TY - JOUR
T1 - Towards an Understanding of the Engagement and Emotional Behaviour of MOOC Students using Sentiment and Semantic Features
AU - TAO, Xiaohui
AU - SHANNON-HONSON, Aaron
AU - DELANEY, Patrick
AU - DANN, Christopher
AU - XIE, Haoran
AU - LI, Yan
AU - O'NEILL, Shirley
N1 - Publisher Copyright:
© 2023 The Author(s)
PY - 2023/1
Y1 - 2023/1
N2 - Online learning and teaching increased in 2020, driven by the COVID-19 pandemic. As many researchers attempted to understand the impact stress had on the emotional behaviours and academic performance of students, most studies explored these pre- and during-COVID behaviours in the context of brick and mortar institutions transitioning to online delivery. There is an opportunity to compare the experiences of students in the MOOC environment in this period, particularly in terms of the difference of engagement, semantics and sentiment/stress behaviours in 2019 and 2020. In this study, we use a dataset from AdelaideX between this time period to identify the most significant features that impact student outcomes. Where previous machine learning approaches used singular features such as student interaction or sentiment in discussion forum posts, we incorporate three feature categories of engagement, semantics and sentiment/stress in an ensemble model is based on voting and stacked methods to determining the relationship between them and academic performance. From our results, we discover that sentiment/stress played little part in academic performance and was relatively unchanged in online courses in this dataset between 2019 and 2020. We present two individual student cases to further contextualise our findings.
AB - Online learning and teaching increased in 2020, driven by the COVID-19 pandemic. As many researchers attempted to understand the impact stress had on the emotional behaviours and academic performance of students, most studies explored these pre- and during-COVID behaviours in the context of brick and mortar institutions transitioning to online delivery. There is an opportunity to compare the experiences of students in the MOOC environment in this period, particularly in terms of the difference of engagement, semantics and sentiment/stress behaviours in 2019 and 2020. In this study, we use a dataset from AdelaideX between this time period to identify the most significant features that impact student outcomes. Where previous machine learning approaches used singular features such as student interaction or sentiment in discussion forum posts, we incorporate three feature categories of engagement, semantics and sentiment/stress in an ensemble model is based on voting and stacked methods to determining the relationship between them and academic performance. From our results, we discover that sentiment/stress played little part in academic performance and was relatively unchanged in online courses in this dataset between 2019 and 2020. We present two individual student cases to further contextualise our findings.
KW - MOOCs
KW - Performance modelling
KW - Sentiment analysis
KW - Stress
UR - http://www.scopus.com/inward/record.url?scp=85147418848&partnerID=8YFLogxK
U2 - 10.1016/j.caeai.2022.100116
DO - 10.1016/j.caeai.2022.100116
M3 - Journal Article (refereed)
SN - 2666-920X
VL - 4
JO - Computers and Education: Artificial Intelligence
JF - Computers and Education: Artificial Intelligence
M1 - 100116
ER -