Problem-Based Learning

AKA

Problem-Based Instruction

Focus

Supporting learning through co-dependent, goal-oriented action among adult learners

Principal Metaphors

  • Knowledge is … scope of possible actions and interpretations
  • Knowing is … doing
  • Learner is … a collaborator (individual) and a problem-solving team (collective)
  • Learning is … developing understanding while application and exploration
  • Teaching is … supervising, facilitating, guiding

Originated

1960s

Synopsis

A type of Active Learning or Inquiry-Based Learning, Problem-Based Learning is a small-group-based classroom approach that is structured around problems that are typically, but not always, open-ended. The orienting goal of Problem-Based Learning is not to generate a solution, but to develop knowledge, communication skills, and collaborative competencies. That is, learning is seen to happen at both individual and group levels. While not limited to adult-education settings, it is was developed in and is most often encountered in professional schools. As might be expected, Problem-Based Learning is commonly associated with:
  • Problem Solving – a phrase that is used to label many different emphases and practices within discussions of learning and teaching. Most commonly, Problem Solving refers to engagement with non-routine exercises, which is seen to support Deep Learning (see Deep vs. Surface Learning) through contextualizing subject matter while requiring learners to think divergently. Opinions vary dramatically over what constitutes good teaching practice around Problem Solving, but empirical evidence points toward precision (and/or opportunities to seek clarity), nuanced scaffolding, instruction in decomposing complicated scenarios, tight linkages to current study, personal relevance, and social supports.
  • Problem-Solving Strategies – a frequent focus in Problem-Based Learning, comprising discrete approaches to grapple with problems. Specific Problem-Solving Strategies include:
    • Algorithm – a well-defined sequence of steps that can be used to generate a solution to a specific type of question (contrast: Heuristic)
    • Brainstorming – a (usually group-based) Problem-Solving Strategy that begins with generating possible solutions to a problem without judgment, after which it proceeds by weaning, testing, and honing suggestions. Specific strategies include:
      • Brainsteering (Kevin Coyne, Shawn Coyne, 2010s) – a Brainstorming strategy concerned with “inside the box” thinking by framing participation with well-defined contexts and issues
      • Gamestorming (Dave Gray, Sunni Brown, James Macanufo, 2010s) – a Brainstorming strategy that emphasized collaborative innovation by exploiting principles of Games and Learning
      • Reverse Brainstorming (unknown, 1970s) – a Brainstorming strategy focused on identifying how problems might be made worse, thus potentially leading to unconventional solutions through radically divergent thinking
      • Starbursting (unknown, 1990s)– a Brainstorming strategy aimed at understanding a problem more deeply by generating relevant questions about it (rather than focusing on solutions)
    • Combining Algorithms (Robert Gagné, 1980s) ­– as the name suggests, using multiple algorithms in combination to generate an appropriate solution
    • Drawing Analogies – seeking clues by extending knowledge and understandings of other, possibly similar or related, situations
    • Generate-and-Test (Guess-and-Test) – a Problem-Solving Strategy that revolves around proposing plausible solutions and then trying them out
    • Heuristic (Cognitive Heuristic) – an experience-based strategy for tackling a problem that typically leads to a solution, often with efficiency (contrast: Algorithm)
    • Means–Ends Analysis – a Problem-Solving Strategy that begins by identifying differences between current conditions and desired ends, and then unfolds as an iterative process of reduces those differences
    • Productive Thinking – holistic and patient pondering on a problem that often enables sudden insight
    • Working Backward – a Problem-Solving Strategy that revolves around parsing the ultimate goal into attainable subgoals. Associated constructs include:
      • Subgoal Labeling – the identification of a definable subset of steps in a larger procedure (e.g., “borrowing” when performing a multi-digit subtraction algorithm)
    • Working Forward (Hill Climbing) – a Problem-Solving Strategy that involves iterative steps, each aimed in the general direction of the ultimate goal, and after each of which one takes stock and makes necessary adjustments
  • Problem-Solving Theory (Allen Newell, Herbert Simon, 1970s) – a domain-defining examination of the nature of Problem Solving and its utility for understanding human cognition. For example, Newell and Simon formulated the concepts of Problem Spaces (see below), distinguished among some Problem Types (see below), and emphasized the use of Heuristics (see above).
  • Problem Space – a term intended to capture the many aspects of engaging with problems, including both those associated with the process (e.g., defining the problem, or checking solutions) and the situational supports made available. Associated constructs include:
    • Joint Problem Space (Jeremy Roschelle, Stephanie Danell Teasley, 1990s) – an elaboration of Problem Space for collaborative contexts, taking into account such aspects as social relations, disciplinary content, and temporality
Several ways of classifying problems have been developed, including David Jonassen's Problem Typology, which locates problems on a continuum from “well-defined” to “ill-defined”:
  • Well-Defined Problem (Well-Structured Problem) – a problem statement that explicitly includes all the information that is necessary to arrive at a solution. Some definitions of a Well-Defined Problem also include the requirement of an established Algorithm (see above).
  • Ill-Defined Problem (Ill-Structured Problem) ­– a problem for that is lacking one or more of the elements of a Well-Defined Problem, meaning that its goal is not clear, it lacks critical information to solve it, and/or it is not associated with an established algorithm
Examples, sequenced from more Well-Defined Problems to more Ill-Defined Problems, include:
  • Algorithmic Problems – situations required in the application of an already-learned algorithm, typically operating as a thinly disguised occasion for rote practice
  • Story Problems – mathematically or logically related elements that are embedded in a thin-but-plausible story structure
  • Puzzles – situations with a specific final states that require knowledge and ingenuity to attain – but that, typically, lend themselves to multiple solution strategies/sequences
  • Rule-Using Problems (Rule Induction Problems) – problems with both clear purposes and correct solutions, but with multiple acceptable paths to those solutions and/or multiple rules governing the solution process
  • Decision-Making Problems – situations that require one to compare and contrast advantages and disadvantages of alternate solutions
  • Troubleshooting Problems – situations that rely on (or that are aimed at developing) a multilayered conceptual model of a system in order to assess possible issues and propose likely causes and useful next steps
  • Policy Problems – situations that call for advice on governance that involve a range of stakeholders, who typically have competing perspectives and/or agendas
  • Design Problems – situations that call for a broad domain knowledge in the development or refinement of an artifact or process. Design Problems generally have multiple, very-varied solutions
  • Dilemmas – situations with complex social and/or ethical entanglements, often involving difficult-but-unavoidable economic, political, cultural, or other trade-offs
  • Wicked Problems – social or cultural problems that are difficult or impossible to solve, owing to vague definition, inadequate knowledge (i.e., incomplete or contradictory), divergent stakeholder opinions, insufficient resources (e.g., funds, time)and/or evolving circumstances
There are ranges of perspectives on what constitutes a good problem and how Problem-Solving Strategies emerge, and each has implications for Problem-Based Learning. Examples include:
  • Adaptive Strategy Choice Model (Strategy Choice Model) (Robert Siegler, 1990s) – a theory that suggests that one has multiple problem-solving strategies that evolve over time, driven principally by competition for use with one another
  • Collaborative Problem Solving (Laurie Nelson, 1990s) – a set of methods and guidelines aimed at the simultaneous development of sophisticated disciplinary knowledge, problem solving skills, critical thinking skills, and collaboration skills.
  • Fermi Problem (Fermi Estimate; Fermi Question; Fermi Quiz; Order Estimation; Order-of-Magnitude Estimate, Order-of-Magnitude Problem) (Enrico Fermi, 1930s) – a type of well-defined question that requires one to consider the broad contours of a complex situation and identify appropriate orders of magnitude (vs. accuracy of measurements) for multiple variables with little or no data. (A classic example is “How many piano tuners are there in Chicago?”)
  • Stage Theory of Strategy Development – an umbrella category that collects perspectives on problem-solving strategies that assume such strategies develop incrementally, with more efficient and effective strategies routinely replacing less useful ones
  • Worked Examples (Example-Based Learning) (John Sweller, 1980s) – a mode of teaching that involves the modeling of both formulation and solution of problems, typically more aimed at introducing or developing key disciplinary principles than at promoting procedural competence

Commentary

Problem-Based Learning was first developed in medical schools, where instructors could assume highly motivated students, intent on profound understandings and refined skill sets – and where competencies in accessing and keeping abreast with emerging insights are as important as solid groundings in established knowledge. Problem-Based Learning would thus seem very well fitted to some professions, but caution should be taken when seeking to generalize to other groups and subject matters. Even in ideal settings, commonly noted issues include time demands, unclear expectations, inadequate scaffolding, and limitations due to instructor knowledge.

Authors and/or Prominent Influences

John Dewey

Status as a Theory of Learning

Problem-Based Learning is not a theory of learning.

Status as a Theory of Teaching

Problem-Based Learning is a theory of teaching that has proven effective in some professional schools.

Status as a Scientific Theory

Problem-Based Learning is founded on scientific theories of learning. Proponents claim a significant body of evidence supporting claims that the approach, properly executed, is associated with higher achievement, more connected understandings, and improved attitudes.

Subdiscourses:

  • Adaptive Strategy Choice Model (Strategy Choice Model)
  • Algorithm
  • Algorithmic Problems
  • Brainsteering
  • Brainstorming
  • Collaborative Problem Solving
  • Combining Algorithms
  • Decision-Making Problems
  • Design Problems
  • Dilemmas
  • Drawing Analogies
  • Generate-and-Test (Guess-and-Test)
  • Fermi Problem (Fermi Estimate; Fermi Question; Fermi Quiz; Order Estimation; Order-of-Magnitude Estimate, Order-of-Magnitude Problem)
  • Gamestorming
  • Heuristic (Cognitive Heuristic; Heuristic Technique)
  • Ill-Defined Problem (Ill-Structured Problem)
  • Joint Problem Space
  • Means–Ends Analysis
  • Policy Problems
  • Problem Solving
  • Problem Space
  • Problem-Solving Strategies
  • Problem-Solving Theory
  • Productive Thinking
  • Puzzles
  • Reverse Brainstorming
  • Rule-Using Problems (Rule Induction Problems)
  • Stage Theory of Strategy Development
  • Starbursting
  • Story Problems
  • Subgoal Labeling
  • Troubleshooting Problems
  • Well-Defined Problem (Well-Structured Problem)
  • Wicked Problem
  • Worked Examples (Example-Based Learning)
  • Working Backward
  • Working Forward (Hill Climbing)

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Please cite this article as:
Davis, B., & Francis, K. (2024). “Problem-Based Learning” in Discourses on Learning in Education. https://learningdiscourses.com.


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