Cognitive Load Assessment in Learning Construction Sensor Data Analytics within an End User Programming Environment
Mohammad Khalid, Abiola Abosede Akanmu, Anthony Olukayode Yusuf, Homero Murzi, Ibukun Awolusi, Nihar James Gonsalves · 2024
Computational thinking-supported educational training can equip the construction workforce with the necessary skills and knowledge to implement sensing technologies and perform sensor data analytics aimed at enhancing construction operations. However, construction students struggle to understand the computational concepts and workflows required to translate low-level sensor data into knowledge for supporting decisions. This study compared the cognitive load of construction students performing similar data analytics tasks using an end-user programming environment (EUP) to conventional MS Excel. Comparative analysis using descriptive and inferential statistics demonstrated that participants using EUP perceived lower levels of cognitive loads and more positive experiences than those who used the conventional techniques. The study’s findings will advance the understanding of the potential cognitive effects of adopting EUPs for construction sensor data analytics. This study contributes to the cognitive theory of multimedia learning by illustrating how multimodal programming environments can influence learners’ cognitive demands.