The document summarizes Hendrik Drachsler's presentation at an NSF expert meeting on big data and privacy in human subjects research. Some key points from Drachsler's presentation include:
- He discussed issues around learning analytics research and how privacy concerns often stop innovation;
- He questioned if big data should be considered the "new truth" and highlighted examples where big data provided inaccurate insights;
- Drachsler advocated for transparency, data security, informed consent and data anonymization to prevent issues like what happened with the inBloom student database project in the US.
Research Data Management in the Humanities and Social SciencesCelia Emmelhainz
This two-part presentation for librarians reviews basic concepts and concerns with research data management, and is targeted to those working with humanists and social scientists. You are free to re-use and modify with attribution.
Slides for presentation given at the first Digital Humanities Congress held in Sheffield from 6 – 8 September 2012 with the support of the Network of Expert Centres and Centernet.
URL http://www.shef.ac.uk/hri/dhc2012
Keynote talk to LEARN (LERU/H2020 project) for research data management. Emphasizes that problems are cultural not technical. Promotes modern approaches such as Git / continuousIntegration, announces DAT. Asserts that the Right to Read in the Right to Mine. Calls for widespread development of contentmining (TDM)
Jan 14 NISO Webinar
Net Neutrality: Will Library Resources be stuck in the Slow Lane?
About the Webinar
Net Neutrality is an issue that has been increasingly in the news, but it is something that has affected libraries for a lot longer. Many public libraries are in underserved communities where patrons may not have personal access to the internet, so the use of the public libraries' resources is critical for them. Without net neutrality, those public libraries may not be able to cost-effectively provide such Internet service. For the scholarly and academic communities, scholarly resources could be resigned to the slow lane of the net, if content providers and libraries don't have the resources to pay for the "fast lane." As resources increasingly go multimedia, requiring greater bandwidth, will libraries and content platform providers be saddled with taking on added costs to ensure reliable access?
Net neutrality begins with the basic idea that the Internet is a fair and democratic platform for all. Organizations such as the American Library Association, the Association of Research Libraries, EDUCAUSE, and Internet2, among others, have spoken out about the critical need for retaining net neutrality in the library, higher education, and research communities.
In this webinar, presenters will help define Net Neutrality, what could happen without it, and how it can impact public and academic libraries, and the wider information community.
Agenda
Introduction
Todd Carpenter, Executive Director, NISO
Network Neutrality Principles and Policy for Libraries & Higher Education
Larra Clark, Deputy Director, Office for Information Technology Policy, American Library Association
Network neutrality: The Public Library Perspective
Holly Carroll, Executive Director, Poudre River Public Library District
Academic Libraries and Net Neutrality
Jonathan Miller, Library Director, Olin Library of Rollins College
Science as an Open Enterprise – Geoffrey BoultonOpenAIRE
Science as an Open Enterprise – Geoffrey Boulton, University of Edinburgh.
University of Minho Open Access Seminar & OpenAIRE Interoperability Workshop (7 Feb. 2013) - Session: Open Science, Open Data and Repositorie.
Research Data Management in the Humanities and Social SciencesCelia Emmelhainz
This two-part presentation for librarians reviews basic concepts and concerns with research data management, and is targeted to those working with humanists and social scientists. You are free to re-use and modify with attribution.
Slides for presentation given at the first Digital Humanities Congress held in Sheffield from 6 – 8 September 2012 with the support of the Network of Expert Centres and Centernet.
URL http://www.shef.ac.uk/hri/dhc2012
Keynote talk to LEARN (LERU/H2020 project) for research data management. Emphasizes that problems are cultural not technical. Promotes modern approaches such as Git / continuousIntegration, announces DAT. Asserts that the Right to Read in the Right to Mine. Calls for widespread development of contentmining (TDM)
Jan 14 NISO Webinar
Net Neutrality: Will Library Resources be stuck in the Slow Lane?
About the Webinar
Net Neutrality is an issue that has been increasingly in the news, but it is something that has affected libraries for a lot longer. Many public libraries are in underserved communities where patrons may not have personal access to the internet, so the use of the public libraries' resources is critical for them. Without net neutrality, those public libraries may not be able to cost-effectively provide such Internet service. For the scholarly and academic communities, scholarly resources could be resigned to the slow lane of the net, if content providers and libraries don't have the resources to pay for the "fast lane." As resources increasingly go multimedia, requiring greater bandwidth, will libraries and content platform providers be saddled with taking on added costs to ensure reliable access?
Net neutrality begins with the basic idea that the Internet is a fair and democratic platform for all. Organizations such as the American Library Association, the Association of Research Libraries, EDUCAUSE, and Internet2, among others, have spoken out about the critical need for retaining net neutrality in the library, higher education, and research communities.
In this webinar, presenters will help define Net Neutrality, what could happen without it, and how it can impact public and academic libraries, and the wider information community.
Agenda
Introduction
Todd Carpenter, Executive Director, NISO
Network Neutrality Principles and Policy for Libraries & Higher Education
Larra Clark, Deputy Director, Office for Information Technology Policy, American Library Association
Network neutrality: The Public Library Perspective
Holly Carroll, Executive Director, Poudre River Public Library District
Academic Libraries and Net Neutrality
Jonathan Miller, Library Director, Olin Library of Rollins College
Science as an Open Enterprise – Geoffrey BoultonOpenAIRE
Science as an Open Enterprise – Geoffrey Boulton, University of Edinburgh.
University of Minho Open Access Seminar & OpenAIRE Interoperability Workshop (7 Feb. 2013) - Session: Open Science, Open Data and Repositorie.
How can we ensure research data is re-usable? The role of Publishers in Resea...LEARN Project
How can we ensure research data is re-usable? The role of Publishers in Research Data Management, by Catriona MacCallum. 2nd LEARN Workshop, Vienna, 6th April 2016
Stereotype and most popular recommendations in the digital library SowiportJoeran Beel
Stereotype and most-popular recommendations are widely neglected in the research-paper recommender-system and digital-library community. In other domains such as movie recommendations and hotel search, however, these recommendation approaches have proven their effectiveness. We were interested to find out how stereotype and most-popular recommendations would perform in the scenario of a digital library. Therefore, we implemented the two approaches in the recommender system of GESIS’ digital library Sowiport, in cooperation with the recommendations-as-a-service provider Mr. DLib. We measured the effectiveness of most-popular and stereotype recommendations with click-through rate (CTR) based on 28 million delivered recommendations. Most-popular recommendations achieved a CTR of 0.11%, and stereotype recommendations achieved a CTR of 0.124%. Compared to a “random recommendations” baseline (CTR 0.12%), and a content-based filtering baseline (CTR 0.145%), the results are discouraging. However, for reasons explained in the paper, we concluded that more research is necessary about the effectiveness of stereotype and most-popular recommendations in digital libraries.
B2: Open Up: Open Data in the Public SectorMarieke Guy
Parallel session [B2: Open Up: Open Data in the Public Sector] run at the Institutional Web Management Workshop 2013 (IWMW 2013) event, University of Bath on 26 - 28th June 2013.
Open Data in a Big Data World: easy to say, but hard to do?LEARN Project
Presentation at 3rd LEARN workshop on Research Data Management, “Make research data management policies work”
Helsinki, 28 June 2016, by Sarah Callaghan, STFC Rutherford Appleton Laboratory
The HathiTrust Research Center: Enabling New Knowledge Through Shared Infrastructure
Robert McDonald - HathiTrust Research Center Executive committee member; Associate Dean for Library Technologies, Indiana University
Data management: The new frontier for librariesLEARN Project
Presentation at 3rd LEARN workshop on Research Data Management, “Make research data management policies work”, by Kathleen Shearer, COAR, CARL/ABCR, RDC/DCR, ARL, SSHRC/CSRH.
How can we ensure research data is re-usable? The role of Publishers in Resea...LEARN Project
How can we ensure research data is re-usable? The role of Publishers in Research Data Management, by Catriona MacCallum. 2nd LEARN Workshop, Vienna, 6th April 2016
Stereotype and most popular recommendations in the digital library SowiportJoeran Beel
Stereotype and most-popular recommendations are widely neglected in the research-paper recommender-system and digital-library community. In other domains such as movie recommendations and hotel search, however, these recommendation approaches have proven their effectiveness. We were interested to find out how stereotype and most-popular recommendations would perform in the scenario of a digital library. Therefore, we implemented the two approaches in the recommender system of GESIS’ digital library Sowiport, in cooperation with the recommendations-as-a-service provider Mr. DLib. We measured the effectiveness of most-popular and stereotype recommendations with click-through rate (CTR) based on 28 million delivered recommendations. Most-popular recommendations achieved a CTR of 0.11%, and stereotype recommendations achieved a CTR of 0.124%. Compared to a “random recommendations” baseline (CTR 0.12%), and a content-based filtering baseline (CTR 0.145%), the results are discouraging. However, for reasons explained in the paper, we concluded that more research is necessary about the effectiveness of stereotype and most-popular recommendations in digital libraries.
B2: Open Up: Open Data in the Public SectorMarieke Guy
Parallel session [B2: Open Up: Open Data in the Public Sector] run at the Institutional Web Management Workshop 2013 (IWMW 2013) event, University of Bath on 26 - 28th June 2013.
Open Data in a Big Data World: easy to say, but hard to do?LEARN Project
Presentation at 3rd LEARN workshop on Research Data Management, “Make research data management policies work”
Helsinki, 28 June 2016, by Sarah Callaghan, STFC Rutherford Appleton Laboratory
The HathiTrust Research Center: Enabling New Knowledge Through Shared Infrastructure
Robert McDonald - HathiTrust Research Center Executive committee member; Associate Dean for Library Technologies, Indiana University
Data management: The new frontier for librariesLEARN Project
Presentation at 3rd LEARN workshop on Research Data Management, “Make research data management policies work”, by Kathleen Shearer, COAR, CARL/ABCR, RDC/DCR, ARL, SSHRC/CSRH.
Presentation delivered at Newport Business School 22 April 2009 as part of their 'Creative Thinking' lunchtime seminar series. Used as a pilot / first draft for some ideas I am developing for longer term work.
This presentation highlighted an artist showcase for an industry trade show. This clean, attractive design starts bold, matching the design for the event collateral, then transitions to open and airy, all the while staying consistent with the over-all branding.
Managing Plone Projects with Perl and SubversionLuciano Rocha
How an in-house solution developed in Perl helped our Plone developers to streamline their work.
From the use of Subversion (and Trac) to keep track of development, sharing code, and bundling packages, to the creation of a program for managing dependencies, building the system, creating release RPMs and tracking deployments.
A test case by Eurotux Informática.
My slides for the IWMW 2010 conference, Sheffield, July 13th 2010. I discuss the use of WordPress in the context of my work at the University of Lincoln.
Is the event market in your area over-saturated with planners, designers, caterers, musicians, florists and more? Then this is the opportunity you have been looking for! Hint: it's in the small things!
Diploma de Innovación Educativa con Tecnologías EmergentesIsmael Burone
Si estás interesado en cómo las tecnologías emergentes pueden ayudar a cambiar la educación este será tu espacio. Una oportunidad de reflexionar con colegas de diversos sectores, con diferentes inquietudes.
Ethics, Openness and the Future of LearningRobert Farrow
What difference does openness make to ethics' This session will examine this question both from the perspective of research into OER and the use of open resources in teaching and learning. An outline of the nature and importance of ethics will be provided before the basic principles of research ethics are outlined through an examination of the guidance provided by National Institutes of Health (2014) and BERA (2014). The importance and foundation of institutional approval for OER research activities is reiterated with a focus on underlying principles that can also be applied openly.
I argue that with a shift to informal (or extra-institutional) learning there is a risk that we lose some clarity over the nature and extent of our moral obligations when working outside institutional frameworks – what Weller (2013) has termed "guerilla" research activity. Innovations of this kind could be free of licensing permissions; they could be funded by kickstarter or public-private enterprise; or they could reflect individuals working as data journalists. But we might also speak of "guerilla" education for innovations taking place on the fringes of institutional activity – from using social media to going full-blown "edupunk" (Groom, 2008). These innovations which employ variants of opennesss can also bring out morally complex situations.
I show how the principles underlying traditional research ethics can be applied openly while noting that, whether working within or outside institutions, there is almost no existing guidance that explains the ethical implications of working openly. Similar issues are raised with MOOC, which operate outside institutions but while drawing on institutional reputations and values. With this in mind I sketch out scenarios we are likely to encounter in the future of education:
- Issues around privacy, security and big data
- Intellectual property conflicts
- Ensuring fair treatment of class students and equivalent online students
- Meeting obligations to content creators
- The ethical status of MOOCs and their obligations to their students
- Moral dimensions of open licenses
- The ethics of learning analytics and the data it produces
I argue that, while models for ethical analysis have been proposed (e.g. Farrow, 2011) more attention should be paid to the ethics of being open. I conclude with an examination of the idea that we have a moral obligation to be open, contrasting prudential and ethical approaches to open education. At the heart of the OER movement, I argue, is a strong moral impulse that should be recognized and celebrated rather than considered the preserve of the ideologue: openness is not reducible to lowering the marginal cost of educational resources. Openness is a diverse spectrum and to leverage its true potential we need to reflect deeply on how technology has the power to challenge the normative assumptions we make about education.
What difference does openness make to ethics? This session will examine this question both from the perspective of research into OER and the use of open resources in teaching and learning. An outline of the nature and importance of ethics will be provided before the basic principles of research ethics are outlined through an examination of the guidance provided by National Institutes of Health (2014) and BERA (2014). The importance and foundation of institutional approval for OER research activities is reiterated with a focus on underlying principles that can also be applied openly.
I argue that with a shift to informal (or extra-institutional) learning there is a risk that we lose some clarity over the nature and extent of our moral obligations when working outside institutional frameworks – what Weller (2013) has termed "guerilla" research activity. Innovations of this kind could be free of licensing permissions; they could be funded by kickstarter or public-private enterprise; or they could reflect individuals working as data journalists. But we might also speak of "guerilla" education for innovations taking place on the fringes of institutional activity – from using social media to going full-blown "edupunk" (Groom, 2008). These innovations which employ variants of opennesss can also bring out morally complex situations.
I show how the principles underlying traditional research ethics can be applied openly while noting that, whether working within or outside institutions, there is almost no existing guidance that explains the ethical implications of working openly. Similar issues are raised with MOOC, which operate outside institutions but while drawing on institutional reputations and values. With this in mind I sketch out scenarios we are likely to encounter in the future of education:
- Issues around privacy, security and big data
- Intellectual property conflicts
- Ensuring fair treatment of class students and equivalent online students
- Meeting obligations to content creators
- The ethical status of MOOCs and their obligations to their students
- Moral dimensions of open licenses
- The ethics of learning analytics and the data it produces
I argue that, while models for ethical analysis have been proposed (e.g. Farrow, 2011) more attention should be paid to the ethics of being open. I conclude with an examination of the idea that we have a moral obligation to be open, contrasting prudential and ethical approaches to open education. At the heart of the OER movement, I argue, is a strong moral impulse that should be recognized and celebrated rather than considered the preserve of the ideologue: openness is not reducible to lowering the marginal cost of educational resources. Openness is a diverse spectrum and to leverage its true potential we need to reflect deeply on how technology has the power to challenge the normative assumptions we make about education.
Dorothea Kleine discussed the importance of understanding the contexts in which children use technologies. Drawing on her recent report, co-authored with David Hollow and Sammia Poveda, Children, ICT and development (2014), Kleine first questioned normative assumptions in the global North, in terms of their often-assumed relevance to the global South, and then offered recommendations for a global research framework. She particularly cautioned against the normative assumptions evident in many established, large-scale surveys (e.g., construction around childhood/adulthood, gender roles, heteronormativity and the nuclear family). She additionally observed that ‘reported behaviour is not the same as behaviour’, and what surveys are bound to record is simply recorded behaviour. She recommended triangulating research methods.
Kleine urged participants to shift from thinking of children as objects of inquiry to co-creators of meaning, and therefore to develop participatory models that involve children and young people at each stage. She also emphasised the importance of involving locals in the research process to get a better sense of local context, a higher sense of ownership and improved chance of project viability and sustainability after the instigators have left. She outlined a research framework, the ‘choice framework’ that considers structural factors (e.g., norms on the use of space or use of time) as well as issues of agency and individual resources, including social resources, psychological resources, cultural resources, information and time. Kleine’s discussion of research methods consistently tied advocacy and intervention goals to the framing and implementation of the research, prioritising children’s voices, envisioning solutions, addressing policy needs throughout the process, treating research as part of a meaningful participatory approach and not as an end in itself. Further, she advocated close links between survey research, participatory action research and policy research and advisory work.
A talk at the Urban Science workshop at the Puget Sound Regional Council July 20 2014 organized by the Northwest Institute for Advanced Computing, a joint effort between Pacific Northwest National Labs and the University of Washington.
Biodiversity—A Healthy Ecosystem Thrives on Fresh Ideas (Part 1 of 3), Phil J...Allen Press
Video of this presentation is available at https://www.youtube.com/watch?v=h38PvZMMJP0&list=PLybpVL27qHff3BVHuNXqYsqTs2e98_MpT&index=8
To maintain the long-term sustainability of the ecosystem, we need a steady flow of innovation and risk and a strong current of entrepreneurial spirit. Wherever ideas are generated—by a small, rebellious start-up or by a long-established player at the top of the food chain—they provide the catalyst and movement that keep things alive and well. We’ll conclude the day by looking at the transformational promise of open, linked, and shared data, the alignment of repository networks, data and metadata exchange, and a wrap-up of the current trends in scholarly publishing from the perspective of the university press.
"Open Science, Open Data" training for participants of Software Writing Skills for Your Research - Workshop for Proficient, Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences, Telegrafenberg, December 16, 2015
Research data management: a tale of two paradigms: Martin Donnelly
Presentation I was supposed to give at "Scotland’s Collections and the Digital Humanities" workshop in Edinburgh on May 2nd 2014. Illness prevented it, but my heroic DCC colleague Jonathan Rans stepped up and delivered the presentation on my behalf.
Research Data Management: A Tale of Two Paradigmstarastar
Presentation by Martin Donnelly, Digital Curation Centre, University of Edinburgh. Invited talk at a workshop for 'Scotland's National Collections and the Digital Humanities,' a knowledge-exchange project hosted at the University of Edinburgh. 2 May 2014. http://www.blogs.hss.ed.ac.uk/archives-now/
The State of Open Data Report by @figshare.
A selection of analyses and articles about open data, curated by Figshare
Foreword by Professor Sir Nigel Shadbolt
OCTOBER 2016
In this webinar, Prof Hendrik Drachsler will reflect on the process of applying learning analytics solutions within higher education settings, its implications, and the critical lessons learned in the Trusted Learning Research Program. The talk will focus on the experience of edutec.science research collective consisting of researchers from the Netherlands and Germany that contribute to the Trusted Learning Analytics (TLA) research program. The TLA program aims to provide actionable and supportive feedback to students and stands in the tradition of human-centered learning analytics concepts. Thus, the TLA program aims to contribute to unfolding the full potential of each learner. It, therefore, applies sensor technology to support psychomotor as well as web technology to support meta-cognitive and collaborative learning skills with high-informative feedback methods. Prof. Drachsler applies validated measurement instruments from the field of psychometric and investigates to what extent Learning Analytics interventions can reproduce the findings of these instruments. During this webinar, Prof Drachsler will discuss the lessons learned from implementing TLA systems. He will touch on TLA prerequisites like ethics, privacy, and data protection, as well as high informative feedback for psychomotor, collaborative, and meta-cognitive competencies and the ongoing research towards a repository, methods, tools and skills that facilitate the uptake of TLA in Germany and the Netherlands.
Smart Speaker as Studying Assistant by Joao ParganaHendrik Drachsler
The thesis by Joao Pargana followed two main goals, first, a smart speaker application was created to support learners in informal learning processes through a question/answer application. Second, the impact of the application was tested amongst various users by analyzing how adoption and
transition to newer learning procedures can occur.
Dieser Entwurf eines Verhaltenskodex richtet sich an Hochschulen, die mittels Learning Analytics die Qualität des Lernens und Lehrens verbessern wollen. Der Kodex kann als Vorlage zur Erstellung von organisationsspezifischen Verhaltenskodizes dienen. Er sollte an Hochschulen, die Learning Analytics einführen wollen, durch Konsultationen mit allen Interessengruppen überprüft und an die Ziele sowie die bestehende Praxis innerhalb der jeweiligen Hochschulen angepasst werden. Der Kodex wurde auf Grundlage einer Analyse bestehender europäischer Kodizes und der in Deutschland geltenden Rechtsgrundlage vom Innovationsforum Trusted Learning Analytics des hessenweiten Projektes "Digital gestütztes Lehren und Lernen in Hessen" entwickelt.
Abstract (English):
This code of conduct can be used as a template for creating organization-specific codes of conduct in Germany. The Code was developed on the basis of an analysis of existing European codes of conduct and the legal basis for the usage of data in higher education in Germany.
Rödling, S. (2019). Entwicklung einer Applikation zum assoziativen Medien Ler...Hendrik Drachsler
Ziel der vorliegenden Bachelorarbeit ist es, den Einfluss von zusätzlicher am Handgelenk wahr-genommener Vibration in Verbindung mit der visuellen Darstellung eines Lerninhaltes auf denLernerfolg zu messen. Der Lernerfolg wird hierbei durch die Lerngeschwindigkeit sowie denUmfang der Wissenskonsolidierung über die Testreihe definiert. Zu diesem Zweck wurde eine Experimentalstudie zumAssoziativen Lernendurchgeführt. Für die Studie verwendeten 33Probanden eine App, die für die vorliegende Arbeit entwickelt wurde. Im Mittel aller Studiener-gebnisse wurden sowohl für die Lerngeschwindigkeit als auch für die Wissenskonsolidierungbessere Werte erzielt, wenn die Probanden die Möglichkeit hatten, den Lerninhalt sowohl visu-ell als auch haptisch zu erfahren. Die festgestellten Unterschiede des Lernerfolges erreichtenjedoch keine statistische Signifikanz. Die Abweichungen der Ergebnisse nach der Umsetzungder vorgeschlagenen Änderungen am Studiendesign sind abzuwarten. Die Bachelorarbeit ist vor allem für den Bildungsbereich interessant.
The present bachelor thesis aims to measure the influence of vibration perceived at the wrist in connection with the visual representation of learning content on the learning success. The learning success is defined by the learning speed and the extent of knowledge consolidation over the test series. For this purpose, an experimental study on Associative Learning was conducted. For the study, 33 test persons used an app, which was developed for the present work. On average of all study results better values were achieved for both learning speed and knowledge consolidation, if the test persons could experience the learning content both visually and haptically. However, the differences in learning outcomes did not reach statistical significance. The results of the deviations after the implementation of the proposed changes to the study design must be awaited. The Bachelor’s thesis is particularly interesting for the education sector.
E.Leute: Learning the impact of Learning Analytics with an authentic datasetHendrik Drachsler
Nowadays, data sets of the interactions of users and their corresponding demographic data are becoming more and more valuable for companies and academic institutions like universities
when optimizing their key performance indicators. Whether it is to develop a model to predict the optimal learning path for a student or to sell customers additional products, data sets to
train these models are in high demand. Despite the importance and need for big data sets it still has not become apparent to every decision-maker how crucial data sets like these are for the
future success of their operations.
The objective of this thesis is to demonstrate the use of a data set, gathered from the virtual learning environment of a distance learning university, by answering a selection of questions in
Learning Analytics. Therefore, a real-world data set was analyzed and the selected questions were answered by using state-of-the-art machine learning algorithms.
Romano, G. (2019) Dancing Trainer: A System For Humans To Learn Dancing Using...Hendrik Drachsler
Masters thesis by Romano, G., (2019). Dancing is the ability to feel the music and express it in rhythmic movements with the body. But learning how to dance can be challenging because it requires proper coordination and understanding of rhythm and beat. Dancing courses, online courses or learning with free content are ways to learn dancing. However, solutions with human-computer interaction are rare or
missing. The Dancing Trainer (DT) is proposed as a generic solution to fill this gap. For the beginning, only Salsa is implemented, but more dancing styles can be added. The DT uses the Kinect to interact multimodally with the user. Moreover, this work shows that dancing steps can be defined as gestures with the Kinect v2 to build a dancing corpus. An experiment with
25 participants is conducted to determine the user experience, strengths and weaknesses of the DT. The outcome shows that the users liked the system and that basic dancing steps were
learned.
In May 2018, the new General Data Protection Regulation (GDPR) will enter into force in the European Union. This new regulation is considered as the most modern data protection law for Big Data societies of tomorrow. The GDPR will bring major changes to data ownership and the way data can be accessed, processed, stored, and analysed in the European Union. From May 2018 onwards, data subjects gain fundamental rights such as ‘the right to access data’ or ‘the right to be forgotten’. This will force Big Data system designers to follow a privacy-by-design approach for their infrastructures and fundamentally change the way data can be treated in the European Union.
The presentation provides an overview of the Trusted Learning Analytics Programme as it has been recently initiated at the University of Frankfurt and the DIPF research institute in Germany. Educational data is under special focus of the GDPR, as it is considered as highly sensitive like data from a nuclear plant. It shows opportunities and challenges for using educational data for learning analytics purposes under the light of the GDPR 2018.
Fighting level 3: From the LA framework to LA practice on the micro-levelHendrik Drachsler
This presentation explores shortcomings of learning analytics for the wide adoption in educational organisations. It is NOT about ethics and privacy rather than focuses on shortcomings of learning analytics for teachers and students in the classroom (micro-level). We investigated if and to what extend learning analytics dashboards are addressing educational concepts. Map opportunities and challenges for the use of Learning Analytics dashboards for the design of courses, and present an evaluation instrument for the effects of Learning Analytics called EFLA. EFLA can be used to measure the effects of LA tools at the teacher and student side. It is a robust but light (8 items) measurement to quickly investigate the level of adoption of learning analytics in a course (micro-level). The presentation concludes that Learning Analytics is still to much a computer science dicipline that does not fulfill the often claimed position of the middle space between educational and computer science research.
Presentation given at PELARS Policy event, Brussles, 09.11.2016. A follow up op the first LACE Policy event in April 2015. Special focus is on the exploitation and sustainability activities for LACE in the SIG LACE SoLAR.
Dutch Cooking with xAPI Recipes, The Good, the Bad, and the ConsistentHendrik Drachsler
This paper presents the experiences of several Dutch projects in their application of the xAPI standard and different design patterns including the deployment of Learning Record Stores. In this paper we share insights and argue for the formation of an international Special Interest Group on interoperability issues to contribute to the Open Analytics Framework as envisioned by SoLAR and enacted by the Apereo Learning Analytics Initiative. Therefore, we provide an overview of the advantages and disadvantages of implementing the current xAPI standard by presenting projects that applied xAPI in very different ways followed by the lessons learned.
Recommendations for Open Online Education: An Algorithmic StudyHendrik Drachsler
Recommending courses to students in online platforms is studied widely. Almost all studies target closed platforms, that belong to a University or some other educational provider. This makes the course recommenders situation specific. Over the last years, a demand has developed for recommender system that suit open online platforms. Those platforms have some common characteristics, such as the lack of rich user profiles with content metadata. Instead they log user interactions within the platform that can be used for analysis and personalization. In this paper, we investigate how user interactions and activities tracked within open online learning platforms can be used to provide recommendations. We present a study in which we investigate the application of several state-of-the-art recommender algorithms, including a graph-based recommender approach. We use data from the OpenU open online learning platform that is in use by the Open University of the Netherlands. The results show that user-based and memory-based methods perform better than model-based and factorization methods. Particularly, the graph-based recommender system proves to outperform the classical approaches on prediction accuracy of recommendations in terms of recall. We conclude that, if the algorithms are chosen wisely, recommenders can contribute to a better experience of learners in open online courses.
Soude Fazeli, Enayat Rajabi, Leonardo Lezcano, Hendrik Drachsler, Peter Sloep
Privacy and Analytics – it’s a DELICATE Issue. A Checklist for Trusted Learni...Hendrik Drachsler
The widespread adoption of Learning Analytics (LA) and Educational Data Mining (EDM) has somewhat stagnated recently, and in some prominent cases even been reversed following concerns by governments, stakeholders and civil rights groups about privacy and ethics applied to the handling of personal data. In this ongoing discussion, fears and realities are often indistin-guishably mixed up, leading to an atmosphere of uncertainty among potential beneficiaries of Learning Analytics, as well as hesitations among institutional managers who aim to innovate their institution’s learning support by implementing data and analytics with a view on improving student success. In this presentation, we try to get to the heart of the matter, by analysing the most common views and the propositions made by the LA community to solve them. We conclude the paper with an eight-point checklist named DELICATE that can be applied by researchers, policy makers and institutional managers to facilitate a trusted implementation of Learning Analytics.
DELICATE checklist - to establish trusted Learning AnalyticsHendrik Drachsler
The DELICATE checklist contains eight action points that should be considered by managers and decision makers planning the implementation of Learning Analytics / Educational Data Mining solutions either for their own institution or with an external provider.
The eight points are:
1. Determination: Decide on the purpose of learning analytics for your institution. What aspects of learning or learner services are you trying to improve?
2. Explain: Define the scope of data collection and usage. Who has a need to have access to the data or the results? Who manages the datasets? On what criteria?
3. Legitimate: Explain how you operate within the legal frameworks, refer to the essential legislation. Is the data collection excessive, random, or fit for purpose?
4. Involve: Talk to stakeholders and give assurances about the data distribution and use. Give as much control as possible to data subjects (permission architecture), and provide access to their data for the individuals.
5. Consent: Seek consent through clear consent questions. Provide an opt-out option.
6. Anonymise: De-identify individuals as much as possible, aggregate data into meta-models.
7. Technical aspects: Monitor who has access to data, especially in areas with high staff turn-over. Establish data storage to high security standards.
8. External partners: Make sure externals provide highest data security standards. Ensure data is only used for intended purposes and not passed on to third parties.
We hope that the DELICATE checklist will be a helpful instrument for any educational institution to demystify the ethics and privacy discussions around Learning Analytics. As we have tried to show in this article, there are ways to design and provide privacy conform Learning Analytics that can benefit all stakeholders and keep control with the users themselves and within the established trusted relationship between them and the institution.
Updated Flyer of the LACE project with latest tangible outcomes and collaboration possibilities.
LACE connects players in the fields of Learning Analytics (LA) and Educational Data Mining (EDM) in order to support the development of a European community and share emerging best practices.
Objectives
-------------
• Promote knowledge creation and exchange
• Increase the evidence base about Learning Analytics
• Contribute to the definition of future directions
• Build consensus on pressing topics like data interoperability, data sharing, ethics and privacy, and Learning Analytics supported instructional design
Activities
• Organise events to connect organisations that are conducting LA/EDM research
• Create and curate a knowledge base to capture evidence for the effectiveness of Learning Analytics
• Produce reviews to inform the LACE community about latest developments in the field
Presentation given at Serious Request 2015, #SR15, Heerlen.
Within the Open University we started a 12 hours marathon college, to collect money for the charity action of radiostation 3FM. The collected money will go to the red cross and support young people in conflict areas.
The Impact of Learning Analytics on the Dutch Education SystemHendrik Drachsler
The article reports the findings of a Group Concept Mapping
study that was conducted within the framework of the Learning Analytics Summer Institute (LASI) in the Netherlands. Learning Analytics are expected to be beneficial for students and teacher empowerment, personalization, research on learning design, and feedback for performance. The study depicted some management and economics issues and identified some possible treats. No differences were found between novices and experts on how important and feasible are changes in education triggered by
Learning Analytics.
Paper available at: http://dl.acm.org/citation.cfm?id=2567617&CFID=427722877&CFTOKEN=73282080
What do analytics on learning analytics tell us? How can we make sense of this emerging field’s historical roots, current state, and future trends, based on how its members report and debate their research?
Challenge submissions should exploit the LAK Dataset for a meaningful purpose. This may include submissions which cover one or more of the following, non-exclusive list of topics:
Analysis & assessment of the emerging LAK community in terms of topics, people, citations or connections with other fields
Innovative applications to explore, navigate and visualise the dataset (and/or its correlation with other datasets)
Usage of the dataset as part of recommender systems
Analysis of the evolution of LAK discipline
Improvement or enrichment of the LAK Dataset
1. Hendrik
Drachsler,
@hdrachsler
Welten
Ins4tute
Research
Centre,
Open
University
of
the
Netherlands
Presenta4on
given
at:
NSF
expert
mee4ng
on
‘Big
Data
and
Privacy
in
Human
Subjects
Research’
(#BDEDU)
11
November
2014
Response
to
talks
at
Big
Data
and
Privacy
in
Human
Subject
Research
(1st
day)
2. 3
• Hendrik
Drachsler,
Open
University
of
the
Netherlands
• Research
topics:
Personaliza4on,
Recommender
Systems,
Learning
Analy4cs,
Mobile
devices
• Applica4on
domains:
Science
2.0
Health
2.0
WhoAmI
5. They
brought
together
some
Super
Hero’s
5
Who
of
you
considers
him-‐
herself
to
be
a
Super
Hero?
• You
are
passionate
about
what
you
are
doing.
• You
shape
the
future
of
society.
• You
touch
ethical
ques4ons
with
your
super
power.
• You
want
to
follow
societal
norms
and
advance
those.
With
Big
Power
comes
great
responsibili5es.
6. Big
Power
-‐>
Big
Data
=
Repurposing
data
6
Jawbone
data
repurposed
to
measure
earthquake
strength
7. 7
Big
Data
is
the
new
truth
(the
ulHmate
truth?)
8. 8
Big
Data
is
the
new
truth
(the
ulHmate
truth?)
Inaccurate
Google
Flue
trend
measures
compared
to
CDC
9. Big
Data
has
the
potenHal
to
change
EducaHon
9
• First
4me
monitoring
learning
while
it
happens
• Personalize
Educa4on
• Iden4fy
students
at
Risk
• Learning
Measures
on
demand
• More
…
10. Some
QuesHons,
Super
Hero’s
10
Some
Demographics:
Who
of
you
are
data
scien4sts,
legal
or
educa4onal
experts?
Who
of
you
read
TOC
of
your
online
services?
Who
of
you
cares
about
his/her
privacy?
Who
sees
Privacy
and
Legal
regula4ons
as
a
burden
we
need
to
overcome?
11. Learning
AnalyHcs
Research
Issues
11
Learning
Analy4cs
research
always
raises
the
P-‐Word
in
EU
(University
of
Amsterdam,
2014)
This
stops
innova4on
and
advancing
research
(dataTEL
2010)
12. 12
• Privacy
changes
overHme
• Privacy
is
bind
to
context
• Privacy
is
bind
to
culture
Slide
supported
byTore
Hoel,
@Tore
13. What
if
I
would
know
…
• How
many
days
you
have
NOT
been
at
school
without
any
excuse.
• All
read
and
wrihen
pages,
and
what
your
annota4ons
have
been.
• The
people
you
hangout
with
in
your
youth.
• If
you
cheated
in
a
test
and
how
many
ahempts
you
needed
for
your
math
class.
• What
if
I
use
all
those
informaHon
and
predict
your
chances
to
be
good
or
bad
in
a
certain
job
aSer
school?
• How
representaHve
and
reliable
is
this
data
I’m
capturing
to
predict
those
chances?
• And
what
if
all
this
informaHon
will
be
last
forever!
13
14. Approaches
to
prevent
another
inBloom
…
• Transparency
(Purpose
of
analysis,
Raw
data
access,
opt-‐out)
• Data
Security
• Contextual
Integrity
(Smart
Informed
Consents)
• Anonymisa4on
&
Data
degrada4on
14
15. Hendrik
Drachsler,
@hdrachsler
Welten
InsHtute
Research
Centre,
Open
University
of
the
Netherlands
Presenta4on
given
at:
NSF
expert
mee4ng
on
‘Big
Data
and
Privacy
in
Human
Subjects
Research’
(#BDEDU)
11
November
2014
Ethics
&
Privacy
Issue
in
the
ApplicaHon
of
Learning
AnalyHcs
(#EP4LA)
16. Who
we
are
16
FP7
LACE
–
Hendrik
Drachsler,
@Hdrachsler,
28
October
2014
LACE
Network
LACE
ConsorHum
17. Building
bridges
between
research,
policy
and
prac4ce
to
realise
the
poten4al
of
learning
analy4cs
in
EU
17
FP7
LACE
–
Hendrik
Drachsler,
@Hdrachsler,
28
October
2014
18. Data
Geology
18
PAST,
single,
centered
IT
solu4ons
with
single
purpose
(loosely
couple
data)
FP7
LACE
–
Hendrik
Drachsler,
@Hdrachsler,
28
October
2014
PRESENT,
mul4ple
ubiquitous
IT
systems
mul4ple
func4onali4es
(highly
connected
but
unstructured
data)
FUTURE,
learner
ac4vity
tracking
of
ubiquitous
systems
(structured
learner
data)
• Are
our
instruments
measuring
what
we
expect
them
to
measure?
• Can
we
isolate
the
noise
in
the
data?
• Are
the
measures
accurate?
20. • $100
million
investment
• Aim:
Personalized
learning
in
public
schools,
through
data
&
technology
standards
• 9
US
states
par4cipated
• In
2013
the
database
held
informa4on
on
millions
of
children
Privacy
as
Showstopper
–
The
inBloom
case
20
21. Privacy
• What is privacy?
– Right to be let alone (Warren and Brandeis)
– Informational self-determination (Westin)
– Degree of access (Gavison)
– … Right to be forgotten …
• Three dimensions (Roessler)
– Informational privacy
– Decisional privacy
– Local privacy
• What it is not
– Anonymity, secrecy, data protection
23. inBloom
example(s)
in
the
Netherlands
23
hhp://www.nu.nl/internet/3938530/
kamer-‐wil-‐uitleg-‐dekker-‐privacy-‐
scholieren.html
24. What
are
the
dangers
of
learning
analyHcs?
– Missing
legal
obliga4ons:
• Data
protec4on
• IRB
• Educa4on
laws
– Inflic4ng
harm:
• Unfair
discrimina4on
• Unjus4fied
discrimina4on
(through
errors)
• Subjec4ve
privacy
harm
(panop4c
effect)
• Unintended
pressure
to
perform
/
wrong
incen4ves?
• Anonymisa4on
is
not
possible
– Viola4ng
human
dignity
– Unintended
changes
of
educa4on
norms?
24
25. ModernizaHon
of
EU
UniversiHes
report
RecommendaHon
14
Member
States
should
ensure
that
legal
frameworks
allow
higher
educa4on
ins4tu4ons
to
collect
and
analyse
learning
data.
The
full
and
informed
consent
of
students
must
be
a
requirement
and
the
data
should
only
be
used
for
educa4onal
purposes.
RecommendaHon
15
Online
plaqorms
should
inform
users
about
their
privacy
and
data
protec4on
policy
in
a
clear
and
understandable
way.
Individuals
should
always
have
the
choice
to
anonymise
their
data.
hdp://ec.europa.eu/educaHon/
library/reports/modernisaHon-‐
universiHes_en.pdf
26. #EP4LA
on
the
European
Agenda
• Round
table
meeHng
‘Ethiek
en
Learning
AnalyHcs’
(Jan
2014)
hhps://www.surfspace.nl/media/bijlagen/ar4kel-‐1499-‐
b315e61001041bf52a6b1c5d80053cea.pdf
• Learning
AnalyHcs
Summer
InsHtute
(July
2014)
hhp://lasiutrecht.wordpress.com/
• Call
for
a
‘Code
of
Ethics
for
LA’
in
NL
(August
2014)
hhps://www.surfspace.nl/ar4kel/1311-‐towards-‐a-‐uniform-‐code-‐of-‐ethics-‐and-‐
prac4ces-‐for-‐learning-‐analy4cs/
• Call
for
a
‘Code
of
Ethics
for
LA’
in
the
UK
(September
2014)
hhp://analy4cs.jiscinvolve.org/wp/2014/09/18/code-‐of-‐prac4ce-‐essen4al-‐
for-‐learning-‐analy4cs/
26
31. Example
Submissions
from
ParHcipants
• Who
is
in
charge
(who
is
the
owners)
of
the
data
created
by
persons?
• What
is
the
impact
of
privacy
concerns
for
the
management?
How
to
deal
with
these
concerns?
• Should
students
be
allowed
to
opt-‐out
of
having
their
personal
digital
footprints
harvested
and
analysed?
• How
to
prevent
reuse
of
collected
data
for
non-‐educa4onal
needs.
(e.g.
finance,
insurance,
research),
or
is
it
no
problem?
Full
list:
hdp://bit.ly/raw_ep4la
32. We
are
pracHcal
people
–
our
approach
32
• Invite
5
legal
experts,
rest
Learning
Analy4cs
experts
• Task
groups
to
answer
requests
of
the
stakeholders
33. Boundaries
of
Learning
AnalyHcs
data
Where
is
the
boundary
on
data
use
for
learning
analy3cs
(courses,
grades,
LMS,
GoogleDrive,
library
system,
residence
halls,
dining
halls,
…)?
– Contextual
Integrity:
context
and
norms
of
learning
environment
– It
depends
on
• Awareness
of
students
about
processes
• Possible
consequences
for
students
• Safeguards
that
are
in
place
33
34. Outsourcing
What
are
the
concerns
when
outsourcing
the
collec3on
and
analysis
of
data?
Who
owns
the
data?
– Concerns:
• Undue
third
country
data
transfers
• Less
control
about
processing
• Less
transparency
for
the
data
subject
– Ownership:
• No
complete
ownership
for
any
party
• Relevant:
data
protec4on
and
intellectual
property
rights
• See
discussion
concerning
`data
portability’
in
DP
regula4on
(NDA
agreement
required)
34
35. Undesirable
data
collecHon
Are
there
any
circumstances
when
collecHng
data
about
students
is
unacceptable/undesirable?
– Yes,
there
are:
• Data
which
is
not
of
any
purpose
• Data
outside
of
the
learning
context
• Data
of
which
the
student
is
not
aware
• Data
which
poses
a
risk
to
the
student
• Data
which
is
not
well
protected
35
36. Data
access
by
students
What
data
should
students
be
able
to
view,
i.e.
what
and
how
much
informaHon
should
be
provided
to
the
student?
– Data
Protec4on
Direc4ve
(ar4cle
12):
• Everything
concerning
them
(at
least
upon
request)
– Human
subjects
research:
• Everything
concerning
study
(at
least
ater
experiment)
• Avoidance
of
decep4on
– But
• Possible
conflict
of
full
data
access
with
goals
of
LA?
• How
to
provide
meaningful
access
while
excluding
other
students
data?
36
37. 9
Themes
around
privacy
(1/3)
1.
LegiHmate
grounds
-‐
Why
are
you
allowed
to
have
the
data?
2.
Purpose
of
the
data
-‐
Repurposing
is
an
issue
vs.
MIT
Social
Machine
lab
3.
Inventory
of
data
-‐
What
data
do
you
have?
-‐
What
can
you
do
with
that
data
already?
38. 9
Themes
around
privacy
(2/3)
4.
Data
quality
-‐
How
good
is
the
data?
(eg.
Bb
log
file
is
weak
predictor)
-‐
When
do
you
I
delete
data
and
what
data?
5.
Transparency
-‐
Informing
students
(Purpose,
Approach)
-‐
Checklist
what
to
communicate
for
researchers
6.
The
rights
of
the
data
subject
to
access
their
data
from
the
data
client
-‐
For
teachers
who
are
employees
other
rights
apply
39. 9
Themes
around
privacy
(3/3)
7.
Outsource
processing
to
external
parHes
-‐
Prevent
external
par4es
to
not
do
addi4onal
analysis
(NDA
agreement)
8.
Transport
data,
legal
locaHon
-‐
e.g.
Safe
Harbour
agreement
9.
Data
Security
-‐>
Shuangbao
Wang
40. SoluHons
Privacy
by
Design
The
7
FoundaHonal
Principles
1.
Proac4ve
not
Reac4ve;
2.
Privacy
as
the
Default
Sevng
3.
Privacy
Embedded
into
Design
4.
Full
Func4onality
5.
End-‐to-‐End
Security
6.
Visibility
and
Transparency
7.
Respect
for
User
hhp://www.ipc.on.ca/images/resources/7founda4onalprinciples.pdf
OWD
2014
–
Ethics
&
Privacy
in
the
ApplicaHon
of
Laerning
AnalyHcs
41. The
Booksprint
Rules
41
• All
voices
are
valid
• Take
ownership
of
the
book
(now
and
later)
• Session
to
be
strongly
facilitated
5
main
elements:
1.
Concept
Mapping
2.
Structuring
3.
Wri4ng
4.
Composi4on
5.
Publica4on
www.booksprints.net
42. 42
Twider
Archive
for
Event
A
Twubs
archive
for
this
event
is
available
at
twubs.com/hashtag
Can
help
in
report-‐wri4ng
and
iden4fying
those
with
similar
interests
45. Using
Lanyrd
The
Lanyrd
social
directory
of
events
provides
access
to
informa4on
about:
• Events
• Speaker
• Par4cipants
The
entry
for
this
event
may
include
access
to:
• Slides
• Twiher
archives
• Reports
Feel
free
to
add
your
details
45
hhp://lanyrd.com/2014/eden14/
46. PracHcal
consideraHons
• Data
protec4on
• Anonymisa4on
• Human-‐subjects
research
• (Contextual
Integrity)
46
47. Data
protecHon
• EU
Data
Protec4on
Direc4ve
• Common
misconcep4ons
– Scope
of
‘personal
data’
– Relevance
of
consent
– Anonymisa4on
• Legal
ground:
consent
or
legi4mate
interest?
• How
to
fulfil
purpose
limita4on?
• Applicability
of
sta4s4cs
exemp4on?
• Automated
decisions?
47
48. AnonymisaHon
• Oten
considered
as
an
‘easy
way
out’
of
DP
obliga4ons
• But
– Other
legal
obliga4ons?
– Right
to
collect
data
in
the
first
place?
– Ethical
considera4ons?
– Uniden4fiability
achievable
for
dataset?
• Useful
guidance:
Opinion
05/2014
of
Art.
29
WP
hhp://ec.europa.eu/jus4ce/data-‐protec4on/ar4cle-‐29/documenta4on/
opinion-‐recommenda4on/files/2014/wp216_en.pdf
48
49. Human-‐subjects
research
• Ethics
approval
required?
– In
NL:
differs
per
university
• Likely
problema4c
points:
– Consent
– Decep4on
– Iden4fiable
data
– Subordinate
posi4on
of
students
49
50. Transparency
• Transparency about
– Data collection
– Profiling activities
– Use of results within institutional process
• Data Protection directive
• Human-subjects research
• Failure of notice and consent
• Legal texts vs. layered notices vs. icons vs. data cockpits
51. Value Sensitive Design
(Batya Friedman)
• Goal:
address
human
values
in
a
technical
design
Source: presentation by Jeroen van den Hoven
54. Contextual
Integrity
(Helen
Nissenbaum)
• Benchmark
for
privacy
in
informa4on
systems
– Recogni4on
of
social
contexts
– Related
to
‘reasonable
expecta4on’
condi4on
• Contextual
integrity
≈
fulfilling
informa4onal
norms
• Contexts:
roles,
ac4vi4es,
norms,
values
• Actors:
senders,
receivers,
subjects
• Ahributes:
data
fields
• Transmission
principles:
confiden4ality,
etc.
54
55. Data Protection aka
Directive 95/46/EC
• Privacy as control / informational self-determiniation
• Not necessarily restricting data collection, but channelling it
• In the European Union
– Directive 95/46/EC
• Elsewhere
– OECD Principles (1980)
– Fair Information Practices (US, not mandatory)
• Upcoming: general data protection regulation
56. 95/46/EC – general obligations
(article 6 (1))
a) Fairly
and
lawfully
b) Purpose
limita4on
(see
also
Opinion
03/2013
of
Art
29
WP)
• Specified,
explicit
and
legi4mate
• Compa4ble
use
(no
secondary
use)
• Excep4ons
for
sta4s4cs
and
science
c) Adequate,
relevant
and
not
excessive
d) Accurate
and
up
to
date
e) Iden4fica4on
no
longer
than
necessary
• Important:
these
provisions
also
hold
if
consent
of
data
subject
has
been
collected!
57. 95/46/EC – legal grounds
(article 7)
a) Consent
b) Performance of a contract
c) Legal obligations
d) Vital interests of the data subject
e) Public interest
f) Legitimate interests
• Legitimate interest should be used more often
• ‘how to’: see Opinion 04/2014 of Art 29 WP
– Balancing test: nature of the interest, impact on data subject,
additional safeguards
58. 95/46/EC -
further considerations
• Special categories of data require extra care (article 8)
– ethnic origin, political opinion, etc.
• Right of access to data (article 12)
– Includes rectification
• Automated decisions (article 15)
– Decisions severely affecting the data subject may not be solely
based on automated profiling
– If so, appeal to decision must be possible
• Security obligation (article 17)
• Notification obligation (article 18)
• Third country data transfers (article 25)
– Only allowed if adequate level of protection
– For the US: Safe Harbour arrangement
59. 95/46/EC - scope
• Only applicable if
– automated processing of ‘personal data’ OR
– part of a ‘filing system’ (article 3)
• Personal data
– Natural person is directly or indirectly identifiable
• à properly anonymised data not subject to 95/46/EC
• However,
– Other legal obligations exist
• e.g. article 7 and 8 EU Charter of Fundamental Rights, article 8 ECHR,
e-Privacy Directive
– Anonymous data can still inflict harm (à group profiling)
– Anonymisation itself is also data processing!
60. Anonymisation – legal considerations
1/2
• Good
introduc4on
in
Opinion
05/2014
of
Art
29
WP
• iden5fica5on
no
longer
than
necessary
can
be
an
obliga4on
for
anonymisaton
• à
anonymisa4on
by
default?
• The
process
of
anonymisa4on
is
also
data
processing
• Principle
of
purpose
limita4on
applies!
• Applying
anonymised
data
to
user
profiles
is
data
processing
• à
only
the
processing
of
already
anonymised
data
by
itself
is
not
subject
to
data
protec4on
61. Anonymisation – legal considerations
2/2
• Pseudonymisation is not anonymisation
– àreplacing identifiers is not enough
• Only anonymous if identification is irreversibly prevented
– Whether that is the case depends on robustness of method
– Opinion 05/2014 assesses different methods in that regard
• Residual risk of identification has to be taken into account
– Monitor and control
62. Anonymisation – Practical
considerations
• Decisive is the ability to single out an individual
– Dropping identifier columns from a DB is likely not enough
– Attributes such as age, gender, etc. might have to be modified
– àrandomization and generalization
• Consider the possibility of further harm (group profiling)
– Unjustified discrimination
– Unfair discrimination
63. Human-subjects research 1/2
• Experiments with Learning Analytics might require approval of a local
ethics committee!
• Nuremberg code, Declaration of Helsinki (excerpt)
– Informed consent
– For the good of society
– Avoidance of harm
– Option to exit
• Problematic points
– Consent
– Deception
– Identifiable data
– Subordinate position
64. Human-subjects research 2/2
• Required by law only for medical research
• àRegulations differ per university
• If review is necessary, often:
– Multiple stages
– Checklists
– Possibly advice for improvement of study
• Requirements for approval tie in with ethical considerations
discussed earlier
65. Conclusions
• No simple recipe or ‘how to ethics’
• Data protection != anonymisation or consent
• Protecting privacy != anonymisation
• Ethics in LA is more than privacy
• Legal constraints (data protection) are not everything
• Think along the lines of contextual integrity
• Keep the students involved
66. Discussion
• Limits of privacy by design
• Security best practices
• Difficulties of meten is weten
– Discussion around publication of CITO scores
• Dangers of misinterpretation
– Safeassign Matching Score
• Data access
– Student, teacher or both
– Granularity of access
• Connection to non-university systems
– Cloud services, social media
• Ways forward
– Code of ethics for LA?
67. “Ethics
&
Privacy
Issues
in
the
Applica4on
of
Learning
Analy4cs”
by
Hendrik
Drachsler,
Open
University
of
the
Netherlands
was
presented
at
LACE
&
SURF
workshop,
Utrecht,
Netherlands
on
28.10.2014.
Hendrik.drachsler@ou.nl,
@hdrachsler
This
work
was
undertaken
as
part
of
the
LACE
Project,
supported
by
the
European
Commission
Seventh
Framework
Programme,
grant
619424.
These
slides
are
provided
under
the
Crea4ve
Commons
Ahribu4on
Licence:
hhp://
crea4vecommons.org/licenses/by/4.0/.
Some
images
used
may
have
different
licence
terms.
www.laceproject.eu
@laceproject
67
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