Choice Learning


Choice Theory


Helping learners to live a “good life”

Principal Metaphors

  • Knowledge is … responsible participation
  • Knowing is … self-directed action
  • Learner is … an agent (individual)
  • Learning is … developing agency
  • Teaching is … empowering learners




Choice Learning begins with the assumption that one’s decisions and behaviors are oriented by a desire for a “good life.” The learner is thus seen as a self-directing individual who seeks to fulfill basic life needs – including physical needs that are encoded in the genome; survival needs having to do with nutrition, shelter, and security; and psychological needs around such matters as caring, acceptance, efficacy, pleasure, choice, and curiosity. Related discourses include:
  • Flexible Learning (Mare Gosper, David Rich, 1990s) – any model of formal education organized around a commitment to enable learners to personalize their experiences through choice of pace, mode, and location of learning
  • Student-Directed Teaching (Don Green, Anne Green, 1990s) – a model of formal education that emphasizes student choice, control, ownership and accountability

Different structures of choice-making are associated with different levels of cognitive demand and can lead to significantly different choices:

  • Sequential Choice – making one selection (of a course, a book, a meal, etc.) at a time, and completing that selection before making the next
  • Simultaneous Choice – making several selections (of courses, books, meals, etc.) at the same time, to be addressed over a prolonged period
Choice Learning is commonly used in business and organizational contexts, where the governing agent or agency can operate from one of two sensibilities:
  • Boss Management (William Glasser, 1990s) – the setting of tasks and standards by an external agent/agency, usually without participant input and often in a coercive manner
  • Lead Management (William Glasser, 1990s) – a collaborative, dialogue-based approach to defining tasks, standards, and roles, leading to clear-but-negotiable expectations that are managed and monitored by participants
Choice Learning (or Choice Theory) should not be confused with Decision Theory (or Theory of Choice). Because they are sometimes conflated, coupled with the fact that Decision Theory has a growing presence in the education literature, we offer the following preliminary details on this major domain of inquiry:
  • Decision Theory (Theory of Choice) – the use of probability and mathematical modeling to better understand the dynamics and consequences of human decision making. Subdiscourses include:
    • Decision Sciences – a domain of inquiry concerned specifically with the development of analytical, data-driven techniques for making decisions at individual, collective, and societal levels. Considerations may include risk analysis, digital modeling, and statistical information.
Three categories of Decision Theory have been  identified:
  • Descriptive Decision Theory – a branch of Decision Theory that focuses on how individuals actually make their decisions. Associated discourses include:
    • Behavioral Decision Theory (Behavioral Decision-Making Theory) (Paul Slovic, 1960s) – an umbrella category that reaches across perspectives aimed at describing how one actually makes decisions and judgments (vs., e.g., perspectives aimed at prescribing how one should make decisions)
    • Behavioral Reasoning Theory (James Westaby, 2000s) – a perspective focused on one’s reasons for decisions, which are seen not as emerging from beliefs, motives, intentions, and actions, but as connecting and influencing them
    • Garbage Can Theory (Anarchic View of Decision Making; Garbage Can ModelGarbage Can Process) (Michael Cohen, James March, Johan Olsen, 1970s) – a model of decision-making within organized anarchies (see Organized Anarchy Theory, under Discourses on Learning Collectives), where the process arises in a chaotic mix of disordered problems, disconnected solutions, and self-interested participants (thus the metaphor of “garbage can”), setting the stage for choice/decision opportunities that are largely defined by chance and opportunism
    • Macrocognition (European Cognitive Systems Engineering, 1980s) – a descriptive term, applied to decision-making associated with real-life matters and encountered in natural (i.e., non-artificial) settings by experts. The notion is founded on the conviction that artificial problems or controlled environments are engaged differently. (Contrast: Microcognition, below.)
    • Microcognition – effectively, the opposite of Macrocognition (see above), involving artificial situations, nonexperts, ambiguous goals, and/or low-stakes matters
    • Naturalistic Decision-Making – a framework developed to study decision-making in high-pressure, real-world situations
    • Recognition-Primed Decision – a perspective on decision-making in complex contexts, such as in emergency medical wards, among stock market traders, and in fire-fighting situations. Among other elements, the model emphasizes extensive experience among decision-makers.
  • Normative Decision Theory – a branch of Decision Theory founded on the theoretical constructs of ideal decision-makers and optimal decisions. Associated discourses include:
    • Logic of Appropriateness (James March, Johan Olsen, 1990s) – the aspect of decision-making based on what feels natural or right – that is, on established norms – as opposed to being based on, for example, rational thought or cost–benefit analysis.
    • Subjective Expected Utility – based on the assumption of a rational actor, the hypothetical probability associated with a possible choice among known alternatives
    • Utility Theory – a subcategory of Normative Choice Theory that includes perspectives focusing on rational or optimal choice behavior
  • Prescriptive Decision Theory – a branch of Decision Theory that focuses on developing models to explain observed behaviors. Examples of Prescriptive Decision Theory include:
    • Behaviorial Economics (Daniel Kahneman, Amos Twersky, 1980s) – an interdisciplinary field originally concerned with making sense of economic inconsistencies and anomalies by focusing on the influence of individuals’ limited (subjective and situational) knowledge on societal functioning. In recent decades, research based in Behavioral Economics has been highly influential in Cognitive Science. Regarding other discourses on this site, Behavioral Economics draws on and contributes to Modes of Reasoning (especially Bounded Rationality) and Cognitive Bias. Other associated discourses include:
    • Cynefin Framework (Dave Snowden, 1990s) – a decision-making framework comprising five distinct domains/categories of experience, along with the defining dynamics, rules, and activities of four of them. The model is intended to support efforts to understand and transform situations. The following diagram, a composite of several online images, sums up key aspects:
    • Regret Theory (Graham Loomes, Robert Sugden, 1980s) – a discourse on decision-making that suggests one’s habits and strategies for choosing tend to be influenced by regrets associated with prior suboptimal decisions
    • Regulatory Focus Theory (Tory Higgins, 1990s) – a perspective on motivation and action that suggests that one’s decision-making leans toward one of two orientations: (1) promotion-focused self-regulation (oriented toward the pleasures associated with gains and accomplishments) or (2) prevention-focused self-regulation (oriented toward security and associated with avoidance of losses and failures). A main implication is that educational, communication, and management efforts should be consistent with one’s dominant orientation.
    • Risk-as-Feelings Theory (George Loewenstein, 2000s) – a perspective on decision-making in risk-bearing situations that foregrounds the role of emotion (esp. negative emotions, such as anxiety)


Choice Learning might be interpreted a mash-up of several currently popular topics of discussion that are clustered among Personal Agency Discourses and Motivation Theories. Its contribution is more pragmatic than theoretical, as it offers direct, step-by-step advice to educators on how to help learners develop senses of control over their lives while growing into responsible and productive members of society.

Authors and/or Prominent Influences

William Glasser

Status as a Theory of Learning

Choice Learning offers no new or innovative insights into how humans learn.

Status as a Theory of Teaching

Choice Learning is properly understood as an educational attitude that is principally concerned with supporting the development of personal agency among learners. In that regard, it is properly described as a discourse on teaching.

Status as a Scientific Theory

Choice Learning is more a philosophy of education than a perspective of learning. While arguably a sound emphasis for modern schooling, it meets none of our criteria for a scientific theory.


  • Behavioral Decision Theory (Behavioral Decision-Making Theory)
  • Behaviorial Economics
  • Behavioral Reasoning Theory
  • Boss Management
  • Decision Sciences
  • Decision Theory (Theory of Choice)
  • Descriptive Decision Theory
  • Flexible Learning
  • Garbage Can Theory (Anarchic View of Decision Making; Garbage Can Model; Garbage Can Process)
  • Lead Management
  • Logic of Appropriateness
  • Macrocognition
  • Microcognition
  • Naturalistic Decision-Making
  • Normative Decision Theory
  • Prescriptive Decision Theory
  • Prospect Theory
  • Recognition-Primed Decision
  • Regret Theory
  • Regulatory Focus Theory
  • Risk-as-Feelings Theory
  • Sequential Choice
  • Simultaneous Choice
  • Student-Directed Teaching
  • Subjective Expected Utility
  • Utility Theory

Map Location

Please cite this article as:
Davis, B., & Francis, K. (2023). “Choice Learning” in Discourses on Learning in Education.

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