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

Abstract

Abstract. In 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 alternative theoretical models are proposed and tested on empirical data. Structural equation modeling (SEM) in MPlus 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.

Advantages of Using the CAS Mathematica in a Study of Supplementary Chapters of Probability Theory

“…Although the degree of their usage varied, a common interest in and increasing motivation to computer teaching are detectable. There are similar conclusions on computer usage in other studies (Ivaniushina et al, 2016;Ivanov et al, 2017;Korres, 2018;Psycharis, 2016).…”

“…In addition, one advantage of using CAS as handheld calculators is their capability to perform algebraic, graphic and numeric calculations (Garner, 2004). In previous research (Emmioğlu, Capa-Aydin, 2012;Ivaniushina et al, 2016;Ivanov et al, 2017), the positive impact of computer teaching methods on education outcomes was established. Moreover, the high individualisation of teaching processes and the variability of teaching purposes achieved were identified.…”

“…The most remarkable aspects for students are the convenience of working on mobile devices and the possibility of fulltext searches. The general positive impact of electronic teaching means has been noted (Ivaniushina et al, 2016;Ivanov et al, 2017;Korres, 2018;Psycharis, 2016). An interesting fact is that the students did not detect a strong connection between interactive templates and presentations that were prepared using the templates.…”

“…The advantages of these program languages include their convenient interfaces and the presentation of mathematical expressions in a form familiar to students (Garner, 2004;Vlasova et al, 2017b). Ivanov et al (2017) discussed the application of the CAS Mathematica when teaching the course Discrete Mathematics. They specifically addressed Mathematica's main advantages and the possible ways to overcome certain difficulties.…”

Using Computer Algebra Systems as a Tool for Disclosure of Interdisciplinary Linkages in Courses of Probabilistic Disciplines

“…В [4] установлено, что те студенты, у которых наблюдался баланс между этими двумя факторами, были более мотивированы и готовы потратить дополнительное время на преодоление трудностей, возникающих при решении задач. В [14] описана структура мотивации и предложена регрессионная модель, связывающая отношения разных компонент мотивации (гендер, образовательная программа и предыдущие успехи) у группы студентов, изучающих курс анализа данных, в котором компьютерные технологии интегрированы в образовательный процесс. Оказалось, что мотивированность является одним из важнейших факторов, влияющих на результаты обучения.…”