A practical evaluation of online self-assisted previewing architecture on rain classroom for biochemistry lab courses - Ben, Songbin; Liu, Chenshu; Yang, Pinyi; Gong, Jiayi; He, Yin (Frontiers in Education, 2024)
In the aftermath of the COVID pandemic, we are fostering online learning as a new teaching style. 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 surveyed on their evaluation of the self-learning segment offered on the Rain Classroom online platform that was assigned before each in-person lab session as preparation. Survey questions consisting of both multiple-choice and free-response questions were carefully constructed to help evaluate students' sentiment towards 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 text-mining to analyze the emotions students express through the free response questions, and an ANOVA analysis was conducted to evaluate the relationship between students' usage of the online interface and overall performance in class. We found that the majority of students think the previewing processing on Rain Classroom is 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 laboratory-related material is helpful for the progression of hands-on laboratory courses. At the same time, we arrived at 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 boost student usage.