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|Title:||Automatically linking digital signal processing assessment questions to key engineering learning outcomes||Authors:||Supraja, S.
Khong, Andy Wai Hoong
DRNTU::Engineering::Electrical and electronic engineering
|Issue Date:||2018||Source:||Supraja, 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.2018.8461373||Abstract:||To 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.||URI:||https://hdl.handle.net/10356/88336
|DOI:||10.1109/ICASSP.2018.8461373||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).||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Conference Papers|
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