S-SOMADS: A new survey to measure student attitudes toward data science
Abstract
Attitudes play an important role in students’ academic achievement and retention, yet we lack quality attitude measurement instruments in the new field of data science. This paper explains the process of creating Expectancy-Value Theory-based instruments for introductory, college-level data science courses, including construct development, item creation, and refinement involving content experts. The family of instruments consist of surveys measuring student attitudes, instructor attitudes, and instructor and course characteristics. These instruments will enable data science education researchers to evaluate pedagogical innovations, create course assessments, and measure instructional effectiveness relating to student attitudes. We also present plans for pilot data collection, and analyses to verify the categorization of items to constructs, as well as ways in which faculty who teach introductory data science courses can be involved.
