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

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.

Supplementary information: The online version contains supplementary material available at 10.1186/s41239-021-00272-z.

Keywords: Interactivity; Learning outcome; Mechanics of solids; Remote trigger; Transactional distance theory; UTM; Virtual labs.

Fig. 1

Transactional distance theory framework for RT-UTM

Fig. 2

Specimen loaded in the UTM for experimentation. a Specimen with circular hole and b specimen with notch

Fig. 3

Design of RT-UTM platform

Fig. 4

Architecture of remote triggered experimental setup

Fig. 5

Schematic representation of instrumentation hardware

Fig. 6

Client-side UI for Young’s modulus experiment

Fig. 7

Client-side UI to study stress concentrations on a plate with a hole

Fig. 8

Specimen with strain gauges loaded in the UTM for Poisson’s Ratio Experiment

Fig. 9

Client-side UI to study stress concentrations on a plate with a notch

Fig. 10

Strain gauge locations on specimen for St. Venant’s Principle

Fig. 11

Block diagram of VL-LMS

Fig. 12

Methodology for assessing RT experimentation effect

Fig. 13

A Box plot showing the range of scores of 14 CUQ of Group A and Group B. Percentages in the brackets showing the percentage increase of average score in each tests. B Figure showing the percentage of students who got correct answers for each MCQs in each tests

Fig. 14

Effectiveness of RT-UTM and PL-UTM from the feedback data