Impact of remote experimentation, interactivity and platform effectiveness on laboratory learning outcomes

• Main Authors: Krishnashree Achuthan, Dhananjay Raghavan, Balakrishnan Shankar, Saneesh P. Francis, Vysakh Kani Kolil
• Format: Article
• Language: English
• Published: SpringerOpen 2021-07-01
• Series: International Journal of Educational Technology in Higher Education
• Subjects: Virtual labs; Remote trigger; Interactivity; Mechanics of solids; UTM; Learning outcome
• Online Access: https://doi.org/10.1186/s41239-021-00272-z

Abstract Access and personalized instruction required for laboratory education can be highly compromised due to regulatory constraints in times such as COVID-19 pandemic or resource shortages at other times. This directly impacts the student engagement and immersion that are necessary for conceptual and procedural understanding for scientific experimentation. While online and remote laboratories have potential to address the aforementioned challenges, theoretical perspectives of laboratory learning outcomes are critical to enhance their impact and are sparsely examined in the literature. Using Transactional Distance Theory (TDT), this paper addresses the gap through a case study on Universal Testing Machine (UTM). By comparing physical (PL-UTM) and remotely triggerable (RT-UTM) laboratory platforms, the structure and interactions as per TDT are analysed. Characterization of interactivity between remote learners and instructors disclose indicative parameters that affect transactional distances and aid in conceptual understanding in remote laboratory learning environment. An extensive pedagogical study through development of two instruments towards assessing conceptual understanding and perception of platform effectiveness that was conducted both on physical laboratory and RT-UTM showed: (1) remote users conducted experiments 3 times more frequently (2) completed assignments in 30% less time and (3) had over 200% improvement in scores when RT-UTM platform was integrated into mainstream learning.