Research and Resources
Contemporary research in the field of student motivation and learning outcomes has been, and continues to be, robust. We summarize several fundamental frameworks and theories that undergird motivation and outcomes. They include: attribution theory (Weiner, 1972); incremental vs. entity views of intelligence (Dweck, 1999); self-efficacy (Bandura, 1989, 1993, 1997); self determination theory (Ryan & Deci, 2000); and expectancy value framework (Eccles, et. al., 1983).
In general, these theories and frameworks attend to achievement motivation, i.e., those psychosocial constructs connected with the degree to which students engage in tasks and activities related to learning and demonstration of learning.
To what does a learner attribute success or failure? Attribution theory (Weiner, 1972) partitions these attributions into 4 components: ability and effort, each of which are personal (internal) domains, and task difficulty and luck, each of which are external to the individual. Further, “perceived causes like ability and task difficulty are consistent across contexts (stable), whereas effort and luck are more variable across contexts and potentially unpredictable (unstable). Moreover, effort and task difficulty can be influenced directly by the student and teacher (controllable), whereas current ability and luck cannot (uncontrollable) (Hulleman, et.al., 2016, pp. 247-248).”
- Pay attention to your language. Limit use of “good luck” and increase acknowledgement of effort.
- Structure your class so that effort is honored.
- Help students with subject specific study skills, help them know how to work smarter, not just harder.
- Encourage study groups; some students feel they must study alone and that is not true.
- You may consider using exam wrappers to assist students in self reflection about their exam preparation and results.
What implications does attribution theory have for your learning environment? If students believe that their success is influenced by uncontrollable factors such as luck, they will be less motivated to put forth appropriate effort for a task or an assessment. Further, students who ascribe their success to an inherent fixed ability, may downshift their effort or engage in self talk reinforcing the perception that they aren’t capable. It is possible that a poor grade on an assignment will reinforce this belief. Dweck’s work (1998) on growth mindset is related to the self-ascribed beliefs individuals’ have about their capabilities.
Your role as an instructor affords you the opportunity to use language and strategies that support effort. Emphasizing effort in your comments to students such as “congratulations on the improvement in your most recent essay draft” shows that student effort is a driving factor for success. Although we all do it, saying “good luck” before a test is counterproductive because it overtly or subliminally suggests that luck will influence a performance.
Self-efficacy influences academic motivation, learning, and achievement (Pajares, 1996). Self-efficacy is characterized by how one feels about themselves and their abilities to perform in domain-specific contexts. Foundational work in this social cognitive perspective, initiated by Bandura (1986) and sustained by myriad scholars, provides us with the understanding of how individuals come to believe they are capable. Contemporary research often examines how the environment impacts self-efficacy.
Although all students benefit from increased academic self-efficacy, some demographic groups’ needs are particularly noteworthy. Eddy and Brownell’s (2016) meta-analysis of self-efficacy for women in STEM provides evidence that women enter higher education with lower self-efficacy in STEM than men. Further, “these measures [self-efficacy, science identity, and belonging] have been shown to be correlated with achievement (Eddy & Brownell, 2016).”
There are four constructs that undergird self efficacy, and each can be impacted by you and the learning environment. Those four are: (1) mastery experiences, (2) vicarious learning, (3) verbal persuasion and (4) emotional and physiological states. Simply put, we feel more positive about our ability to perform in a specific domain if we have already been successful.
Self Determination Theory (SDT)
Ryan and Deci’s (2000) research on self determination theorizes that human motivation is based in the human condition to grow and develop competency. SDT posits that there are three fundamental psychological needs related to motivation: (1) the need for competence, (2) the need for autonomy, and (3) the need for relatedness. Further, when educators fulfill these three, students become more motivated, regulate their learning, and perform better (Niemiec & Ryan, 2009).
The need for students’ competence can be supported by learning challenges at the appropriate level for growth. Vygotsky (1978) described Zone of Proximal Development (ZPD) as “the distance between the actual developmental level as determined by independent problem solving and the level of potential development as determined through problem solving under adult guidance or in collaboration with more capable peers (p. 86).” Choosing appropriately scaled challenges for your students to develop competence (along with your guidance) is a motivator for students.
You can support students’ need for autonomy by providing them with some amount of choice to demonstrate mastery. Where possible, ask your students for input on the course content. Recognize that your adult learners and many younger undergraduates already exhibit autonomy in other aspects of their lives. Consider how those additional expectations for student responsibilities outside your learning environment can be accommodated, and even leveraged to encourage motivation, as you design your course.
You are supporting relatedness when your students can make meaningful connections to the content and the tasks asked of them. If your lived experience is dissimilar from your students’, you will want to think about how you can bring relevancy more to the forefront. Are you representing a variety of scholars’ history and demographics in your field? Are you using contemporary technology that is relevant to how students learn now? Consider conferring with your students (current and former) and colleagues in your field for ideas as you examine the course for relevancy.
The expectancy value framework (Eccles, et. al, 1983) builds on deep literature bases in both expectancies and value research. In brief, the expectancy value framework brings together students’ perceptions of (or expectancy for) being able to complete a task and the value ascribed to spending effort to master or complete a task. These two together form the motivation for individuals to engage with learning tasks. All decisions on whether to be motivated to engage with a task inherently include an evaluation of the cost for the engagement. What would one miss out on if time and attention were directed to something else? Although a thorough treatment of this framework is beyond the purpose of our website, readers are encouraged to look at the tables in the pdf to find sources of expectancy related beliefs, sources of value, and sources of cost.
As you think about this framework and implications for your students, consider how you could increase student expectancies for mastery, increase students’ perceptions of the value of your course work, and be aware of the costs students may balance in this analysis.
You might share your professional research interests and experiences: Why is the content important to you? What are your stories about working in this field of study? It’s important for students to see the relevance of the materials as it pertains to their future plans. Clearly link concepts/lessons to industry or a broader purpose, future classes/activities, or other transferable skills that are used in the field. Another relevancy-based strategy is to challenge students with deep learning (case studies, community-based learning, collaborative projects, etc.).
If you and your students have developed a strong relationship and you have helped students understand why your course is important to them, you will likely find that they engage deeply with you and their learning.
Belcher, A., Hall, B. M., Kelley, K., & Pressey, K. L. (2015) An analysis of faculty promotion of critical thinking and peer interaction within threaded discussions. Online Learning, (19)4. https://doi.org/10.24059/olj.v19i4.544
Canning, E. A., Muenks, K., Green, D. J., & Murphy, M. C. (2019). STEM faculty who believe ability is fixed have larger racial achievement gaps and inspire less student motivation in their classes. ScienceAdvances, 5(2). DOI: 10.1126/sciadv.aau4734
Chaiklin, S. (2003). The zone of proximal development in Vygotsky’s analysis of learning and instruction. In Kozulin, A., Gindis, B., Ageyev, V. S. & Miller, S. M. (Eds.), Vygotsky’s educational theory in cultural context (39-64). Cambridge: Cambridge University Press.
Claro, S., Paunesku, D. & Dweck, C.S. (2016). Growth mindset tempers the effects of poverty on academic achievement. Proceedings of the National Academy of Sciences of the United States of America, (116)31, 8664-8668. https://doi.org/10.1073/pnas.1608207113
Dweck, C. (June 26, 2018). Growth mindset interventions yield impressive results. The Conversation. Retrieved from https://theconversation.com/growth-mindset-interventions-yield-impressive-results-97423
Eccles, J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motivation (pp. 74–146). San Francisco: W.H. Freeman.
Eddy, S. L. & Brownell, S. E. (2016). Beneath the numbers: A review of gender disparities in undergraduate education across science, technology, engineering, and math disciplines. Physical Review Physics Education Research, 12(2). DOI https://doi.org/10.1103/PhysRevPhysEducRes.12.020106
Good, C., Rattan, A., & Dweck, C. S. (2012). Why do women opt out? Sense of belonging and women’s representation in mathematics. Journal of Personality and Social Psychology, 102(4), 700-717. http://dx.doi.org/10.1037/a0026659
Guo, P. J. (2013, November 13) Optimal video length for student engagement. Retrieved from https://blog.edx.org/optimal-video-length-student-engagement
Hulleman, C. S., Barron, K. E., Kosovich, J. J. & Lazowski, R. A. (2016). Student motivation: Current theories, constructs, and interventions within an expectancy-value framework. In Lipnevich, A. A., Preckel, F. & Roberts, R. D. (Eds.), Psychosocial skills and school systems in the 21st century. (pp. 241-278). Switzerland: Springer.
Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, competence, and relatedness in the classroom: Applying self-determination theory to educational practice. Theory and Research in Education, 7(2), 133–144.
Pajares, F. (1996). Self-efficacy beliefs in achievement settings. Review of Educational Research, 66, pp. 543-578.
Sisk, V. F., Burgoyne, A. P., Jingze, S., Butler, J. L. & Macnamara, B. N. (2018). To what extent and under which circumstances are growth mind-sets important to academic achievement? Two Meta-analyses. Psychological Science, 29 (4), 549-571. https://doi.org/10.1177/0956797617739704
Vygotsky, L. S. (1978). Interaction between learning and development (M. LopezMorillas, Trans.). In M. Cole, V. John-Steiner, S. Scribner, & E. Souberman (Eds.), Mind in society: The development of higher psychological processes (pp. 79–91). Cambridge, MA: Harvard University Press.
Weiner, B. (1972). Theories of motivation: From mechanism to cognition. Oxford, England: Markham.
Zhou, H. (2015). A systematic review of empirical studies on participants’ interactions in internet-mediated discussion boards as a course component in formal higher education settings. Online Learning (19)3. https://doi.org/10.24059/olj.v19i3.495