Code of practice for learning analytics

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ensure that learning analytics is carried out responsibly, appropriately and effectively.</p> </div> <div class="l-side-panel"> <div class="boxout"> <div class="linklist"> <p class="linklist__title">About this guide</p> <p class="linklist__subtitle">Authors</p> <ul> <li class="linklist__item"><p class="field field-type-text">Niall Sclater</p> Consultant and director, Sclater Digital Ltd </li>  <li class="linklist__item"> <a href="/web/20221006152755/https://www.jisc.ac.uk/staff/paul-bailey"> Paul Bailey </a> <p> Head of co-design </p> </li>  <li class="linklist__item"> Published: <time property="dc:date" datatype="xsd:dateTime" content="2015-06-04T00:00:00+01:00" datetime="2015-06-04T00:00:00+01:00">4 June 2015</time> </li> <li class="linklist__item"> Updated: <time>15 August 2018</time> </li> </ul> </div>  </div>  <div class="linkbox"> <a target="_blank" href="http://web.archive.org/web/20221006152755/https://repository.jisc.ac.uk/6985/1/Code_of_Practice_for_learning_analytics.pdf"> <div class="duo"> <div class="duo__side"> <span class="ico ico-pdf-white"></span> </div> <div class="duo__body"> <b>Download as PDF</b><br> </div> </div> </a> </div>  </div>  </header> </div> </div> <div class="l-centre-offset"> <section class="content-longform__main localnav__wrapper"> <div class="book-menu span-9"> </div> <div class="row"> <div class="col span-2"> <div class="l-gutter--right"> <nav class="localnav"> <p class="localnav__title"> Contents </p> <ul><li><a class="in-page" href="#Introduction">Introduction</a></li><li><a class="in-page" href="#Responsibility">Responsibility</a></li><li><a class="in-page" href="#Transparency">Transparency and consent</a></li><li><a class="in-page" href="#Privacy">Privacy</a></li><li><a class="in-page" href="#Validity">Validity</a></li><li><a class="in-page" href="#Access">Access</a></li><li><a class="in-page" href="#Enablingpositiveinterventions">Enabling positive interventions</a></li><li><a class="in-page" href="#Minimisingadverseimpacts">Minimising adverse impacts</a></li><li><a class="in-page" href="#Stewardship">Stewardship of data</a></li></ul> </nav>  </div> </div> <div class="col span-10"> <div class="l-gutter--right"> <div class="auto-section article-full__body"> <h2><a id="Introduction" name="Introduction"></a>Introduction</h2><p>Learning analytics uses data about students and their activities to help institutions understand and improve educational processes, and provide better support to learners.</p><p>Learning analytics should be used for the benefit of students. This might be to assist them individually or through using aggregated and anonymised data to help other students. Learning analytics might also be used to improve the educational experience more generally. It is distinct from assessment, and should be used for formative rather than summative purposes.</p><p> <figure class="media media-element media--fullwidth file-default"> <div class="media__inner"> <img data-delta="1" typeof="foaf:Image" src="http://web.archive.org/web/20221006152755im_/https://www.jisc.ac.uk/sites/default/files/learning-analytics.jpg" alt="" title=""/> <div class="media__attribution"> <div class="media__attribution__title"> <span class="ico ico-i">Creative Commons attribution information</span> </div> <div about="" class="media__attribution__info"> <div xmlns:cc="http://creativecommons.org/ns#" xmlns:dct="http://purl.org/dc/terms/" class="last-child"> <div property="cc:title" class="cc__title"><a property="dct:title" rel="cc:attributionURL" href="http://web.archive.org/web/20221006152755/http://www.istockphoto.com/photo/teacher-helping-mature-student-with-studies-in-library-46818882">Working together at a computer</a></div><div class="cc__author">©<span property="cc:attributionName">monkeybusinessimages</span> via <span>iStock</span></div><div class="cc_license last-child">All rights reserved</div> </div> </div> </div> </div> </figure> </p><p>The effective use of learning analytics will initially involve the deployment of new systems along with changes to institutional policies and processes. New data may be collected about individuals and their learning activities. The data will be analysed and interventions may take place as a result. This presents opportunities for positive engagements and impacts on learning, as well as misunderstandings, misuse of data and adverse impacts on students.</p><aside class="article-full__callout article-full__callout--hang-right"><blockquote><p><p>Complete transparency and clear institutional policies are essential</p></p></blockquote></aside><p>Complete transparency and clear institutional policies are therefore essential regarding the purposes of learning analytics, the data collected, the processes involved, and how they will be used to enhance the educational experience.</p><p>This code of practice aims to set out the responsibilities of educational institutions to ensure that learning analytics is carried out responsibly, appropriately and effectively, addressing the key legal, ethical and logistical issues which are likely to arise.</p><p>Educational institutions in the UK already have information management practices and procedures in place and have extensive experience of handling sensitive and personal data in accordance with the Data Protection Act 2018. They must now also comply with the UK General Data Protection Regulation (<span class="lexicon-term has-tooltip" title="General Data Protection Regulation"><abbr title="General Data Protection Regulation">GDPR</abbr></span>).</p><p>By transferring and adapting this expertise to regulate the processing of data for learning analytics, institutions should establish the practices and procedures necessary to process the data of individuals lawfully and fairly.</p><p><a id="Responsibility" name="Responsibility"></a></p><h2>Responsibility</h2><p>Institutions must decide who has overall responsibility for the legal, ethical and effective use of learning analytics. They should allocate specific responsibility within the institution for:</p><ul><li>The collection of data to be used for learning analytics</li><li>The anonymisation of the data where appropriate</li><li>The analytics processes to be performed on the data, and their purposes</li><li>The interventions to be carried out</li><li>The retention and stewardship of data used for and generated by learning analytics</li></ul><p>Student representatives and key staff groups at institutions should be consulted about the objectives, design, development, roll-out and monitoring of learning analytics.</p><p><a id="Transparency" name="Transparency"></a></p><h2>Transparency and consent</h2><p>Institutions will define the objectives for the use of learning analytics, what data is necessary to achieve these objectives, and what is out of scope. The data sources, the purposes of the analytics, the metrics used, who has access to the analytics, the boundaries around usage and how to interpret the data must be explained clearly to staff and students.</p><p>Institutions should also clearly describe the processes involved in producing the analytics to students and staff or make the algorithms transparent to them.</p><aside class="article-full__callout article-full__callout--hang-right"><blockquote><p><p>Institutions should clearly describe the processes involved in producing the analytics to students and staff</p></p></blockquote></aside><p>Student consent is required for the collection and use of "special category data", such as ethnicity, as defined by <span class="lexicon-term has-tooltip" title="General Data Protection Regulation"><abbr title="General Data Protection Regulation">GDPR</abbr></span>.</p><p>Students will also normally be asked for their consent for personal interventions to be taken based on the learning analytics. This will usually be done at the point where a specific intervention is proposed. However, there may be legal, safeguarding or other circumstances where students are not permitted to opt out of such interventions. If so these must be clearly stated and justified.</p><p>New learning analytics projects may not be covered by the institution’s existing arrangements. Collection and use of data for these may require further measures, such as data protection impact assessments and obtaining additional consent.</p><p>Options for granting consent must be clear and meaningful, explaining the expected consequences of granting or withholding consent. Students should be able easily to amend their decisions subsequently.</p><p><a id="Privacy" name="Privacy"></a></p><h2>Privacy</h2><p>Access to student data and analytics should be restricted to those identified by the institution as having a legitimate need to view them.</p><p>Where data is to be used anonymously particular care must be taken by institutions to avoid:</p><ul><li>Identification of individuals from metadata</li><li>Re-identification of individuals by aggregating multiple data sources</li></ul><p>The use of “special category data” for the purposes of learning analytics requires additional safeguards. Circumstances where data and analytics could be shared externally - eg requests from educational authorities, security agencies or employers - will be made explicit to staff and students, and may require additional consent.</p><aside class="article-full__callout article-full__callout--hang-right"><blockquote><p><p>The use of “special category data” for the purposes of learning analytics requires additional safeguards</p></p></blockquote></aside><p>Institutions should ensure that student data is protected when third parties are contracted to store data or carry out learning analytics on it.</p><p>Institutions may have a legal obligation to intervene, and hence override some privacy restrictions, where data or analytics reveal that a student is at risk. Such circumstances should be clearly specified.</p><p><a id="Validity" name="Validity"></a></p><h2>Validity</h2><p>It is vital that institutions monitor the quality, robustness and validity of their data and analytics processes in order to develop and maintain confidence in learning analytics and ensure it is used to the benefit of students. Institutions should ensure that:</p><ul><li>Inaccuracies in the data are understood and minimised</li><li>The implications of incomplete datasets are understood</li><li>The optimum range of data sources is selected</li><li>Spurious correlations are avoided</li></ul><p>All algorithms and metrics used for predictive analytics or interventions should be understood, validated, reviewed and improved as appropriate by qualified staff.</p><p>Data and analytics may be valid but should also be useful and appropriate; learning analytics should be seen in its wider context and combined with other data and approaches as appropriate.</p><p><a id="Access" name="Access"></a></p><h2>Access</h2><p>Students should be able to access all learning analytics performed on their data in meaningful, accessible formats, and to obtain copies of this data in a portable digital format. Students have a legal right under the <span class="lexicon-term has-tooltip" title="General Data Protection Regulation"><abbr title="General Data Protection Regulation">GDPR</abbr></span> to be able to correct inaccurate personal data held about themselves.</p><aside class="article-full__callout article-full__callout--hang-right"><blockquote><p><p>Students have a legal right to be able to correct inaccurate personal data held about themselves</p></p></blockquote></aside><p>They should normally also be able to view the metrics and labels attached to them. If an institution considers that the analytics may have a harmful impact on the student’s academic progress or wellbeing it may withhold the analytics from the student, subject to clearly defined and explained policies. However, the student must be shown the data about them if they ask to see it.</p><p><a id="Enablingpositiveinterventions" name="Enablingpositiveinterventions"></a></p><h2>Enabling positive interventions</h2><p>Institutions should specify under which circumstances they believe they should intervene when analytics suggest that a student could benefit from additional support. This may include advising students that they should not continue on a particular pathway. Students may also have obligations to act on the analytics presented to them – if so these should be clearly set out and communicated to the students.</p><p>The type and nature of interventions, and who is responsible for carrying them out, should be clearly specified. Some may require human rather than digital intermediation. Predictions and interventions will normally be recorded, and be auditable, and their appropriateness and effectiveness reviewed.</p><p>The impact of interventions on staff roles, training requirements and workload should be considered; addressing this requires support from senior management. Institutions should also be clear about the priority given to learning analytics in relation to other requirements.</p><p>Institutions should decide how to allocate resources for learning analytics appropriately for learners with different requirements and ensure that diverse groups and individuals are treated equitably.</p><p><a id="Minimisingadverseimpacts" name="Minimisingadverseimpacts"></a></p><h2>Minimising adverse impacts</h2><p>Institutions must recognise that analytics can never give a complete picture of an individual’s learning and may sometimes ignore personal circumstances.</p><p>Institutions should take steps to ensure that trends, norms, categorisation or any labelling of students do not bias staff, student or institutional perceptions and behaviours towards them, reinforce discriminatory attitudes or increase social power differentials.</p><aside class="article-full__callout article-full__callout--hang-right"><blockquote><p><p>ensure that trends, norms, categorisation or any labelling of students do not bias staff, student or institutional perceptions and behaviours towards them</p></p></blockquote></aside><p>Analytics systems and interventions should be carefully designed and regularly reviewed to ensure that:</p><ul><li>Students maintain appropriate levels of autonomy in decision making relating to their learning, using learning analytics where appropriate to help inform their decisions</li><li>Opportunities for “gaming the system” or any benefit to the student from doing so are minimised</li><li>Knowledge that their activity is being monitored does not lead to non-participation by students or other negative impacts on their academic progress or wellbeing</li><li>Adverse impacts as a result of giving students and staff information about the students’ performance or likelihood of success are minimised</li><li>Staff have a working understanding of legal, ethical and unethical practice</li></ul><p><a id="Stewardship" name="Stewardship"></a></p><h2>Stewardship of data</h2><p>Data for learning analytics must comply with existing institutional data policies and the <span class="lexicon-term has-tooltip" title="General Data Protection Regulation"><abbr title="General Data Protection Regulation">GDPR</abbr></span> and, in particular, be:</p><ul><li>Kept to the minimum necessary to deliver the purposes of the analytics reliably</li><li>Processed in the UK, European Economic Area or, if elsewhere, only in accordance with the <span class="lexicon-term has-tooltip" title="General Data Protection Regulation"><abbr title="General Data Protection Regulation">GDPR</abbr></span></li><li>Retained only for appropriate and clearly defined periods</li></ul><p>On request by students any personal data used for or generated by learning analytics should be destroyed or anonymised, with the exception of certain, clearly specified data fields required for educational or statutory purposes such as grades.</p><section class="is-collapsible is-collapsible--blue user-created "><h3 class="cx_collapsibles__trigger">Citations</h3><div class="cx_collapsibles__target"><p>Please cite this report as Jisc (2015): Code of practice for learning analytics.</p><p>Published under the <a href="http://web.archive.org/web/20221006152755/http://creativecommons.org/licenses/by/4.0/">CC BY 4.0 licence.</a></p></div></section> </div> <footer class="article-full__footer"> <dl class="article-full__tags l-gutter--top"> <dt>Explore more on this topic</dt> <dd><a href="/web/20221006152755/https://www.jisc.ac.uk/guides?f%5B0%5D=field_project_topics%3A416" typeof="skos:Concept" property="rdfs:label skos:prefLabel" datatype="">Course data</a></dd><dd><a href="/web/20221006152755/https://www.jisc.ac.uk/guides?f%5B0%5D=field_project_topics%3A449" typeof="skos:Concept" property="rdfs:label skos:prefLabel" datatype="">Data and analytics</a></dd><dd><a href="/web/20221006152755/https://www.jisc.ac.uk/guides?f%5B0%5D=field_project_topics%3A490" typeof="skos:Concept" property="rdfs:label skos:prefLabel" datatype="">Legal</a></dd> </dl> <section class="content-longform__section content-longform__section--about"> <div class="l-main-panel"> <h3 class="section__title">About the authors</h3>  <section class="duo profile profile--large profile--long" itemscope itemtype="http://schema.org/Person"> <div class="duo__side"> <img src="http://web.archive.org/web/20221006152755im_/https://www.jisc.ac.uk/sites/default/files/styles/featured_contact/public/paul-bailey.jpg?itok=ttSvnkIZ" alt="Paul Bailey"/> </div> <div class="duo__body profile__info"> <h4 class="profile__name"> <span class="name"> <a href="/web/20221006152755/https://www.jisc.ac.uk/staff/paul-bailey"> Paul Bailey </a> </span> </h4> <h5> <span itemprop="jobTitle"> Head of co-design, Jisc </span> </h5> <p>I work in Jisc R&D to develop new products and services, working with the sector to explore new ideas for how technology can help our members.</p> <dl class="profile__details"> <dt class="visuallyhidden"> Phone <dd> <a href="http://web.archive.org/web/20221006152755/tel:07823 416303" class="phone-link">07823 416303</a> </dd> </dt> <dt class="visuallyhidden"> Email </dt> <dd> <a href="http://web.archive.org/web/20221006152755/mailto:paul.bailey@jisc.ac.uk" itemprop="email" class="email-link">paul.bailey@jisc.ac.uk</a> </dd> </dl> </div> </section> <section class="duo profile profile--large profile--long" itemscope itemtype="http://schema.org/Person"> <div class="duo__side"> <img src="http://web.archive.org/web/20221006152755im_/https://www.jisc.ac.uk/sites/default/files/styles/featured_contact/public/niall-sclater.jpg?itok=L1PTJQ6T"/> </div> <div class="duo__body profile__info"> <h4 class="profile__name"> <span itemprop="jobTitle"> Niall Sclater </span> <span class="name"> </span> </h4> <p><p>Niall Sclater, consultant and Director of <a href="http://web.archive.org/web/20221006152755/http://sclater.com/blog/">Sclater Digital Ltd</a>, has more than 20 years of experience of innovation in online and distance education, specialising in e-learning, educational technology and distance learning. Current work includes research for Jisc in learning analytics.</p></p> </div> </section> </div> <div class="l-side-panel"> <div class="boxout"> <div class="linklist"> <p class="linklist__title">About this guide</p> <p class="linklist__subtitle">Authors</p> <ul> <li class="linklist__item"><p class="field field-type-text">Niall Sclater</p> Consultant and director, Sclater Digital Ltd </li>  <li class="linklist__item"> <a href="/web/20221006152755/https://www.jisc.ac.uk/staff/paul-bailey"> Paul Bailey </a> <p> Head of co-design </p> </li> <li class="linklist__item">Published: <time property="dc:date" datatype="xsd:dateTime" content="2015-06-04T00:00:00+01:00" datetime="2015-06-04T00:00:00+01:00">4 June 2015</time> </li> <li class="linklist__item">Updated: <time>15 August 2018</time></li> </ul> </div>  </div>  </div>  </section> </footer> </div>  </div>  </div> </section> </div> </article> </div> </section> <input type="hidden" id="affinity-tags" value="Data and analytics"> <script> 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