In the words of the guys at Rustici: “Tin Can API (sometimes known as the Experience API) is a brand new specification for learning technology that makes it possible to collect data about the wide range of experiences a person has (online and offline)”.
One of the things this means is that learning professionals could become accumulators and analysers of 'big data', with all the implications for handling data responsibly that come with this.
For any learning engagement to be successful it is vital for the educators to gain and keep the trust of the participants. In this 'big data' world, where the digital trail we leave behind can be used to target us for goods and services or can fall into the hands of identity thieves and fraudsters, we are all becoming more aware and more wary of who knows what about us. Those of us who work with Tin Can (xAPI) need to think carefully about how we use this new capability, and how it is presented to our learners.
A Bit of Tin Can in the Real World
Consider this scenario which we encountered recently in a Tin Can project with a good partner of ours. (OK, they're actually a customer… but it feels like a partnership!)
An organisation has a multiple choice interaction which tests for mastery of a subject. For compliance purposes, the organisation must record the happy event of someone passing the test and be able to produce evidence of it if challenged in the future.
The data being recorded is a timestamped set of specific answers, tagged with the passing student's identity and their score.
Who has an interest in this tracked information?
The organisation… needs it to demonstrate training has been delivered and assessed to the satisfaction of compliance requirements. This data may be vital in avoiding a fine or other penalty.
The student… has a positive interest in this data being tracked. Since 'mastery' is a condition of participation in certain of the organisation's activities and projects, it is a fair assumption that passing this test is important to them; it may even be a condition of continued employment.
Let's develop the scenario a bit further.
In order to help the student learn and become proficient, the organisation is happy for the student to attempt the interaction as many times as they like.
In order for the subject matter experts, instructional designers and organisation stakeholders to get feedback on the efficiency of the learning interaction, the organisation would like to know the patterns of wrong and right responses to specific questions. This will help in honing the questions and improving the learning strategy and resources. Analysis like this does not require knowledge of who gave which answer, so these response records could be captured and held anonymously.
The data being recorded is a timestamped set of specific responses, but tagged as anonymous. Our client is a public organisation and this test is being distributed to public volunteers, so they're being very careful about what data is captured and minimising the amount of personal data stored.
So who has an interest in this set of tracking data?
The subject matter experts, instructional designers and organisation stakeholders… want to ensure that the test is fit for purpose, and could gain value from monitoring patterns of responses to questions.
The student… is unlikely to want their identity to be recorded on failed attempts. When these attempts are anonymous the student can feel free to try and fail (and learn!), and would probably be OK with this data being recorded to help improve the learning programme.
So what does this amount to?
By being careful with what identities are reported on the Tin Can statements, we can aim to minimise the amount of data which can be traced back to the individual. And by gathering anonymous data, the organisation can improve its learning service for future students.
Although as instructional designers and learning interaction engineers we don't have total control over student privacy, establishing and maintaining trust with the student should always be a vital concern for us. By only recording data that is relevant and purposeful, optimising the amount of personalised data, and communicating very clearly and honestly with the learner what we're doing with the data, we establish trust and contribute towards a solid contract for learning with the student.
So in summary, here are the aims and principles that guide us as instructional designers.
Principles of data collection
To talk more about Tin Can, instructional design, or any other elearning issue, give us a call on 01959 543900. We're looking forward to hearing from you!