Jisc, Apereo and the Lace Project held a workshop in Paris on 6th February to discuss the ethical and legal issues of learning analytics. The focus of this meeting was the draft taxonomy of issues that I prepared previously. It was extremely helpful to have comments from experts in the area to refine the list, which is forming the basis for Jisc’s Code of Practice for Learning Analytics. I have subsequently reworked the taxonomy based on the group’s comments.
Re-ordering
I’ve now re-ordered the table to reflect a slightly more logical lifecycle view of learning analytics moving from issues of ownership and control to seeking consent from students, ensuring transparency, maintaining privacy, ensuring validity in the data and the analytics, enabling student access to the data, carrying out interventions appropriately, minimising adverse impacts and stewarding the data.
Type
I’ve added a “Type” column which states whether the issue is primarily one of ethics, legalities or logistics. It’s become clear to me that many of the issues in the literature around ethics and privacy for learning analytics are more about the logistics of implementation than about doing what’s right or keeping within the law. I’ve therefore renamed the taxonomy to reflect the fact it’s about logistics as well.
Rank
The Paris group suggested scoring the issues on the basis of their importance and we began to rate them on a scale of 1 to 5, highlighting the most important ones. I have subsequently reduced the scale to three points, roughly equating to: 1 – Critical; 2 – Important; 3 – Less important / may not arise. I have reflected the views of the group in the rankings but have had to make many choices as to their relative importance myself. I’d like to find some more rigorous way of rating the issues though the ranking will always be dependent on the nature and priorities of the institution.
Responsibility
The group added a stakeholder column. Subsequently I divided this into Stakeholders most impacted and Stakeholders responsible. I then found that the most impacted stakeholders were almost always students so the column wasn’t particularly helpful and I’ve just included a Responsibility column which shows who is primarily responsible for dealing with the issue. Again there’s a level of subjectivity here on my part and these roles will be constituted differently depending on the institution. I’ve listed six stakeholders:
- Senior management – the executive board of the institution.
- Analytics committee – the group responsible for strategic decisions regarding learning analytics. This might be a learning and teaching committee, though some of the issues may be the responsibility of a senior champion of learning analytics rather than a more representative committee.
- Data scientist – while the analytics committee may decide on particular issues, there is a need for data scientists or analysts to advise on issues relating to the validity of the dataset and how to interpret it.
- Educational researcher – some issues would be best dealt with by staff with detailed knowledge of the educational issues who are able to monitor the impact of analytics on students. This role may be carried out by teachers or tutors or those more dedicated to educational research.
- IT – the institutional information technology department will take primary responsibility for some aspects of the analytics processes.
- Student – while students are potentially impacted by almost every issue here, they are primarily responsible themselves for dealing with a few of them.
| Group | Name | Question | Type | Rank | Responsibility |
|---|---|---|---|---|---|
| Ownership & Control | Overall responsibility | Who in the institution is responsible for the appropriate and effective use of learning analytics? | Logistical | 1 | Senior management |
| Control of data for analytics | Who in the institution decides what data is collected and used for analytics? | Logistical | 1 | Senior management | |
| Breaking silos | How can silos of data ownership be broken in order to obtain data for analytics? | Logistical | 2 | Analytics Committee | |
| Control of analytics processes | Who in the institution decides how analytics are to be created and used? | Logistical | 1 | Analytics Committee | |
| Ownership of data | How is ownership of data assigned across stakeholders? | Legal | 1 | Analytics Committee | |
| Consent | When to seek consent | In which situations should students be asked for consent to collection and use of their data for analytics? | Legal / Ethical | 1 | Analytics Committee |
| Consent for anonymous use | Should students be asked for consent for collection of data which will only be used in anonymised formats? | Legal / Ethical | 3 | Analytics Committee | |
| Consent for outsourcing | Do students need to give specific consent if the collection and analysis of data is to be outsourced to third parties? | Legal | 3 | Analytics Committee | |
| Clear and meaningful consent processes | How can institutions avoid opaque privacy policies and ensure that students genuinely understand the consent they are asked to give? | Legal / Ethical | 1 | Analytics Committee | |
| Right to opt out | Do students have the right to opt out of data collection and analysis of their learning activities? | Legal / Ethical | 1 | Analytics Committee | |
| Right to withdraw | Do students have the right to withdraw from data collection and analysis after previously giving their consent? | Legal | 3 | Analytics Committee | |
| Right to anonymity | Should students be allowed to disguise their identity in certain circumstances? | Ethical / Logistical | 3 | Analytics Committee | |
| Adverse impact of opting out on individual | If a student is allowed to opt out of data collection and analysis could this have a negative impact on their academic progress? | Ethical | 1 | Analytics Committee | |
| Adverse impact of opting out on group | If individual students opt out will the dataset be incomplete, thus potentially reducing the accuracy and effectiveness of learning analytics for the group | Ethical / Logistical | 1 | Data scientist | |
| Lack of real choice to opt out | Do students have a genuine choice if pressure is put on them by the insitution or they feel their academic success may be impacted by opting out? | Ethical | 3 | Analytics Committee | |
| Student input to analytics process | Should students have a say in what data is collected and how it is used for analytics? | Ethical | 3 | Analytics Committee | |
| Change of purpose | Should institutions request consent again if the data is to be used for purposes for which consent was not originally given? | Legal | 2 | Analytics Committee | |
| Legitimate interest | To what extent can the institution’s “legitimate interests” override privacy controls for individuals? | Legal | 2 | Analytics Committee | |
| Unknown future uses of data | How can consent be requested when potential future uses of the (big) data are not yet known? | Logistical | 3 | Analytics Committee | |
| Consent in open courses | Are open courses (MOOCs etc) different when it comes to obtaining consent? | Legal / Ethical | 2 | Analytics Committee | |
| Use of publicly available data | Can institutions use publicly available data (e.g. tweets) without obtaining consent? | Legal / Ethical | 3 | Analytics Committee | |
| Transparency | Student awareness of data collection | What should students be told about the data that is being collected about them? | Legal / Ethical | 1 | Analytics Committee |
| Student awareness of data use | What should students be told about the uses to which their data is being put? | Legal / Ethical | 1 | Analytics Committee | |
| Student awareness of algorithms and metrics | To what extent should students be given details of the algorithms used for learning analytics and the metrics and labels that are created? | Ethical | 2 | Analytics Committee | |
| Proprietary algorithms and metrics | What should institutions do if vendors do not release details of their algorithms and metrics? | Logistical | 3 | Analytics Committee | |
| Student awareness of potential consequences of opting out | What should students be told about the potential consequences of opting out of data collection and analysis of their learning? | Ethical | 2 | Analytics Committee | |
| Staff awareness of data collection and use | What should teaching staff be told about the data that is being collected about them, their students and what is being done with it? | Ethical | 1 | Analytics Committee | |
| Privacy | Out of scope data | Is there any data that should not be used for learning analytics? | Ethical | 2 | Analytics Committee |
| Tracking location | Under what circumstances is it appropriate to track the location of students? | Ethical | 2 | Analytics Committee | |
| Staff permissions | To what extent should access to students’ data be restricted within an institution? | Ethical / Logistical | 1 | Analytics Committee | |
| Unintentional creation of sensitive data | How do institutions avoid creating “sensitive” data e.g. religion, ethnicity from other data? | Legal / Logistical | 2 | Data scientist | |
| Requests from external agencies | What should institutions do when requests for student data are made by external agencies e.g. educational authorities or security agencies? | Legal / Logistical | 2 | Senior management | |
| Sharing data with other institutions | Under what circumstances is it appropriate to share student data with other institutions? | Legal / Ethical | 2 | Analytics Committee | |
| Access to employers | Under what circumstances is it appropriate to give employers access to analytics on students? | Ethical | 2 | Analytics Committee | |
| Enhancing trust by retaining data internally | If students are told that their data will be kept within the institution will they develop greater trust in and acceptance of analytics? | Ethical | 3 | Analytics Committee | |
| Use of metadata to identify individuals | Can students be identified from metadata even if personal data has been deleted? | Legal / Logistical | 2 | Data scientist | |
| Risk of re-identification | Does anonymisation of data become more difficult as multiple data sources are aggregated, potentially leading to re-identification of an individual? | Legal / Logistical | 1 | Data scientist | |
| Validity | Minimisation of inaccurate data | How should an institution minimise inaccuracies in the data? | Logistical | 2 | Data scientist |
| Minimisation of incomplete data | How should an institution minimise incompleteness of the dataset? | Logistical | 2 | Data scientist | |
| Optimum range of data sources | How many and which data sources are necessary to ensure accuracy in the analytics? | Logistical | 2 | Data scientist | |
| Validation of algorithms and metrics | How should an institution validate its algorithms and metrics? | Ethical / Logistical | 1 | Data scientist | |
| Spurious correlations | How can institutions avoid drawing misleading conclusions from spurious correlations? | Ethical / Logistical | 2 | Data scientist | |
| Evolving nature of students | How accurate can analytics be when students’ identities and actions evolve over time? | Logistical | 3 | Educational researcher | |
| Authentication of public data sources | How can institutions ensure that student data taken from public sites is authenticated to their students? | Logistical | 3 | IT | |
| Access | Student access to their data | To what extent should students be able to access the data held about them? | Legal | 1 | Analytics Committee |
| Student access to their analytics | To what extent should students be able to access the analytics performed on their data? | Legal / Ethical | 1 | Analytics Committee | |
| Data formats | In what formats should students be able to access their data? | Logistical | 2 | Analytics Committee | |
| Metrics and labels | Should students see the metrics and labels attached to them? | Ethical | 2 | Analytics Committee | |
| Right to correct inaccurate data | What data should students be allowed to correct about themselves? | Legal | 1 | Analytics Committee | |
| Data portability | What data about themselves should students be able to take with them? | Legal | 2 | Analytics Committee | |
| Action | Institutional obligation to act | What obligation does the institution have to intervene when there is evidence that a student could benefit from additional support? | Legal / Ethical | 1 | Analytics Committee |
| Student obligation to act | What obligation do students have when analytics suggests actions to improve their academic progress? | Ethical | 2 | Student | |
| Conflict with study goals | What should a student do if the suggestions are in conflict with their study goals? | Ethical | 3 | Student | |
| Obligation to prevent continuation | What obligation does the institution have to prevent students from continuing on a pathway which analytics suggests is not advisable? | Ethical | 2 | Analytics Committee | |
| Type of intervention | How are the appropriate interventions decided on? | Logistical | 1 | Educational researcher | |
| Distribution of interventions | How should interventions be distributed across the institution? | Logistical | 1 | Analytics Committee | |
| Conflicting interventions | How does the institution ensure that it is not carrying out multiple interventions with conflicting purposes? | Logistical | 2 | Educational researcher | |
| Staff incentives for intervention | What incentives are in place for staff to change practices and facilitate intervention? | Logistical | 3 | Analytics Committee | |
| Failure to act | What happens if an institution fails to intervene when analytics suggests that it should? | Logistical | 3 | Analytics Committee | |
| Need for human intermediation | Are some analytics better presented to students via e.g. a tutor than a system? | Ethical | 2 | Educational researcher | |
| Triage | How does an institution allocate resources for learning analytics appropriately for learners with different requirements? | Ethical / Logistical | 1 | Analytics Committee | |
| Triage transparency | How transparent should an institution be in how it allocates resources to different groups? | Ethical | 3 | Analytics Committee | |
| Opportunity cost | How is spending on learning analytics justified in relation to other funding requirements? | Logistical | 2 | Senior management | |
| Favouring one group over another | Could the intervention strategies unfairly favour one group over another? | Ethical / Logistical | 2 | Educational researcher | |
| Consequences of false information | What should institutions do if a student gives false information e.g. to obtain additional support? | Logistical | 3 | Analytics Committee | |
| Audit trails | Should institutions record audit trails of all predictions and interventions? | Logistical | 2 | Analytics Committee | |
| Unexpected findings | How should institutions deal with unexpected findings arising in the data? | Logistical | 3 | Analytics Committee | |
| Adverse impact | Labelling bias | Does labelling or profiling of students bias institutional perceptions and behaviours towards them? | Ethical | 1 | Educational researcher |
| Oversimplification | How can institutions avoid overly simplistic metrics and decision making which ignore personal circumstances? | Ethical | 1 | Educational researcher | |
| Undermining of autonomy | Is student autonomy in decision making undermined by predictive analytics? | Ethical | 2 | Educational researcher | |
| Gaming the system | If students know that data is being collected about them will they alter their behaviour to present themselves more positively, thus distracting them and skewing the analytics? | Ethical | 2 | Educational researcher | |
| Abusing the system | If students understand the algorithms will they manipulate the system to obtain additional support? | Ethical | 3 | Educational researcher | |
| Adverse behavioural impact | If students are presented with data about their performance could this have a negative impact e.g. increased likelihood of dropout? | Ethical | 1 | Educational researcher | |
| Reinforcement of discrimination | Could analytics reinforce discriminatory attitudes and actions by profiling students based on their race or gender? | Ethical | 1 | Educational researcher | |
| Reinforcement of social power differentials | Could analytics reinforce social power differentials and students’ status in relation to each other? | Ethical | 2 | Educational researcher | |
| Infantilisation | Could analytics “infantilise” students by spoon-feeding them with automated suggestions, making the learning process less demanding? | Ethical | 3 | Educational researcher | |
| Echo chambers | Could analytics create “echo chambers” where intelligent software reinforces our own attitudes and beliefs? | Ethical | 3 | Educational researcher | |
| Non-participation | Will knowledge that they are being monitored lead to non-participation by students? | Ethical | 2 | Educational researcher | |
| Stewardship | Data minimisation | Is all the data held on an individual necessary in order to carry out the analytics? | Legal | 1 | Data scientist |
| Data processing location | Is the data being processed in a country permitted by the local data protection laws? | Legal | 1 | IT | |
| Right to be forgotten | Can all data regarding an individual (expect that necessary for statutory purposes) be deleted? | Legal | 1 | IT | |
| Unnecessary data retention | How long should data be retained for? | Legal | 1 | Analytics Committee | |
| Unhelpful data deletion | If data is deleted does this restrict the institution’s analytics capabilities e.g. refining its models and tracking performance over multiple cohorts? | Logistical | 2 | Data scientist | |
| Incomplete knowledge of data sources | Can an institution be sure that it knows where all personal data is held? | Legal / Logistical | 1 | IT | |
| Inappropriate data sharing | How can data sharing be prevented with parties who have no legitimate interest in seeing it or who may use it inappropriately? | Legal | 1 | IT | |
