According to Training magazine, American companies spent over $90.6 billion USD on training and development in 2017 (Training, 2017, p. 21). Other estimates put this number even higher at $164.2 billion USD in 2012 (Beer, Finnstrom, & Schrader, 2016, p. 1). This amounts to about $1,075 USD per employee. Training and development spends in Canada are similar. In a 2016 survey of 90 large Canadian private-sector employers, companies reported spending between $500 and $1,000 CDN annually per employee with 45% of companies spending more than $1,000 CDN per employee (AON Hewitt and The Business Council of Canada, 2016, p.12).
Despite the amount spent on training, many organizations are not seeing the return on their investment in learning and development. According to Singaraju, Carroll, and Park (2015) (as cited in Bersin, Geller, Wakefield, & Walsh, 2016, p. 7) only 37% of companies believe their programs are effective. Beer et al., (2016) refer to the limited return on investment (ROI) for corporate training as the, “great training robbery” (p. 1). They assert that investments in corporate learning do not translate into positive changes for organizations (Beer et al., 2016, p.1). This belief that training is ineffective was reflected in a 2008 study of internal and family medicine residents who participated in an online tutorial about diabetes guidelines. The study showed that participants lost half of their knowledge gains within 8 days of completing the module. Gains in knowledge were insignificant 55 days following completion of the course despite learners rating the tutorial “very good” or “excellent” (Bell et al., 2008, p. 1164).
At the same time, the ability for employees to learn and adapt may be more critical than ever. According to the 2016 Deloitte Human Capital Survey, 84% of executives cited learning as “important” or “very important” (Bersin, Geller, Wakefield, & Walsh, 2016, p. 6). A study published in the MIT Sloane management review (Kane et al., 2016) indicated that 90% of managers or executives, “anticipate that their industries will be disrupted by digital trends to a great or moderate extent” (p. 3). Gratton and Scott (2016) predict that the working world of the future will shift and evolve significantly along with the skills workers require. Many of the skills workers learn today will quickly become obsolete in a rapidly changing digital environment.
The rapid pace of change is compounded by instant access to information with smart phones, instant messaging and ever increasing internet speeds. A 2014 report by Bersin by Deloitte described modern learners as overwhelmed, distracted, impatient, untethered, collaborative, and empowered (Tauber & Wang-Audia, 2014, p. 4-6). They suggested that designers developing online courses have, “5-10 seconds to grab someone’s attention before they click away” (p.10). Consistent with that, a 2015 study by Microsoft estimated the attention span of the average adult to be less than that of a goldfish (Microsoft, 2015, p.6).
One potential mechanism to address the lack of ROI associated with corporate training could be to better adapt training programs to how employees learn. Current research in neuroscience has greatly advanced our understanding of how we take in, process and retain information leading to memory formation which is necessary for learning (Collins, 2016, p. 50). Our understanding of neuroscience may provide clues as to how we can increase the positive impact of corporate development programs. This literature review will examine the current research on the neuroscience of memory formation and its potential application to transfer of learning in a corporate setting.
What does neuroscience say about how we learn?
Neuroscience is an, “emerging, but rapidly growing field” driven by advances in neuroimaging including Functional Magnetic Resonance Imaging (fMRI) and Positron Emission Tomography (PET) (Stuart, 2014, p.3). Recent advances in fMRI and PET scan have allowed researchers to view neurochemical activity in the brain during encoding, consolidation and recall of information necessary for memory formation (Stuart, 2014, p.3). This has expanded researchers understanding of how learning occurs in the brain and the factors that enhance or impede learning.
There are multiple different definitions of neuroscience as it blends different scientific fields including, “…psychology, physiology, philosophy and even computer science, engineering and physics” (Collins, 2016, p. 7). This literature review will use the definition of cognitive neuroscience proposed by Rugg in 1997, “Cognitive neuroscience aims to understand how cognitive functions, and their manifestations in behavior and subjective experience, arise from activity in the brain” (p. 1). This literature review will encompass studies both on the activity of the brain, as evidenced through neuroimaging, and the output or manifestation of this activity through cognition or learning.
As there are different definitions of neuroscience, there are also different definitions of learning and different types of learning which can crossover with, and be confused with, memory. According to Collins (2016), “We need our memories to learn but having a memory isn’t enough to qualify as learning” (p. 50). In order for learning to take place we need to be able to, “recall that memory later in order to demonstrate learning” (p. 50). As such, the formation of declarative memories (or memories that can be expressed) and learning are tightly linked. In order for declarative memories to be formed, information must be encoded, stored and retrieved at a later date (Collins, 2016, p. 141; Davachi, Keifer, Rock, D., & Rock, L., 2010, p. 2; Rasch & Born, 2013, p. 683). The hippocampus, part of the limbic system, plays a key role in mediating the formation of declarative memory (Collins et al., 2016, p. 56). Studies show that the hippocampus needs to be sufficiently activated in order for memories to be encoded (Davachi et al., 2010, p. 2). Other areas of the brain discussed in this literature review and involved in memory formation include the pre-frontal cortex, amygdala and caudate nucleus.
Principles of cognitive neuroscience in learning
For declarative memories to be formed, the learner needs to fully focused on the material being taught (Davachi et al., 2010. p.2; Collins, 2016, p. 108). A study by Kensinger, Clarke, and Corkin demonstrated that, when learners were trying to pay attention to both auditory and visual tasks at the same time, the hippocampus was not as active (2003, ). This study also showed less activation in the prefrontal cortex (PFC) when the subjects were multi-tasking, particularly when the task was challenging. The PFC plays an important role in learning and memory through the its interaction with the hippocampus (Collins, 2016, p. 143). It is well established that that PFC can only process a certain amount of information at a time (Rock, 2009, p. 7) and when we are focused on a task/ paying full attention, there is more active encoding of information in the hippocampus, leading to increased retention of information (Davachi et al., 2010, p. 2).
Research has confirmed that multi-tasking reduces attention and learning. Studies showed that, when attention was divided during learning, long term memory was negatively impacted. (Craik et al., 1996, p. 174; Kensinger et al., 2003, p. 2407). Studies have also shown that students multi-tasking on their laptops during a class performed significantly poorer on tests of content than students who weren’t multitasking (Hembrooke & Gay, 2003. p. 58; Sana, Weston, & Cepeda, 2013, p. 29). A somewhat contrary study showed that students who were texting while reading took longer to read but achieved the same levels of performance when tested afterwards (Bowman, Levine, Waite, & Gendron, 2010, p. 930).
Full attention is also dependent on the optimal balance of dopamine and norepinephrine in the brain (Davachi et al., 2010, p. 3). A 2010 review by Shohamy and Adcock states, “evidence indicates that dopamine release before, during, and after an event supports hippocampal plasticity and episodic memory formation” (p. 470). Dopamine is associated with positive emotions and anticipated rewards and can be increased in multiple ways. For example, dopamine is increased when people see the relevance of the information being presented (Davachi et al., 2010, p. 3). This is consistent with a 1997 meta-analysis by Symons and Johnson showing that people were more likely to remember information they were able to relate to themselves (p. 386). Norephinephrine is released when we experience increased stress, pressure or threat and also helps us pay attention and form memories (Collins, 2016; Davachi et al., 2010). Thus introducing a moderate amount of stress into training situations can also increase memory formation.
Research would indicate that human attention spans (Microsoft, 2015, p.6) are decreasing and learners are highly distracted (Tauber & Wang-Audia, 2014, p. 4). This means the link between attention and memory formation is an important consideration for instructional designers and corporate trainers. Introducing novelty in training, by changing up the delivery method, can increase attention by introducing a small amount of stress and increasing dopamine levels (Collins, 2016, p. 152; Davachi et al., 2010, p. 3).
Curiosity has also been shown to positively impact memory (Gruber, Gelman, & Ranganath, 2014, 491; Kang et al., 2009, p. 971). This is due in part to the fact that learners will pay greater attention to a topic in which they are interested thus increasing their focus. Curiosity however is also closely related to dopamine release in the brain (Collins, 2016, p. 84) and the interaction of dopamine and the hippocampus (Gruber, Gelman, & Ranganath, 2014, p. 491). Curiosity has also been shown to activate the caudate nucleus, an area of the brain associated with anticipated reward and associated with better recall (Kang et al., 2009, p. 971). Kang et al. (2009) conducted both neuroimaging and behavioural studies to examine the impact of curiosity on learning. In addition to affirming that curiosity positively impacted memory recall, they discovered a link to intrinsic motivation. In one experiment, students were provided with tokens or time they could exchange to find out answers to test questions. They discovered that students were more willing to exchange limited resources for questions about which they were curious and concluded that that, “curiosity is a form of reward anticipation” (Kang et al., 2009, p. 971). This means that when people are curious about what they are learning they will be willing to work harder for the intrinsic reward of learning.
Davis et al. (2014) define generation as, “the act of creating (and sharing) your own connections to new ideas” (p.5). When we connect new knowledge to existing knowledge we are tapping into and strengthening existing neuronal connection making it easier to retrieve the information when needed (Davachi et al., 2010, p. 4). A study by Davachi and Wagner (2002, p. 987) demonstrated that recall was superior when subjects were asked to relate the items being tested to one another as opposed to trying to memorize them by rote.
Simply asking people to solve at a problem or arrive at an answer themselves before presenting them with a solution can increase long term memory. A 2016 study sought to determine if there was a difference in learning when learners solved a problem themselves (generated a solution) vs arriving at an answer via an a-ha moment. Their research suggested that both methods of learning were effective at improving long term memory but that there may be a difference in the way the brain processes the two different methods. (Kizilirmak, Gomes da Silva, Imamoglu, & Richardson-Klavehn, 2016, p. 1072).
Asking people questions can also contribute to generation by increasing attention on the topic and forcing people to search for and retrieve the information. Asking questions can also create an optimal level of stress if people know they will be called on (Collins, 2016, p. 117). Asking learners to evaluate the content, think about how they would apply it, or compare it to their own experiences can all increase generation (Davachi et al., 2010, p. 4). Asking people to “teach back” the content they have learned may also increase generation and long term retention of information. A 2014 study by Nestojko, Bui, Kornell and Bjork demonstrated that effect of telling learners that they would be asked to teach the content they were learning on retention. Subjects who expected to teach the content had improved recall for key points, compared to those who expected to be tested. The authors suggested that students expecting to teach would be encouraged to use a range of study methods (e.g. organizing of information, identification of key points) that they would not have used otherwise (p. 1045).
Another form of generation is self-reference. Studies have shown that people are more likely to pay attention to information and thus remember it when they are able to relate the information to themselves or their own experiences (Collins, 2016, p. 114; Davachi et al., 2010. p. 4, Davis et al., 2014). A meta-analysis by Symons and Johnson (1997) showed that when participants were asked to relate information they were learning to themselves they were more likely to remember it. They refer to this as the “self-reference effect” and suggest that it may be effective because we are linking new knowledge to knowledge about self, that is already well organized and deeply encoded in our brains.
Retrieval or testing of information also has a positive impact on long term memory. Antony, Ferreira, Norman, and Wimber (2017) assert that retrieval is more effective than repetition of the same content (p. 574). A study by Karpicke and Blunt (2011), demonstrated that asking people to retrieve content was more effective than concept mapping, an elaborative learning technique (p. 774). Elaborative learning techniques, as the name would suggest, are learning techniques which, “involve[s] making connections among ideas you are trying to learn and connecting the material to your own experiences, memories, and day-to-day life” (Smith & Weinstein, 2016, n.p.). As such concept mapping could be considered a form of generation.
In a subsequent study, Blunt and Karpicke, tested the combination of concept mapping and retrieval (2014). Although, concept mapping was effective when used as a method of retrieval, the impact was attributable to the act of retrieval and not the format of retrieval (p. 857). Their study would suggest that the act of retrieving information and engaging in retrieval practice promotes learning rather than the learning technique used (i.e. concept mapping).
Several factors may impact the effectiveness of the act of retrieval. Studies have shown that showed that increasing the number of times learners practice retrieving the information increases retention with three practice sessions seeming to be optimal. This is true even after a positive recall, a point at which learners may feel they have mastered the information (Karpicke, 2009, p. 483). However, when students were given control over their own study methods and not forced to practice retrieval many of them stopped after one successful retrieval event. Single retrieval practice resulted in poorer learning outcomes when compared to multiple proactive retrieval sessions (Karpicke, 2009, p. 483). Multiple retrieval attempts are even more effective when learners receive feedback in between retrieval attempts and these attempts are spaced out. Hays, Kornell, and Bjork (2012) built on previous studies and demonstrated that immediate feedback following a failed attempt improved memory retention (p. 294).
Retrieval can be effective in both classroom and online training. A 2013 study by Szpunar, Khan, and Schacter demonstrated that interspersing video content with short test questions related to the content being taught increased attention and improved retention of content. Interestingly, interspersed testing also reduced final test anxiety (p. 6316).
The principal of retrieval in improving long term memory applies even when people are asked to guess an answer and are unsuccessful. (Kornell, Hays, & Bjork, 2009, p. 995; Yan, Yu, Garica, & Bjork, 2014, p. 1382). In a 2009 study, Kornell, Hays and Bjork. demonstrated that, “unsuccessful retrieval attempts followed by feedback led to more learning than did spending an equal amount of time studying the cue and target together” (p.995). Guessing an answer, and getting it wrong, was more effective than not guessing at all.
Learning through insights or “a-ha” moments has been show to positively increase long term memory retention (Davis et al., 2014, p. 7; Davis, Chesebrough, Rock & Cox, 2015, p. 4). Davis et al. define insights as, “the eureka moment when the unconscious mind solves a problem” (2014, p, 7). “Insights are directly related to generation by connecting ideas in ways they have not been previously connected” (Davis et., 2014, p.7)
Studies have shown that, when study subjects are experiencing insights, there is increased activity in, among other areas, the amygdala, the area of the brain linked to emotions (Ludmer, Dudai, & Rubin, 2011, 1009; Zhao et al., 2013, p.6). The potential link between emotion and insight was investigated by Shen, Yuan, Liu and Luo (2016). They demonstrated that insights or a-ha moments were associated with positive feelings such as, “ease and certainty” (p. 293).
Research would show that certain factors can contribute to insight or a-ha moments. Contrary to the data supporting the importance of attention during learning to the generation of long term memories, insights seem more likely to occur when the mind is allowed to wander from task at hand (Davis et al., 2014, p. 7). Insights arrive when we are not focusing on them or are not directly effort towards them (Rock, 2011, p. 47). Additionally, Rock would suggest that when learners are quiet and inward looking they are more likely to arrive at insights. He recommends, “reducing social threat, increasing certainty, and finding ways to quieten the brain” as routes to insight (p.49). Given that positive emotions are also associated with dopamine release which increases focus and attention, it would appear that trainers should consider how they can create a positive and low-threat environment in their classrooms or online courses to increase the chance of insights (p. 49).
There is extensive literature to show that chunking and spacing the presentation of information over time is more effective at improving long term retention than equivalent massed study time (Cepeda, Pashler, Vul, Wixted, & Rohrer, 2006, p. 371; Kornell, 2009, p. 1314). The effectiveness of spacing seems to increase with time between study sessions up to a certain point that varies with the retention interval or the gap between the study session and the time at which the information needs to be recalled (Cepeda et al., 2006, p. 366).
While spacing has been shown to be highly effective at aiding memory retention, it is difficult to determine the optimum spacing interval. The positive effect of spacing on long term memory seems to increase with increasing interstudy interval (ISI) (time between study sessions of same content) but is dependent on retention interval (time between last study session and retrieval test) (Cepeda, Vul, Rohrer, Wixted, & Pashler, 2008, p.1100). The correlation however between increasing ISI and retention interval is complex making it difficult to draw conclusions about an optimal time of spacing (Davachi et al., 2010, p. 7). There are different theories as to why spacing is so effective at increasing long term memory. The effects of spacing may be due to consolidation effect, or the time required for the brain to, “reorganize, distribute and consolidate” new content (Davachi et al., 2010, p. 9).
The ideal length of spacing may be less dependent on the ISI and retention interval and more dependent on the effects of sleep. Studies would demonstrate that memory consolidation continues during sleep and aids in the retention of memories and increases recall (Payne et al., 2012, p. 5; Rasch & Born, 2013, p. 681). Bell et al. (2014) investigated the effect of spacing on long term memory, with and without sleep in between study session. Results showed that a 12-hour spacing gap that included sleep was superior for increasing long-term memory, when compared to a 12-hour gap without sleep and similar to the 24-hour gap (p. 281). The 12-hour gap with no sleep was no better than the massed learning (p. 281).
Spacing within a training session (not just between study sessions) may also help improve long term memory (Kornell et al., 2009, p. 1311; Davis et al., 2014, p. 9) and positively impact motor skill acquisition (Boettcher et al., 2018). A group of surgeons being trained to perform lacroscopic surgery was divided into two groups. Each group received 3 hours of total training but in one group the training was massed and in the second group 40 minutes of instruction was interspersed with 20 minutes walking breaks. Not only was overall performance superior in the spaced group, but their anxiety and perception of how challenging the task was, were both reduced (Boettcher et al., 2018, p. 158).
In addition to increasing long term retention, spacing may also reduce required study time even with complex information. Spaced learning of complex content (e.g. biology, physics) was as effective as, and more time-efficient than, traditional learning in preparing secondary school students for final exams (Kelley & Whatson, 2013, p. 6). In this particular study, spaced learning was defined as, “three intensive instruction elements of the same content with minor variations each lasting 20 minutes or less (stimuli), spaced by two distractor (physical) activities of 10 min (spaces without the stimuli)” (p. 4). It’s important to note that the development of the spaced learning approach in the classroom was developed over several years and involved a collaborative partnership between teachers, social scientists and neuroscientists (p. 3). The theory of spacing alone was insufficient; a significant amount of time was investing in developing instructional methods that work in the classroom and supporting teachers in delivering instructions using these methods.
Neuroscience applied in corporate training context
There is evidence to show that when the principles of neuroscience are applied to the design, development and delivery of corporate learning there is greater learning transfer. Salas, Tannenbaum, Kraiger and Smith-Jentsch (2012) define transfer of learning as, “the extent to which learning during training is subsequently applied on the job or affects later job performance” (p. 77).
A 2005 study by Chiaburu and Marinova examined the factors that best predict learning transfer of learning in organizations. They confirmed previous studies that show that participant’s motivation coming into a training session is predictive of learning transfer (p. 117). Consistent with the principles of neuroscience, one can presume that those who were motivated to attend a training course would be more curious and more likely to pay attention to the content. Participants were more motivated to attend the training when they had an expectation of being successful, or were confident in their abilities (p. 118) and set a positive goal of mastering the content (p. 118). The expectation of success and mastery could reasonably be assumed to increase positive emotions leading to dopamine release and better attention. Contrary to what might have been expected, support from one’s manager did not influence skills transfer whereas peer support did (p. 118). The authors suggested additional research and analysis to better understand, and disaggregate, the role of the peer and manager from other organizational and environmental factors that may impact transfer of learning. The positive impact of peer support, but not supervisor support, on transfer of learning was reconfirmed in a 2014 study by Homklin, Takahashi and Techakanont with auto workers in Thailand (p. 126). The authors hypothesize that the results may be due to the team-based nature of work in the automotive industry. Regardless, given the rapid increase in team based or collaborative work, the idea of introducing peer support pre and post training may be an interesting way for those designing and delivering training to increase transfer of learning. This can also be a low or no cost way for organizations to build employees networks and increase their ROI on training.
A 2011 analysis by Grossman and Salas summarized the factors that support transfer of learning dividing them into trainee characteristics, training design and work environment. Similar to the Chiaburu and Marinova (2006) study, they found that self-efficacy and motivation led to increase transfer of learning (p. 107). They also found that learner’s perceived utility of training positively impacted transfer of learning (p. 107). Again, one can assume that when learners can see the value of the training they are more likely to pay attention and generate new neural connections as they think about ways to apply what they learn. Contrary to other studies (Chiaburu & Marinova, 2006; Homklin, Takahashi, & Techakanont 2014), they found that both peer and supervisor support positively predicted transfer of learning (p. 108). They suggest that supervisors can help support transfer of learning through expectation setting with the employee pre-training and goal setting for application of skills after the training (p. 113) Supervisors can also support transfer of skills learned through recognition and feedback (p. 113). They also found that the opportunity for learners to practice what they had learned back on the job and receive follow up led to transfer of learning (p. 108). This would be consistent with the principles of spacing and retrieval and the idea that each time learners need to retrieve the information they have learned they are strengthening and deepening the neural connections and consolidating the learning.
A 2016 study by Mind Gym compared outcomes for learners who attended a 90-minute Mind Gym session on Influence or covered similar content in a one or two-day session. The Mind Gym session incorporated neuroscience concepts of attention, curiosity, generation, self-reference and spacing (Mind Gym, 2016). Those who attended a 90-minute session had better Level two outcomes (knowledge and understanding of influencing) compared to those who attended a one-day program (p. 7). Those however who attended a two-day program had the greatest increases in their knowledge and understanding. However, when it came to participant’s ability to apply what they had learned (Level three) outcomes for the 90-minute session were superior to both the one-day and two-day sessions (p. 7). These findings have potential implications for organizations wanting a greater return on their time investment in employee training and should be replicated in other setting with other instructional content.
A 2017 meta-analysis by Lacerenza, Reyes, Marlow, Joseph and Salas looked specifically at the factors that determined learning transfer in leadership development. They found that programs developed using a needs analysis resulted in greater transfer of learning than those that did not have a learning analysis (p. 16). The authors hypothesize that programs created following a needs analysis would be more relevant to learners. This would be consistent with the self- reference effect and the idea that learners retain information better when they are able to related to themselves and existing (Symons & Johnson,1997). They also found that voluntary attendance at training increased transfer of learning (p. 16) consistent with the principle that curiosity and motivation contribute to better learning outcomes. Interestingly though, organizational results were better for programs where attendance was mandatory. Contrary to previous studies they found that spaced training was no better at improving learning than massed training but did produce better transfer of learning with the effect being more pronounced for those programs that spaced sessions weekly (p. 16). This has significant implications for trainers looking to increase transfer of learning from programs. They also found that the use of practice, multiple learning methods and feedback increased learning transfer (p. 17). Again, this would be support the theory that novelty increases attention and generation (Davachi et al., 2010, p.3).
Over the past 10-15 years a growing body of evidence has emerged on the cognitive neuroscience of learning using advanced imaging techniques to examine the activity of the brain during encoding, consolidation and retrieval of memory. Out of this has emerged principles of neuroscience that, when applied, can improved long term memory or declarative memory. These principles have potential implications for instructional designers and facilitators responsible for learning in a corporate setting but also have several limitations.
One of the key limitations of the studies would be the study population. Many, if not the majority, of these studies have been conducted in controlled environments in an institutional or educational setting using students as test subjects. Thus, it can be difficult to extrapolate results to older workers in a corporate or industrial setting.
Also, these studies demonstrate improvement in, what Kirkpatrick would refer to as, level two learning defined as, “the extent to which participants change attitudes, improve knowledge, and/or increase skill as a result of attending the program” (Kirkpatrick, D.L., & Kirkpatrick, J.D., 2006, p. 22). This may be valuable when the desired outcome is the ability to write a test or exam. In a corporate setting however, the desired outcome is usually a change in employee behavior (Kirkpatrick level three) and a positive impact on business results or ROI (level four) (Kirkpatrick, D.L., & Kirkpatrick, J.D., 2006, p. 21).
Finally, it can be difficult to draw a direct link between neuroscience and transfer of learning in a corporate setting given the many factors at play in any corporate system. These include the role of the manager, interaction with peers, monetary and non-monetary reward and recognition and systemic support for the desired change in behavior. Thus, neuroscience is just one of many factors influencing the transfer of learning. Additionally, there are logistical challenges to applying many of these principles in a corporate setting such as scheduling, cost of training and availability of workers. Preconceived mindsets around how training is delivered can also be a barrier. If an employee is being asked to attend a full day session they will probably expect the organization is optimizing their time away from their job by filling the time with as much content as possible —a concept that runs contrary to what is now known about how we learn best.
Finally, many of the principles emerging from the neuroscience of learning are consistent with well know principles of adult learning instructional design. As such, they don’t necessarily add to the practical body of knowledge for corporate trainers or instructional designers.
Future studies should specifically address the application of the principles of neuroscience in a corporate setting. Certain principles are clearly established and may not need to be tested such as attention, curiosity and generation through self-reference. There may be room however to test the mechanisms by which one increases attention, curiosity and generation in a corporate environment in which workers have limited time and are highly distracted by the increasing demands of a rapidly changing workforce.
The use of spacing in a corporate setting could be an interesting area for ongoing study. Although some studies on spacing or have been done, additional studies would be helpful with more rigorous measurement/evaluation criteria. For example, what is the minimum amount of time required for a topic to be learned and retained? Is spaced learning as effective for training that may be a combination of skill, behavior and mindset shift (e.g. collaboration, effective listening, feedback and coaching) as it is for more easily observable and measureable purely skill based training (e.g. vs compliance, safety training or skills based surgery example)? Can spaced learning be used for retrieval and follow-up, and not just initial learning.
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