TY - GEN
T1 - Early intervention for improving student performance by detecting non-engagement
AU - Barlaskar, Esha
AU - Cutting, David
AU - McDowell, Andrew
AU - Anderson, Neil
AU - Ahmaderaghi, Baharak
AU - Collins, Matthew
PY - 2023/6/24
Y1 - 2023/6/24
N2 - During the Covid-19 pandemic, both teachers and students had to face many challenges, especially due to the lack of in-person classes. To address those challenges and to make up for the lack of in-person lectures, teaching modalities had been changed, which yielded unexpected benefits. For example, students started to engage more in online lecture sessions via chats, polls, and quizzes. Online lectures were recorded enabling students to revisit them as a valuable study aid, this was particularly useful for international students where English is not their first language. Post Covid-19 pandemic once the teaching was back on-campus, we decided to go with the blended mode of teaching, where we adopted all the positive things that helped in engaging students during our online teaching such as live quizzes, breakout rooms, polls, making lecture content recorded for online viewing, etc. and tried to implement them in the in-person classroom. Running this mode of teaching and learning for 2022, we have received mixed student feedback and academic results. In this paper we first reflect on the strengths and weaknesses of this approach, highlighting what worked for us and what did not, and then we propose ways to mitigate those weaknesses. Specifically, we propose an approach to tackle the lack of student engagement in the modules by identifying the students who are not engaging in the module and making early interventions either to modify the classroom activities or to motivate those students so that they re-engage. There are some key indicator metrics for identifying the non-engaging students, such as attendance in the practical lab/support sessions, log-in details to the servers where the experiments are being run, and results of classroom interactive activities like quizzes, polls, etc. It would benefit the students if these key metrics are utilised right from the start of the module in order to detect the students who might fall behind and perform poorly.
AB - During the Covid-19 pandemic, both teachers and students had to face many challenges, especially due to the lack of in-person classes. To address those challenges and to make up for the lack of in-person lectures, teaching modalities had been changed, which yielded unexpected benefits. For example, students started to engage more in online lecture sessions via chats, polls, and quizzes. Online lectures were recorded enabling students to revisit them as a valuable study aid, this was particularly useful for international students where English is not their first language. Post Covid-19 pandemic once the teaching was back on-campus, we decided to go with the blended mode of teaching, where we adopted all the positive things that helped in engaging students during our online teaching such as live quizzes, breakout rooms, polls, making lecture content recorded for online viewing, etc. and tried to implement them in the in-person classroom. Running this mode of teaching and learning for 2022, we have received mixed student feedback and academic results. In this paper we first reflect on the strengths and weaknesses of this approach, highlighting what worked for us and what did not, and then we propose ways to mitigate those weaknesses. Specifically, we propose an approach to tackle the lack of student engagement in the modules by identifying the students who are not engaging in the module and making early interventions either to modify the classroom activities or to motivate those students so that they re-engage. There are some key indicator metrics for identifying the non-engaging students, such as attendance in the practical lab/support sessions, log-in details to the servers where the experiments are being run, and results of classroom interactive activities like quizzes, polls, etc. It would benefit the students if these key metrics are utilised right from the start of the module in order to detect the students who might fall behind and perform poorly.
M3 - Conference contribution
SN - 9789893510636
VL - 2
T3 - Education and New Developments
SP - 149
EP - 153
BT - Proceedings of the International Conference on Education and New Developments, END 2023
A2 - Carmo, Mafalda
PB - inScience Press
T2 - Education and New Developments Conference 2023
Y2 - 24 June 2023 through 26 June 2023
ER -