With more than a third of all students taking at least one online course during their postsecondary education (Ginder, Kelly-Reid, & Mann, 2019), preparing the next generation of science, technology, engineering, and math (STEM) professionals requires judiciously discerning how online learning tools can be used to facilitate student understanding of STEM disciplinary content. It is well known that students learn better from words and pictures (i.e., multimedia learning) than from words alone (Mayer, 2005). This is especially believed to be true for content that is not readily visible in the natural world, such as in the domain of chemistry where the structures and processes occur at a scale that is invisible to the learner. As a result, a number of online simulations have been developed by chemistry researchers and educators to help introductory students visualize and understand naturally invisible chemistry concepts (Stieff, 2011; Tasker & Dalton, 2006). Prior research on multimedia learning suggests that the effectiveness of an online learning tool is dependent on a number of different factors, several of which pertain to the characteristics of the learner (Doolittle, Terry, & Mariano, 2009; Kriz & Hegarty, 2007). What is unknown is how to differentiate instruction using online learning tools to facilitate learning for students with different cognitive skill sets.
In this project, the project team will design and determine effective online formative assessments and the associated feedback for online simulations presenting content about two fundamental introductory chemistry concepts: molecular structures and their properties. Within a two-year timespan, two experiments, one focusing on each concept, will be conducted using student volunteers. The experiments will compare student learning gains between three distinct learning conditions: 1) simulation-based instruction using online formative assessments and online feedback; 2) simulation-based instruction using online formative assessments and in-person feedback; and 3) in-person classroom-type instruction. Findings from this project will inform the kinds of formative assessments and feedback to be used to support student learning from online chemistry simulations.
Doolittle, P. E., Terry, K. P., & Mariano, G. J. (2009). Multimedia learning and working memory capacity. In R. Zheng (Ed.), Multimedia Learning and Working Memory Capacity (pp. 17–33). Premier Reference Source London.
Ginder, S. A., Kelly-Reid, J. E., & Mann, F. B. (2019). Enrollment and Employees in Postsecondary Institutions, Fall 2017; Financial Statistics and Academic Libraries, Fiscal Year 2017. National Center for Education Statistics (NCES).
Kriz, S., & Hegarty, M. (2007). Top-down and bottom-up influences on learning from animations. International Journal of Human-Computer Studies, 65(11), 911–930
Mayer, R. E. (2005). Cognitive theory of multimedia learning. The Cambridge Handbook of Multimedia Learning, 41, 31–48.
Stieff, M. (2011). Improving representational competence using molecular simulations embedded in inquiry activities. Journal of Research in Science Teaching, 48(10), 1137–1158.
Tasker, R., & Dalton, R. (2006). Research into practice: visualisation of the molecular world using animations. Chemistry Education Research and Practice, 7(2), 141–159.