Designing Data Science Courses to Support Non-STEM Undergraduate Students
Insights Based on Expectancy-Value Theory
DOI:
https://doi.org/10.52731/lir.v004.305Keywords:
Learning Support, Educational Practice, Expectancy Value Theory, MotivationAbstract
This study employed the Expectancy-Value Theory framework to organize the educational prac-tices of a data science minor course attended by a mixed cohort of humanities and science stu-dents. This approach aims to identify the elements that support humanities students in learning data science. The findings indicate that integrating course content with the students' major fields of study, simplifying the process of setting up technical environments, and offering prompt feed-back through information and communication technology are essential.
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