The Structure of Students' Motivation: Expectancies and Values in Taking Data Science Course

Valeria Ivaniushina - Candidate of Sciences in Biology; Leading Research Fellow of the Laboratory of Sociology in Education and Science, National Research University Higher School of Economics (Saint Petersburg). E-mail: ivaniushina@hse.ru Daniil Alexandrov - Candidate of Sciences in Biology; Head of the Laboratory of Sociology in Education and Science, National Research University Higher School of Economics (Saint Petersburg). E-mail: dalexandrov@hse.ru Ilya Musabirov - Junior Research Fellow of the Laboratory of Sociology in Education and Science, National Research University Higher School of Economics (Saint Petersburg). E-mail: ilya@musabirov.infoAddress: 16 Soyuza Pechatnikov ul., 190121 St. Petersburg, Russian FederationIn this paper we explore motivational structure of students taking a challenging university course. The participants were second-year undergraduate students majoring in Economics, Sociology, Management and Humanities, enrolled in the Data Science minor. Using expectancy-value theory as a framework, we aim (1) to analyze gender differences in motivation; (2) to identify the link between the components of motivation and academic achievement; (3) to estimate the role of the previous academic achievement and educational choices. Two alternativetheoretical models are proposed and tested on empirical data. Structural equation modeling (SEM) in M Plus 7.31 was used for analysis. We found that the course is more popular among males students, who also demonstrate higher level of expectancy for success. However, there is no gender difference in academic performance. Students majoring in Sociology and Economics perceive Data Science as more interesting and useful than Management and Humanities students. SEM analysis empirically validated the model in which expectancy of success directly influences academic achievement, and values influence is mediated by expectancies. The final model that includes motivation, gender, student's major, and previous achievement explains 34% of variance in academic performance. We discuss the role of different components of student motivation and practical significance of our results.