Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88336
Title: Automatically linking digital signal processing assessment questions to key engineering learning outcomes
Authors: Supraja, S.
Tatinati, Sivanagaraja
Hartman, Kevin
Khong, Andy Wai Hoong
Keywords: Learning Outcomes
Assessment
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
http://hdl.handle.net/10220/47656
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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