Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88336
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSupraja, S.en
dc.contributor.authorTatinati, Sivanagarajaen
dc.contributor.authorHartman, Kevinen
dc.contributor.authorKhong, Andy Wai Hoongen
dc.date.accessioned2019-02-13T03:33:03Zen
dc.date.accessioned2019-12-06T17:01:00Z-
dc.date.available2019-02-13T03:33:03Zen
dc.date.available2019-12-06T17:01:00Z-
dc.date.copyright2018-01-01en
dc.date.issued2018en
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
dc.identifier.urihttps://hdl.handle.net/10356/88336-
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
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en
dc.format.extent5 p.en
dc.language.isoenen
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
dc.subjectLearning Outcomesen
dc.subjectAssessmenten
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleAutomatically linking digital signal processing assessment questions to key engineering learning outcomesen
dc.typeConference Paperen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.contributor.conference2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018)en
dc.contributor.researchDelta-NTU Corporate Laboratoryen
dc.contributor.researchCentre for Research and Development in Learning (CRADLE)en
dc.identifier.doi10.1109/ICASSP.2018.8461373en
dc.description.versionAccepted versionen
dc.identifier.rims204214en
item.grantfulltextopen-
item.fulltextWith Fulltext-
Appears in Collections:EEE Conference Papers
Files in This Item:
File Description SizeFormat 
ICASSP2018a.pdf458.9 kBAdobe PDFThumbnail
View/Open

SCOPUSTM   
Citations 50

3
Updated on Jul 16, 2022

Page view(s)

307
Updated on Aug 12, 2022

Download(s) 50

76
Updated on Aug 12, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.