Tore Hoel

Introduction of learning analytics to education opened up the can of worms related to privacy issues that come with big data. Privacy issues are increasingly ‘wicked problems’ that call for a rethinking of the key artefacts involved. Global information systems make privacy a challenge that go to the center of solution design and information science research. In this dissertation research we exemplify the long and winding process from capturing questions of concern, to constructing conceptual artefacts to begin discussing the concerns, to proposing the first constructs that could lead to technical solutions—all within the context of technology enhanced learning and education. Learning analytics is a new discipline based on an increasing access to data, which will be extended by introduction of more and more sensors that are part of smart classrooms and intelligent campus projects. There is a gap between people’s online sharing of personal data and their concern about privacy. However, online practices are volatile, which make action design research and design science research an appropriate approach to explore conditions for design of privacy solutions. The research has been carried out taking part in two practice communities, the learning analytics knowledge community, and the learning technologies standards community. The contributions of this PhD research are both theoretical and practical. Privacy is defined in the context of big data; the theory of contextual integrity is extended to include the concept of ‘context trigger’, and design proposals explore the role of privacy policies in regulating data sharing. Risks and benefits of data sharing is explored to develop a learning analytics design space model. In addition, other constructs to facilitate discourse on data sharing in context are developed. Keywords: privacy, privacy engineering, contextual integrity, personal data, learning analytics, big dataOppimisen analytiikan käyttöönotto koulutuksessa avasi runsaasti kysymyksiä yksityisyydestä Big data -analyysiä hyödynnettäessä. Tietosuojakysymykset ovat yhä ’pahempia ongelmia’ ja uudenlaista ajattelua tarvitaan niiden selvittämiseksi. Globaalit tietojärjestelmät tekevät yksityisyydestä haasteellista ja tästä syystä on tärkeää keskittyä ratkaisujen suunnitteluun ja tietojärjestelmätieteen tutkimukseen alalla. Tässä väitöskirjatutkimuksessa havainnollistetaan aihealueen keskeisimmät kysymykset, konseptualisoidaan keskeiset käsitteet, joita tarvitaan keskusteluun yksityisyyden huolenaiheista. Lisäksi tässä väitöskirjassa ehdotetaan ensimmäisiä askelia yksityisyyden teknisiin ratkaisuihin teknologiavälitteisen opetuksen ja koulutuksen saralla. Oppimisen analytiikka on uusi tieteenala, joka perustuu ulottuvillamme olevaan kasvavaan tiedon määrään. Tulevaisuudessa älykkäiden luokkahuoneiden ja –kampusten kasvava sensorimäärä tulee lisäämään oppimisen analytiikan hyödyntämistä. Ihmisten henkilökohtaisten tiedonjakamistottumusten ja heidän yksityisyyttä koskevien huoliensa välillä vallitsee kuilu. Ihmisten verkkokäyttäytyminen on ailahtelevaa, mistä syystä toimintatutkimus ja suunnittelutiede soveltuvat hyvin yksityisyyden ratkaisujen suunnittelemiseen. Tämä tutkimus on toteutettu toimintatutkimuksena osallistumalla kahteen toimintayhteisöön: oppimisanalytiikan yhteisöön ja oppimisteknologioiden standardisointiyhteisöön. Tämän väitöskirjatutkimuksen vaikutukset ovat sekä teoreettisia että käytännöllisiä. Yksityisyys on määritelty Big datan kontekstissa; asiayhteyden eheysteoriaa on laajennettu ‘asiayhteyden laukaisijan’ käsitteellä ja suunnitteluehdotuksissa tutkitaan yksityisyyden käytänteiden roolia tiedon jakamisen sääntelyssä. Tiedonjakamisen riskejä ja hyötyjä tutkimalla on kehitetty oppimisen analytiikan suunnittelutilamalli. Lisäksi väitöskirjassa on kehitetty muita käsitteitä helpottamaan tiedonjakamiseen liittyvää tieteellistä keskustelua. Asiasanat: yksityisyys, yksityisyyden suunnittelu, asiayhteyden eheys, henkilökohtainen tieto, oppimisen analytiikka, Big dat

Open learning analytics (OLA) aims to meet diversified needs for insights into different stakeholders’ efforts to improve learning and learning contexts integrating heterogeneous learning analytics techniques. From an abstract point of view, OLA aligns well with the ideas of open and distance education institutions, of which Shanghai Open University (SOU) is a learning Chinese representative. The paper reports on the design of an OLA framework for SOU, based on different users’ service demands and the diverse sources of data and multiple platforms in use at the university. The proposed architecture is based on a discussion of the general characteristics of OLA architecture. The final model is achieved through an iterative development method.

Abstract This paper describes the first experiences in development and usage of an environment to support a community in collaborative modeling efforts in the area of Technology Enhanced Learning (TEL). With first priority, the domain of interest, the sub-...

Doric column and link to Issue Home Page Issue 66 January 2011 Doric column and link to Issue Home Page. ... diagram by Scott Wilson (below) seems to suggest that the solution is to put 'open' in front of every ... Educational Technology Research and Development, 53(4), 5-23. ...

In recent years e-Schoolbag is established as an overarching concept of large-scale Chinese implementation of digital textbooks and related services. Chinese authorities have promoted e-Textbooks and e-Schoolbag projects for international standardization, creating a strong interest from the international community to learn more about new design ideas behind e-Schoolbag and current practices. This paper presents a case study based on Chinese research literature, exploring the evidence base and research focus of the Chinese e-Schoolbag initiatives.

Discussions on MOOCs, on digital textbooks and new authoring and publishing models, on privacy issues related to digital practices, etc. have led to increased interest in Learning Analytics and Educational Data Mining. The European 7th Framework Coordination and Support action project, LACE (Learner Analytics Community Exchange) is devoted to structure this discussion and build communities that explore the potentials and pitfalls of the unprecedented availability of data related to education and learning. This paper gives a basic introduction to Learning Analytics and Educational Data Mining, presenting a generic framework and outlining how this will be used to support community building and discourse on different aspects of these themes within schools, higher education and the workplace. Learning Analytics and Educational Data Mining both only recently brought to the top of the educational technology trend lists, this paper argues that a discourse and co-design approach would be th...

Open learning analytics (OLA) aims to meet diversified needs for insights into different stakeholders’ efforts to improve learning and learning contexts integrating heterogeneous learning analytics techniques. From an abstract point of view, OLA aligns well with the ideas of open and distance education institutions, of which Shanghai Open University (SOU) is a learning Chinese representative. The paper reports on the design of an OLA framework for SOU, based on different users’ service demands and the diverse sources of data and multiple platforms in use at the university. The proposed architecture is based on a discussion of the general characteristics of OLA architecture. The final model is achieved through an iterative development method.

This chapter reviews current trends in the development of artificial intelligence (AI) ethics in order to suggest two implications for the field of higher education research and practice. In recent years, a vast assortment of businesses, national and regional governments, supra-national organisations, NGOs, academic groups, standardisation organisations, and public interest bodies have sought to define ethical codes for the use of AI. Various research projects have analysed this terrain, often suggesting the convergence of universal and definitive principles. However, less attention has been given to an emerging area of research and practice related to the participative co-design of AI, involving ongoing processes of multi-stakeholder discussion, participation, and exchange. This chapter draws on this emerging work to suggest ways in which higher education might better approach ‘ethics by design', established around two key ideas: situating ethical issues within already existing...

This outline of the workshop and the accompanying briefing document (Foresight of Technology Enhanced Learning, by Hoel and Hollins) address how Stakeholders within the Technology Enhanced Learning (TEL) community view their own future scope to use advanced learning technologies. The briefing document discusses arguably problematic methodologies and processes used within TEL to predict the future. Roadmapping; Scenario Planning; Foresight; Road Maps

University libraries offer services that generate data about how students and faculty use knowledge sources and engage with teaching and learning. In an era of Big Data there is mounting pressure to use these data, something that challenges the professional ethics of librarians. This paper explores how Norwegian librarians position themselves in relation to the new phenomenon of learning analytics, which would like to process library data to help improve learning and its contexts. A literature review shows that librarians in general are highly sceptical to let any information that is not anonymised out of their hands to be used by other professions. However, library data is increasingly being shared with third parties as part of development of library systems and practices. In a survey presented in this paper Norwegian librarians were asked about their willingness to take part in analytics and data sharing. The findings show that even if librarians in general do not want to share da...

This paper brings together two perspectives on current developments in information and communications technology (ICT) utilized for the purposes of learning, education, and training (LET). One perspective is concerned with governance and legitimacy issues regarding the process and relevance of standards development; the other, is concerned with the role of discerning relevant questions in LET and the potential scope for innovation that might develop tools to support such discernment. Discussion of information systems architecture is introduced as a means to set the context and provide a framework for bringing together an overall narrative and coherence to these two perspectives. Questions initiated by “why” are of particular interest because they typically demand an explanation in natural language to satisfy the questioner. By focusing on “why” as a key “primitive” of enquiry the authors present an approach that might inform improved processes of ICT for LET (a.k.a ITLET) standardiz...

This short paper addresses how we make sense of future technologies within the Technology Enhanced Learning (TEL) community. The paper highlights a number of methodologies and processes available to those predicting the future and is illustrated by cases from European practice. The study identifies inadequacies of the process that could, arguably, limit their effectiveness.

This paper explores the field of ICT standardisation related to learning analytics, a new class of technologies being introduced to schools, universities and further education as a consequence of increased access to data from learning activities. Learning analytics has implication for how the individual manages data and knowledge about herself and her learning, highlighting issues of privacy, ownership of data, and consent to share and use data, – issues that are not yet been fully discussed in the field of learning technology development in general, and standardisation of learning technologies in particular. What do these issues mean for standardisation and design of LA architectures? Based on requirements of open architecture, transparency and trust, and ownership and consent this paper proposes a search architecture for learning analytics based on open and linked data. The proposed middle layer highlights dynamic usage agreements and student agency and represents an alternative a...

This paper is motivated by paradoxes and frustrations in the practice of learning technology standardisation. Case studies and participatory observations suggest that stakeholder engagement is key to an improved process. As industry involvement is hard to achieve in a fragmented market, the author suggests that government interests should step up their engagement to speed up the development cycle of idea formation, specification and implementation.

This paper explores the field of ICT standardisation related to learning analytics, a new class of technologies being introduced to schools, universities and further education as a consequence of increased access to data from learning activities. Issues related to requirements for data sharing are registered; and the challenges of privacy, ownership of data, consent are identified as major obstacles for successful implementation of learning analytics. What does this mean for standardisation and design of LA architectures? Based on requirements of open architecture, transparency and trust, and ownership and consent this paper proposes a search architecture for learning analytics based on open and linked data. The proposed middle layer highlights dynamic usage agreements and end user agency and represents an alternative approach to the LA architectures now being developed in international standardisation fora. Keywords—learning analytis; data sharing; interoperability; privacy; data o...

Moving learning analytics from the research labs to the classrooms and lecture halls requires data sharing. Data are not any longer gathered in controlled settings but have to be combined from different sources within the institution and maybe beyond. This scaling up of learning analytics raises a host of questions on behalf of the data subjects providing requirements for design of new solutions and practices. This paper analyses a corpus of more than 200 questions gathered by a European learning analytics support action and explores how these questions could be used to understand the problem space of data sharing and the solution space to be carved out by research and development within this emerging field of learning technologies. The paper concludes the discussion on data sharing and big data for education is still at an early stage, where conceptual issues dominate and there is a long way to go before one can move towards solving issues of technical development and implementation.

There is a gap between people’s online sharing of personal data and their concerns about privacy. Till now, this gap is addressed by attempting to match individual privacy preferences with service providers’ options for data handling. This approach has ignored the role different contexts play in data sharing. This paper aims at giving privacy engineering a new direction putting context centre stage and exploiting the affordances of machine learning in handling contexts and negotiating data sharing policies. This research is explorative and conceptual, representing the first development cycle of a design science research project in privacy engineering. The paper offers a concise understanding of data privacy as a foundation for design extending the seminal contextual integrity theory of Helen Nissenbaum. This theory started out as a normative theory describing the moral appropriateness of data transfers. In our work, the contextual integrity model is extended to a socio-technical the...

The European Learning Analytics Community Exchange (LACE) project is responsible for an ongoing series of workshops on ethics and privacy in learning analytics (EP4LA), which have been responsible for driving and transforming activity in these areas. Some of this activity has been brought together with other work in the papers that make up this special issue. These papers cover the creation and development of ethical frameworks, as well as tools and approaches that can be used to address issues of ethics and privacy. This editorial suggests that it is worth taking time to consider the often intertangled issues of ethics, data protection and privacy separately. The challenges mentioned within the special issue are summarised in a table of 22 challenges that are used to identify the values that underpin work in this area. Nine ethical goals are suggested as the editors’ interpretation of the unstated values that lie behind the challenges raised in this paper.