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TinCan2PROV: exposing interoperable provenance of learning processes through experience API logs

Tom De Nies (UGent) , Frank Salliau (UGent) , Ruben Verborgh (UGent) , Erik Mannens (UGent) and Rik Van de Walle (UGent)
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Abstract
A popular way to log learning processes is by using the Experience API (abbreviated as xAPI), also referred to as Tin Can. While Tin Can is great for developers who need to log learning experiences in their applications, it is more challenging for data processors to interconnect and analyze the resulting data. An interoperable data model is missing to raise Tin Can to its full potential. We argue that in essence, these learning process logs are provenance. Therefore, the W3C PROV model can provide the much-needed interoperability. In this paper, we introduce a method to expose PROV using Tin Can statements. To achieve this, we made the following contributions: (1) a formal ontology of the xAPI vocabulary, (2) a context document to interpret xAPI statements as JSON-LD, (3) a mapping to convert xAPI JSON-LD statements into PROV, and (4) a tool implementing this mapping. We preliminarily evaluate the approach by converting 20 xAPI statements taken from the public Tin Can Learning Record Store to valid PROV. Where the conversion succeeded, it did so without loss of valid information, therefore suggesting that the conversion process is reversible, as long as the original JSON is valid.

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MLA
De Nies, Tom et al. “TinCan2PROV: Exposing Interoperable Provenance of Learning Processes Through Experience API Logs.” WWW’15 Companion : Proceedings of the 24th International Conference on World Wide Web. New York, NY, USA: Association for Computing Machinery (ACM), 2015. 689–694. Print.
APA
De Nies, T., Salliau, F., Verborgh, R., Mannens, E., & Van de Walle, R. (2015). TinCan2PROV: exposing interoperable provenance of learning processes through experience API logs. WWW’15 companion : proceedings of the 24th international conference on world wide web (pp. 689–694). Presented at the 24th International conference on World Wide Web (WWW 2015), New York, NY, USA: Association for Computing Machinery (ACM).
Chicago author-date
De Nies, Tom, Frank Salliau, Ruben Verborgh, Erik Mannens, and Rik Van de Walle. 2015. “TinCan2PROV: Exposing Interoperable Provenance of Learning Processes Through Experience API Logs.” In WWW’15 Companion : Proceedings of the 24th International Conference on World Wide Web, 689–694. New York, NY, USA: Association for Computing Machinery (ACM).
Chicago author-date (all authors)
De Nies, Tom, Frank Salliau, Ruben Verborgh, Erik Mannens, and Rik Van de Walle. 2015. “TinCan2PROV: Exposing Interoperable Provenance of Learning Processes Through Experience API Logs.” In WWW’15 Companion : Proceedings of the 24th International Conference on World Wide Web, 689–694. New York, NY, USA: Association for Computing Machinery (ACM).
Vancouver
1.
De Nies T, Salliau F, Verborgh R, Mannens E, Van de Walle R. TinCan2PROV: exposing interoperable provenance of learning processes through experience API logs. WWW’15 companion : proceedings of the 24th international conference on world wide web. New York, NY, USA: Association for Computing Machinery (ACM); 2015. p. 689–94.
IEEE
[1]
T. De Nies, F. Salliau, R. Verborgh, E. Mannens, and R. Van de Walle, “TinCan2PROV: exposing interoperable provenance of learning processes through experience API logs,” in WWW’15 companion : proceedings of the 24th international conference on world wide web, Florence, Italy, 2015, pp. 689–694.
@inproceedings{7035698,
  abstract     = {A popular way to log learning processes is by using the Experience API (abbreviated as xAPI), also referred to as Tin Can. While Tin Can is great for developers who need to log learning experiences in their applications, it is more challenging for data processors to interconnect and analyze the resulting data. An interoperable data model is missing to raise Tin Can to its full potential. We argue that in essence, these learning process logs are provenance. Therefore, the W3C PROV model can provide the much-needed interoperability. In this paper, we introduce a method to expose PROV using Tin Can statements. To achieve this, we made the following contributions: (1) a formal ontology of the xAPI vocabulary, (2) a context document to interpret xAPI statements as JSON-LD, (3) a mapping to convert xAPI JSON-LD statements into PROV, and (4) a tool implementing this mapping. We preliminarily evaluate the approach by converting 20 xAPI statements taken from the public Tin Can Learning Record Store to valid PROV. Where the conversion succeeded, it did so without loss of valid information, therefore suggesting that the conversion process is reversible, as long as the original JSON is valid.},
  author       = {De Nies, Tom and Salliau, Frank and Verborgh, Ruben and Mannens, Erik and Van de Walle, Rik},
  booktitle    = {WWW'15 companion : proceedings of the 24th international conference on world wide web},
  isbn         = {9781450334730},
  language     = {eng},
  location     = {Florence, Italy},
  pages        = {689--694},
  publisher    = {Association for Computing Machinery (ACM)},
  title        = {TinCan2PROV: exposing interoperable provenance of learning processes through experience API logs},
  url          = {http://dx.doi.org/10.1145/2740908.2741744},
  year         = {2015},
}

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