Open AccessImplementation of Asynchronous Educational Modules to Improve Student Understanding of Statistical Analysis in STEM Undergraduate Courses
Author(s) -
Sophia Barber,
Sophia Isabella Ibargüen,
Chloe Sharp,
Janet Teng,
Daisy Kim,
Richard Luu,
Jared Ashcroft,
Yu-Chung Chang-Hou
Publication year - 2021
Publication title -
frontiers in education technology
Language(s) - English
Resource type - Journals
eISSN - 2576-1854
pISSN - 2576-1846
DOI - 10.22158/fet.v4n2p108
Subject(s) - coursework , asynchronous communication , statistical analysis , mathematics education , computer science , covid-19 , distance education , undergraduate research , medical education , psychology , mathematics , medicine , statistics , computer network , disease , pathology , infectious disease (medical specialty)
Due to the COVID-19 pandemic, many undergraduate students have been given no other option but to take their classes remotely. This has provided many challenges for both students and instructors, especially in the STEM field due to the required laboratory coursework. For this reason, alternative methods of distance learning are needed to optimize student laboratory experiences. The sudden transition to a remote format and adjusting to a new learning environment has proven to be difficult for both students and faculty. It has also been established throughout the pandemic that students perform substantially worse in on-line coursework compared with traditional, in-person classes. Students in a general chemistry course were introduced to innovative asynchronous lab modules that could be performed at home with the additional opportunity of conducting statistical analysis tests. These modules utilize discussion boards, graphing assessments, and labs to teach students how to perform different statistical tests and to familiarize students with the DataClassroom, Google Sheets, and Microsoft Excel platforms. This asynchronous learning format will promote both overall student engagement in STEM courses and student understanding of statistical analysis, thus exhibiting the potential to implement these modules in future undergraduate STEM coursework.