Self-Directed Learning Is a Social Activity (and Not a Generalized Skill)

Self-Directed Learning Is a Social Activity (and Not a Generalized Skill)

David S. Porcaro
DOI: 10.4018/978-1-6684-6500-4.ch003
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Abstract

The perspective of self-directed and self-regulated learning as a generalized skill discounts the fact that a major driver of self-direction is an individual's motives centered on communities of practice. Effective design of learning experiences that foster agency, self-direction, and curiosity requires a clearer understanding of this relationship. Activity theory provides a helpful model for explaining the internal and external interactions of learning, both individually and within a community. Without this perspective, educators too often blame learners as failing to have self-regulation when in reality these learners are working under a different set of goals, roles, rules, or tools than those of the learning designer. Using this framework, this theoretical overview will demonstrate ways an immersive adult technology training academy helps support novices striving to enter technology-related careers, as they develop the identity, skills, and self-regulation of coding, data, and design professions.
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Introduction

This chapter’s exploration begins with the premise that self-directed learning is the ultimate goal of education. Ideally, adults set goals for their lifelong learning, and make choices to follow and evaluate the effectiveness of different learning pathways, surround themselves with communities of practice, connect to peers and mentors, and apply their learning in a variety of contexts that expand throughout their lives. Self-directed learning, then, is the agentic development and exercise of learning goals.

In essence, this self-direction is what separates adult learning from other forms of formal education, including primary and secondary education. For the most part, adults choose what and how they learn, and are motivated (sometimes for social or economic reasons) to pursue different learning and development paths (Knowles, 1975). Children, on the other hand, are often compelled to learn how to divide mixed numbers, recall dates of battles, verbalize the spelling of rare words, or other tasks that they did not choose, and neither have much use in most of their day-to-day interactions or are connected to their own short and long-term learning or life goals.

Within the larger and more external concept of self-directed learning is the related subconcept of self-regulated learning (SRL; see Linkous, 2021; Neelen & Kirschner, 2020). Self-regulated learning includes the mechanisms that drive someone to start, persist or put in the mental effort (Clark & Saxberg, 2018) needed to learn and perform toward their goals.

Since the 1980s, researchers have been investigating how SRL leads to academic outcomes (Schunk & Greene, 2018b). Since that time, many educators have developed the perspective that SRL is a concept they have or don’t (see, Kirwan et al., 2014), and that can be measured with tools like LASSI (Weinstein et al., 2016), MSLQ (Pintrich & Others, 1991), etc. and that can be developed (or not). Through the subsequent generations, a more dynamic, two-way relationship between learning achievement and self-regulated learning has developed, leading to a better understanding of the more complex nature of SRL development.

While this dynamic and reciprocal relationship between SRL operations and achievement outcomes was a welcome development of the 2000s, this approach is insufficient in one major aspect. Self-directed learning is not a generalizable, self-standing skill that one has or does not have. It’s rather a highly community-, discipline-, and even context- dependent collection of constructs (Merrienboer, 1997). Much like the attention paid to grit, the more one looks for a single stable construct, the less there is to see (Credé et al., 2017).

SRL is an extremely complex subject, and much has been said about it. In fact, SRL is in essence an overarching theory of learning that actually encompasses many subsystems of learning, including motivation, cognition, behavior, affect, social interactions, performance and culture (see Panadero, 2017). It is not the intention of this chapter to recreate that work.

The reader who wants a recent and more in-depth overview of many of the concepts discussed here may refer to Schunk and Greene’s (2018) second edition of the Handbook of Self-Regulation of Learning and Performance (Schunk & Greene, 2018a). In an attempt for breadth, this chapter may leave many of the details of the key concepts, relationships, and theories underdeveloped or even unstated. This chapter builds off this strong framework of SRL to layer on the additional insights of Activity Theory (also referred to as Cultural-Historical Activity Theory; Engeström, 1999) to demonstrate how one can better understand how learners exercise agency to navigate complex community relationships and activity systems in their attempts to self-regulate their learning.

Key Terms in this Chapter

Roles: The expected place and behaviors of individuals within a community. These may be formalized and hierarchical, or informal and unstated. Individuals with more central roles in a community hold power to define and enforce community rules. Roles are constantly in flux in a community and an individual can act out different roles in different communities.

Community: An activity system of individual agents who share a common goal, such as a community of practice, a cultural or location-based community, or a sub-community within a field, practitioners, etc. For instance, a community of Python using data analysts in the Euro-American finance industry.

Motives: Unverbalized intentions and values of individual agents. Even within an individual, motives may conflict with each other, and longer-term goals may require deprioritizing immediate motives.

Rules: The norms and expected behaviors of a community or activity system, both formalized and spoken, or unexpressed. One of the major learning goals of communities is to enculturate individuals to the rules of a community, which are constantly in flux as individuals exercise agency and adapt or create goals, roles, and tools.

Operations: The automatic and internalized activities of individuals, often expressed as long-term memory. As operations become more automatic, they can become non-conscious.

Tools: Tools are mediating objects (e.g., physical or digital) designed to support efficiency in the behaviors/activities that contribute to shared goals. Tools that are non-physical, such as language, story, or symbols, are called signs. The introduction or alteration of tools can change the goals, roles and rules of a community.

Goals: The verbalized intentions of an individual agent. Communities are formed when individuals share goals with others in a community. While goals are relatively stable over time, they can change as objects expand through activity or competing motives are prioritized.

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