TY - GEN
T1 - MORPH
T2 - 7th International Conference on Learning Analytics and Knowledge, LAK 2017
AU - Jeremic, Zoran
AU - Kumar, Vive
AU - Graf, Sabine
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/3/13
Y1 - 2017/3/13
N2 - While there is high potential in using learning analytics to provide educational institutions as well as teachers and learners with actionable information and improve learning experiences, currently only very few learning analytics tools are actually used in educational institutions. In this paper, we introduce MORPH, a platform that facilitates the integration of learning analytics modules and tools into institutional learning systems. MORPH provides a robust distributed architecture which combines batch, stream and real-time data processing using a parallel processing model to enable and support efficient processing of large amounts of data. Furthermore, it provides common management and administration features that enable the seamless integration of learning analytics research modules and tools into existing institutional learning systems.
AB - While there is high potential in using learning analytics to provide educational institutions as well as teachers and learners with actionable information and improve learning experiences, currently only very few learning analytics tools are actually used in educational institutions. In this paper, we introduce MORPH, a platform that facilitates the integration of learning analytics modules and tools into institutional learning systems. MORPH provides a robust distributed architecture which combines batch, stream and real-time data processing using a parallel processing model to enable and support efficient processing of large amounts of data. Furthermore, it provides common management and administration features that enable the seamless integration of learning analytics research modules and tools into existing institutional learning systems.
KW - Batch processing
KW - Dashboards
KW - Data streaming
KW - Institutional learning environments
KW - Learning analytics
KW - Real-time processing
UR - http://www.scopus.com/inward/record.url?scp=85016449251&partnerID=8YFLogxK
U2 - 10.1145/3027385.3029478
DO - 10.1145/3027385.3029478
M3 - Published Conference contribution
AN - SCOPUS:85016449251
T3 - ACM International Conference Proceeding Series
SP - 596
EP - 597
BT - LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference
Y2 - 13 March 2017 through 17 March 2017
ER -