TY - GEN
T1 - Adaptive Remediation with Multi-modal Content
AU - Tu, Yuwei
AU - Brinton, Christopher G.
AU - Lan, Andrew S.
AU - Chiang, Mung
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Remediation is an integral part of adaptive instructional systems that provide a supplement to lectures in case the delivered content proves too difficult for a user to fully grasp in a single class session. To extend the delivery of current remediation methods from single type of sources to combinations of different material types, we propose an adaptive remediation system with multi-modal remediation content. The system operates in four main phases: ingesting a library of multi-modal content files into bite-sized chunks, linking them based on topical and contextual relevance, then modeling users’ real-time knowledge state when they interact with the delivered course through the system and determining whether remediation is needed, and finally identifying a set of remediation segments addressing the current knowledge weakness with the relevance links. We conducted two studies to test our developed adaptive remediation system in an advanced engineering course taught at an undergraduate institution in the US and evaluated our system on productivity. Both studies show that our system is effective in increasing the productivity by at least 50%.
AB - Remediation is an integral part of adaptive instructional systems that provide a supplement to lectures in case the delivered content proves too difficult for a user to fully grasp in a single class session. To extend the delivery of current remediation methods from single type of sources to combinations of different material types, we propose an adaptive remediation system with multi-modal remediation content. The system operates in four main phases: ingesting a library of multi-modal content files into bite-sized chunks, linking them based on topical and contextual relevance, then modeling users’ real-time knowledge state when they interact with the delivered course through the system and determining whether remediation is needed, and finally identifying a set of remediation segments addressing the current knowledge weakness with the relevance links. We conducted two studies to test our developed adaptive remediation system in an advanced engineering course taught at an undergraduate institution in the US and evaluated our system on productivity. Both studies show that our system is effective in increasing the productivity by at least 50%.
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U2 - 10.1007/978-3-030-22341-0_36
DO - 10.1007/978-3-030-22341-0_36
M3 - Conference contribution
AN - SCOPUS:85069746780
SN - 9783030223403
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 455
EP - 468
BT - Adaptive Instructional Systems - 1st International Conference, AIS 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Proceedings
A2 - Sottilare, Robert A.
A2 - Schwarz, Jessica
PB - Springer Verlag
T2 - 1st International Conference on Adaptive Instructional Systems, AIS 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCI International 2019
Y2 - 26 July 2019 through 31 July 2019
ER -