Statistics achievement is one’s level of success or proficiency in the field of statistics. It is critical to enhance college students’ statistics achievement because it benefits their future research endeavors (Onwuegbuzie 2004). However, many college students, particularly those in traditionally less quantitative programs in social sciences, are shown to exhibit unsatisfying academic behaviors that result in statistics underachievement, such as procrastination and disengagement (Graham and Weiner 1996; Onwuegbuzie 2004; Paechter et al. 2017; Xu, Lem, and Onghena 2021). Therefore, strategies to enhance student positive academic behaviors in statistics courses should be considered with the goal of promoting their statistics achievement. In this reflection, I propose that incorporating utility value into courses may be a way to achieve this goal. Based on my observations when teaching statistics at a research university, students exhibited more positive academic behaviors, including less procrastination, more help-seeking, and delving deeper into the learning task when utility value was incorporated, which allowed them to reflect on the usefulness of statistics to their fields of interest and future goals. This reflection provides implications for college statistics teachers to consider incorporating utility value into their courses as a way to enhance students’ statistics achievement. 

Observations as a Statistics Teaching Assistant

I am a graduate teaching assistant in a statistics course at a research university. It is a required course for graduate and advanced undergraduate students majoring in social sciences programs to acquire fundamental statistical knowledge and practical statistical skills for conducting quantitative research studies in social sciences. This course encompasses two content areas: descriptive statistics and inferential statistics. Descriptive statistics involve various techniques for summarizing and visualizing data, which encompass measurements, tables, and graphs, as well as summary indices including central tendency and variability. On the other hand, inferential statistics involve statistical inference and probability theory, including binomial, normal, and t distributions, hypothesis testing, power analysis, as well as correlation and regression techniques. In this course, students are assigned a project that requires them to answer a series of research questions based on a dataset by conducting a variety of statistical tests using SPSS software and performing data analysis and interpretation. Students are required to independently conduct this project by referring to the project guidelines and class notes. During this process, students are expected to learn how to run different tests and under what conditions to run them. As a teaching assistant, I am responsible for answering students’ questions on the project during weekly help sessions and office hours.

During one semester, I served as a teaching assistant for two sections of this course taught by two different professors, Professor A and Professor B. I had previously worked with Professor A, whereas Professor B was a new incoming professor. Instead of providing a dataset for the project, as other professors tended to do, Professor B asked students to use a dataset in their research fields according to their preferences. Based on the dataset, students were also responsible for formulating a series of research questions and selecting appropriate statistical tests to address them. In class, Professor B emphasized that conducting this project would equip students with statistical knowledge and skills useful for their future research. For example, a student majoring in educational psychology may select the large-scale open dataset, the National Longitudinal Survey of Youth 1979 (NLSY79), to conduct the project. The student may raise the question, “Is there any difference between the effects of parents’ and students’ expectations on students’ academic achievement?” To address this question, the student may conduct a paired t-test and analyze and interpret the result. If a student was unsure about what dataset to choose for their project, Professor B would facilitate their search for a dataset of interest to them in their field.

Based on my observations, students in Professor B’s section exhibited more positive academic behaviors compared to those in Professor A’s section. Specifically, students in Professor B’s section asked me questions when conducting the project more frequently during my help sessions and office hours, indicating a higher tendency to seek help (Zusho et al. 2007). Students in Professor B’s section began the project early, including searching for the dataset for use and formulating research questions early, indicating low procrastination and high engagement in learning statistics (Johnson and Sinatra 2013; Onwuegbuzie 2004). In the project reports, students in Professor B’s section delved deep into research questions by conducting detailed data analysis and interpretation and linking them to their own research fields, whereas those in Professor A’s section answered predetermined research questions like those shown in examinations. 

Discussion

One possible explanation for the more positive academic behaviors of students in Professor B’s section is the incorporation of utility value. Coined in expectancy-value theory in motivational research, task utility value is an academic motivational factor that refers to the perceived importance of a learning task because of its usefulness for other tasks or aspects of an individual’s professional and personal life (Eccles et al. 1993). Drawing on various motivational theories, there is substantial recognition that academic motivational factors can influence academic behaviors (e.g., Bandura 1977; Deci and Ryan 1985; Eccles et al. 1993). Thus, it is possible that when students perceive the importance of a course because of its usefulness for certain aspects of their professional lives, they tend to exhibit more positive academic behaviors, such as actively engaging in class discussions, effectively managing their study time, completing their homework on time, and seeking help and resources when needed. For instance, empirical studies have found that utility value is positively related to engagement among college students majoring in social sciences programs (Johnson and Sinatra 2013; Sutter et al. 2022). In the course project scenario, by searching for datasets for the project themselves in their fields and coming up with their own research questions, students had a chance to consider the question, “How is this useful to my field or future goal?” That is, they had a chance to consider the utility value of this project and the relation of the course to their fields of interest, future research plans, and long-term academic and career goals. For example, a student majoring in educational psychology may find the course valuable because they can use the statistical testing and software operating skills developed in this course when conducting research studies involving quantitative data analysis in the field of educational psychology in the future. Compared to Professor A’s section, in Professor B’s section, students were more allowed to realize and reflect on the usefulness of statistics. Therefore, it is understandable that they procrastinated less, engaged more, sought more help, and delved deeper into the course project. These positive academic behaviors would, in turn, promote their statistics achievement.

“When teaching various types of statistical tests, teachers should keep in mind that it is important not only to teach students the definitions of these tests (i.e., knowledge about “what”), the procedures for conducting these tests (i.e., knowledge about “how”), and the conditions under which to conduct them (i.e., knowledge about “when”), but also, more importantly, to help students understand why acquiring knowledge about “what,” “how,” and “when” for these tests is essential for them.”

Practical Implications for Statistics Teachers

This reflection provides practical implications for college statistics teachers to consider incorporating utility value into their courses as a way to enhance students’ statistics achievement. For instance, when teaching various types of statistical tests, teachers should keep in mind that it is important not only to teach students the definitions of these tests (i.e., knowledge about “what”), the procedures for conducting these tests (i.e., knowledge about “how”), and the conditions under which to conduct them (i.e., knowledge about “when”), but also, more importantly, to help students understand why acquiring knowledge about “what,” “how,” and “when” for these tests is essential for them. When it comes to testing students’ knowledge of statistical tests through course projects, teachers can allow students to select their datasets and formulate their questions according to their preferences, thus encouraging them to ponder the usefulness of statistics. In a more explicit way, teachers can explain statistics’ usefulness in class. 

Future Research Directions

Based on my observations as a statistics teaching assistant, I propose that incorporating utility value into statistics courses can be a way to promote students’ positive academic behaviors and, thus, enhance their statistics achievement. While it is widely recognized that academic motivational factors influence behaviors, it is essential to acknowledge that causal inferences about the effect of adding utility value on students’ academic behaviors cannot be made because no empirical study involving data collection and analysis was conducted. This reflection may provide promising future directions for researchers, who may consider conducting experimental or longitudinal studies to test the causal relationship between utility value and students’ academic behaviors, such as procrastination, engagement, and help-seeking, and statistics achievement. To draw solid insights on the effects of utility value on academic behaviors and statistics achievement, studies should be well-designed, incorporating controls for the effects of confounding variables, such as those related to teachers and students. Researchers may also conduct qualitative studies to understand how certain strategies adopted by teachers may promote students’ perceived usefulness of statistics and how their perceived usefulness may influence their academic behaviors and statistics achievement. By conducting rigorous research, we can advance our understanding of the beneficial role of utility value in statistics learning and provide insights in both practical and theoretical ways.

About the Scholar

References

Bandura, Albert. 1977. “Self-Efficacy: Toward a Unifying Theory of Behavioral Change.” Psychological Review 84 (2): 191–215. https://doi.org/10.1037/0033-295X.84.2.191.

Deci, Edward L., and Richard M. Ryan. 1985. Self-Determination and Intrinsic Motivation in Human Behavior. Plenum Press.

Eccles, Jacquelynne, Allan Wigfield, Rena D. Harold, and Phyllis Blumenfeld. 1993. “Age and Gender Differences in Children’s Self- and Task Perceptions during Elementary School.” Child Development 64 (3): 830–47. https://doi.org/10.2307/1131221.

Graham, S., and Bernard Weiner. 1996. “Theories and Principles of Motivation.” In Handbook of Educational Psychology, 63–84.

Johnson, Marcus Lee, and Gale M. Sinatra. 2013. “Use of Task-Value Instructional Inductions for Facilitating Engagement and Conceptual Change.” Contemporary Educational Psychology 38 (1): 51–63. https://doi.org/10.1016/j.cedpsych.2012.09.003.

Onwuegbuzie, Anthony J. 2004. “Academic Procrastination and Statistics Anxiety.” Assessment & Evaluation in Higher Education 29 (1): 3–19. https://doi.org/10.1080/0260293042000160384.

Paechter, Manuela, Daniel Macher, Khatuna Martskvishvili, Sigrid Wimmer, and Ilona Papousek. 2017. “Mathematics Anxiety and Statistics Anxiety. Shared but Also Unshared Components and Antagonistic Contributions to Performance in Statistics.” Frontiers in Psychology 8: 1196.

Sutter, Claudia C., Chris S. Hulleman, Karen B. Givvin, and Mary Tucker. 2022. “Utility Value Trajectories and Their Relationship with Behavioral Engagement and Performance in Introductory Statistics.” Learning and Individual Differences 93: 102095. https://doi.org/10.1016/j.lindif.2021.102095.

Xu, Chao, Stephanie Lem, and Patrick Onghena. 2021. “Examining Developmental Relationships between Utility Value, Interest, and Cognitive Competence for College Statistics Students with Differential Self-Perceived Mathematics Ability.” Learning and Individual Differences 86: 101980. https://doi.org/10.1016/j.lindif.2021.101980.

Zusho, Akane, Stuart Karabenick, Christina Bonney, and Brian Sims. 2007. “Contextual Determinants of Motivation and Help Seeking in the College Classroom.” In The Scholarship of Teaching and Learning in Higher Education: An Evidence-Based Perspective, 611–59. https://doi.org/10.1007/1-4020-5742-3_14.