An Overview of Usage Data Formats for Recommendations in TEL

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Presentation of the paper "An Overview of Usage Data Formats for ecommendations in TEL" held at the RecSysTel WS 2012 in Saarbrücken, Germany - paper available at CEUR-896 (http://ceur-ws.org/Vol-896/)

Technology Education

An Overview of Usage Data Formats for
Recommendations in TEL
Short paper

Katja Niemann
Maren Scheffel
Martin Wolpers

© Fraunhofer-Institut für Angewandte Informationstechnik FIT

Usage Data

 Focus here on the following domains
 Learning analytics
 Educational data mining

 Examples of successful use
 Identification of irregularities of learning behaviour of students
 Reflection and comparison of learning activities among students of a
learning group
 Personalized recommender systems

© Fraunhofer-Institut für Angewandte Informationstechnik FIT

Comparison of prominent formats

 Contextualized Attention Metadata

 Activity Streams

 Learning Registry Paradata

 NSDL Paradata

  Enhance the interoperability among usage data analysis tools
and usage data storage silos

© Fraunhofer-Institut für Angewandte Informationstechnik FIT

Contextualized Attention Metadata (CAM)

Schmitz, H.-C., Wolpers, M., Kirschenmann, U., Niemann, K. 2012. ‚Contextualized Attention Metadata’.
Human Attention in Digital Environments,
Eds: Claudia Roda, Cambridge University Press, Cambridge, US, 2012
http://www.cup.es/catalogue/catalogue.asp?isbn=9780521765657

Core features
 Event representation links entities without fixed roles
 Linked Data approach to entities and events (open definition)

© Fraunhofer-Institut für Angewandte Informationstechnik FIT

Activity Streams

Snell, J., Atkins, M., Norris, W., Messina, C., Wilkinson, M., Dolin, R. 2012. JSON Activity Streams 1.0,
http://activitystrea.ms/specs/json/1.0/

Core features
 Activity representation links entities with fixed roles
 Sequence of activity objects represent activity stream
 Aims to provide usage info about single objects

© Fraunhofer-Institut für Angewandte Informationstechnik FIT

Learning Registry Paradata

Paradata Specification V1.0.
https://docs.google.com/document/d/1IrOYXd3S0FUwNozaEG5tM7Ki4_AZPrBn-pbyVUz-Bh0/edit?hl=en_US&pli=1#

Core features
 Modified Activity Streams format
 Used for aggregate representations

© Fraunhofer-Institut für Angewandte Informationstechnik FIT

NSDL Paradata
NSDL Paradata, 2012.
https://wiki.ucar.edu/display/nsdldocs/comm_para+%28paradata+-+usage+data%29

Core features
 Object-centric representation of usage of resource
 Collection of all usage info in one representation per object

© Fraunhofer-Institut für Angewandte Informationstechnik FIT

Conclusion and next steps

Automatic Mapping: no one-size-fits-all mapping among the formats is
possible!
 Extendable schemas
 Vocabularies not specified completely
 Open definition of some elements  metadata schemas that can
include unknown schemas

 Automatic mapping: rules sets based on specific usage scenarios!
 Then: Generalize over rules sets to create less scenario specific rule
sets.

© Fraunhofer-Institut für Angewandte Informationstechnik FIT

Thanks a lot!

Fraunhofer Institut für Angewandte Informationstechnik FIT

Katja Niemann
Maren Scheffel
Martin Wolpers

First.last@fit.fraunhofer.de

Schloss Birlinghoven
53754 Sankt Augustin

© Fraunhofer-Institut für Angewandte Informationstechnik FIT

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An Overview of Usage Data Formats for Recommendations in TEL

1. An Overview of Usage Data Formats for Recommendations in TEL Short paper Katja Niemann Maren Scheffel Martin Wolpers © Fraunhofer-Institut für Angewandte Informationstechnik FIT

2. Usage Data  Focus here on the following domains  Learning analytics  Educational data mining  Examples of successful use  Identification of irregularities of learning behaviour of students  Reflection and comparison of learning activities among students of a learning group  Personalized recommender systems © Fraunhofer-Institut für Angewandte Informationstechnik FIT

3. Comparison of prominent formats  Contextualized Attention Metadata  Activity Streams  Learning Registry Paradata  NSDL Paradata   Enhance the interoperability among usage data analysis tools and usage data storage silos © Fraunhofer-Institut für Angewandte Informationstechnik FIT

4. Contextualized Attention Metadata (CAM) Schmitz, H.-C., Wolpers, M., Kirschenmann, U., Niemann, K. 2012. ‚Contextualized Attention Metadata’. Human Attention in Digital Environments, Eds: Claudia Roda, Cambridge University Press, Cambridge, US, 2012 http://www.cup.es/catalogue/catalogue.asp?isbn=9780521765657 Core features  Event representation links entities without fixed roles  Linked Data approach to entities and events (open definition) © Fraunhofer-Institut für Angewandte Informationstechnik FIT

5. Activity Streams Snell, J., Atkins, M., Norris, W., Messina, C., Wilkinson, M., Dolin, R. 2012. JSON Activity Streams 1.0, http://activitystrea.ms/specs/json/1.0/ Core features  Activity representation links entities with fixed roles  Sequence of activity objects represent activity stream  Aims to provide usage info about single objects © Fraunhofer-Institut für Angewandte Informationstechnik FIT

6. Learning Registry Paradata Paradata Specification V1.0. https://docs.google.com/document/d/1IrOYXd3S0FUwNozaEG5tM7Ki4_AZPrBn-pbyVUz-Bh0/edit?hl=en_US&pli=1# Core features  Modified Activity Streams format  Used for aggregate representations © Fraunhofer-Institut für Angewandte Informationstechnik FIT

7. NSDL Paradata NSDL Paradata, 2012. https://wiki.ucar.edu/display/nsdldocs/comm_para+%28paradata+-+usage+data%29 Core features  Object-centric representation of usage of resource  Collection of all usage info in one representation per object © Fraunhofer-Institut für Angewandte Informationstechnik FIT

8. Conclusion and next steps Automatic Mapping: no one-size-fits-all mapping among the formats is possible!  Extendable schemas  Vocabularies not specified completely  Open definition of some elements  metadata schemas that can include unknown schemas  Automatic mapping: rules sets based on specific usage scenarios!  Then: Generalize over rules sets to create less scenario specific rule sets. © Fraunhofer-Institut für Angewandte Informationstechnik FIT

9. Thanks a lot! Fraunhofer Institut für Angewandte Informationstechnik FIT Katja Niemann Maren Scheffel Martin Wolpers First.last@fit.fraunhofer.de Schloss Birlinghoven 53754 Sankt Augustin © Fraunhofer-Institut für Angewandte Informationstechnik FIT

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