Online education evaluation for signal processing course through student learning pathways

• Main Authors: Ng, Kelvin Hongrui, Tatinati, Sivanagaraja, Khong, Andy Wai Hoong
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2019
• Subjects: DRNTU::Engineering::Electrical and electronic engineering; Deep Learning; Online Education
• Online Access: https://hdl.handle.net/10356/88335; http://hdl.handle.net/10220/47974; https://2018.ieeeicassp.org/Papers/PublicSessionIndex3.asp?Sessionid=1180
• Institution: Nanyang Technological University

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.