A practical evaluation of online self-assisted previewing architecture on rain classroom for biochemistry lab courses

Abstract

In the aftermath of the COVID pandemic, we have been promoting online learning as a new learning tool. This study investigated the design of the optimal structure of online self-assisting coursework for laboratory courses that can assist students to better prepare for hands-on experiments. Undergraduate students from Liaoning University, who took the “Biochemistry Experiments” laboratory class, were asked to evaluate a self-learning segment offered on the Rain Classroom, an online platform that was provided to students before each in-person lab session as a preparation. Survey questions consisting of both multiple-choice and free-response questions were carefully designed to help evaluate students' sentiment toward the online previewing platform. Cramér's V correlation was used to determine the correlation between multiple choice answers, WordCloud and topic modeling analysis were conducted after textmining to analyze the emotions students express through the free response questions, and an analysis of variance (ANOVA) was conducted to evaluate the relationship between students' usage of the online interface and overall performance in class. The majority of students believe the previewing of the content on Rain Classroom to be helpful for their upcoming hands-on practice in the in-person laboratory course. At the same time, helpful insights were drawn from students' feedback through sentiment analysis from their entries in free-response questions in the survey. We showed that having online tools to pre-expose students to the laboratory-related material is helpful in preparing the students for hands-on laboratory courses. At the same time, we also offer a few suggestions that may guide the design of future online resources for laboratory classes such as involving multi-modality media to improve engagement and perfecting the interactive feature to increase its usage by students.