dc.contributor.authorSupraja, S.
dc.contributor.authorTatinati, Sivanagaraja
dc.contributor.authorHartman, Kevin
dc.contributor.authorKhong, Andy Wai Hoong
dc.date.accessioned2019-02-13T03:33:03Z
dc.date.available2019-02-13T03:33:03Z
dc.date.copyright2018-01-01
dc.date.issued2018
dc.identifier.citationSupraja, S., Tatinati, S., Hartman, K., & Khong, A. W. (2018). Automatically linking digital signal processing assessment questions to key engineering learning outcomes. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018). doi:10.1109/ICASSP.2018.8461373en_US
dc.identifier.urihttp://hdl.handle.net/10220/47656
dc.description.abstractTo deliver on the potential outcome-based teaching and learning holds for engineering education, it is important for engineering courses to provide students with different types of deliberate practice opportunities that align to the program’s learning outcomes. Working from these requirements, we increased the design and measurement intentionality of a digital signal processing (DSP) course. To align the course’s learning outcomes more constructively with its assessment measures, we automated the process of classifying DSP questions according to learning outcomes by introducing a model that integrates topic modeling and machine learning. In this work, we explored the effect of pre-processing procedures in terms of stopword selection and word co-occurrence redundancy issue in question classification inferences. In this work, we proposed a customized variant of the Word Network Topic Model, q-WNTM, which is able to use its pre-classified DSP questions to reliably classify new questions according to the course’s learning outcomes.en_US
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en_US
dc.format.extent5 p.en_US
dc.language.isoenen_US
dc.rights© 2018 Institute of Electrical and Electronics Engineers (IEEE). All rights reserved. This paper was published in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018) and is made available with permission of Institute of Electrical and Electronics Engineers (IEEE).en_US
dc.subjectLearning Outcomesen_US
dc.subjectAssessmenten_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleAutomatically linking digital signal processing assessment questions to key engineering learning outcomesen_US
dc.typeConference Paper
dc.contributor.conference2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018)en_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/ICASSP.2018.8461373
dc.description.versionAccepted versionen_US
dc.contributor.organizationDelta-NTU Corporate Laben_US
dc.contributor.organizationCentre for Research and Development in Learningen_US
dc.identifier.rims204214


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