Category Archives: Graduate Coursework

Research Focus Statement for Verbal Protocol Analysis of Individuals Accessing Their Banking and Credit Accounts

This is an assignment I completed writing on 2017-10-03 for the class, EDF 7475: Qualitative Research in Education taught by David Boote, Ph.D. at University of Central Florida. During the remainder of the semester I will be further developing this proposal and think this is one I will actually conduct in some form.

EDF 7475 Research Focus Statement
Richard Thripp
University of Central Florida

My proposed study is primarily a verbal protocol analysis of how people interact with their bank and credit accounts from their mobile devices and/or home computers. The focus of my research will be on American adults who have web-accessible banking and/or credit accounts. The main purpose is to discover and explore how they interact with the web or mobile interfaces of their accounts when reviewing account activity, imagining future account activity, paying bills, and making transfers. This will be conducted via protocol analysis, a qualitative “think-aloud” methodology (Ericsson, 2006) that I will leverage to identify and document their thought processes as they engage or pretend to engage in these tasks. A second purpose is to elucidate the strategies individuals use for managing their day-to-day finances, including provoking them to reflect on recent spending. This will be situated within the literature on financial behaviors among low- and middle-income Americans.

I intend to use maximal variation sampling to focus, in particular, on at least one individual who is lackluster at managing his/her finances, one who is accomplished, and perhaps one who is average. Because participants will be sitting down with me to access their actual personal accounts, I anticipate the protocol analysis will have to be augmented with prompts to imagine conditions or scenarios that are not presently occurring for them (e.g., “imagine you just received your paycheck,” “imagine you are considering making a $500 purchase,” etc.). Further, they may be asked to verbally reflect on their recent financial behaviors, to reveal their financial strategies, or lack thereof.

Research Questions

My main research question is: What are individuals’ thought processes related to accessing, reviewing, anticipating, and initiating activity on their bank and credit accounts? Potentially, this will be augmented with the following sub-questions, although the data collected may not address all of them. All of these sub-questions are intended as advance organizers to guide my qualitative data collection and analysis with my purpose and main research question in mind.

1. How do they plan around periodic bills and income sources?
1a. Are their approaches different for fixed versus unpredictable amounts?
2. How do they avoid or cope with overdraft fees and other bank fees?
3. Do they approach debit and unsecured-credit spending differently?
3a. Do they use one payment method religiously, or a mix depending on the situation (e.g., cash, debit, credit, checks)?
4. How and why do they access account activity and periodic statements?
5. How do they account for nonperiodic debits that do not yet appear in posted or pending account activity (e.g., written checks not yet cashed, fuel purchases that place only a $1.00 hold and take several days to post)? This may differ for debit versus credit users.
6. For bills, do they prefer automatic or manually scheduled payments, and why?
7. What do they find frustrating about their financial institution(s) and banking interface(s)?
8. Do they treat some income differently than others (e.g., windfall bias)?
9. Do they segregate liquid monies into multiple deposit accounts and/or different types of deposit accounts (e.g., checking, savings, money market), and why?
9a. How do they handle moving money between accounts?
10. Are they satisfied with their spending decisions?


This study will contribute to research on financial literacy and behaviors, particularly with respect to consumer inattention (e.g., Grubb, 2015), payment methods and spending behavior (e.g., Soman, 2001), and checking overdraft fees (e.g., Stango & Zinman, 2009, 2014). There have been hundreds of attempts at financial education interventions, predicated on a wealth of survey data showing a lack of financial literacy in the United States, Europe, and beyond (Lusardi & Mitchell, 2014). Critically, educators and policymakers presume educational programs are efficacious, when in fact, they appear to yield negligible long-term effects. For a rigorous meta-analysis, see Frenandes, Lynch, and Netemeyer (2014), which found only 0.1% explanatory power across 201 studies.

The proposed verbal protocol analysis will qualitatively explore how individuals interact with and think about their bank and credit accounts. This approach is fundamentally different from typical questionnaire-based research, such as the Jump$tart Coalition’s Survey of Personal Financial Literacy Among Students or the FINRA Investor Education Foundation’s National Financial Capability Study. Consequently, it may be of both methodological and practice-oriented significant. Moreover, using maximal variation sampling will show how different types of people interact with bank accounts and other financial products—potentially, key differences between financial experts and novices may be revealed that could be leveraged to improve financial education and credit counseling practices.

Preliminary Source List

Campbell, J. Y. (2006). Household finance. The Journal of Finance, 61, 1553–1604.

Carlin, B. I., & Robinson, D. T. (2012). What does financial literacy training teach us? Journal of Economic Education, 43, 235–247.

Ericsson, K. A. (2006). Chapter 13: Protocol analysis and expert thought: Concurrent verbalizations of thinking during experts’ performance on representative tasks. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman, (Eds.), The Cambridge handbook of expertise and expert performance (pp. 223–242).

Fernandes, D., Lynch, J. G., Jr., & Netemeyer, R. G. (2014). Financial literacy, financial education, and downstream financial behaviors. Management Science, 60, 1861–1883.

Grubb, M. D. (2015). Consumer inattention and bill-shock regulation. Review of Economic Studies, 82, 219–257.

Hershey, D. A., Walsh, D. A., Read, S. J., & Chulef, A. S. (1990). The effects of expertise on financial problem solving: Evidence for goal-directed, problem-solving scripts. Organizational Behavior and Human Decision Processes, 46, 77–101.

Hilgert, M. A., Hogarth, J. M., & Beverly, S. G. (2003). Household financial management: The connection between knowledge and behavior. Federal Reserve Bulletin, 89, 309–322.

Karger, H. (2015). Curbing the financial exploitation of the poor: Financial literacy and social work education. Journal of Social Work Education, 51, 425–438.

Lee, J., Marlowe, J. (2003). How consumers choose a financial institution: Decision-making criteria and heuristics. International Journal of Bank Marketing, 22, 2, 53–71.

Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52, 5–44.

Morgan, D. P., Strain, M. R., & Seblani, I. (2012). How payday credit access affects overdrafts and other outcomes. Journal of Money, Credit, and Banking, 44, 519–531.

Nye, P., & Hillyard, C. (2013). Personal financial behavior: The influence of quantitative literacy and material values. Numeracy, 6(1), 1–24.

Soman, D. (2001). Effects of payment mechanism on spending behavior: The role of rehearsal and immediacy of payments. Journal of Consumer Research, 27, 460–474.

Stango, V., & Zinman, J. (2009). What do consumers really pay on their checking and credit card accounts? Explicit, implicit, and avoidable costs. The American Economic Review, 99, 424–429.

Stango, V., & Zinman, J. (2014). Limited and varying consumer attention: Evidence from shocks to the salience of bank overdraft fees. The Review of Financial Studies, 27, 990–1030.

Note: On 10/04/2017 I changed “critical case” to “maximal variation” sampling.

Critique of “Perceiving and managing business risks: Differences between entrepreneurs and bankers” by Sarasvathy, Simon, & Lave (1998)

This is a critique of a qualitative, protocol-analysis empirical study by Sarasvathy, Simon, and Lave (1998) that I wrote on 2017-09-22 for the class, EDF 7475: Qualitative Research in Education taught by David Boote, Ph.D. at University of Central Florida.

EDF 7475 Article Critique One
Richard Thripp
University of Central Florida

Article Citation

Sarasvathy, D., Simon, H. A., & Lave, L. (1998). Perceiving and managing business risks: Differences between entrepreneurs and bankers. Journal of Economic Behavior & Organization33, 207–225.


The authors wrote a series of business problems, some involving risk of financial loss, and others involving uncertainty of workers dying or getting cancer. They administered these written problems to successful entrepreneurs (n = 4) and seasoned bankers (n = 4) who were recruited from continuing education participants and alumni of Carnegie Mellon University. The authors used verbal think-aloud protocols to analyze participants’ responses and thought processes to the written problems. Through cluster and protocol analyses, the authors concluded that entrepreneurs and bankers conceptualize risk and uncertainty differently. Bankers tend to hold returns fixed and try to decrease risk, while entrepreneurs accept the given levels of risk and focus on increasing returns. When confronting potential cancer or death due to carcinogens and other hazards in the workplace, bankers spoke in the third person and restricted their problem space to financial, legal, and ethical issues, often refusing to make a decision; contrastingly, entrepreneurs put their personal values first and looked for external solutions (e.g., being acquired by a larger company) to provide the millions of dollars required to improve safety.

Contribution to the Field

Methodologically, this article contributed by using a qualitative approach, which is unusual in finance and economics. The stark conceptual differences that emerged from protocol analysis of the bankers as compared to the entrepreneurs contributes to future studies of different financial perspectives, and more broadly, to management and leadership studies. Moreover, the business problems the authors developed are of value, although unfortunately from a web search it appears no one else has ever used these problems in the 21 years since this article’s acceptance.

Strengths and Weaknesses

Ericsson (2002) says a protocol analysis should ideally include other indicators like response times and brain activity. While the authors verbal coding and analysis was parsimonious yet sufficiently detailed, they did not include any information on how long respondents took on each question nor the entire instrument. One strength was that many utterances along each cluster dimension were included in Table 1, although the amount of time respondents spent pondering the problems could also have been included. However, the inclusion of quantitative participant-level statistics in Tables 3–4 is laudable and should be followed by other qualitative researchers, albeit the value here is dubious due to the small amount of data.

In delivering the instrument, procedural rigor was lacking. This is abundantly clear in Appendix A, where Banker 2 mistook an amount in Problem 5 to be $1 million instead of $5 million, because “apparently he was given the copy of the problems used by the previous subject who, after completing the protocol, had discussed the possibility of making it one million and had changed the number 5 to number 1” (pp. 224–225). Another oversight is that Appendix B: Results of Cluster Analysis only refers readers to statistics in Tables 3–4 which were included in Appendix A, but these tables should have been moved to Appendix B. Finally, participants may be identifiable—we know the entrepreneurs founded companies with $5–30 million annual revenues—but social desirability bias is never mentioned, despite the nature of the problems. Given the prestige of the journal and institution, these shortcomings are surprising.

There is no getting around the fact this was a convenience sample. Entrepreneurs who were participating in the authors’ continuing education program were solicited to participate, and bankers were alumni “selected based on geographical accessibility and convenience of scheduling” (p. 208). Moreover, the design of the study suggests the authors may have held a priori assumptions about the differences between bankers and entrepreneurs. Further, the writing and design of the problems, including rationale for offering only di- or trichotomous choices, is absent. It is not even clear the authors wrote the problems— “five problems were used” (p. 208) is what they state, although I surmise they wrote the problems based on an absence of web results when searching portions of the exact text of the problems, besides this article itself.

Nevertheless, this was a concise, enjoyable read that resulted in surprising findings and insights. For example, in Problem 1, I find bankers’ preference for Project 2 stunning given the high probability of low returns contrasted with only a 0.5–0.75% increase in summed returns, based on cumulative probability. Even with discretionary monies, accepting enormous uncertainty for such marginal gains seems overly rational—almost inhuman. Finally, it was smart of the authors to note that causality could flow counterintuitively—perhaps bankers self-select due to their pre-existing aversion to risk, rather than developing the aversion on the job?

Contribution to My Understanding

This article showed me that conducting a verbal protocol analysis is not an insurmountable challenge. The authors’ coding scheme was concise and readily accessible, while their consistent emphasis on participants’ quotes gave me a clear window into several perspectives and lines of reasoning. Moreover, the article made it starkly clear that qualitative research involves many value judgments—there were certainly hundreds of utterances the authors did not see fit to include, and a detailed copy of their encoding protocols is only available upon request. However, trusting in their judgments and techniques, I feel reading their selections and analyses is preferable to having the raw recordings or transcripts. Finally, I now understand that protocol analysis can be used not just for comparing experts and novices, but also different kinds of experts, and that many inferences can be inductively drawn from the results.

Personal Intro to EDF 7474 Peers

Here is an introduction discussion post I wrote to my peers in Dr. Hahs-Vaughn’s EDF 7474: Multilevel Data Analysis course at University of Central Florida this semester (Fall 2017). Here, I recap my first year in the Ph.D. program, discuss my plans for the fall semester, and share an anecdote about the time I sold a photograph to NBC.

Hello, my name is Richard Thripp and I’m a 2nd-year Ed. Ph.D. Instructional Design & Technology student. I like math and statistics, and am in the Advanced Quantitative Methodologies certificate program.

The area I am most passionate about is financial literacy education and research, although in the past year I have been all over the place, working on an engineering testing center (a manuscript on which I am Author # 4 of 4 is under review), threshold concepts for doctoral students (abandoned this), Florida 7th grade civics test data (a manuscript on which I am Author # 3 of 5 is under review), and National Financial Capability Study (NFCS) data. Tentatively, this semester I want to further analyze NFCS data in this course and in Dr. Witta’s Quantitative Methods II course, leading to a publication, while an unrelated project will be observing consumer behavior at Starbucks for Dr. Boote’s qualitative methods class.

This semester is my first one teaching. I have 70 students in two sections (35 each) of EME 2040: Introduction to Technology for Educators. So far I have enjoyed it a lot.

An interesting fact about me is that in 2011, I sold this framed copy of this image of a grasshopper, a photograph I took in 2006 and removed the background, on Etsy to NBC, for $20. It appeared in Max Burkholder’s room in Season 3 of Parenthood. I still haven’t watched the show, and could not find it while skimming through it, but was told by one other person they saw it.

Message from NBC on Etsy

EME 6646 Assignment on Moral Emotions, Self-Regulation of BOLD Signals, and Monetary Rewards

Assignment 4, Part A: Individual Explanation of Rewards and Emotions
For EME 6646: Learning, Instructional Design, and Cognitive Neuroscience
By Richard Thripp
University of Central Florida
June 8, 2017

Moral Emotions

Using a task where participants passively viewed morally charged pictures (e.g., starving children and warfighting), Moll et al. (2002) found that while such images activated the amygdala, thalamus, and upper midbrain just like basic emotions, images evoking “moral emotions” additionally activated the orbital and medial regions of the prefrontal cortex, as well as the superior temporal sulcus region, both of which were previously known to be important for perception and social behavior. Moll et al. (2002) argue that these functional magnetic resonance imaging (fMRI) results indicate that humans automatically assign moral values to social events, and that this is an important function of human social behavior.

While traditionally, the prevailing paradigm was that moral judgments are guided by reason, neuroimaging evidence has shown us that emotion plays a vital role. For example, Greene, Sommerville, Nystrom, Darley, and Cohen (2001) tackled the issue by presenting fMRI-connected participants with a battery of moral dilemmas, with moral–personal (e.g., pushing a bystander off a bridge to stop a trolley that would kill five people), moral–impersonal (e.g., voting in favor of a referendum that would result in many deaths), and non-moral conditions (e.g., whether to stack coupons at the grocery store). Moral–personal dilemmas activated brain regions (i.e., medial frontal gyrus, posterior cingulate gyrus, and angular gyrus) that were significantly less active in the other conditions. Moreover, reaction time was higher in instances where a participant responded an action was “morally appropriate” (it was a dichotomous choice between this and “morally inappropriate”) when this was emotionally incongruent—for example, when participants said “appropriate” to sending the bystander to his or her death to stop the trolley from killing five people. The authors specifically compared this to the Stroop test, contending that this was a similar phenomenon in that it required extra processing time. Overall, emotions can help us understand why the majority will say it is acceptable to flip a switch that changes the direction of the trolley, killing a bystander to save five others, while a majority will say it is unacceptable to push the bystander in front of the trolley to stop it, even though the outcome is the same. Greene et al. (2001) say the latter is more emotionally salient. While the trolley problem is a philosophical paradox if considering only reason, adding emotion resolves it.

If moral dilemmas light up different parts of the brain, and if emotional salience is important to judging whether an issue is morally unacceptable, educators can use this to design instruction to engage moral emotions. For instance, the music industry has long argued that illegally downloading a song is no different than shoplifting the CD from Target. The former might be compared with flipping the trolley switch, while the latter is like pushing the bystander in front of the trolley—far fewer would shoplift than illegally download a song. Casting academic integrity in a similar light could help promote ethical and prosocial behaviors among students. Marketing research implies that most people are honest to a fault—they would not be grossly dishonest to get ahead, but if they can profit while continuing to believe they are righteous, they will do so (Mazar, Amir, & Ariely, 2008). In addition to promoting academic honesty, moral emotions can be evoked in instruction through vignettes, case studies, or interrogatories (e.g., “What would you do if you could save five people by harvesting the organs of a cerebrally dead 22-year-old who is an organ donor but whose family actively protests?”). Integrating these as both individual and group activities may be useful. Group activities invite going along with the group, so individually completion might precede group discussion. Sadly, while Walt Disney Studios appeals to our moral emotions and emotions of all forms in their motion pictures, instructors typically leave this engagement opportunity untapped.

Self-Regulation of BOLD Signals and Monetary Rewards

Recently, Sepulveda et al. (2016) combined measurement via real-time FMRI neurofeedback (NF) with instructing participants to increase their blood-oxygen-level dependent (BOLD) signals (i.e., self-regulation of brain physiology), in a between-groups study with four groups (n = 20 with five per group) which received either NF only (a.k.a. contingent feedback), NF and motor-imagery training, NF and monetary reward, and NF + motor-imagery training + monetary reward. The BOLD signal is a proxy for “volitional control of supplementary motor area” (Sepulveda et al., 2016, p. 3155)—this ability can improve “planning and execution of motor activity” (p. 3155), and may be important to self-regulation, learning, academic success, et cetera. Interestingly, while all groups were successful at up-regulating their BOLD signals, monetary reward resulted in the greatest increase, while motor-imagery training did not even result in a statistically significant enhancement. That is to say, the participants who were evidently the most motivated to increase their BOLD signals were the ones who received NF and an on-screen dollar amount where the amount increased in proportion to their real-time increase in BOLD signal. While the authors were careful to note that monetary rewards—which are by definition an extrinsic motivator—lose their effectiveness over time and thus should be used as an initial motivator that is withdrawn over time (hopefully giving way to intrinsic motivation), their discussion does not mention that this neuroimaging evidence may be important to the use of monetary rewards for academic and organizational success.

Monetary Rewards May Be Ineffective in Academic Settings

In contrast to Sepulveda et al. (2016), Mizuno et al. (2008) found that while learning motivated by monetary rewards activated the putamen bilaterally much like self-reported level of motivation learning, the intensity of activity (measured via fMRI BOLD signals) increased with higher levels of motivation for learning, but not with increased monetary rewards. This may suggest that, at least in an academic context, greater monetary rewards do not increase motivation. While it did not employ neuroimaging, a study of 300 middle schoolers by Springer, Rosenquist, and Swain (2015) may be relevant. They offered either no incentive, $100, or a “certificate of recognition signed by the district superintendent” (p. 453) to students who attended tutoring regularly. While the preceding fMRI research may lead us to believe that the monetary incentive would have been effective, in fact it had no significant differences from the control group, while the certificate of recognition was a highly effective motivator. Therefore, for academic motivation, financial rewards may be inferior to other forms of extrinsic motivation (e.g., a certificate), or to intrinsic motivation. Nevertheless, they may be a useful tool for the unimaginative instructor, particularly in contexts where a grading scheme cannot be implemented (e.g., some forms of organizational training). For a more typical academic setting, grades and “extra” credit opportunities (which, ironically, are available even to students who achieve far less than 100% on their work) may basically take the place of what would have been monetary rewards in another setting.


Greene, J. D., Sommerville, R. B., Nystrom, L. E., Darley, J. M., & Cohen, J. D. (2001). An fMRI investigation of emotional engagement in moral judgment. Science, 293, 2105–2108.

Mazar, N., Amir, O., & Ariely, D. (2008). The dishonesty of honest people: A theory of self-concept maintenance. Journal of Marketing Research, 45, 633–644.

Mizuno, K., Tanaka, M., Ishii, A., Tanabe, H. C., Onoe, H., Sadato, N., & Watanabe, Y. (2008). The neural basis of academic achievement motivation. NeuroImage, 42, 369–378.

Moll, J., de Oliveira-Souza, R., Eslinger, P. J., Bramati, I. E., Mourão-Miranda, J., Andreiuolo, P. A., & Pessoa, L. (2002). The neural correlates of moral sensitivity: A functional magnetic resonance imaging investigation of basic and moral emotions. Journal of Neuroscience22, 2730–2736.

Sepulveda, P., Sitaram, R., Rana, M., Montalba, C., Tejos, C., & Ruiz, S. (2016). How feedback, motor imagery, and reward influence brain self-regulation using real-time fMRI. Human Brain Mapping, 37, 3153–3171.

Springer, M. G., Rosenquist, B. A., & Swain, W. A. (2015). Monetary and nonmonetary student incentives for tutoring services: A randomized controlled trial. Journal of Research on Educational Effectiveness, 8, 453–474.

EME 6646 Assignment on Long-Term Potentiation, Learning Strategies, Memory Consolidation, and Sleep

Assignment 3, Part A: Individual Explanation of Learning and Memory
For EME 6646: Learning, Instructional Design, and Cognitive Neuroscience
By Richard Thripp
University of Central Florida
June 2, 2017

Long-Term Potentiation

In their 2014 literature review, Granger and Nicoll lament that long-term potentiation (LTP) has never been precisely defined. They explain that “the broadest definition is a long-lasting enhancement in synaptic strength following a brief high-frequency stimulation” (p. 1), which might be summarized as “neurons wire together if they fire together” (Löwel & Singer, 1992, p. 211). While the debate over whether long-term potentiation occurs pre- or postsynaptically continues, it is perhaps tangential to the educational ramifications. Fundamentally, the discovery of long-term potentiation in the 1970s was critical to our understanding of the mammalian brain (Teyler & DiScenna, 1987), showing us that the brain is not a static object, but that, even in the short term, neural pathways can be strengthened by mental exercise, not unlike physical exercise. This synaptic plasticity is important for many types of declarative memory (Byrne, n.d.), which, for educators, suggests that an important component of learning is repeated activation of the relevant synapses. Consequently, practice of the to-be-acquired skill should be integrated early and throughout a program of study. For example, given our knowledge of LTP, it would be inappropriate to structure a course on driving with 15 weeks of reading a textbook followed by one week of application behind the wheel. In the realm of teacher education, LTP might be accounted for by integrating field experiences early, which may also narrow the theory–practice gap (Coffey, 2010), rather than inefficiently delaying such fieldwork for a senior-year internship.

Learning Strategies

The strategy used during learning is a determinant of what type(s) of memory systems are engaged, and subsequently, the degree of success (Squire, 2004). This was discovered, in part, by inhibiting the hippocampus in rats, which aided them in navigating a maze tailored for non-declarative memory by solidifying the supremacy of the caudate nucleus. In fact, hippocampal lesions allowed the rats to perform better at this task! In humans, one can imagine a renegade researcher invasively inhibiting regions of the anterior cingulate and dorsolateral prefrontal cortices in an attempt to improve human performance on the Stroop test. More practically, educators can attempt to evoke efficient learning strategies for the materials at hand. For example, trying to learn a habit while also trying to memorize the requisite steps can result in failure on both counts (Squire, 2004). Therefore, educators might specifically instruct learners to focus on repetition in one trial and memorization in another. For example, if the task is executing a mathematical operation with a series of sub-steps, a textbook author could encourage habit learning by providing several problems with a reference sheet containing the sub-steps in view. Then, to encourage memorization, the reference sheet could be subsequently confined to a separate sheet requiring a page turn, with the learner being explicitly directed to test his or her memorization of the requisite steps.

Memory Consolidation and the Essentiality of Sleep

While it is easily observed via EEG oscillations that normal human sleep consists of 90-minute cycles including rapid eye-movement sleep (REM) and four stages of non-REM sleep, how memory consolidation occurs during sleep is less clear (Stickgold, 2005). However, that it has occurred is abundantly clear because certain tasks such as finger-tapping, rotation adaption, and visual texture discrimination have been experimentally shown to be enhanced subsequent to sleep, but when tested after 4 to 12 hours of wakefulness without sleep, the enhancement does not occur. Moreover, for some such tasks, improvement occurs even more when re-tested 72 hours later rather than 24, suggesting that additional nights of sleep further enhance these forms of procedural learning. Stickgold’s (2005) literature review goes on to summarize what he dubs as “converging evidence” (p. 1276) at the molecular, cellular, and higher levels, showing that sleep helps cell membranes, myelin, and cortical neuronal responsiveness. Further, at least in the Zebra finch, songs rehearsed during wakefulness appear to continue to be rehearsed in sleep, based on detection of similar “patterns of neuronal excitation” (Stickgold, 2005, p. 1277). Therefore, strong evidence exists that several forms of procedural learning are consolidated via sleep, although the evidence for declarative memory is less conclusive.

More recently, Tononi and Cirelli (2014) have argued, with molecular, electrophysiological, and structural evidence, for the synaptic homeostasis hypothesis (SHY). This basically says that “sleep is the price the brain pays for plasticity” (Tononi & Cirelli, 2014, p. 12). The essentiality of sleep is supported by neuroscience and behavioral evidence, yet this advice is often not followed by students nor educators and other professionals. Educators might encourage learners to get adequate sleep by directly exposing them to neuroscience research on sleep’s importance and by discourage cramming or “all-nighters” with adequate instructional scaffolding (e.g., setting draft and format review deadlines prior to a final submission deadline). Finally, researchers have recognized (e.g., Piffer, Ponzi, Sapienza, Zingales, & Maestripieri, 2014) that some humans are geared toward “morningness” (i.e., “early birds”) while others have a propensity toward “eveningness” (i.e., “night owls”).

As an individual with a lifelong propensity toward eveningness, I am baffled by the culture in America and elsewhere that favors early birds while mocking and ridiculing night owls for their purported laziness. The machinations of society are organized to confer privilege upon early birds—such as requiring children to go to school early in the morning, businesses and government offices that open early in the morning, and a majority of employment opportunities requiring us to wake up shortly after sunrise. Now that most people have electricity, if we are to facilitate learning, memory consolidation, and human performance in general, why not provide parallel structures and opportunities for night owls? Surely, would it not relieve congestion on Orlando’s roads if instead of a majority of workers working in the neighborhood of 9 a.m. to 5 p.m., if workplaces could be staffed in the evening and overnight so that unused nighttime road capacity might be leveraged? What about tweens and teenagers who would be better served if school was from noon to 7 p.m. rather than requiring them to rise before dawn and be tired all day? In higher education, afternoon and night course offerings can cater to night owls while not necessarily being punitive toward early birds. To further improve memory consolidation, housing developments and apartment complexes might be constructed with soundproofing and futuristic windows that use amorphous metal oxides to become completely opaque at the flip of a switch (Llordés, Garcia, Gazquez, & Milliron, 2013), facilitating daytime quiet and darkness for improved sleep, memory consolidation, and learning.


Byrne, J. H. (n.d.). Chapter 7: Learning and memory. Neuroscience online: An electronic textbook for the neurosciences. Retrieved from

Coffey, H. (2010). “They taught me”: The benefits of early community-based field experiences in teacher education. Teaching and Teacher Education, 26, 335–342.

Granger, A. J., & Nicoll, R. A. (2014). Expression mechanisms underlying long-term potentiation: A postsynaptic view, 10 years on. Philosophical Transactions of the Royal Society, 369(1633), 1–6.

Llordés, A., Garcia, G., Gazquez, J., & Milliron, D. J. (2013). Tunable near-infrared and visible-light transmittance in nanocrystal-in-glass composites. Nature, 500, 323–326.

Löwel, S., & Singer, W. (1992). Selection of intrinsic horizontal connections in the visual cortex by correlated neuronal activity. Science, 255, 209–212.

Piffer, D., Ponzi, D., Sapienza, P., Zingales, L., & Maestripieri, D. (2014). Morningness–eveningness and intelligence among high-achieving US students: Night owls have higher GMAT scores than early morning types in a top-ranked MBA program. Intelligence, 47, 107–112.

Squire, L. R. (2004). Memory systems of the brain: A brief history and current perspective. Neurobiology of Learning and Memory, 82, 171–177.

Stickgold, R. (2005). Sleep-dependent memory consolidation. Nature, 437, 1272–1278.

Teyler, T. J., & DiScenna, P. (1987). Long-term potentiation. Annual Review of Neuroscience, 10, 131–161.

Tononi, G., & Cirelli, C. (2014). Sleep and the price of plasticity: From synaptic and cellular homeostasis to memory consolidation and integration. Neuron, 81, 12–34.