R3 1.16 August 31, 2023 Inside the Brains of Anxious STEM Students; New AI Resource for Faculty
This issue of R3 reviews a Trends in Neuroscience and Education article exploring the connections between anxiety, attention, and academic reasoning among college students in STEM fields.
This issue of R3 reviews a Trends in Neuroscience and Education article exploring the connections between anxiety, attention, and academic reasoning among college students in STEM fields. The work tells an intriguing story about the ways in which anxiety impedes learning, namely by interfering with collaboration among brain areas that normally work together to allow us to focus on the task at hand.
I also want to share a smashing new AI- related resource created by Cynthia Alby of Georgia College & State University: AI Prompts for Teaching: A Spellbook. This is an evolving document that contains a wealth of ready-to-use, cut-and-paste AI chat prompts, including some time-savers and brainstorming aids for our own faculty work. I plan to tap into it as I head into an academic year that will be heavily influenced by ChatGPT and the like (a topic I wrote about in this recent article over at the Chronicle of Higher Education). Kudos to Cynthia Alby for generously sharing such a timely and thoughtful resource!
Citation:
Smith, D. D., Meca, A., Bottenhorn, K. L., Bartley, J. E., Riedel, M. C., Salo, T., Peraza, J.A., Laird, R.W., Pruden, S.M., Sutherland, M.T., Brewe, E., & Laird, A.R. (2023). Task-based attentional and default mode connectivity associated with science and math anxiety profiles among university physics students. Trends in Neuroscience and Education, 32(June), 100204.
DOI:
https://doi.org/10.1016/j.tine.2023.100204
Paywall or Open:
Paywall
Summary:
Previous research has shown that high-anxiety individuals display a pattern of irregular or reduced coordination among brain regions involved in attention. Of particular interest are the dorsal attention network (involved in staying focused on goals), ventral attention network (involved in paying attention to external stimuli), and the default mode network (involved in diffuse or undirected internal thought processes). This study sought to track this pattern of disrupted attention within students who have anxiety specifically tied to academics, especially math and science. Undergraduate STEM students were tested to see if they fit high-or low-anxiety profiles with respect to science and math, then their brain activity was scanned while they completed different types of physics problems. The results illustrate how the emotional experience of anxiety places extra demands on cognitive resources that are needed to carry out academic work, resulting in less efficient and effective cognitive processing.
Research Questions:
- Can STEM students be sorted into different levels and types of anxiety, based on their answers to surveys tapping into anxiety about learning science and math?
- Does brain activity (especially that involved in attention) differ for students with high and low levels of science and/or math anxiety?
- Could these patterns plausibly lead to less efficient processing during STEM learning activities?
Sample:
One hundred twenty-three undergraduate students, average age 19.8, 56 female and 67 male; all were enrolled in introductory physics courses required for a range of STEM majors.
Method/Design:
Participants completed a battery of survey-style tests designed to reveal different types of anxiety. These included two distinct tests pertinent to STEM: one on science-related anxiety and one on math anxiety. These tests were used to place participants into four different categories: low anxiety on science and math, high anxiety on science and math, high science anxiety only, high math anxiety only.
Those in the low-anxiety and high math anxiety groups (83 in total) then completed different types of physics activities while their brain activity was scanned using functional magnetic resonance imaging (fMRI). (There were not enough students in the high science and math anxiety group to enable analysis, thus this group was excluded). Analyses of the brain activity data focused on connectivity among and within three attention-related networks in the brain. Connectivity, in this context, essentially means correlations between activity in different structures or regions, such that when one area becomes more active, it appears to trigger activity in another area.
Key Findings:
There did appear to be distinct sub-groups of students, including some who are high in science and math anxiety and some who are low. These sub-groups did not appear to correlate to demographic characteristics such as gender or ethnicity.
High-math-anxiety students showed reduced connectivity among attentional networks, reflecting disrupted coordination among as well as within the different attention networks, especially during the later stages of solving the problems. This might reflect intrusive thoughts, alertness to threat, rumination or similar kinds of cognitive processes beginning to build up as an individual works on a STEM-related problem.
Choice Quote from the Article:
While findings suggest that STEM retention is a multi- faceted problem, one notable psychosocial barrier that students commonly report facing when choosing whether or not to remain in their programs is STEM-related anxiety, which is defined as apprehension or fear towards STEM-related activities [6]. STEM-related anxiety has been associated with underperformance in STEM courses [7,8], avoidance of effortful and effective study strategies [9], and is a significant contributing factor to withdrawal from introductory university STEM courses [7].
Why it Matters:
This article caught my eye as a systematic look at anxiety connected to specific academic activities and topics, tracing the impacts of these worries on the brain’s systems for maintaining attention. I have long been interested in the connections between emotions, motivation, and cognition, and attention looks like it could be a thread binding those seemingly separate facets of the mind.
Notably, performance did not generally differ for the different tests, even though coordination did. In other words, highly anxious students might achieve the same results as less anxious ones, but at a higher cost in terms of mental effort. This important distinction helps characterize what it is like to learn as an anxious person.
Practically speaking, I wonder how students might benefit if these results were shared with them, perhaps as an extension of the belonging or mindset interventions that are widespread these days. Or, perhaps STEM students could be screened for high math or science anxiety, and offered supports that specifically target attention, such as mindfulness practice, guided meditation, or positive self-talk.
Most Relevant For:
Instructional designers; those interested in the neuroscience of learning; faculty and leaders involved in STEM and student success; those interested in anxiety and related mental health issues among college students; student success program leaders
Limitations, Caveats, and Nagging Questions:
As the authors acknowledge, the sample was relatively small when it was winnowed down to only the high- and low-anxiety groups. It’s also important to note that this sample is unlikely to resemble the general population with respect to overall math and science anxiety. This is reflected in the low number who are in the High categories for both kinds of anxiety (about 6.5% overall). This makes sense given that students who are highly anxious about science and math would be less likely to choose STEM majors, so I don’t think it is a major flaw with the work – but it needs to be considered if the work is extended to students outside STEM.
There is a fair amount of dense, technical material here on analysis of the fMRI data, and similarly, on the assignment of students to different profiles based on their anxiety test scores. I don’t think that readers need to grasp every one of those details to benefit from the article. However, I’ve seen this type of work get misinterpreted, especially when it comes to the default mode network (whose precise purpose and workings are still not fully understood). Those wanting to apply or extend the work, therefore, might do well to refresh on how fMRI data are interpreted, especially the connectivity concept. The article also frequently references a framework called Attentional Control Theory (ACT), and concludes that the results found provide further support for this theory. I don’t think that ACT is necessarily germane to readers generally interested in applying the findings to teaching. It is an interesting way to think about the role of attention in different kinds of cognitively demanding tasks, but general readers shouldn’t be put off by this particular emphasis.
Lastly, it’s worth noting that there are quite a few nonsignificant/null findings in the article, and it is not always clear exactly why not all of the predicted or possible relationships held up (as the authors readily acknowledge). I don’t see this as a fatal flaw, but some readers might be concerned about the overall chance that the significant findings resulted from Type I error (essentially, a false alarm), given how many overall tests were run. To me, the interpretation of the significant findings seems reasonable, and the overall pattern of when there were group differences, and when there were not, is well explained. Taken together, it tells a coherent story of what goes on in the minds of anxious students confronting challenging reasoning tasks in STEM courses.
If you liked this article, you might also appreciate:
Eickhoff, S.B., Yeo, B.T.T. & Genon, S. (2018). Imaging-based parcellations of the human brain. Nature Reviews Neuroscience, 19, 672–686. https://doi.org/10.1038/s41583-018-0071-7
Finell, J., Sammallahti, E., Korhonen, J., Eklöf, H., & Jonsson, B. (2022). Working memory and its mediating role on the relationship of math anxiety and math performance: A meta-analysis. Frontiers in Psychology, 12(January), 1–14. https://doi.org/10.3389/fpsyg.2021.798090
Raichle, M. E. (2015). The brain’s default mode network. Annual Review of Neuroscience, 38(1), 433–447. https://doi.org/10.1146/annurev-neuro-071013-014030
Rogers, B. P., Morgan, V. L., Newton, A. T., & Gore, J. C. (2007). Assessing functional connectivity in the human brain by fMRI. Magnetic Resonance Imaging, 25(10), 1347–1357. https://doi.org/10.1016/j.mri.2007.03.007
File under: neuroscience; attention; STEM teaching and learning; anxiety; student mental health