal data. The degree of data protection, however, may interfere with transparency, which is a key value of research integrity. The goal of this research project is to investigate the conflict between publication of research data and the issues of privacy, and to identify and test solutions, considering both differences between disciplines and between cultural perspectives.
In this chapter the authors explore the handling of Learning Analytics research data with a focus on the publication process. It begins with a comprehensive introduction into the movement of Open Science, and then proceeds to the topic of Open Research Data. This foundation is necessary to understand the difficult situation in the field of Learning Analytics regarding this movement. The chapter continues with a look at the barriers of publishing research data in Learning Analytics, based on studies conducted in Germany, Peru, India and China. In the final part of the chapter, the authors intend to provide guidance to scientists in Learning Analytics. Furthermore, the authors offer possible practical solutions for the publication of research data in this discipline. The chapter ends with a conclusion.
Key Terms in this Chapter
Research Integrity: Research Integrity refers to a set of principles that lead to good scientific practice. These include: reliability, honesty, respect and accountability.
Altmetrics: An alternative way to record and document the use and impact of science.
Research Data: Data that are produced during the research process. It includes all data from the planning of the process to the outcome thereof.
Open Data: Data that can be freely accessed, modified, processed and re-used by everyone for any purpose.
Metadata: Structured data that provides basic description of other data.
Metadata Standard: Used for the standard definition of related data in terms of content and structure.
Research Data Management: Includes all activities related to the collection, storage, preservation and publication of research data.
Repository: Infrastructure and the corresponding service that enables digital resources (e.g. data, code or documents) to be permanently, efficiently and sustainably stored.