This document describes a mapping between the Experience API (xAPI) format for logging learning experiences and the W3C PROV standard for representing provenance. It outlines how xAPI statements can be converted to JSON-LD and then mapped to equivalent PROV concepts to make the learning logs interoperable. The mapping was tested on real xAPI statement data and allows logging of learning processes in a way that is machine-interpretable and can be queried and analyzed at scale. Going forward, the mapping will be used and tested in educational projects and systems to start leveraging the power of linked data for learning analytics.

1. ELIS – Multimedia Lab
Tom De Nies, Frank Salliau, Ruben Verborgh,
Erik Mannens, Rik Van de Walle
Ghent University – iMinds – MMLab
http://research.tomdenies.be
tom.denies@ugent.be
@TomDeNies
TinCan2PROV:
Exposing Interoperable Provenance of Learning
Processes through Experience API Logs

2. ELIS – Multimedia Lab
Logging learning processes
Tin Can a.k.a. the Experience API
W3C PROV: the standard for provenance
Mapping Tin Can to PROV
Put it to the test
What’s next?
Conclusion
Outline

3. ELIS – Multimedia Lab
Logging Learning Processes

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Digital learning records can give us insight in:
 Student performance
 Learning circumstances/context
 Correlation between students, assignments, teachers,
materials, …
 And much more!
Logging Learning Processes

5. ELIS – Multimedia Lab
(a.k.a. the Experience API, a.k.a. the xAPI)
Currently the go-to solution to effectively log (learning) experiences.
Pros: developer-friendly JSON format, easy-to-understand basics,
plenty of Learning Record Store (LRS) options to manage the logs.
Cons: no full-fledged Linked Data, some awkward design choices
How can we log learning experiences?

6. ELIS – Multimedia Lab
{
"actor": {
"mbox": "mailto:tom.denies@ugent.be",
"name": "Tom De Nies",
"objectType": "Agent"
},
"verb": {
"id": "xapi-verbs:completed",
"display": { "en": "completed" }
},
"object": {
"id": "http://www.example.org/exercise1",
"objectType": "Activity",
"definition": { "name": { "en": "Example Activity" } }
}
}
Time for an example
“I”
“did”
“this”

7. ELIS – Multimedia Lab
See
http://tincanapi.com/
statements-101/
(of course there’s more to be logged…)

8. ELIS – Multimedia Lab
We could, but then we would miss some opportunities:
 There’s just 1 serialization: JSON
 This would limit the choices in database technology …
 … which would limit the choices when building a LRS
 Tin Can’s machine-interpretability is sub-optimal
(e.g., the URL for the specification is either a PDF or a Github blob)
 Interoperability could be improved
Why not just use Tin Can as is?

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Let’s look at that earlier Tin Can statement in another way:
What can we do about this?
Tom De Nies
completed
Example Activity

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… called provenance
“Provenance is information about entities, activities, and people
involved in producing a piece of data or thing.” – W3C PROV
http://www.w3.org/TR/prov-dm/
So if learning experience logs describe the things that led to a
result … they are actually provenance!
… and that means we can model it in PROV!
This reminds me of something…

11. ELIS – Multimedia Lab
PROV has the power to address our previous concerns:
 There’s many serializations: PROV-N, RDF, XML, JSON, …
 This could go in any database: mongodb, relational, a triple
store, linked data fragment, …
 … we could use this to build a LRS ourselves!
 Everything is machine-interpretable
 Interoperability is PROV’s raison d’être…
Why PROV?

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Learning logs could be cross-referenced with:
Imagine the possibilities…
Version history of educational software
Lineage and inventory of the study material
Provenance of the teaching staff, IT
infrastructure, …
And so on!

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Overview of our workflow

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Step 1: bring the statement closer to actual Linked Data
http://www.w3.org/TR/json-ld/
In theory, all we need is to add an “@context” :
“http://semweb.mmlab.be/ns/tincan2prov/tincan2prov.jsonld”
which maps every JSON field to a concept in an ontology.
In practice, this revealed some flaws with the xAPI
specification…
JSON-LD

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There was no ontology (at least no machine-interpretable one).
So we created one: http://semweb.mmlab.be/ns/tincan2prov/
Adding this to root of JSON object should work now:
“@context”:
http://semweb.mmlab.be/ns/tincan2prov/tincan2prov.jsonld,
“@type”: “xapi:Statement”
Converting xAPI JSON to JSON-LD (1/2)

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Hiccups encountered:
- The field “id” is reserved for the URI in JSON-LD, so we need to
add "@context": { "id": "@id"} to every verb and object property
- Language tags: xAPI conforms to RFC 5646, whereas JSON-LD
conforms to RFC 4646
- Extensions & attachments are use case dependent
- Equally named fields don’t always have the same datatype
(e.g., id can sometimes be a UUID, other times a URI)
Converting xAPI JSON to JSON-LD (2/2)

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{
"@context":
“http://semweb.mmlab.be/ns/tincan2prov/tincan2prov.jsonld",
"@type": "http://semweb.mmlab.be/ns/tincan2prov/Statement",
"actor": {
"mbox": "mailto:tom.denies@ugent.be",
"name": "Tom De Nies",
"objectType": "Agent"
},
Our running example in JSON-LD (1/2)

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"verb": {
"@context": { "id" : "@id" },
"id": "xapi-verbs:completed",
"display": { "en": "completed",
"nl": "afgewerkt"
}
},
"object": {
"@context": { "id" : "@id" },
"id": "http://www.example.org/exercise1",
"objectType": "Activity",
"definition": {
"name": { "en": "Example Activity" }
}
}
}
Our running example in JSON-LD (1/2)

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[] xapi:actor [ a xapi:Agent;
xapi:name "Tom De Nies"@en;
foaf:mbox <mailto:tom.denies@ugent.be> ];
xapi:verb xapi-verbs:completed ;
xapi:object <http://www.example.org/exercise1> .
xapi-verbs:completed
xapi:display "completed"@en ,
"afgewerkt"@nl .
<http://www.example.org/exercise1>
a xapi:Activity ;
xapi:definition [
xapi:name
"Example Activity"@en
] .
…or in RDF/Turtle

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xapi:actor → prov:Agent
xapi:verb → prov:Activity
xapi:object → prov:Entity
xapi:result, xapi:score, xapi:context, xapi:attachments→
prov:Entity
xapi:contextActivities → prov:Collection of xapi:Activity
xapi:stored → prov:wasGeneratedBy
xapi:timestamp → prov:qualifiedStart
Infer PROV assertions (in a nutshell)

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The result

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Try it yourself at http://tincan2prov.org

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How do you evaluate a mapping?
→ by using it extensively in practice
Again, only experience will tell…
For now:
checked with 20 diverse statements from the public Tin Can LRS
http://tincan2prov.org/evaluation.html
- Smooth conversion (when input was valid)
- No loss of information observed (so mapping can be
reversed if need be)
Put it to the test

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Flemish project Edutablet
http://www.iminds.be/en/projects/2014/03/20/edutab
User-driven development and optimization of digital teaching
aids in education
Real-life tests in schools running as we speak
The end-game: a fully LD/PROV-based LRS
What’s next?

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The foundations for interoperable learning logs are there.
Now we need to start harnessing the power of Linked Data in
education…
… and TinCan2PROV is a small step in the right direction!
http://tincan2prov.org
Conclusion