Improving Student Outcomes Using Cognitive Load Theory-Based Multiple-Assessments throughout Online Learning of the Musculoskeletal Block During the COVID-19 Pandemic

Asty Amalia Nurhadi, Shelly Salmah, Gita Vita Soraya, Bau Dilam Ardiansyah, Sitti Rafiah
2022 Nusantara Science and Technology Proceedings  
Since its first emergence in China, the global spread of COVID-19 had reached 269.721.683 cases in over 200 countries in December 2021. The rapid spread of this virus had urged WHO and governments to establish policies on physical distancing. Restriction of offline learning activities in Indonesia forced the educational institution to rapidly adapt. As consequence, we had to change all learning and assessment processes into online forms. We found many challenges in the implementation,
more » ... ly in the assessment processes. Therefore, in consideration of Cognitive Load Theory, we decided to intervene in these problems by implementing multiple assessment strategies in Musculoskeletal System Course, for 2020 batch students. We compared the results of the 2020 cohort with the results of the 2019 cohort in the Musculoskeletal System using the Mann-Whitney test (SPSS version 24). There were 256 students (2020 cohort) who participated in this System in comparison to 318 students (2019 cohort). Statistical analysis showed that there was a significant difference between the outcomes of the 2020 cohort that experienced multiple assessments before final examination in comparison to 2019 cohort who only experienced single-end examination (p value <0.000). Mean outcomes of 2020 cohort (334.34) where significantly higher compared to 2019 cohort (240.96). Our study had shown that students' outcome during online learning due to COVID-19 pandemic can be improved by chunking the volume of learning materials into several parts. This method was in line with Cognitive Load Theory and gave possibility for students to organize their learning as well as to get multiple feedback for their improvement.
doi:10.11594/nstp.2022.2209 fatcat:2rhmq5mqhbdbnedbvon3g55tdu