Preparing Students for the Era of the General Data Protection Regulation (GDPR)

Abstract – One of the main goals of the General Data Protection Regulation (GDPR) is to protect the personal data of individuals. Each organization (company, association, school, institution, university, etc.) has an obligation to protect all of the individual data that it obtains. Those data can belong to employees, members, students, clients, etc. The research in this paper is related to the higher education students in Croatia. This study is being conducted in three parts. The first part was conducted in April of 2017 (N=159) and the second in April/May of 2018 (N=141), in a period before the GDPR became valid (May 25th, 2018). In this paper, we are analysing the results of the second part of the study. Additionally, we are discussing risks that might appear if students do not know the GDPR. Risk matrix results are used to represent a basis which higher education administrations can utilize to make corrective decisions. The main conclusion of the research is that there are still issues with understanding the basic concepts of personal data and the GDPR, which may cause some problems during studying process. The main recommendation for HEIs or students organizations (such as student councils) is to organize lectures and workshops related to the GDPR

Data Management in Learning Analytics: Terms and Perspectives

On-line teaching environments (like all online environments) acquire extremely high granularity data both on users' personal profiles and on their behavior and results. The modern Analytics environments allow, at various levels and profiles, to have access to data both in aggregate and in individual form. One of the characteristic elements of online teaching environment is that the data is not anonymous but reproduces a personalization and identification of the profiles. Identifiability of the subject is implicit in a teaching process, but access to Analytics techniques reveals a fundamental question: "What is the limit?". The answer to this question should be preliminary to any use of data by users (students) or teachers or instructors or managers of online learning environments. Nowadays, we’re also experiencing a particular moment of change: the entry into force of the European General Data Protection Regulation 679/2016, the general regulation on the protection of personal data which aims to standardize all national legislation and adapt it to the new needs dictated by the evolving technological context. The objective of this work is to propose a list of the problems connected to data management in the context of Digital Education. To this end, an examination of the current legislation (both Italian and European) was conducted with particular reference to the contrast between the need for access (openness) and privacy (protection of users) in online teaching processes. Three points of view were evaluated: the institution that provides, the teacher who produces and the student who uses. The contribution aims to provide an in-depth analysis on the issue of data protection and management that can help the figures involved in the online educational process to understand the evolution of legal instruments regarding the production, management of OER so as to "use" them correctly in a current two-speed context: that of technology and that of legislation

Effective usage of Learning Analytics: What do practitioners want and where should distance learning institutions be going?

The implementation of learning analytics may empower distance learning institutions to provide real-time feedback to students and teachers. Given the leading role of the Open University UK (OU) in research and application of learning analytics, this study aims to share the lessons learned from the experiences of 42 participants from a range of faculty, academic and professional positions, and expertise with learning analytics. Furthermore, we explored where distance learning institutions should be going next in terms of learning analytics adoption. The findings from the Learning Analytics User Stories (LAUS) workshop indicated that four key areas where more work is needed: communication, personalisation, integrated design, and development of an evidence-base. The workshop outputs signalled the aspiration for an integrated analytics system transcending the entire student experience, from initial student inquiry right through to qualification completion and into life-long learning. We hope that our study will spark discussion on whether (or not) distance learning institutions should pursue the dream of an integrated, personalised, and evidence-based learning analytics system that clearly communicates useful feedback to staff and students, or whether this will become an Orwellian nightmare

Small data as a conversation starter for learning analytics: Exam results dashboard for first-year students in higher education

Purpose - The purpose of this paper is to draw attention to the potential of “small data” to complement research in learning analytics (LA) and to share some of the insights learned from this approach. Design/methodology/approach - This study demonstrates an approach inspired by design science research, making a dashboard available to n=1,905 students in 11 study programs (used by n=887) to learn how it is being used and to gather student feedback. Findings - Students react positively to the LA dashboard, but usage and feedback differ depending on study success. Research limitations/implications - More research is needed to explore the expectations of a high-performing student with regards to LA dashboards. Originality/value - This publication demonstrates how a small data approach to LA contributes to building a better understanding

Moving Forward with Learning Analytics: Expert Views

Learning analytics involve the measurement, collection, analysis, and reporting of data about learners and their contexts, in order to understand and optimise learning and the environments in which it occurs. Since emerging as a distinct field in 2011, learning analytics has grown rapidly, and early adopters around the world are already developing and deploying these new tools. This paper reports on a study that investigated how the field is likely to develop by 2025, in order to make recommendations for action to those concerned with the implementation of learning analytics. The study used a Policy Delphi approach, presenting a range of future scenarios to international experts in the field and asking for responses related to the desirability and feasibility of these scenarios, as well as actions that would be required. Responses were received from 103 people from 21 countries. Responses were coded thematically, inter-rater reliability was checked using Cohen’s kappa coefficient, and data were recoded if kappa was below 0.6. The seven major themes that were identified within the data were power, pedagogy, validity, regulation, complexity, ethics, and affect. The paper considers in detail each of these themes and its implications for the implementation of learning analytics

Understanding privacy and data protection issues in learning analytics using a systematic review

The field of learning analytics has advanced from infancy stages into a more practical domain, where tangible solutions are being implemented. Nevertheless, the field has encountered numerous privacy and data protection issues that have garnered significant and growing attention. In this systematic review, four databases were searched concerning privacy and data protection issues of learning analytics. A final corpus of 47 papers published in top educational technology journals was selected after running an eligibility check. An analysis of the final corpus was carried out to answer the following three research questions: (1) What are the privacy and data protection issues in learning analytics? (2) What are the similarities and differences between the views of stakeholders from different backgrounds on privacy and data protection issues in learning analytics? (3) How have previous approaches attempted to address privacy and data protection issues? The results of the systematic review show that there are eight distinct, intertwined privacy and data protection issues that cut across the learning analytics cycle. There are both cross-regional similarities and three sets of differences in stakeholder perceptions towards privacy and data protection in learning analytics. With regard to previous attempts to approach privacy and data protection issues in learning analytics, there is a notable dearth of applied evidence, which impedes the assessment of their effectiveness. The findings of our paper suggest that privacy and data protection issues should not be relaxed at any point in the implementation of learning analytics, as these issues persist throughout the learning analytics development cycle. One key implication of this review suggests that solutions to privacy and data protection issues in learning analytics should be more evidence-based, thereby increasing the trustworthiness of learning analytics and its usefulness.publishedVersio

The influence of data protection and privacy frameworks on the design of learning analytics systems

Learning analytics open up a complex landscape of privacy and policy issues, which will influence how learning analytics systems and practices are designed. Research and development is governed by regulations for data storage and management, and by research ethics. Consequently, when moving solutions out the research labs implementers meet constraints defined in national laws and justified in privacy frameworks. This paper explores how the OECD, APEC and EU privacy frameworks seek to regulate data privacy, with significant implications for the discourse of learning, and ultimately, an impact on the design of tools, architectures and practices that now are on the drawing board. A detailed list of requirements for learning analytics systems is developed, based on the new legal requirements defined in the European General Data Protection Regulation, which from 2018 will be enforced as European law. The paper also gives an initial account of how the privacy discourse in Europe, Japan, South-Korea and China is developing and reflects upon the possible impact of the different privacy frameworks on the design of LA privacy solutions in these countries. This research contributes to knowledge of how concerns about privacy and data protection related to educational data can drive a discourse on new approaches to privacy engineering based on the principles of Privacy by Design. For the LAK community, this study represents the first attempt to conceptualise the issues of privacy and learning analytics in a cross-cultural context. The paper concludes with a plan to follow up this research on privacy policies and learning analytics systems development with a new international study

An Investigation of Privacy and Utility Tensions in Learning Analytics

Through learning analytics (LA), higher education institutions have put student data to various uses which aim to be beneficial for students, lecturers, and the institutions. Despite the potential benefits of LA, however, there are research gaps in understanding inherent privacy and utility tensions. Using four research studies, this thesis is an investigation of factors that contribute to these tensions. An examination, using Delphi study techniques, of how LA experts (n=12) conceptualised privacy in LA and what they thought of as key privacy issues demonstrated a collective agreement on institutional responsibility, including to empower students to manage their privacy. As such, the findings exposed gaps between existing institutional applications of LA and the views of the experts. A laboratory study (n=111) with follow-up semi-structured interviews (n=4) identified that students are not concerned about the use of their data for LA. However, knowing that student data could be shared with third parties evoked feelings of discomfort. The qualitative data suggested that students’ privacy concepts differed from those of the LA experts, highlighting a need to operationalise LA with a shared understanding of what privacy means for stakeholders. Using an experimental design (n=447), privacy concern was further examined through the lens of students’ data use preferences. The findings suggested that participants’ data use preferences were not influenced by an awareness of the possible privacy risks and benefits of data use for LA. Consequently, other factors might influence students’ data use preferences. The qualitative data shed light on a “dual nature” to participants’ data use preferences, suggesting both support for and reservation about the use of student data for LA, the latter due to ethics and privacy concerns. Further examination using follow-up interviews (n=15) suggested a need to align institutional data use practices with students’ expectations. Taken together, the research findings suggest that privacy in LA combines several concepts, expressed in different ways across stakeholder groups. To better understand students’ perspectives of privacy in LA requires unpacking the dimensions of privacy that contribute to students’ privacy concern, or lack thereof. Most importantly, while some uses of student data for LA do not concern students, other data uses might not meet their expectations. Taking steps to address these tensions will contribute towards ethical LA