Exploration-exploitation tradeoffs and information-knowledge gaps in self-regulated learning: Implications for learner-controlled training and development

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Human Resource Management Review journal homepage: www.elsevier.com/locate/hrmr

Exploration-exploitation tradeoffs and information-knowledge gaps in self-regulated learning: Implications for learner-controlled training and development ⁎

Jay H. Hardy IIIa, , Eric Anthony Dayb, Winfred Arthur Jrc a

Oregon State University, United States University of Oklahoma, United States c Texas A&M University, United States b

ABS TRA CT

Learning in modern organizations often involves managing a tradeoff between exploration (i.e., knowledge expansion) and exploitation (i.e., knowledge refinement). In this paper, we consider the implications of this tradeoff in the context of learner-controlled training and development. We then propose a model that integrates research on control theory, curiosity, and skill acquisition to explain how information knowledge gaps (i.e., gaps between what learners believe they know and what they desire to know) guide resource allocation decisions throughout the learning process. Using this model, we present testable propositions regarding (a) the different approaches learners take when resolving exploration-exploitation tradeoffs, (b) how systematic changes in learner perceptions translate into changes in systematic learner behavior, and (c) how common biases in key learner perceptions can undermine the functioning of self-regulated learning in training and development contexts. We finish with a discussion of the model's implications for the science and practice of training and development.

1. Introduction In an effort to equip employees with the requisite knowledge and skill needed to succeed in the more self-directed dynamic performance contexts of the modern workplace, it is becoming increasingly common for organizations to delegate the responsibility for making key instructional decisions to their employees. This has led to a notable increase in the use of learner-centric approaches to training and development in which workers are given an unprecedented amount of control over which courses they take and must decide on their own when, what, and how much training material they should engage (American Society for Training and Development, 2015; Bell, Tannenbaum, Ford, Noe, & Kraiger, 2017; Kraiger & Jerden, 2007). In response to this trend, the prevailing paradigm within the field of training and development has recently shifted toward one with a greater emphasis on the potential of technology-based training, informal learning, and active learning for developing depth and adaptability in employee knowledge and skill (Bell & Kozlowski, 2008, 2010; Noe, Clarke, & Klein, 2014). Thus far, these efforts have enabled the identification of several key self-regulatory mechanisms that contribute to the success of learner-controlled approaches (Bell & Kozlowski, 2008; Kozlowski et al., 2001) and have produced a number of learner-centric interventions that have been shown to be highly effective for developing employee knowledge and skill (e.g., error management training, guided exploration, and mastery training; Debowski, Wood, & Bandura, 2001; Frese et al., 1991; Keith & Frese, 2005; Kozlowski et al., 2001; Wood, Kakebeeke, Debowski, & Frese, 2000). However, development in this area has slowed in recent years as researchers wrestle with some of the more perplexing questions pertaining to how to best develop and implement learner-controlled training and development initiatives.

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Corresponding author at: Oregon State University, College of Business, 352 Austin Hall, Corvallis, OR, United States. E-mail address: [email protected] (J.H. Hardy).

https://doi.org/10.1016/j.hrmr.2018.07.004 Received 1 August 2016; Received in revised form 13 July 2018; Accepted 18 July 2018 1053-4822/ © 2018 Elsevier Inc. All rights reserved.

Please cite this article as: Hardy, J.H., Human Resource Management Review (2018), https://doi.org/10.1016/j.hrmr.2018.07.004

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We believe one of the biggest barriers to progress in this area is that our collective understanding of the natural processes guiding how learners make decisions regarding how and where to allocate their limited attentional resources within the context of learnercontrolled training and development is surprisingly limited. For instance, it remains unclear what psychological mechanisms learners use to decide how much they need to learn or how they go about determining when they have learned enough. Moreover, we know very little about the specific types of learning strategies individuals use to resolve gaps in their understanding, why they choose one strategy over the other, or what the implications of these strategies might be for the acquisition of knowledge and skill. Answering these questions is of the utmost importance, given that the assumption that learner decisions are effective is foundational to the success of learner-centric approaches. In order to best design learner-controlled interventions, we must first understand the psychological mechanisms and decision making processes learners utilize to manage their limited resources when acquiring knowledge and skill on their own. Without this understanding, it can be difficult to determine when, where, and why learner-controlled training is most likely to be effective (and perhaps more importantly why it sometimes is not). Along these lines, the purpose of the present paper is to advance research in this area by presenting an integrated theoretical framework that outlines key processes and mechanisms underlying how learners make decisions regarding where and when to allocate their limited effort and attention within the context of learner-controlled training and development. Specifically, the theoretical model presented here focuses on the resolution of the exploration-exploitation tradeoff, which is one of the most common dilemmas learners face when given control over the learning process. Although implications of exploration-exploitation tradeoffs have been considered in the literature on macro-level organizational learning (March, 1991), they have generally been overlooked at the individual-level where learning is less dependent on social exchanges and more on a series of systematic, dynamic, and progressive learning processes. After considering the broader implications of exploration-exploitation tradeoffs for knowledge and skill acquisition in learner-controlled training and development, we then turn to integrating principles from the literatures on curiosity theory (Berlyne, 1954, 1966; Loewenstein, 1994), control theory (Carver & Scheier, 1982; Powers, 1973; Vancouver, 2005), and skill acquisition (Anderson, 1982; Anderson et al., 2004; Kanfer & Ackerman, 1989) to produce a model of self-regulated learning we call the Dynamic Exploration-Exploitation Learning (DEEL) model. A defining characteristic of the DEEL model is its central regulatory mechanism, the information-knowledge gap (or information-gap for short), which we argue is a critical mechanism for understanding how learners modify their behavior to resolve exploration-exploitation tradeoffs. After introducing the model, we then turn to demonstrating DEEL's theoretical utility by showing how DEEL speaks to common flaws in key learner perceptions that form information-knowledge gaps and how they conspire to limit the effectiveness of selfregulated learning and decisions to engage in training and development opportunities. We then use computational modeling to show how DEEL can account for systematic, dynamic changes in learner behavior at various stages of the learning process and discuss how these changes affect the development of generalizable, adaptable knowledge and skill capacity. We finish with a discussion of how DEEL relates to empirically supported instructional techniques relevant to learner-controlled training, with an eye towards how these techniques can be better leveraged and integrated to promote effective self-regulated learning and training engagement. 2. Background assumptions Training and development contexts can vary widely in both function and structure. Therefore, we begin by explicitly specifying key assumptions inherent to the type of learning contexts to which we argue these principles apply. To start, a foundational assumption underlying our model's development is that the majority of learner behavior conforms to the principles of bounded rationality (Simon, 1991). That is, we assume learners generally attempt to make decisions that maximize their learning outcomes, but that the quality of their decisions are inherently limited by characteristics of the task to be learned, time and resource constraints, and their own cognitive limitations (Kanfer & Ackerman, 1989). In this regard, our model is somewhat unique, in that it acknowledges that learner limitations and perceptual biases directly influence the self-regulated learning process—a problem more traditionally rationalistic theories are unable to resolve. Nevertheless, it should be noted that by adopting this assumption, we are conceding the fact that our model is not designed to account for instances of wholly irrational behavior that (although rare) can have a large impact on the learning process. We make a similar set of assumptions in regards to characteristics of the learning environment. Specifically, like Kozlowski, Toney, et al. (2001), we assume that the presence and recognition of some form of diagnostic feedback and a structural emphasis on learner rather than instructor control over the learning process are defining characteristics of effective learner-controlled training. As such, our model applies only to learning contexts where these instructional components (i.e., feedback and learner control) are present. Furthermore, to use the terminology of Norman and Bobrow (1975), our model assumes that the task to be learned is resource-limited rather than data limited. That is, changes in attention and effort devoted to the task should result in measureable changes in task performance for the principles in our model to apply. As such, our model is likely to be more relevant to open-ended, complex task learning contexts characteristic of active learning environments than to simple, closed tasks for which traditional proceduralized training is more likely to be relevant (Bell & Kozlowski, 2008). With these assumptions and task characteristics in mind, the challenge was developing a theoretical model that can be applied to better understand how learners resolve explorationexploitation tradeoffs in learner-controlled training and development contexts. We turn to this topic next. 3. Exploration-exploitation tradeoffs in self-regulated learning A defining characteristic of learner-controlled training and development is that learners rarely have the requisite time, energy, effort, and/or attention to acquire the full breadth and depth of knowledge and skill required for achieving optimal levels of task 2

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performance. As such, learners must carefully navigate a complex set of decisions when determining how to most efficiently allocate their limited resources to achieve their goals. A natural response to these constraints is to invest resources in areas where the potential performance impact appears to be greatest and fewer resources in areas where they may be wasted. The various psychological processes guiding these goal-directed, resource-allocation decisions within learning contexts are collectively known as self-regulated learning, defined as the “modulation of affective, cognitive, and behavioral processes throughout a learning experience to reach a desired level of achievement” (Sitzmann & Ely, 2011, p. 421). In the present paper, we focus on the role of self-regulated learning in resolving exploration-exploitation tradeoffs, which is one of the most common dilemmas facing learners engaged in self-regulated learning. The general concept of exploration-exploitation tradeoffs is relatively straightforward. Exploration involves regularly switching between options with the purpose of developing a broader and richer repertoire of potential solutions (Hardy, Day, Hughes, Wang, & Schuelke, 2014) and encompasses behaviors like information search, strategy variation, risk-taking, experimentation, and discovery (March, 1991). Exploitation, on the other hand, involves staying with a single option (or a limited set of alternatives) with the purpose of maximizing the potential of a preferred solution, and encompasses behaviors like refinement, implementation, and execution (March, 1991). An exploration-exploitation tradeoff emerges when learners are more motivated to learn than to fulfill competing drives (e.g., motivations to simply fulfill obligations or to avoid costs), but their available resources are sufficiently limited as to prohibit full application of both exploratory and exploitative approaches. Exploration-exploitation dilemmas are pervasive in the human experience. The common figure-of-speech “jack of all trades, master of none” describes individuals with a great breadth of knowledge—allegedly due to their eagerness to explore a variety of knowledge and skills—who never develop expertise because they fail to invest time mastering any particular topic.1 Similar sayings can be found in nearly every major language, including Arabic: [he] “who does several trades, is incapable of managing any”, Cantonese: “equipped with knives all over, yet none is sharp”, Mandarin: “all trades known, all trades dull”, and French: “he who embraces too much, has a weak grasp”. Yet, despite a corresponding interest in exploration-exploitation tradeoffs across a number of literatures, developing a formal model of this dilemma has proven difficult. There are two primary reasons for this. First, researchers have only recently begun to acknowledge that exploration and exploitation likely operate on a continuum rather than as antithetical alternatives (Mehlhorn et al., 2015). Second, definitions of exploration and exploitation may change depending on whether one is referring to characteristics of the situation (i.e., inputs), the exhibited behavioral response (i.e., processes), or the consequences of the behavioral response (i.e., the outcome; Mehlhorn et al., 2015). Given this ambiguity, it is important to consider implications of exploration-exploitation tradeoffs as they apply to various aspects of the specific context and learner in question and to conceptualize these tradeoffs as points on a continuum rather than as a binary choice. In the following sections, we consider the implications of exploration-exploitation tradeoffs in within-person, dynamic training and development contexts using the input-process-outcome heuristic depicted in Fig. 1.

3.1. Input: Uncertainty Not all exploration-exploitation tradeoff decisions are alike. The relative merit of exploration and exploitation fluctuate dramatically depending on characteristics of both the environment and the individual (Mehlhorn et al., 2015). However, the first step to resolving any exploration-exploitation tradeoff is to consider situational characteristics that define the relative benefits and/or risks of various approaches (i.e., the paths) to achieving the individual's goals. In the context of training and development, much of this information can be found in the primary focus of the learner's efforts, which is the resolution of uncertainty within the task environment. We use the term uncertainty here to encompass both perceptions of unresolved novelty and complexity in the environment (i.e., the specific training program or the broader job and career context) and the availability of competing alternatives to the one that is preferred. Perceptions of unresolved novelty and complexity represent the extent to which individuals believe the environment contains new, unfamiliar, or changing information (Berlyne, 1970; Dember & Earl, 1957). Novelty and complexity perceptions communicate to learners what they can expect in terms of the costs of the information search process (e.g., cognitive effort, time) relative to its benefits (Bröder, 2000; Charnov, 1976; Newell & Shanks, 2003). High novelty perceptions spur exploration, because novelty suggests that there is something to be gained by venturing away from a favored approach. Low novelty perceptions favor a greater emphasis on exploitation because the perceived absence of new information in the environment reduces the potential value of the information search relative to its costs. A second factor that shapes perceptions of uncertainty is the perceived availability of competing alternatives to one's preferred approach. When learners believe there are many different ways to perform a task, they will be more willing to spend time exploring alternatives with the hope of expanding their strategic repertoire. In contrast, when learners believe there is only one best way to perform a task, they will be more motivated to focus on learning that procedure than to waste their time attempting alternative, ostensibly less optimal approaches. For this reason, exploration is better suited to learning complex tasks with dynamic, decisionmaking characteristics, whereas exploitation is better suited for simple or potentially hazardous tasks for which the optimal approaches to the task problem are already well known (Hardy et al., 2014).

1 Interestingly, the complete saying reads “jack of all trades, master of none, though oftentimes better than master of one.” This second line modifies the meaning of the saying to suggest that there is value in exploration, even to the detriment of expertise development.

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Fig. 1. Input-process-output heuristic framework of exploration-exploitation tradeoffs.

3.2. Process: Learning approach Another way to conceptualize the exploration-exploitation dilemma is to consider the behavioral patterns exhibited by the individual (Mehlhorn et al., 2015). In training and development contexts, this behavior involves the learning approach used to acquire knowledge and skill. An approach can be described as exploratory if it emphasizes engaging a wide variety of alternatives (task strategies, training material, opportunities for development) with little focus on identifying a preferred alternative. In this regard, exploration reflects a systematic process whereby learners identify and resolve novelty relevant to effective performance (Hardy et al., 2014). In support of its perceived potential to enhance the learning process, exploratory behavior is featured prominently in a number of established training and development interventions. For example, in variable practice training, learners are exposed to novel instantiations of task information to be learned with the hope that they will gain a greater understanding of the complete task context as a result of continuous exploration (Magill & Hall, 1990; Shea & Morgan, 1979). In a similar vein, the purpose of job rotation is to provide learning opportunities that help employees develop variety in their perspectives, knowledge, and skills (Ortega, 2001). By engaging greater variety in their exposure to roles and responsibilities critical to organizational functioning, the hope is that employees who participate in job rotation programs can become more flexible and versatile employees (Campion, Cheraskin, & Stevens, 1994). On the other hand, a learning approach can be described as exploitative if it focuses on a single or restricted set of alternatives. For instance, under a constant practice training schedule, learner resources are intentionally directed toward behavior that is narrow in focus with an emphasis on reducing errors and mastering what has been identified as the optimal way to perform a given action. The idea is that constant exposure to a limited range of task characteristics will allow learners to develop a deeper, more thorough understanding of the material. It is important to note here that although exploitation is more focused in scope than exploration, our conceptualization of exploitation implies that it is still fundamentally learning-oriented in its purpose. Like exploration, the goal of exploitation in learning contexts is building knowledge and skill. Although exploration is the learning approach most often associated with training and development, exploitation also represents a viable learning strategy, one that is even more important than exploration for developing highly specialized knowledge and skills. 3.3. Outcome: Knowledge and skill Finally, the exploration-exploitation tradeoff can be conceptualized in terms of the outcomes associated with each approach. In the context of training and development, this includes both the types of knowledge and skill typically acquired through exploration and exploitation respectively and the improved performance capacity that ultimately results from the newly acquired knowledge and skill. In this section, we consider the implications of the exploration-exploitation tradeoff for the development of both analogical capacity, defined as the capability to use one's existing knowledge and skill in response to demands similar to those encountered within the training and development context, and adaptive capacity, defined as the capability to use one's existing knowledge and skill in response to novel (e.g., more difficult, complex, and dynamic) performance demands (Ivancic & Hesketh, 2000). The primary benefit of exploration is that it is associated with the development of breadth in knowledge and skill. This breadth is particularly well-suited to the development of adaptive capacity because possessing a broader repertoire of responses increases the likelihood that individuals will be able to identify and apply the correct response across a variety of situations. This reduces the need for employees to constantly be re-trained on how to perform new or changing tasks because they will be prepared to apply what they have learned across a wider range of potential problem situations. Moreover, breadth in knowledge and skill associated with exploration benefits analogical capacity on complex tasks or in work contexts that require learners to know and apply a broad range of

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task principles (Hardy et al., 2014). However, individuals who adopt a purely exploratory approach risk foregoing the development of depth in their understanding. On this point, it has been argued that the surface-level characteristics of knowledge gained through exploratory learning approaches may partially explain the failure of discovery-based learning techniques to meaningfully benefit learning outcomes in the absence of structure or guidance (Mayer, 2004). Exploitative, which contributes primarily to the development of depth, rather than breadth, in knowledge and skill, avoids this problem altogether. By focusing exclusively on a single approach or set of approaches, learners engaged in sustained exploitation gain a deeper level of understanding consistent with the development of routine expertise (i.e., expertise in a limited domain), which is a critical component of analogical capacity. Supporting this notion, Stanislaw, Hesketh, Kanavaros, Hesketh, and Robinson (1994) found that computer programmers with expertise in a single language were generally better equipped to solve problems relevant to their favored language's capabilities than novices (Stanislaw et al., 1994). However, like purely exploratory approaches, an overemphasis on exploitation can also be problematic. Research shows that people who possess routine expertise tend to struggle when characteristics of the problem domain change (Devine & Kozlowski, 1995; Sternberg & Frensch, 1992). Similarly, Stanislaw et al. (1994) found that when operating in adaptive contexts, programmers who favored a broader variety of languages outperformed individuals who specialized in a single language because they were better able to select and adapt the appropriate language to suit the problem. As such, there is evidence to suggest that excessive exploitation can be a liability when it comes at the expense of developing adaptive expertise (Hesketh, 1997). Given the arguments presented above, it seems that an extreme commitment to either exploration or exploitation will leave learners with problematic gaps in their understanding, particularly in their adaptive capacity. So how then are learners supposed to manage exploration/exploitation's respective tradeoffs? To answer this question, it is important to acknowledge that in the real world, learners rarely adopt a purely exploratory or exploitative learning approach, but instead attempt to manage their limited resources in such a way that they are able to find an optimal balance between the two different strategies (Gupta, Smith, & Shalley, 2006). We argue this balancing act serves an adaptive function, in that individuals who are able to effectively balance their allocation of resources to depth (exploitation) and breadth (exploration) respectively will gain access to a synergy in learning that goes beyond the summative knowledge and skill acquired through a single approach. This is because the depth of exploitation adds usefulness to the breadth of exploration and vice versa. Supporting this notion in the organizational learning literature, Katila and Ahuja (2002) found an interaction between exploration (i.e., product search scope) and exploitation (i.e., product search depth) such that robotics firms that balanced their organizational learning strategies produced a greater number of new products than other firms that focused primarily on specialization or innovation. We argue a similar strategy will be beneficial at the individual level, such that the optimal approach to learning likely involves an oscillation between identifying new task strategies through exploration and “test driving” them through exploitation. This idea is consistent with research showing observed learning curves comprise the aggregation of a series of smaller curves associated with individual shifts in strategy selection (e.g., Delaney, Reder, Staszewski, & Ritter, 1998). Effective learners show a tendency to transition back and forth between the identification of new task strategies, and practice devoted to improving their effectiveness within each strategy they discover (Schunn, McGregor, & Saner, 2005) and exploiting and mastering simpler task strategies may facilitate the development of more advanced strategies (Donner & Hardy, 2015). By switching back and forth between exploration and exploitation, learners can develop a broad repertoire of potential task strategies as well as an understanding of which strategies are most effective and in which particular circumstances (Schunn et al., 2005). 3.4. Exploration-exploitation tradeoffs summary and propositions In the preceding sections, we argued that the exploration-exploitation tradeoff in the context of training and development can be understood by considering the presence of informational uncertainty, the learning approach, and the knowledge and skill outcomes associated with exploration and exploitation. Specifically, exploration is associated with higher levels of uncertainty, behavior focused on engaging a wide variety of alternatives with little focus, and the development of breadth rather than depth in knowledge and skill. In contrast, exploitation is associated with lower levels of uncertainty, behavior focused on a narrower set of alternatives, and the development of depth rather than breadth in knowledge and skill. We also argued that a synergy can emerge when learners adopt an optimal mix of exploration and exploitation to meet the needs of their particular situation. These arguments are summarized in the following three propositions. Proposition 1 Exploration will be more effective in situations where (a) there is a large amount of novelty to be resolved, (b) there are many alternatives to consider, and (c) there is a need for generalizable, adaptable knowledge and skill. Proposition 2 Exploitation will be more effective in situations where (a) there is a small amount of novelty to be resolved, (b) an optimal alternative is clear, and (c) there is a need for highly specialized knowledge and skill. Proposition 3 Exploration and exploitation will produce a synergistic effect on learning when effort allocated to discovering new alternatives through exploration is coupled with corresponding efforts allocated to exploiting newly identified alternatives. 4. Dynamic Exploration-Exploitation Learning model Having considered the implications of exploration-exploitation tradeoffs in training and development contexts, we next turn to

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Fig. 2. A visual depiction of the Dynamic Exploration-Exploitation Learning model (DEEL). a Exploitation = 0; exploration = 1.

the question of how self-regulation helps learners navigate these tradeoffs. To do so, we offer DEEL (see Fig. 2), which is a model of self-regulated learning that combines tenets from curiosity theory (Berlyne, 1954, 1960, 1966; Loewenstein, 1994), control theory (Carver & Scheier, 1982; Powers, 1973; Vancouver, 2005), and research on skill acquisition (Anderson, 1982; Anderson et al., 2004; Kanfer & Ackerman, 1989) to describe how a simple regulatory mechanism known as the information-knowledge gap can act as a bridge between learner perceptions of their situation and the decisions they make regarding how much and where to allocate resources in order to effectively resolve exploration-exploitation tradeoffs. Our goals in developing DEEL were to (a) present a theoretical description of a series of mechanisms underlying how learners use self-regulation to resolve exploration-exploitation tradeoffs, (b) account for the iterative and dynamic nature of these decisions in relation to knowledge and skill acquisition, and (c) consider the implications of this model for understanding and predicting learner behavior in learner-controlled training and development. Definitions of prominent components in the DEEL model can be found in Table 1. The theoretical foundation for the information-knowledge gap was originally articulated by George Loewenstein, who presented it as a mechanism useful for understanding characteristics of epistemic curiosity that were otherwise difficult to explain (Loewenstein, 1994). Essentially, information-knowledge gaps are functional discrepancies between two key perceptions: (1) what learners perceive they currently know/can do, which we refer to as competence beliefs; and (2) what learners want or need to know/be able to do, which we refer to as novelty motives. Competence beliefs are a sub-dimension of self-efficacy in learning contexts that represent the learner's accounting of their capabilities independent of environmental constraints. Novelty motives, on the other hand, encompass the perceived novelty in the environment and the learner's desire to make sense of it. Essentially, novelty motives are a type of learning goal, with the caveat that their focus is primarily proficiency-based (i.e., focused on obtaining knowledge and skill) rather than achievement-oriented (i.e., focused on meeting a particular performance goal). When learners' novelty motives are elevated above their competence beliefs, they experience gaps in the knowledge that cultivate curiosity, which is a motivated state characterized by the activating feelings of interest and knowledge deprivation (Litman, 2008; Litman & Jimerson, 2004; Loewenstein, 1994). We argue that information-knowledge gaps operate in a similar manner to the goal-performance discrepancies described by control theorists

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Table 1 Definitions of prominent components in the DEEL model. Model component

Definition

Information-knowledge gap

Perceived discrepancies between what learners believe they currently know/can do (i.e., competence beliefs) and what learners want or need to know/be able to do (i.e., novelty motives)

Competence beliefs

Learner perceptions of their own capabilities (i.e., what they believe they currently know/can do) independent of environmental constraints

Novelty motives

Learner perceptions of levels of novelty in the learning environment and their desire to make sense of it

Motivation to learn

The collective result of motivational forces both internal and external that influence learner desires to allocate learning-oriented effort in response to perceived information-knowledge gaps

Learning effort

Learner resources (e.g., time, attention, and energy) that are specifically allocated toward accomplishing learning goals

Strategy tradeoff

Learner decisions regarding how best to allocate their limited pool of learning resources among exploratory vs exploitative strategies, cognitions, and behaviors in order to maximize learning outcomes

Exploration

Process-level learning strategies, cognitions, and behaviors that emphasize engaging a wide variety of alternatives (e.g., task strategies, training material, opportunities for development) with little focus on identifying a single or limited set of preferred alternatives

Exploitation

Process-level learning strategies, cognitions, and behaviors that emphasize focusing on a single or restricted set of alternatives (e.g., task strategies, training material, opportunities for development) that primarily focus on identifying and effectively executing a preferred alternative

Analogical capacity

The capability to use one's existing knowledge and skill in response to demands similar to those encountered within the training and development context

Adaptive capacity

The capability to use one's existing knowledge and skill in response to novel (e.g., more difficult, complex, and dynamic) performance demands

Competence bias

Perceptual miscalibrations between what a learner believes they know/can do and their objective levels of performance capability

Novelty recognition bias

Perceptual miscalibrations between how much novelty and complexity in a task remains to be learned and objective levels of novelty and complexity that actually remains to be learned

(Carver & Scheier, 2001; Powers, 1973; Vancouver, 2005) in that we propose motivational potential in learning contexts is a function of the magnitude of perceived information discrepancies. The larger the perceived discrepancy between what learners want to know (and want to be able to do) and what they currently know (and can do), the more motivated they will be to devote additional resources to their accepted learning goals.2 4.1. The role of information-knowledge gaps in resolving exploration-exploitation tradeoffs In the DEEL model, information-knowledge gaps serve two primary purposes. First, the reality of many modern training and development contexts is that learning goals must compete with a variety of other demands for limited time, attention, and effort resources. Consequently, employees must make decisions regarding which information and opportunities to pursue and which to ignore. In this respect, information-knowledge gaps help learners determine the optimal amount of resources they should allocate to learning goals relative to other competing demands by providing a calculated assessment of their informational (i.e., knowledge and skill) needs. We call this the resource-preservation function, because its purpose in self-regulation is to enable learners to be more prudent in the way that they use their limited time, attention, and effort. As shown in Fig. 2, the resource-preservation function is depicted in DEEL as the direct path from the information-knowledge gap to the overall pool of learning effort. Larger informationknowledge gaps signal that more resources are needed to satisfy one's novelty motives whereas smaller gaps signal that resources should be preserved or allocated to other goals. If the learner's perceptions of her capabilities are accurate, then reducing the pool of resources allocated toward learning effort in response to improvements in knowledge and skill capacity is an adaptive response, as fewer resources are required in later stages of the learning process to produce similar outcomes (Kanfer & Ackerman, 1989). 2

It should be noted that critics of control theory (e.g., Bandura, 2012; Bandura, 2015) have claimed that the proposed positive link between goal discrepancies and effort is inconsistent with existing theory and research suggesting that allocated effort increases as people approach goal attainment (e.g., Förster, Higgins, & Idson, 1998; Gersick, 1988; Schmidt & Deshon, 2007). However, in their computational model of multiple-goal pursuit, Vancouver, Weinhardt, and Schmidt (2010) demonstrated that these increases in effort are not necessarily the result of decreases in discrepancy magnitude, but are instead a by-product of hyperbolic discounting that occurs in response to impending performance deadlines. Furthermore, the process of learning an open-ended skill is somewhat unique in that it is not defined by “ultimate achievement on some higher order accomplishment,” but instead by progress made toward developing “individual component skill” (Kanfer & Ackerman, 1989, p. 660). Thus, we argue that the motivational forces associated with observed increases in motivational effort as deadlines draw near are less relevant to effort production in learner-controlled training for complex task domains than the negative feedback loop described by control theorists. Nevertheless, we acknowledge that (in certain situations and under certain contingencies) other factors beyond perceived discrepancies can indeed influence effort allocation decisions. 7

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Fig. 3. Relationship between information-knowledge gaps and exploration-exploitation tradeoffs.

Second, after deciding how much effort to allocate to the learning process, learners must then determine the optimal strategy for utilizing that effort productively. Thus, the second function of information-knowledge gaps in self-regulated learning is to inform decisions on how to allocate learning effort. We call this the learning function, because its purpose is to help learners decide how to best utilize their limited pool of learning resources to maximize learning outcomes. As shown in Fig. 2, the learning function is depicted in DEEL by the moderating role of strategy tradeoff on the paths from learning effort to exploration and exploitation behaviors. When information-knowledge gaps are large, the amount of uncertainty in the learning environment is high. As previously discussed, high uncertainty signals a greater need for exploration relative to exploitation. In response, learners are more likely to devote a greater proportion of their learning effort to exploration with the goal of developing breadth over depth in their learning. Conversely, when information-knowledge gaps are small, uncertainty is lower. In response, learners devote a greater proportion of their learning effort to exploitation and the development of depth over breadth in their learning. Both of these functions are relevant to effectively resolving exploration-exploitation tradeoffs. The resource-preservation function determines the significance of the exploration-exploitation tradeoff in question based on the magnitude of the information-knowledge gap. The learning function, on the other hand, helps learners determine the optimal mix of exploration and exploitation to maximize learning outcomes across a variety of situations. As shown in Fig. 3, we propose that the way learners resolve exploration-exploitation tradeoffs is rarely with an “either/or” response. In some cases, information-knowledge gaps dictate that learners should strive for a balance between exploration and exploitation, where they use a combination of exploring new information and task strategies and exploiting newly acquired information and identified task strategies. In other cases, they may favor either exploration or exploitation.3 These arguments are summarized in the following propositions. Proposition 4 Learners will devote more overall effort to learning goals when information-knowledge gaps are large and less effort when information-knowledge gaps are small. Proposition 5 Learners will devote more learning effort to exploration when information-knowledge gaps are large and more learning effort to exploitation when information-knowledge gaps are small. Another important factor to consider here is the learner's motivation to learn. Simply perceiving that an information-knowledge

3 An interesting theoretical implication of this idea is that exploration and exploitation might be positively correlated, even when they compete for the same pool of learning effort. This is because learners who devote a greater proportion of their resources to learning goals will be more likely to have a large pool of resources devoted to learning. As a result, they will utilize both exploration and exploitation approaches more often when information-gaps are large relative to when information knowledge gaps are small.

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gap exists is not enough to guarantee a learner will be motivated to devote their limited resources to resolve it. Learners who do not value the acquisition of knowledge and skill as an end itself will be less likely to allocate resources toward a learning goal, even in the presence of a large information-knowledge gap. Similarly, learners who believe they are not capable of learning or those who are more motivated by alternative goals (e.g., satisficing a training requirement or avoiding all costs) will also be unlikely to respond to perceived information-knowledge gaps by allocating effort to either exploration or exploitation. Therefore, as shown in Fig. 2, we include motivation to learn in our model as a moderator of the relationship between information-knowledge gaps and the learner decisions to allocate effort in pursuit of learning goals. Proposition 6 Motivation to learn moderates the effect of information-knowledge gap magnitude on learning effort such that lower levels of motivation to learn will attenuate the effect of information-knowledge gap on learning effort. 4.2. Information-knowledge gaps in the context of dynamic learning Self-regulation and learning are both inherently dynamic processes that occur at the within-person level and unfold over time. How that time is structured depends largely on characteristics of the task to be learned. Some tasks can be performed effectively after only an hour or two of concentrated effort. Others can take months, years, or even decades to master. Still others are learned more sporadically, with regulatory effort applied only when the individual has a spare moment to devote to the learning process. However, one thing that is consistent across all learner-controlled training and development contexts is that changes in learner behavior associated with self-regulation generally correspond with changes in the external learning environment, typically in the form of performance feedback. For this reason, the basic unit of time in DEEL is intentionally left ambiguous. This provides DEEL with the theoretical flexibility to enable it to be used to explain behavior in a variety of different learning contexts. One way feedback shapes the regulatory process is by defining the characteristics of information-knowledge gaps perceived by the learner. Specifically, we argue that information-knowledge gaps enable learners to adjust and refine their preferred approach in response to knowledge and skill acquisition and to changes in the external learning environment. Kanfer and Ackerman's information processing model (Kanfer, 1990; Kanfer & Ackerman, 1989) posits that the early stages of skill acquisition require a greater investment of resources in the learning process than do later stages where performance is more automatic. This implies a necessary shift in resource allocation strategies on the part of the learner as they transition from declarative to procedural forms of knowledge. We argue that the process of navigating this transition, referred to as compilation, involves the allocation of attentional resources to some combination of both exploration and exploitation. Moreover, because learning is dynamic, we argue that the specific combination of exploration and exploitation favored by learners will change throughout the learning process in response to corresponding changes in the learner and learning environment. For its part, exploration plays a greater role early in skill development when perceived novelty and complexity is high because it fosters the development of a wide range of basic and strategic knowledge components. Exploitation, on the other hand, enables learners to develop depth in understanding through the refinement of existing knowledge components into more consolidated forms of procedural knowledge and can thus be expected to play a greater role in the middle and later stages of knowledge acquisition. To be clear, we are not saying exploration is irrelevant to the development of procedural knowledge or that exploitation is irrelevant to the development of declarative knowledge. Rather, our model suggests that the relative utility of exploration versus exploitation gradually evolves in response to changes in the learner or in learning environment. In this regard, DEEL is somewhat unique in that it accounts for a range of dynamic shifts within the learning process—a theoretical advantage that can help overcome several key limitations of the self-regulation literature (Lord, Diefendorff, Schmidt, & Hall, 2010; Sitzmann & Ely, 2011). As shown in Fig. 3, we propose there are two general categories of exploration-exploitation shifts that can emerge in training and development contexts: (1) shifts toward exploitation that occur in response to increases in knowledge and skill (and more automated performance), and (2) shifts toward exploration that occur in response to changes in the learning environment. We discuss the structure and relevance of these two shifts next. 4.2.1. Shifts toward exploitation We expect a natural reduction in the information-knowledge gap will occur as learners acquire new knowledge and skill. This reduction is the result of increases in competence beliefs that accompany improvements in knowledge and skill capacity (Heggestad & Kanfer, 2005; Sitzmann & Yeo, 2013). In this regard, competence beliefs provide a crucial perceptual link between performance feedback resulting from objective changes in learner performance capacity and learner behavior. Specifically, we argue that higher competence beliefs reduce the magnitude of perceived information-knowledge gaps. Given that smaller information-knowledge gaps are associated with a greater emphasis on exploitation, the end result of this chain of events is that learners will naturally transition from exploration to exploitation. This transition has the potential to be adaptive because it helps learners tailor their learning approach to shifting demands within the learning process. However, as we discuss later, learners must first avoid the potentially disruptive influence of bias in the process of translating objective performance information into capability beliefs before realizing the full benefits of this shift, a rational ideal that is realized much less frequently than is often assumed.

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Nevertheless, it should be noted here that characterizing the impact of competence beliefs on information-knowledge gaps as only discrepancy reduction-oriented is overly simplistic because it ignores the role of expectancy beliefs in the positive discrepancy creation process (Bandura & Locke, 2003; Scherbaum & Vancouver, 2010). In this context, positive discrepancy creation refers to the tendency for people to set higher novelty motives for themselves in response to increases in performance capability, an act that contributes to sustained motivational effort resulting from sustained goal-performance discrepancies. Expectancies like competence beliefs contribute to this process through their positive impact on goal setting (Bandura, 1997). As performance improves, individuals develop the confidence to strive for increasingly more difficult goals. As shown in Fig. 2, we posit that a similar phenomenon exists in the formation of information-knowledge gaps. Specifically, we expect that as learners become more knowledgeable and skilled, and thus more confident, they develop an interest in and capacity for identifying and seeking out nuances in the task (job or career) environment that they previously overlooked (Hardy et al., 2014). In turn, novelty motives are spurred, thus stimulating informationknowledge gaps and tempering the transition toward greater exploitation described above. Early research on stimulus selection decisions supports the notion that individuals prefer to engage novelty that is slightly more complex than their current level of competence (Earl, Franken, & May, 1967; May, 1963). Thus, we make the following propositions. Proposition 7 The development of knowledge and skill capacity will have a positive effect on competence beliefs (i.e., how much learners perceive they know). Consequently, increases in competence beliefs will result in reductions in information-knowledge gaps as learning progresses. Proposition 8 The development of knowledge and skill capacity will have a positive effect on novelty motives (i.e., learners wanting to know more). Consequently, increases in novelty motives will slow the rate at which information-knowledge gaps are reduced.

4.2.2. Shifts toward exploration Information-knowledge gaps also allow learners to respond to changes in the training environment or job that are external to the learner. This is because changes in the underlying task structure, the introduction of new work responsibilities, or changes in policy or regulations that alter the nature of the job introduce learners to previously unencountered sources of novelty. These changes cause information-knowledge gaps to expand via spurred novelty motives. Learners then respond to this widening of information-knowledge gaps by devoting additional resources to learning goals and by shifting the allocation of those resources to learning strategies that more strongly emphasize exploration. In this way, information-knowledge gaps ensure that learner behavior is responsive to the complexity of the training and development context. Furthermore, this mechanism suggests that introducing changes to the training and development environment will expose learners to additional sources of novelty, which prompts them to sustain their exploration over a longer period of time. Like shifts toward exploitation as a result of knowledge and skill acquisition discussed above, shifts toward exploration in response to changes in the environment are potentially adaptive in that they increase the chance that learners will be able meet the demands of the changing learning environment. Larger information-knowledge gaps that emerge following external changes in the learning environment signal to learners that a favored approach that worked well before the change, may not work quite as well after the change, and that learners may now lack information needed to be effective. As a result, exploration becomes more valuable to learners following an external change in the learning environment relative to its costs. This is the logic underlying the marginal value theorem (Charnov, 1976), which has been applied to explain human behavior during information search and has even been shown to have a physiological basis (Daw, O'Doherty, Dayan, Seymour, & Dolan, 2006). We summarize this proposed effect in the following proposition. Proposition 9 Changes in the learning environment will increase novelty motives. Consequently, increased novelty motives will result in expansions in information-knowledge gaps.

4.3. The effect of bias on information-knowledge gaps and learning In many respects, the DEEL model speaks to the promise of giving more autonomy to employees in training and development contexts and trusting that they can effectively manage their development of knowledge and skill capacity. Potential benefits of the systematic processes guided by information-knowledge gaps include (a) allowing learners to effectively manage their limited pool of resources within the learning context and beyond, (b) helping learners to determine the optimal resolution of exploration-exploitation tradeoffs, and (c) equipping learners to remain responsive to dynamic changes in the learning process from both within the learner and the external environment. However, to realize the full potential of information-knowledge gaps in self-regulated learning, two key conditions must be met. First, as with other regulatory discrepancies, the effectiveness of information-knowledge gaps in regulating learner behavior is moderated by the availability and quality of task-relevant feedback (Kluger & Denisi, 1998). Without effective feedback, it becomes

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difficult to form realistic performance expectancies, which are instrumental in the learning process (Bandura, 1997). Research in performance contexts has shown that individuals tend to rely on biased perceptions of performance capability in the absence of diagnostic feedback, which contributes to suboptimal performance (Schmidt & Deshon, 2010). These biases are even more problematic in learning contexts where some degree of self-doubt provides incentive for learning (Bandura, 1997; Bandura & Locke, 2003). Fortunately, a rich history of research shows that feedback can help facilitate positive outcomes for individuals in learning and performance contexts (Hattie & Timperley, 2007; Kluger & Denisi, 1996). This supports the notion that feedback can help learners overcome the detrimental effects of bias in learner-controlled training and development contexts. However, this research has also shown that the expected impact of feedback can vary widely across interventions and is heavily dependent on characteristics of both the task and the learner (Hattie & Timperley, 2007; Kluger & Denisi, 1996; Kluger & Denisi, 1998). As such, it is likely unrealistic to expect that the inclusion of basic forms of diagnostic feedback alone can fully mitigate the disruptive impact of bias on the formation of competence beliefs. Along these lines, a second condition necessary for effective functioning of information-knowledge gaps is that learners must be able and willing to use feedback that they receive to form accurate and complete understandings of information-knowledge gaps. That is, closer alignment between learner perceptions underlying novelty motives and competence beliefs and the objective characteristics of the learning environment is positively related to the effectiveness of learner functioning within learner-controlled training and development contexts. However, again there is a large body of evidence suggesting that biases in competence beliefs and novelty motives are prevalent across a variety of contexts and individuals. Starting with biases in competence beliefs, research indicates that humans show a pervasive tendency to overestimate their own level of competence relative to reality (Dunning, 2012). For example, 93% of drivers rate themselves as more skilled than the average driver (Svenson, 1981), 90% of university faculty members rate themselves as above-average teachers (Cross, 1977), and 87% of MBA students rated their academic performance as superior to their peers (Zuckerman & Jost, 2001)—values that are far removed from the mathematical reality of only 50% of people being above-average in any given domain. Unskilled individuals are particularly susceptible to overconfidence biases because the metacognitive skills that facilitate success are the same skills that enable people to form accurate perceptions of their own capability (Ehrlinger, Johnson, Banner, Dunning, & Kruger, 2008; Kruger & Dunning, 1999). From a social-psychological perspective, this overconfidence bias can be thought of as adaptive in the sense that beliefs in false superiority help people to cope in the face of adversity (Taylor & Brown, 1988). However, overconfidence can be problematic in learning contexts, where possessing accurate perceptions of one's capabilities are necessary for the identification of strengths and resolution of weaknesses. For example, an overconfident student driver may not see the need to devote time and energy preparing for an upcoming driving test. Similarly, an overconfident manager may not see the benefit in spending her weekend attending a well-respected, but time-consuming leadership training workshop. In both cases, overconfidence stifled learning motivation by reducing the magnitude of the learner's perceived information-knowledge gaps, which leads overconfident individuals to be less likely to devote their limited resources to learning goals. This proposed effect is summarized in the following proposition. Proposition 10 Learners will tend to overestimate how much they think they know and can do relative to what they actually know and can do. This bias in competence beliefs will decrease learners' perceptions of their information-knowledge gaps. Research also shows that people display a tendency to underestimate the amount of novelty and complexity that exists in their environment and to oversimplify their perceptions of what remains to be learned (Döerner, 1980). These underestimates are the result of tendencies within individuals (particularly in novices) to oversimplify their understanding of the environment by focusing on its surface rather than deep-level characteristics (Haerem & Rau, 2007). Although often considered in a negative light, oversimplification provides many benefits to people in day-to-day life. For example, human perceptual systems rely on oversimplifications of the information gathered by the sensory organs to make sense of and adapt to one's surroundings. Oversimplification also allows learners to cognitively reduce the level of effort they believe is needed to solve problems (Shah & Oppenheimer, 2008). Although this reduction in required effort can be instrumental in motivating learners to engage in the learning process in the first place, it can interfere with the functioning of information-knowledge gaps. Specifically, we argue that oversimplification biases negatively influence novelty motives, which lead to underestimates of perceived information-knowledge gaps. These underestimates subsequently prevent learners from identifying critical sources of novelty in their environment. We summarize this proposed effect in the following proposition. Proposition 11 Learners will tend to underestimate the amount of novelty and complexity in the learning environment. This bias in perceived novelty and complexity will stifle novelty motives and decrease learners' perceptions of their information-knowledge gaps. Both competence biases and novelty recognition biases are problematic in self-regulated learning because they undermine the ability of information-knowledge gaps to provide accurate reflections of the learner's current level of knowledge and skill relative to the novelty remaining in the environment. Consequently, these biases lead learners to underestimate the total amount of learning effort needed to achieve their learning goals and contribute to suboptimal resolutions of the exploration-exploitation dilemma. In

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support of this notion, research has shown that trainees show a tendency to quickly converge on a restricted set of suboptimal solutions, even when more viable solutions remain unexplored (Gopher, Weil, & Siegel, 1989; Seagull & Gopher, 1997). However, learners who are able to resist this tendency and subsequently sustain their exploration for longer periods of time are rewarded with more rapidly acquired yet adaptive knowledge and skill capacity (Hardy et al., 2014). Along these lines, DEEL predicts that underestimates of the information-knowledge gap cause learners to reduce their overall amount of learning effort and to shift too quickly to exploitative learning strategies when a more balanced approach is more appropriate. We summarize this effect in the following proposition. Proposition 12 Miscalibrated information-knowledge gaps contribute to suboptimal resolutions of the exploration-exploitation dilemma. The results of these suboptimal resolutions reduce the overall effectiveness of the self-regulated learning process and undermine the development of knowledge and skill capacity. 5. Computational model Given the complexity and dynamic nature of the arguments underlying DEEL, we took the additional step of formally specifying the model shown in Fig. 2 using the computational modeling software Vensim (VensimPLE, 2013). This novel approach to theory development enables a priori simulations of how variables in a system are expected to change over time given a set of starting values (Vancouver, Tamanini, & Yoder, 2010). A key advantage of the computational modeling of theories is that they ensure logical consistency when specifying a complex pattern of interrelationships among multiple constructs over multiple iterations. In addition, computational modeling enables the production of specific predictions that are uniquely falsifiable, which allows for more targeted model testing and refinement. The added depth permitted by computational modeling is particularly well-suited to studying selfregulatory processes and their dynamic nature (Vancouver, Weinhardt, & Schmidt, 2010; Vancouver, Weinhardt, & Vigo, 2014). We use computational modeling here to provide a visual representation of the expected outcomes communicated in our propositions and as a demonstration of the logical consistency of our theoretical arguments. We then highlight a few examples of unique insights derived from simulations based on our model. The first step in developing a computational model is to represent each of the proposed relationships in mathematical terms. These mathematical representations are specified in a manner that captures the qualitative nature (i.e., the direction, linearity, etc.) of the proposed effects. For example, the current model's competence belief parameter is defined at each iteration by a function of the model's prior performance score plus some degree of bias (a value that can be adjusted to simulate model functioning at various levels of bias). This specification of competence beliefs is consistent with research suggesting that performance expectancies are influenced by both prior performance experiences (Heggestad & Kanfer, 2005; Sitzmann & Yeo, 2013) and some degree of confidence bias (Kruger & Dunning, 1999). The parameter representing information-knowledge gaps is defined at each iteration as a function of current novelty motives minus current competence beliefs. Again this specification is consistent with our theoretical conceptualization of information-knowledge gaps. To allow for easy replication, pseudo code detailing how each parameter was specified in the model is provided in the Appendix A. Moreover, full details on each step of the model's specification, along with a downloadable version of the full model in Vensim are available in the Supplementary Materials. With the model specified, our next step was to conduct a sensitivity analysis on key model parameters. The purpose of a sensitivity analysis is to ensure that the fundamental nature of the proposed effects do not change across a range of reasonable model starting values (e.g., complex versus simple learning environments, difficult versus simple goals) and that functioning of the proposed effects are not limited to a set of overly-restrictive assumptions. This check on the model's internal validity speaks to the robustness of the theoretical logic underlying the predictions across a variety of contexts and situations (Davis, Eisenhardt, & Bingham, 2007). Details on the sensitivity analysis for this model can be found in the Supplementary Materials. In general, these tests revealed that information-knowledge gaps closed more rapidly in simple task environments where skill acquisition occurred very quickly relative to complex task environments where skill acquisition was much slower. Furthermore, more difficult initial novelty goals sustained information-knowledge gaps longer than simpler initial novelty goals. Although in both cases, the model was influenced by changes in the learner or learning environment, the rationale guiding the functioning of the DEEL model did not change. As such, the sensitivity analyses supported the successful functioning of the mechanisms underlying the model's functioning across a variety of conditions, which helps bolster the model's internal validity and increases the confidence in the results of further simulations. With the model specified and the sensitivity analyses complete, we next turned to simulating the progression of the learning process in a variety of different conditions and contexts. These simulations allow us to (a) check the internal consistency of the model and (b) understand how DEEL allows for self-regulatory dynamics in the learning process to influence learning outcomes. In the current paper, we ran three sets of simulations to demonstrate the relevance of the model's theoretical arguments in three different situations: (1) stable learning contexts, (2) changing learning contexts, and (3) in the presence of learner bias. Fig. 4 displays the results of a computational model simulating the progression of the learning process for a single individual over the course of 200 simulated learning trials in a stable (i.e., unchanging) learning context. In the simulation, the model responded to

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increases in knowledge and skill capacity during each simulated learning trial through the theoretical mechanisms depicted in Fig. 2 and described in the preceding sections. The results of this simulation confirmed the logical consistency of Propositions 4, 5, 7, and 8. As shown in Fig. 4, the model exhibited more learning effort overall when information-knowledge gaps were larger (e.g., trial 10) than when they were smaller (e.g., trial 150; Proposition 4). In other words, the model was more likely to devote a greater proportion of its available resources to a wide range of self-regulated learning processes when information-knowledge gaps were larger and fewer when information-knowledge gaps were smaller. Furthermore, the model was strategic, in that it showed a tendency to devote a greater proportion of allocated learning effort to exploration-focused strategies when information-knowledge gaps were larger and more to exploitation-focused strategies when information-knowledge gaps were smaller (Proposition 5). These proposed effects are important, because together they demonstrate that DEEL can successfully reconcile the seemingly contradictory findings pertaining to the existence of iterative, bidirectional, and self-correcting relationships among exploration and exploitation-focused self-regulated learning processes at the within-person level (Hardy, Day, & Steele, 2018) with the overwhelming positive between-person correlations among self-regulated learning constructs reported in the Sitzmann and Ely (2011) meta-analysis. Furthermore, as shown in Fig. 4, both competence beliefs and novelty motives showed a tendency to increase as a function of time spent acquiring knowledge and skill. Increases in competence beliefs contributed to reductions in the information-knowledge gap as

Fig. 4. Results of computational simulation of DEEL demonstrating shifts in exploration-exploitation as a result of systematic increases in knowledge and skill capacity.

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learning progressed (Proposition 7), which led to a reduction in both overall effort and exploration as learning progressed. However, this effect was somewhat mitigated by systematic increases in novelty motives (Proposition 8). Again, these predictions are consistent with recent data showing that learner exploratory behavior tends to decrease over the course of practice as learners shift their focus from exploratory behavior to exploitation (Hardy et al., 2014; Hardy et al., 2018). Fig. 5 displays the results of a second simulation in which a change in the environment was introduced halfway through the learning process (i.e., during the 100th simulated learning trial). The results of this simulation confirmed the logical consistency

Fig. 5. Results of computational simulation of DEEL demonstrating shifts in exploration-exploitation as a result of an increases in environmental complexity halfway through the learning process (i.e., during the 100th trial).

of Proposition 9. Specifically, changes in the task environment halfway through the learning process contributed to a sudden influx of novelty, which subsequently triggered a corresponding expansion in its information-knowledge gap (Proposition 9). In response, the model adjusted its strategic approach to devote additional resources to learning effort, and a greater proportion of that effort to exploration. Interestingly, this change ultimately benefited knowledge and skill capacity for the model, because the model was able to adjust its approach to engage and resolve additional sources of novelty that were not available in the stable learning context. Fig. 6 displays the results of a final set of simulations in which novelty recognition bias and competence bias were introduced in a stable learning context. Results were then compared to the original model which was free of bias. As shown in Fig. 6, introducing competence bias (i.e., overestimates of an individual's capabilities relative to their performance level) and novelty recognition bias (i.e., underestimates of the novelty and complexity of a task to be learned) into the model generated smaller perceived informationknowledge gaps relative to the simulation that did not include these biases (Propositions 10 and 11). More constricted information-

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Fig. 6. Results of computational simulation of DEEL demonstrating the effect of competence bias (i.e., overestimates of the model's capabilities relative to its performance level) and novelty recognition bias (i.e., underestimates of the novelty and complexity of a task to be learned) on information-knowledge gaps, learning effort, resolution of the exploration exploitation tradeoff, and knowledge and skill capacity.

knowledge gaps resulted in reductions in learning effort and a premature shift to suboptimal resolutions of the exploration-exploitation tradeoff, which subsequently resulted in less knowledge and skill capacity (Proposition 12). This effect was further compounded when both competence and novelty recognition biases were present. These findings are consistent with a growing body of research and a recent meta-analysis arguing that self-efficacy can have a null or even negative relationship with learning and performance (Beck & Schmidt, 2018; Halper & Vancouver, 2015; Sitzmann & Yeo, 2013), particularly when performance ambiguity is high (Schmidt & Deshon, 2010; Vancouver & Purl, 2017)—a condition in which learners are more likely to rely on biased perceptions of their own capability. 6. Implications of DEEL for supporting self-regulation and active learning By offering a within-person model that emphasizes exploration-exploitation tradeoffs in relation to information-knowledge gaps, 15

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Table 2 Example theoretical and practical insights and implications of the DEEL model. Model insights

Relevant propositions

Example implication for research on training and development

Example implication for the design of learning interventions

Insight 1: Exploration synergizes with exploitation to elevate learning outcomes for learners who can effectively manage explorationexploitation tradeoffs

Proposition 1 Proposition 2 Proposition 3

Conceptualizing the relationship between exploration and exploitation simply as a tradeoff may overlook the possibility that exploration and exploitation work in tandem to positively influence learning outcomes

Interventions should regularly supplement an emphasis on learner exploration with opportunities to apply (i.e., exploit) what they have learned, taking care not to place too great an emphasis on either exploration or exploitation

Insight 2: Information-knowledge gaps play an important role in guiding learner resource allocation decisions in learnercontrolled training and development contexts

Proposition 4 Proposition 5 Proposition 6

Identifying and understanding factors that shape information-knowledge gaps can aid in the development of a better understanding of when and why learner behavior changes and what those changes mean for the learning process

There may be value in attempting to track or even intentionally re-shape the structure of learner information-knowledge gaps to help focus resources where they will offer the greatest immediate and long-term benefit

Insight 3: Shifts in learner behavior can be attributed in part to predictable and systematic changes in both the learner and the learning environment throughout the learning process

Proposition 7 Proposition 8 Proposition 9

Understanding how information-knowledge gaps help learners manage dynamics in the learning process can provide insights into how learners allocate and reallocate effort as the learning process progresses

Timing is a critical component of intervention design, because the motivations and perceptions of learners early in the learning process differ from those later in the learning process

Insight 4: Common biases in learner perceptions of the environment and their own capability can be expected to undermine learning outcomes if not properly addressed

Proposition 10 Proposition 11 Proposition 12

In the absence of guidance, it is unlikely that natural learner regulatory processes will enable learner-centric instruction to realize its potential of facilitating the development of employee adaptability

Interventions designed to increase the accuracy of learner perceptions of the complexity of the task to be learned and their own capability may promote learning outcomes

the impetus for the present paper is to provide a dynamic view of self-regulated learning that reconciles the promises and potential pitfalls of learner-controlled, employee-centric approaches to training and development. The DEEL model is unique in a number of ways. For instance, DEEL is one of the only theories of self-regulated learning that explicitly addresses how changes in the way learners perceive themselves and their environment underlie the process of developing knowledge and skill capacity, which encompasses specific training programs as well as the broader job, organizational, and career contexts. A central idea underlying DEEL is that changes in perceptions can enable efficient learning. However, DEEL also maintains that common perceptual biases conspire to impede learning effectiveness. In Table 2, we list and describe four unique insights that emerge from this perspective and provide example implications of what these insights could mean for future research and practice within the training and development literature. To be clear, we do not view the DEEL model as a replacement for existing models of active learning (e.g., Bell & Kozlowski, 2010; Molloy & Noe, 2010; Sitzmann & Weinhardt, 2015), nor do we see it as predominantly contradictory. Rather, we view DEEL as a means by which existing active learning theory and practice in training and development contexts can be better leveraged. As a means of reconciling a conflict between proceduralized and discovery-based learning approaches, active learning has emerged over the last couple of decades as one of the more prominent approaches to training. Active learning theory is based on a constructivist perspective (Bell & Kozlowski, 2010), which argues that learning is primarily an inductive process in which learners explore training material and infer rules, principles, and strategies governing effective application (Bruner, 1961). Consequently, active-learning theory is thought to be particularly well-suited to the ever-increasing complexity in today's work world and need for adaptability (Bell & Kozlowski, 2008, 2010; Salas, Tannenbaum, Kraiger, & Smith-Jentsch, 2012). Although exploration through extensive hands-on practice with limited “step-by-step” or “how to” instructions is fundamental to active learning, active-learning approaches also involve the incorporation of instructional elements to support trainees' self-regulation (e.g., mastery goal instructions, adaptive guidance, and error management training; Keith & Wolff, 2015). In presenting DEEL, we have argued that the pitfalls of learner-controlled approaches involve inaccurate estimates of information-knowledge gaps and consequently poor decisions regarding the overall amount of effort needed and how such effort should be apportioned to specific learning strategies. With these pitfalls in mind, we believe that specific design elements commonly advocated to support trainee self-regulation can be better understood and leveraged. 6.1. Effective learning techniques Although we are unaware of any research that has directly examined an interplay between exploration and exploitation behaviors in a training context, we believe the literature on effective learning techniques provides preliminary support for the importance of such an interplay, and thus should be considered in the design of active-learning environments as a means of supporting trainee selfregulation. Empirical research shows that certain practice and study strategies benefit learning more so than others and that learners (teachers, trainers, and coaches as well) tend to misperceive less effective strategies as effective and vice versa (Dunlosky, Rawson, Marsh, Nathan, & Willingham, 2013; Soderstrom & Bjork, 2015). More effective strategies include practice testing, distributed practice and interleaved practice, variable practice, elaborative interrogation, and self-explanation, with practice testing and distributed and interleaved practice receiving the most empirical attention and yielding robust effects across a variety of learners, 16

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criterion tasks, and contexts (Dunlosky et al., 2013). Conversely, highlighting (and underlining), summarizing, rereading, and massed practice are popular yet less effective techniques (Dunlosky et al., 2013). Although explanations for the relative effectiveness of these learning strategies are commonly discussed and debated in terms of cognitive theory (and rightfully so) viewing these strategies from an exploration-exploitation perspective can help provide an accompanying behavioral component. In general, learning strategies that engage learners in a mix of exploration and exploitation behaviors yield more effective learning, while learning strategies that lack engagement in exploration and exploitation or overemphasize one at the expense of the other are less effective. We expand on these issues using practice testing, and distributed practice and interleaved practice as examples. In contrast to commonly used yet less effective strategies of highlighting, summarizing, and rereading, practice testing (compared to additional time rereading) compels learners to translate knowledge discovered into performance, and consequently involves exploring various task and solution strategies. Benefits to learning can occur directly from practice testing itself without further opportunities for study and practice (Dunlosky et al., 2013). Such direct effects show the importance of initially exploring task and solution strategies to learning. Practice testing can also indirectly benefit learning due to its influence on subsequent study and practice behaviors. These indirect effects reflect the importance of opportunities to further explore to-be-learned material as a result of how the testing informs information-knowledge gaps. With the addition of objective feedback regarding correct responses, constructive information about information-knowledge gaps is provided, which in turn helps learners make subsequent decisions about what to explore or exploit. Similarly, repeated testing with feedback helps prevent the occurrence of repeated errors and can be especially useful for correcting errors that are made with high confidence (Dunlosky et al., 2013). As a result, feedback regarding correct responses helps prevent premature settling on suboptimal task and solution strategies and promotes an efficient and effective transition from exploration to exploitation. Greater benefits occur if practice testing is distributed across multiple if not many sessions rather than massing the time in a single or fewer sessions. Because memories of what was previously discovered and practiced are not perfectly accurate, with more distributed practice there is greater potential for the discovery of new information and strategies. In turn, learners are likely to recycle through the process of exploration-exploitation switches in a way that not only leads to greater retention but also leads to a better understanding of the nuances of a given task domain and thus greater adaptive capacity. The processes by which interleaved practice benefits learning are similar to those for distributed practice given that with interleaved practice learners necessarily go back and forth across multiple content domains (e.g., training modules, task components) rather than in a blocked sequence of completing one domain before moving on to another (Dunlosky et al., 2013; Soderstrom & Bjork, 2015). Thus, like distributed practice, interleaved practice involves repeated exposure to information-knowledge gaps and greater exploration and oscillation between exploration and exploitation behaviors. As highlighted with the use of practice testing, comparing the various learning techniques (e.g., variable practice, elaborative interrogation, and self-explanation) in terms of exploration and exploitation behaviors provides insight into how to combine the techniques in ways to better support learner-controlled training programs. The general intent would not simply be to induce greater learning-oriented effort or exploration in general, but rather it would be to encourage sustained exploration in the context of more oscillation between exploration and exploitation behaviors. More broadly put, the intent would be to facilitate efficient yet effective learning by optimizing the exploration-exploitation transition. Certainly advice can be given to trainees to devote less time to generic highlighting, reviewing, and summarizing activities and more time to self-testing. Similarly, trainees can be given advice on how to distribute and interleave their practice and study time as well as how to engage a wide variety of material and task strategies. In particular, we posit that combining practice testing with interleaved and variable practice would optimize the learning process. While interleaved and variable practice prompt revisiting the learning process and engaging in a variety of viable task strategies, incorporating iterations of practice testing compels trainees to maximize their performance on occasion. Thus, this combination of techniques likely exposes information-knowledge gaps in a way that promotes more sustained exploration and efficient transitions to exploitation. 6.2. Active-learning instructional elements The interplay between exploration and exploitation strategies provides a behavioral basis for understanding the beneficial effects of the instructional elements advocated in active-learning training. As reviewed by Keith and Wolff (2015), examples of instructional elements that support self-regulation include mastery instructions, error management training, emotion-control strategies, adaptive guidance, and metacognitive prompts. Of these instructional elements, we see mastery instructions and adaptive guidance as most likely to sustain exploration as well as an efficient yet effective interplay between exploration and exploitation behaviors. Mastery instructions (and learning goals) ask learners to focus their learning efforts on discovering and understanding task information and task strategies, which in turn promote and sustain exploration (Kozlowski & Bell, 2006). Performance instructions (and performance goals) push learners to achieve objective performance outcomes, and promote an erroneously rapid transition to exploitation as learners look for quick, if not simple, solutions for handling task demands (Locke & Latham, 1990). The connection between mastery instructions and the development of knowledge and skill capacity at the within-person level seems fairly straightforward as increases in a mastery focus are akin to increases in novelty motives. As such, an increased focus on mastery goals stimulates information-knowledge gaps and consequently prompts learners to continue exploring task demands and strategies for addressing these demands (Yeo, Loft, Xiao, & Kiewitz, 2009). The role of a performance focus at the within-person level is more nuanced. Performance goals can draw needed attentional resources away from task demands to social comparisons and concerns regarding ability, especially when large goal-performance discrepancies occur. However, a performance focus also encourages learners to seek performance improvements (Yeo et al., 2009). As 17

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such, we see promise in coupling mastery and performance foci. Nonetheless, how mastery and performance foci are coupled should be considered in relation to learners' skill levels with the benefits of a performance focus likely occurring when it is emphasized later in training (Kanfer & Ackerman, 1989; Latham, Seijts, & Crim, 2008). The broader point that we advance is that mastery instructions might be more effective if they encourage learners to appropriately balance exploration with exploitation strategies over the course of learning. With this broader point in mind, we see adaptive guidance as particularly promising. Although not explicitly characterized in terms of the exploration-exploitation distinction, we contend that empirical examples of adaptive guidance (Bell & Kozlowski, 2002) help learners manage exploration-exploitation tradeoffs by sustaining overall levels of exploration in the context of specific explorationexploitation switches. Adaptive guidance entails tailoring learning goals to meet the needs of individual learners as they progress. With adaptive guidance, help is given to learners on where they should specifically direct their effort as they accomplish certain learning goals. As learners discover and begin to exploit certain task strategies, new learning goals are provided to essentially help learners decide what to explore next. As such, learners continue to exploit strategies for some task components while exploring new strategies in other components (Gopher et al., 1989). This process could iterate to facilitate exploration-exploitation switches across the array of task components. We posit that such iterations sustain learning effort as contractions to the information-knowledge gap due to increases in competence beliefs are offset as new learning goals stimulate novelty motives. Thus, adaptive guidance facilitates efficient yet effective learning by optimizing the exploration-exploitation transition. Likewise, we believe it would also be useful to consider how other active-learning instructional elements such as error management training, metacognitive prompts, and emotion-control strategies influence exploration-exploitation behaviors. However, it is not as clear to us if these other techniques sustain exploration as much as they promote an efficient transition to exploitation. Accordingly, we call for researchers and training designers to consider how active-learning instructional elements effectively expose information-knowledge gaps and promote more sustained exploration with an efficient transition to exploitation. We also encourage researchers and training designers to consider how to combine active-learning instructional elements with the aforementioned empirically supported learning techniques. For example, combining mastery instructions or adaptive guidance with bouts of distributed testing later in training could be especially worthwhile in promoting both analogical and adaptive capacity. 7. Boundary conditions and extensions The purpose of the DEEL model is to describe how a simple self-regulated learning mechanism can enable learners to respond to and resolve exploration-exploitation tradeoffs in modern, learner-centric, dynamic training and development contexts. As noted above, we do not intend for DEEL to replace existing models (e.g., Bell & Kozlowski, 2010; Molloy & Noe, 2010; Sitzmann & Weinhardt, 2015) but instead hope that it can supplement existing theory and research in this area. Given this scope, there are important predictors of skill acquisition and learning not included in our model. In this section, we outline boundary conditions of the DEEL model and offer suggestions for future model development. To start, a key assumption underlying the DEEL model is that learner decisions are generally shaped by logic and reason, even when this logic and reason is clouded by bias. It should be noted that this assumption is not unique to DEEL. Nearly all prominent theories of learner motivation make similar assumptions because adopting the belief that learner behavior is guided by rational decision making allows for more avenues for understanding (and potentially correcting) patterns of learner behavior than the alternative. Nerveless, we acknowledge that such assumptions can be limiting in that they do not directly account for the possibility that instances of wholly irrational behavior can have an important impact on the learning process. That being said, the notion that irrationality can play a non-trivial role in the learning process is not entirely incompatible with the DEEL model. For instance, the model allows for the influence of factors that transcend pure rationality (e.g., learner bias) and there is room for theoretical development identifying other ways in which irrationality influences the learning process. Future model development should focus on generating a more comprehensive list of biases and other forms of seemingly irrational behavior that influence learner behavior and explicitly identifying the way that they influence the learning process. Toward this end, DEEL can provide a framework through which seemingly irrational behavior can be interpreted and understood. Second, the primary purpose of DEEL is to describe a generalizable process that applies to a wide range of learners across a variety of learning contexts. As such, when introducing the model, there is no mention of individual differences such as cognitive ability, conscientiousness, goal orientation, locus of control, and trait anxiety in the learning process (Chen, Gully, Whiteman, & Kilcullen, 2000; Colquitt, Lepine, & Noe, 2000). To be clear, we do not believe that these differences do not exist or that they have no influence on model functioning. Rather, the model is designed to be easily expandable such that it offers a number of potential explanatory mechanisms for researchers interested in understanding how individual differences influence self-regulated learning which opens up potentially fruitful avenues for future research. Third, a key assumption of the DEEL model is that certain characteristics of the learning environment such as the qualitative amount of novelty in the learning environment (e.g., high versus low), the rate of skill acquisition that can be expected (e.g., fast versus slow), and the general stability of the information to be learned (e.g., stable versus fluctuating) are knowable and predictable. In the real world, macro- and meso-level factors such as strategic training initiatives, training culture, and organizational support can enhance or undermine micro-level regulatory processes in ways the model is currently not designed to capture (Sitzmann & Weinhardt, 2015). Furthermore, the model is not well suited for explaining behavior in more motivationally complex contexts such as instances where multiple goals must compete for limited time or attentional resources (Vancouver, Weinhardt, & Schmidt, 2010). As such, caution should be exercised when attempting to develop predictions from DEEL in motivationally complex or ill-defined learning situations. Nevertheless, as was the case for individual differences, the model is designed to be easily expandable to account 18

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for the effect of exogenous situational factors and multiple goal conflicts on model functioning. In general, we believe the DEEL model can be applied to better understand the effects of organizational climate and culture variables on both training transfer and development activities by considering how these factors might influence employees' subsequent exploration and exploitation behaviors. A focus on exploration-exploitation tradeoffs may also help address the mixed results often found in these literatures (Kraiger & Cavanagh, 2015; London & Mone, 2015). Via the DEEL model, we can develop a better understanding of the potential positive and negative effects of various feedback and development interventions (Hattie & Timperley, 2007; Kluger & Denisi, 1998; Shute, 2008) by modeling and then testing the predicted effect of specific variables on mechanisms such as bias in learner novelty motives, bias in competence beliefs, overall degree of learning effort, and resolution of the explorationexploitation tradeoff through strategy selection. Moreover, questions concerning the effectiveness of specific interventions and other exogenous variables should address whether exploration and exploitation behaviors are appropriate in relation to both employee and organizational goals as the tradeoff between the two can be viewed quite differently from different perspectives. Thus, we believe that extending DEEL to include macro- and meso-level factors could be useful in addressing a variety of training and development issues. Finally, although some internal validation is provided in this paper and in the Supplementary Materials, this is only the first step in computational model validation (Naylor & Finger, 1967; Sargent, 2013). Additional work is needed to increase confidence in the model's explanatory power across a wide range of learning contexts. Given that the DEEL model necessarily shows the learning process to be dynamic, it is important that future training and development research incorporates longitudinal designs. In particular, we regard research that models growth while examining interactions between exogenous variables and within-person changes in selfregulatory variables as particularly worthwhile (e.g., Sitzmann & Ely, 2010).

8. Conclusions Training and development in modern organizations is inherently a dynamic, complex, and largely learner-centric process in which learners must balance the allocation of resources devoted to the exploration of new possibilities and the exploitation of old certainties. However, existing theories often struggle to account for these key characteristics of the learning process. In this paper, we examine the implications of the exploration-exploitation tradeoff for learning in modern organizations before presenting the DEEL model as a way to advance work in this area. The primary advantage of DEEL is that it can be used to understand how, when, and why learner behavior changes throughout the learning process and why self-regulatory processes sometimes fail. Collectively, the conceptualization of self-regulated learning advanced here is an important step toward advancing an understanding of the learning process as it occurs in modern organizations that is more closely aligned with the iterative, dynamic nature of learning.

Appendix A Adaptive capacity = INTEG ((Increased breadth in understanding + "Depth/breadth synergy") * (Rate of knowledge and skill acquisition / 100), Initial knowledge and skill / 3) Analogical capacity = INTEG ((Increased depth in understanding + "Depth/breadth synergy") * (Rate of knowledge and skill acquisition / 100), Initial knowledge and skill / 3) Competence beliefs = Performance scores + Competence bias Competence bias = 0 "Depth/breadth synergy" = (Exploration * Exploitation) Environ change = Delay fixed(15 * Environmental change, 100, 0) Environmental change = 1 Exploitation=Learning effort * (1 – Strategy tradeoff) Exploration = Learning effort * (Strategy tradeoff) Final time = 200 Increased breadth in understanding = Exploration Increased depth in understanding = Exploitation "Information-knowledge gap" = If then else(Novelty motives > Competence beliefs, Novelty motives – Competence beliefs, 0) Initial competence goals = 50 Initial knowledge and skill = 10 Initial time = 0 Learning effort = ("Information-knowledge gap" / 10) * Motivation to learn Motivation to learn = 1 Novelty motives = Initial competence goals – Novelty recognition bias + (Competence beliefs * 0.5) + Environ change Novelty recognition bias = 0 Performance scores = Adaptive capacity + Analogical capacity – Environ change Rate of knowledge and skill acquisition = 1 – Environmental change * 0.5 Saveper = Time step Strategy tradeoff = "Information-knowledge gap" / 100 Time step = 1 19

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