Why UCF should allow faculty and staff to change Windows 10 taskbar display settings

June 21, 2017

My bid to get University of Central Florida’s (UCF) I.T. department to allow education faculty and staff to change taskbar settings so they could ungroup Windows 10 taskbar items and be able to display labels in addition to icons was shot down. I am told this issue does not affect job performance in any way and that there is no need for changes because work is not being impeded. My concluding remarks:

Thanks, [redacted], for your help! I disagree with [redacted]—faculty in the education department are provided with dual monitors, even though by this standard, single monitors would not impede work. I believe that like dual monitors, being able to ungroup items on the taskbar and being able to display labels instead of icons would improve productivity. However, I will take no further action.

EME 6646 Assignment on Measuring Creativity, Neuroimaging, Psychometrics, and Methods

Assignment 5, Part A: Individual Explanation of Imagination and Creativity
For EME 6646: Learning, Instructional Design, and Cognitive Neuroscience
By Richard Thripp
University of Central Florida
June 15, 2017

Measuring Creativity: Neuroimaging or Psychometrics?

When researchers using neuroimaging techniques seek to compare brain activity between people who are especially creative and people who are of average creativity, how do they do so? One might think this would be accomplished by using neuroimaging techniques to determine who is more creative. However, the pretty pictures of brain activity we see in many journal articles are actually the result of averaging and subtraction (Sawyer, 2011). In truth, most of the brain is active almost all the time—what we are really looking at is whether particular regions are comparatively less or more active than others, and this difference is often only 3% if we are lucky (Sawyer, 2011). Brain scans where certain “creative” regions of the brain are shown in bright red may lead the reader astray, not suggesting such a tiny differential in brain activity.

Perhaps because our current ability to measure actual brain activity is not a useful indicator of creativity, neuroimaging cannot yet be directly used to determine an individual’s level of creativity. Thus, even studies employing neuroimaging typically fall back on psychometric measures. For example, Jaušovec (2000) empirical investigation is titled “Differences in cognitive processes between gifted, intelligent, creative, and average individuals while solving complex problems: An EEG study” (p. 213). At first glance, one might think electroencephalogram (EEG) is being used to determine whether someone fits into the four categories of “gifted,” “intelligent,” “creative,” or “average.” However, Jaušovec actually used the Weschler Adult Intelligence Scale (WAIS or “IQ test”) and the Torrance Test of Creative Thinking (TTCT) to organize participants into these categories, defining “gifted” as doing well on both tests, “average” as not doing well on either, and the other categories as doing well on one test but not the other. Then, he found minor differences in EEG readings when participants solved open- or closed-problem tasks, and concluded that intelligence and creativity are probably different, and that patterns of brain activity are related to creativity and intelligence. Knowing that even the best psychometric tests have substantial measurement error (e.g., IQ tests measure not only intelligence, but familiarity with written language and academic environments), that grouping people as Jaušovec (2000) did introduces further error (I have reproduced his grouping table below), and that EEG itself lacks spatial resolution, Jaušovec’s methods seem so muddy as to be unfit to produce any conclusions. However, it is not as though I have cherry-picked an unknown, dubious study—according to Google Scholar his article has an impressive 239 citations! With recent arguments further suggesting that EEG’s temporal resolution is overblown (Burle et al., 2015), our confidence ability to draw conclusions diminishes further.

Jausovec (2000) Table 4

Figure 1. Grouping table for intelligence and creativity categories by Jaušovec (2000).

While EEG is not in the same vein of neuroimaging as magnetic-resonance imaging (MRI), near-infrared spectroscopy (NIRS), or positron emission tomography (PET), the use of psychometrics as an organizing device, and of subtractive averaging as a method to present pretty pictures implying big results, remain applicable. I have difficulty seeing the ethical differences between subtractive averaging and removing the zero axis on a bar chart to show bars of vastly different heights that would otherwise have been only slightly different.

Neuroimaging and Psychometrics in Creativity Research: A Corroboration Model

Psychometrics, the science of mental measurement, by definition is messy and imprecise. However, corroborating psychometric instruments with neuroimaging techniques may help us more accurately understand creativity. This is what Arden, Chavez, Grazioplene, and Jung (2010) advocate in their literature review and position piece on neuroimaging creativity. Researchers are all using different criteria to measure and interpret creativity, but there has been no concerted effort toward detailing the “psychometric properties of creative cognition” (Arden et al., 2010, p. 152), which is needed to be able to compare studies to each other. Nevertheless, employing neuroimaging has already allowed us to debunk, or at least fail to find support for, common hypotheses such as creativity being linked to the right brain or improved neural function (Arden et al., 2010). If we continue to improve the reliability and validity of creativity research along both psychometric and neuroimaging dimensions, we will improve our limited understanding of creativity, which is particularly needed areas such as novelty and originality (Fink, Benedek, Grabner, Staudt, & Neubauer, 2007). Limited spatial resolution prevents us from accurately isolating brain activity, while at the same time, the prevailing paradigm of neuroscience creativity research remains oriented toward finding the specific areas of the brain are associated with creativity (Arden et al., 2010; Sawyer, 2011), while the correct answer may be that all of them are—although some more so than others. Modern techniques as reviewed by Jung, Mead, Carrasco, and Flores (2013), such as structural magnetic-resonance imaging (sMRI), diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H-MRS) are critical to isolating the structural characteristics of creative cognition, and might be seen as a complement, rather than a replacement, to the proxy measures that psychometrics constitute. Finally, lesion studies reveal that areas of the brain may actually compete in parallel to reach creative solutions, with the right medial prefrontal cortex (mPFC) winning out in healthy subjects, even though it produces inferior results (Jung et al., 2013). When corroborated with psychometric measures, this may lead us to an amusing finding whereby high creativity might be associated with brain problems (i.e., lesions in the left language errors).

Methodological Issues in Neuroscience-Based Creativity Research

Even recent creativity research is often devoid of neuroimaging. For example, Anderson, Potočnik, and Zhou’s (2014) “Innovation and creativity in organizations: A state-of-the-science review, prospective, commentary, and guiding framework,” published in Journal of Management and focused on 2002–2013 research, defines creativity as “idea generation” and looks at studies that solely use observational and self-report data. In an organizational context, it is still unheard of to use MRI, DTI, 1H-MRS, et cetera, and even EEG is rare. Moreover, the research corpus itself is scattered and disjointed (Batey & Furnham, 2006). Consequently, sound methods are even more important for the few researchers who are able to use neuroimaging methods.

A big issue exemplified in Jaušovec (2000), and reiterated by Arden et al. (2010), are case-control designs whereby subjects are unnecessarily dichotomized into high- and low-creativity buckets, instead of respecting the continuous nature of creativity. Even psychometric measures such as Torrance tests do not classify people in binary, but rather across a range of scores. Respecting this continuity can improve statistical power.

Using expensive and cumbersome technologies such as PET or fMRI requires lying down, perfectly still, with loud whirring noises (Sawyer, 2011). Even EEG requires electrodes attached to one’s head, which impairs many creative activities. Methodologically, this is a large problem that is presently not surmountable. There is no way to measure creativity with an fMRI while a subject plays a violin (except, perhaps, a pizzicato performance). Moreover, neuroimaging studies do not measure novelty or usefulness, unlike common definitions of creativity used by non-neuroscience researchers (Sawyer, 2011).

Lastly, although there are many other methodological issues, neuroscience creativity research would be furthered by accurate reporting and disclosure of averaging, subtraction techniques, and the actual activation levels that were observed temporally and/or spatially (Sawyer, 2011). Speculation about causation should be clearly marked as such. Finally, researchers should refrain from labeling a region of the brain as a center for any specific creative task, or for creativity in general (Arden et al., 2010). Even though it generates popular press, such determinations are typically inaccurate.


Anderson, N., Potočnik, K., & Zhou, J. (2014). Innovation and creativity in organizations: A state-of-the-science review, perspective, commentary, and guiding framework. Journal of Management, 40, 1297–1333. http://doi.org/10.1177/0149206314527128

Arden, R., Chavez, R. S., Grazioplene, R., & Jung, R. E. (2010). Neuroimaging creativity: A psychometric view. Behavioural Brain Research, 214, 143–156. http://doi.org/10.1016/j.bbr.2010.05.015

Batey, M., & Furnham, A. (2006). Creativity, intelligence, and personality: A critical review of the scattered literature. Genetic, Social, and General Psychology Monographs, 132, 355–429. http://doi.org/10.3200/MONO.132.4.355-430

Burle, B., Spieser, L., Roger, C., Casini, L., Hasbroucq, T., & Vidal, F. (2015). Spatial and temporal resolutions of EEG: Is it really black and white? A scalp current density view. International Journal of Psychophysiology, 97, 210–220. http://doi.org/10.1016/j.ijpsycho.2015.05.004

Fink, A., Benedek, M., Grabner, R. H., Staudt, B., & Neubauer, A. C. (2007). Creativity meets neuroscience: Experimental tasks for the neuroscientific study of creative thinking. Methods, 42, 68–76. http://doi.org/10.1016/j.ymeth.2006.12.001

Jaušovec, N. (2000). Differences in cognitive processes between gifted, intelligent, creative, and average individuals while solving complex problems: An EEG study. Intelligence, 28, 213–237. http://doi.org/10.1016/S0160-2896(00)00037-4

Jung, R. E., Mead, B. S., Carrasco, J., & Flores, R. A. (2013). The structure of creative cognition in the human brain. Frontiers in Human Neuroscience, 7, 1–13. http://doi.org/10.3389/fnhum.2013.00330

Sawyer, K. (2011). The cognitive neuroscience of creativity: A critical review. Creativity Research Journal, 23, 137–154. http://doi.org/10.1080/10400419.2011.571191

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. http://doi.org/10.1126/science.1062872

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. http://doi.org/10.1509/jmkr.45.6.633

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. http://doi.org/10.1016/j.neuroimage.2008.04.253

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. http://doi.org/10.1016/j.bandc.2016.07.007

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. http://doi.org/10.1002/hbm.23228

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. http://doi.org/10.1080/19345747.2015.1017679

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 http://neuroscience.uth.tmc.edu/s4/chapter07.html

Coffey, H. (2010). “They taught me”: The benefits of early community-based field experiences in teacher education. Teaching and Teacher Education, 26, 335–342. http://doi.org/10.1016/j.tate.2009.09.014

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. http://doi.org/10.1098/rstb.2013.0136

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. http://doi.org/10.1038/nature12398

Löwel, S., & Singer, W. (1992). Selection of intrinsic horizontal connections in the visual cortex by correlated neuronal activity. Science, 255, 209–212. http://doi.org/10.1126/science.1372754

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. http://doi.org/10.1016/j.intell.2014.09.009

Squire, L. R. (2004). Memory systems of the brain: A brief history and current perspective. Neurobiology of Learning and Memory, 82, 171–177. http://doi.org/10.1016/j.nlm.2004.06.005

Stickgold, R. (2005). Sleep-dependent memory consolidation. Nature, 437, 1272–1278. http://doi.org/10.1038/nature04286

Teyler, T. J., & DiScenna, P. (1987). Long-term potentiation. Annual Review of Neuroscience, 10, 131–161. http://doi.org/10.1146/annurev.ne.10.030187.001023

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. http://doi.org/10.1016/j.neuron.2013.12.025

Poster Presentation at ICEL: Fortifying Asynchronous Online Learning With Digitally Delivered In-Person Assessments to Leverage the Testing Effect

I am re-posting this here as a WordPress “post” but the permanent and more convenient “page” URL is thripp.com/epc-poster.

Here is our Fortifying Asynchronous Online Learning With Digitally Delivered In-Person Assessments to Leverage the Testing Effect poster by Richard Thripp, M.A.; Ronald DeMara, Ph.D.; Baiyun Chen, Ph.D.; and Richard Hartshorne, Ph.D., presented on June 2, 2017 at the 12th International Conference on e-Learning (ICEL) hosted at University of Central Florida (UCF).

This poster focuses on innovations implemented at the Evaluation and Proficiency Center at UCF’s College of Engineering and Computer Science, but these strategies could be used at many institutions.

EPC Poster

I am also making the poster available in PDF format. Feel free to share a link to this page with your colleagues.

Writing on education, finance, psychology, et cetera