Keywords

1 Introduction

The environment in which the U.S. military now operates in is characterized by increased complexity. In order to address this changing environment, U.S. military training doctrine now emphasizes the development of agile, adaptable leaders with broad critical thinking skills. Conventional wisdom suggests that the development of these skills is not suited to classroom activity and is best achieved through experiential learning. While experiential learning has major advantages over formal training methods, in practice the benefits can be difficult to achieve. Even with exposure to the appropriate experiences, without proper support skill acquisition can be hampered.

Skill acquisition refers to the development of technical and complex skills through four phases; recollection, recognition, decision, and awareness [1]. Skill acquisition is done consistently throughout the course of a life cycle, and the complexity of the skill acquired depends on the task at hand. Skill acquisition begins with the recollection phase, where a learner is able to understand parts of a situation. Leading to the recognition phase, where a learner holistically links parts of a situation together to understand the situation as a whole. Followed by the decision phase, where the learner makes analytical or intuitive decisions about the learning experience. Finally leading to the awareness phase, where the learner is able to be consumed in his or her performance [1].

Skill acquisition consists of the development of all kinds of skills. Simple skills, technical skills, hard skills, soft skills, complex skills, etc. are all skills developed in skill acquisition. In this paper, we will be focusing on two specific sets of skills; technical skills and complex interpersonal skills. For the rest of this paper, we will be referring to complex interpersonal skills as complex skills. Technical skills refer to skills that are needed in specific areas of expertise [2]. Complex skills, on the other hand, are the skills that are needed when interpreting highly volatile, uncertain, complex, and ambiguous (VUCA) environments [3]. See Table 1 for further distinction between technical and complex skills.

Table 1. List of Technical vs Complex skills
Full size table

The purpose of this paper is to introduce Guided Mindfulness (GM) as a tool that can be utilized for skill acquisition through experiential learning in complex environments. GM is a technology assisted platform that optimizes experiential learning and guides its users to recollect, recognize, decide, and be aware of new learning experiences through specific prepare and reflect experiences. These experiences are facilitated through an artificial intelligence feature in the app that prompts its users to answer a series of questions to prepare them for a new learning experience followed by a series of reflection questions after the completion of the learning experience. Through this process, GM can more effectively develop technical and complex skills and more systematically facilitate the skill acquisition process. GM can aid in developing these skills in complex dynamic and organizational environments, where learning opportunities can often be lost in rapid operational tempo. Overall, GM will aid its users by reinforcing effective memory and event preparation strategies across a potentially wide range of competencies.

1.1 Overview of Guided Mindfulness (GM)

Although approximately 70% of learning is expected to stem from experiential processes [4], whether individuals actually learn from their experiences is not guaranteed [5]. For instance, various individual differences (e.g., conscientiousness) may play a role in whether individuals have a propensity to reflect meaningfully upon their experiences and effectively learn from them [6]. Therefore, a more systematic, or guided, approach to experiential learning may be required to facilitate learning across individuals with varying degrees of learning dispositions and abilities. GM is proposed as a technology assisted platform intended to facilitate its users’ abilities to effectively and strategically learn from experiences.

GM capitalizes on complex learning from experiences by engaging the users in event-based preparation and reflection activities grounded in self-regulatory and mindfulness principles [3]. Users are first assessed on various competencies deemed relevant (based on the Service’s mission, job requirements, the user’s career trajectory, etc.). The users’ current standings on these competencies aid in their self-awareness and become the baseline for learning improvements. GM then initializes the prepare phase upon the users’ identification of an important event where learning opportunities may take place. In the prepare phase, GM may prompt the user to consider which of the assessed competencies are necessary for successful performance, their level of proficiency on those competencies, and possible barriers or roadblocks that may interfere with successful performance. Through this preparation phase, GM helps the users become more self-aware by understanding their strengths and weaknesses and by identifying discrepancies between their current standings and their competency goals, which is an important aspect of self-regulation. This phase also draws attention to situational and social cues that are critical to learning. Directing the users’ attention to these cues can heighten their salience during the event, increasing the users’ ability to be mindful of them while in action. Being mindful of these cues while engaging in the event can help users recognize when adjustments to their actions are required (e.g., based on non-verbal feedback from the social environment) and will help them more accurately assess the event during the reflection phase.

After the event, the reflection phase is initiated. Specifically, GM prompts the users to reflect by providing a structured sensemaking process that allows the users to think through and analyze important aspects of the event. Through this process, users make meaning of what happened during event, including what they did and did not do well, resulting in new or revised mental models [3]. Upon this sensemaking, GM prompts the users to simulate alternative actions that could prove useful in future, similar events. This simulation is expected to result in greater internalization of what was learned as well as a greater number of strategies the users have in their repertoires for application in future events [3]. All data gathered from the prepare and reflect phases is stored in a GM database and can be sorted and reviewed to aid in future meta-learning (e.g., identification of problem areas across various events or certain types of events). Further, through ongoing engagement with GM, users are expected to become more efficient and effective in learning from their experiences overtime; ultimately enhancing their self-regulatory and mindfulness capabilities.

1.2 GM and Technical Skill Acquisition

Technical skills, also known as “hard skills”, generally refer to skills or competencies related to an individual’s particular area of expertise. Although they are often associated with the use of machinery, tools, or equipment, these competencies go beyond just engineering or mechanical tasks [2]. Technical skills are considered critical aspects of performance among surgeons, pilots, graphic designers, etc. [7, 8]. Technical skills can also range from simple to complex. Simple technical skills are fairly straightforward, can be learned quickly, and usually involve one or very few motor activities. Complex technical skills, however, require a combination of specific knowledge and psychomotor performance; these skills take longer than one session to learn and can be a clear differentiator between novices and experts [9]. In challenging modern organizations, technological advancements and dynamic work environments have made almost every technical skill a complex one.

Acquiring technical skills usually proceeds in three phases [10]. In the first phase, trainee develops basic cognitive understanding of the task. This cognitive phase is characterized by erratic performance that proceeds in distinctive steps. Through practice and feedback, the learner moves from cognitive knowledge to motor performance, also known as integrative phase. This phase is characterized by more stable performance and blurring of the distinctive steps into one fluid action; however, the learner still has to pay attention to the task. In the final, autonomous phase, the learner is able to perform smoothly and with minimal, if any, attention to the activity [11].

More recently, it has been proposed that acquisition of technical skills is followed by a fourth phase: the application of the knowledge in new and complex situations. In this phase, experiential learning has been identified as a critical training mechanism that enables knowledge application [12]. In order for experiential learning to be effective, the learner must have an opportunity to reflect on his own experience, and through reflection, broaden the understanding of self, her/his action, and the context in which the action occurs [13]. This translates into an ability to apply knowledge beyond the training context and effectively adapt that application of knowledge when necessary, at which point the trainee becomes an expert.

As critical as experiential learning is to the development of expertise, this part of technical skills training is often neglected. We propose that GM can be a powerful tool that can assist learner capitalize on experiential learning by leveraging preparation and reflection during skill acquisition as well as during application of skills in real-life settings.

1.3 GM and Complex Skill Acquisition

As task complexity increases, so does the gap in performance between practiced professionals and beginners [14]. As such, is it important that the learning process of complex skills is fostered within novices, especially when such acquisition is necessary for success or must occur within a short period of time. It is a commonly held belief that the majority of complex skills are most successfully acquired through experiential learning methods [15]. GM aids in complex skill acquisition through experiential methods, namely by improving learners’ self-regulatory mechanisms, such as awareness (self, situational, social), sensemaking, and simulation.

The importance of self-regulation to learning outcomes is rooted in control theory [16, 17]. Control theory describes a negative feedback loop where an individual deliberately compares his or her current state to a desired state [18]. As such, a plan is set into motion to reach the desired state if there is a discrepancy. For complex skills, individuals must take stock of skill and competency states in addition to social and situational factors external to the individual to reach their desired end state. The individual then uses the feedback gathered from the assessment of these states and factors to reflect and integrate their learning experiences and further improve [3]. However, as task and situation complexity increases, individuals may be unable to effectively synthesize learning in real-time due to cognitive overload and new task demands. GM thus serves as a facilitating mechanism by which individuals can effectively manage their learning time to optimize experiential learning. The GM platform is designed to prompt users with insightful preparation and reflection questions that prompt users to formulate internal feedback, providing insight into their performance and learning.

In order for self-regulation to be successful in terms of behavior, cognition, and affect, individuals must first be self-aware [19]. Self-awareness is defined as an individual’s ability to understand his or her own skills, affect, values, strengths, weaknesses, assumptions of the world and others, and biases [20]. As control theory is predicated on the ability to compare desired states to current states [18], self-awareness is the next step in directing an individual to achieve their goals. Through pre-event preparation, GM guides its users to assess their own standing on skills and competencies, allowing users to become more aware of the automatic ways in which they interact with the environment and pinpoint strengths and ways to improve [3]. Post-event reflection allows for a comparison to pre-event expectations of the event itself and anticipated vs. actual behavior in the event. Such reflection allows for learners to seek and receive feedback from themselves and the environment, improving overall self-awareness. When tasked with acquiring complex skills, individuals face higher levels of cognitive load and may be required to access various bodies of knowledge, skills, and competencies at once to effectively perform [14, 21]. In this case, pre-event preparation allows learners to assess their abilities while refraining from judgment [22], making them better prepared for the reality of the upcoming learning event. Additionally, making feedback self-guided was found to be beneficial for performance since learners are forced to learn how to identify and correct their own errors [21, 23]. Thus, providing support that the self-guided nature of post event reflection is advantageous to users who wish to learn complex skills.

Situational awareness refers to an individual’s understanding of a situation and the ability to rapidly adjust their understanding as the environment changes [24]. Situational awareness is paramount in VUCA environments. In today’s high-speed business world, acquiring complex skills oftentimes happens in VUCA environments, and the process of acquiring complex skills is often filled with uncertainty, complexity, and ambiguity. Situational awareness requires individuals to have accurate mental models of the given circumstance and all related components [25], allowing for thorough understanding of the environment where possible. Situational awareness, similar to self-awareness, is critical to self-regulation because it allows individuals to process in-time environmental feedback and direct cognitive resources to the learning event [3]. Prior to an event, GM directly prompts individuals to identify contextual factors relevant to his or her ability to perform in the event and asks, more specifically, how each factor may influence the event. Following the event, the user is then asked to confirm or disconfirm how each situational factor mentioned previously affected the event. In doing so, the learner’s attention is brought to situational cues critical to performance in the future and allows for the restructuring and improvement of associated mental models [3].

Social awareness focuses on the understanding of appropriate behaviors between individuals in addition to group-level dynamics [26]. Learning, especially complex skills such as global leadership or cross-cultural competence, rarely, if ever, occurs within a vacuum. Navigating social interaction and obtaining feedback from such environments is a crucial aspect of the learning process. Complex social environments require more than automatic processing; individuals must dedicate mindful attention to social interactions to notice and successfully perceive social cues, recognize patterns of relationships between others, and understand networks of power and social influence [27]. A better understanding of the social climate is likely to lead to better outcomes for individuals engaging in social interaction [28]. GM implores users to investigate the social environment by asking users to determine relevant stakeholders and other players in given contexts relevant to their goals and learning. Following events, GM helps guide mindful reflection of social interaction to provide feedback on an individual’s behavior and overall performance during the event.

Sensemaking and simulation are two of the more forward-focused components to the GM platform and as such, have especially unique implications for complex skill acquisition. Sensemaking is accomplished through reflection and is defined by Griffith and colleagues (2017) as “the process by which people infer meaning from an event and use that derived meaning to decide on a future course of action,” (p. 8) [3]. Sensemaking necessitates thorough and effective self, situational, and social awareness [29]. Sensemaking involves the complex process of inferring meaning from relevant cues (i.e., feedback from various sources) to adjust mental models and inform future decisions [3]. Sensemaking aids individuals in meaningfully categorizing and clarifying inputs [30]. This process allows tacit knowledge to develop following experiences individuals have, making learned knowledge explicit and viable. As such, sensemaking is a vastly important process for those learning complex skills [3]. GM aims to improve individual sensemaking by utilizing structured and unstructured reflection strategies that require critical thinking and recognition of interrelated concepts and mental models. Pre-event prompts call attention to salient contextual cues that users can attune to in real-time, consistently updating their cognitive frameworks and adapting as needed. Mental models that are updated in real-time may expedite the learning process of complex skills by allowing users to know which elements need to be practiced or more fully understood and where to ask questions or seek direct feedback from others.

GM considers simulation as a sort of “mental trial” of relevant events. Successful simulation occurs following a thorough sensemaking process, where individuals can more effectively answer the various, “what if’s” of any given situation [3]. This forward-thinking exercise allows individual to prepare for any upcoming complex situation. For complex skill learners, simulation is especially important as such learners are often forced to operate “on all cylinders” in terms of the variety of knowledge, skills, and competencies. Simulating an upcoming event, GM prompts its users to consider which knowledge, skills, and competencies they will have to incorporate and account for in the upcoming event, making them better prepared and better positioned to obtain favorable outcomes. Simulation is also prompted following the event, where users are tasked with considering alternative courses of action and their possible ramifications. This sort of debrief aims to more deeply embed the learning associated with the event, further clarifying and expanding upon cognitive frameworks across environments.

1.4 Adapting to Complex/Dynamic Environments

Complex dynamic environments are environments where tasks are more intricate, leading to tasks that take longer to complete, increasing the variety of possible outcome decisions [31]. There are a myriad of environments that can be classified as complex dynamic environments that include the following: uncertainty, complexity, ill structured problems, time pressures, shifting opposing goals, poorly defined goals, high stakes, feedback loops, multiple players, multiple elements, under vigilance or pressure to perform to organizational goals and norms [32]. Typically individuals in complex dynamic environments engage in complex dynamic control tasks (CDC tasks) that use some form of cognitive activity such as decision making, critical thinking, problem solving, or cognitive flexibility [33].

Complex dynamic environments are the “normal” milieu of the military. Warfighters operate in complex situations riddled with ambiguity and volatility. When warfighters are sent into a VUCA environment, in many instances their mission is not easily accomplished due to a variety of contextual factors (the elements, lack of information and equipment, etc.). Each of these factors can change at any given time leading to different consequences for each chosen action. Another aspect to the complexity in the environment of the warfighter are the series of mission changes and new environments that consist of novel elements and variables that require immediate transfer of skills and knowledge.

Failure to acquire the skills needed to adapt to complex dynamic environments is due to a multitude of factors. Spiro, Feltovich, Coulson [33] explain a factor as individuals possessing a reductive world view known as the “inappropriate lessening or oversimplification of complexity” in which “parts of complex systems are assumed to ‘add up’ to wholes” (p. S52). Another factor is the lack of experts sent into these complex dynamic environments. Many studies support the notion that experts do better than novices in complex and dynamic environments and require less effort to be proficient [34,35,36,37,38,39,40]. An expert is defined as someone with more experience and knowledge whereas, a novice is defined as someone less skilled with less experience and knowledge [34]. This factor may be influenced by the lack of training time needed to effectively adapt to a complex dynamic environment. As such, even if there is time and training for novices to become experts, a number of studies demonstrate that novices do just as well as experts in decision-making tasks [34]. This is only the case if novices are capable of having a more expansive and flexible cognitive view on processing complexity [33]. GM can aid in creating expertise and cognitive flexibility which can support performance in uncertain dynamic environments [3, 41].

1.5 Integrating GM Over the Learning Cycle-Adapting to the Environment, not so Much Learning and Improving Complex Skills

Based on the previously mentioned skill developments, GM is expected to be best utilized by integrating over a full learning or training cycle of these complex and technical skills. Therefore, organizations can capitalize on learning, training and development through implementing GM as an ongoing tool to manage learning and acquiring skills. GM can help with both the individual’s learning and the organization’s learning by capitalizing on the experiential aspect of learning for the former and the organizational learning cycle for the latter.

Individual’s learn in several ways: direct experience, verbal transmitting of information (e.g., reading and writing), and reorganizing previously learned information [42, 43]. Experiential learning consists of four stages: concrete experience, reflective observation, abstract conceptualization, and active experimentation [13]. Unfortunately, information is lost over the experiential learning cycle due to the lack of time to properly engage in reflective observation and abstract conceptualization; therefore, GM is a possible solution to this loss of information by prompting individuals to reflect and make sense of experiences in real-time after a learning event occurs [13].

Organizational learning typically occurs over four stages: generating, integrating, interpreting, and acting [42]. Generating refers to the collection of external data and the generation of new ideas within the organization. Integrating refers to the combination of the information into an organization’s context so that departments and individuals within an organization share this information and can act in concert with each other. Interpreting refers to when organizational members digest and clarify the information generated collectively. Acting refers to when organizational members have access to the previously mentioned information and make decisions based on this information. Often organizations fail at this stage by placing decision making in the hands of leaders therefore undermining the learning process [42]. Integrating GM across the latter stages of organizational learning will serve organizational learning well since it will allow for organizational members to take the information generated during the generation phase and allow for the integration and interpretation of that information. However, organizations will still need to empower employees to make decisions at the final stages to allow for the full organizational learning cycle to be in effect (i.e., allow employees to act on information learned during the former stages).

When GM is integrated over a full experiential learning cycle, it can allow for complex interpersonal skills to be developed [44]. The GM approach is designed to be competency neutral, so competencies can be either complex or technical; however, it is expected that learning complex skills will benefit from GM more than technical skills, due to the capitalization on the experiential learning of the individual.

Since GM is best used to capitalize on experiential learning, it is expected that GM will be best served over a period of time throughout the learning cycle rather than just one point of intervention. Experiential learning should make up 70% of learning in an organization through the on-the-job experiences [4]. Since GM capitalizes on self-regulated learning, GM would best be integrated over a full cycle of learning where time is required for feedback loops. Therefore, GM is not expected to be a one-time intervention during skill acquisition, rather GM should be implemented into daily experiences and learning on-the-job as it enhances the latter stages of organizational learning (i.e., integrating and interpreting).

1.6 Future Directions

This paper addressed the use of GM for skill acquisition. Future direction for GM can be geared towards developing the platform for specific skills. GM can be used to examine what types of skills are best learned through the platform; complex skills or technical skills. In a time where cross-cultural research and practice is at a peak, GM can assess skill acquisition among different cultures. GM can assess if there are cultural differences in skill acquisition. This may prove to be beneficial for leaders that lead cross cultural teams – GM may help certain team members to develop skills needed for team improvement. GM may also be used to examine age group differences in skill acquisition. It is clear that adults learn differently from children and adolescents. What can be further examined is how well adults in different age groups learn comparatively.