Please use this identifier to cite or link to this item:
Title: Online education evaluation for signal processing course through student learning pathways
Authors: Ng, Kelvin Hongrui
Tatinati, Sivanagaraja
Khong, Andy Wai Hoong
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Deep Learning
Online Education
Issue Date: 2018
Source: Ng, K. H., Tatinati, S., & Khong, A. W. H. (2018). Online education evaluation for signal processing course through student learning pathways. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018), Calgary, Canada.
Abstract: Impact of online learning sequences to forecast course outcomes for an undergraduate digital signal processing (DSP) course is studied in this work. A multi-modal learning schema based on deep-learning techniques with learning sequences, psychometric measures, and personality traits as input features is developed in this work. The aim is to identify any underlying patterns in the learning sequences and subsequently forecast the learning outcomes. Experiments are conducted on the data acquired for the DSP course taught over 13 teaching weeks to underpin the forecasting efficacy of various deeplearning models. Results showed that the proposed multi-modal schema yields better forecasting performance compared to existing frequency-based methods in existing literature. It is further observed that the psychometric measures incorporated in the proposed multimodal schema enhance the ability of distinguishing nuances in the input sequences when the forecasting task is highly dependent on human behavior.
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [].
metadata.item.grantfulltext: open
metadata.item.fulltext: With Fulltext
Appears in Collections:EEE Conference Papers

Files in This Item:
File Description SizeFormat 
ICASSP2018b.pdf210.15 kBAdobe PDFThumbnail

Google ScholarTM


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