Make-Believe: Problem-Based Learning¶

Strategy · All strategies
Evidence · 4 claims (4 for) · 9 studies (5 review, 2 quant-synthesis, 1 causal, 1 theoretical), q2–q4 · 2 of 9 report an effect size

Description¶

Make-believe problem-based learning places learners inside a simulated real-world situation — a role, a scenario, a stake — and asks them to solve a complex, open-ended problem collaboratively, as practitioners in that world would. The "make-believe" frame (a kidnapped teacher, a restaurant the class must launch, a climate summit) gives authentic problems a narrative container that makes them emotionally engaging and manageable within a classroom. Learners work alongside expert guidance, identify what they need to know, and develop solutions that are evaluated against real-world criteria rather than a single correct answer.

Design Implications¶

Problem-based scenarios leverage Active Learning: learners constructing solutions to authentic problems outperform passive instruction on application and skill measures, though effects on basic knowledge acquisition are more mixed Active learning improves exam performance. [+S]. Scenario-based and case formats improve exam performance when problems are well-structured around target concepts Case-based learning improves exam performance. [+M]. The make-believe frame raises task value — learners persist more when problems connect to real-world stakes they care about Task value increases motivation and engagement. [+M] — and successful navigation of a challenging scenario builds self-efficacy that supports persistence Self-efficacy predicts academic persistence. [+M].

Context¶

Requirements¶

• A rich, plausible scenario with authentic stakes and a role for each learner (Problem Presentation or Case Studies)
• Access to resources learners can consult as they identify knowledge gaps (Assigned Readings, expert interviews, datasets)
• Ongoing facilitation: tutors question, probe, and model expert reasoning rather than lecture (Coaching, Cognitive Apprenticeship)
• Collaborative structures with defined roles so group work distributes expertise (Collaboration)
• A culminating product or performance evaluated with authentic criteria (Application of Knowledge, Assessment)

Constraints¶

• Minimally guided versions overload novices: without prior knowledge or strong scaffolding, learners flounder in unstructured search and learn less than with guided instruction [~S] — the expertise-reversal pattern means the approach that helps advanced learners can hurt beginners
• The narrative frame can crowd out the content: learners remember the story of the kidnapped teacher but not the forensic science, unless debriefs explicitly extract the target concepts
• Resource- and time-intensive; poorly designed scenarios collapse into unfocused play
• Group free-riding is common without individual accountability structures

Implementation Variability¶

• Structured PBL: tutor-posed problems with scaffolded knowledge-gathering cycles (medical school model) — appropriate for novices
• Open make-believe projects: learner-driven scenarios with light facilitation — appropriate for learners with prior knowledge
• Hybrid: direct instruction on core concepts first, then a make-believe problem for application and transfer
• Scale varies from a single lesson (a one-hour mystery) to a semester-long simulation (running a mock company)

Target Learners¶

• Learners with sufficient prior knowledge to navigate open-ended problems productively; novices need more scaffolding or prior direct instruction [~S]
• Middle school through higher education; the narrative frame is especially motivating for adolescents
• Learners building professional identities (pre-service teachers, medical, business students) who benefit from practicing roles before enacting them for real

Target Learning Goals¶

• Problem-solving and diagnostic reasoning in ill-structured domains
• Collaboration and communication within authentic role structures
• Motivation and engagement through task value and authentic stakes Task value increases motivation and engagement. [+M]
• Transfer: applying concepts to novel, messy situations rather than recalling them

Instructions¶

1. Design the scenario: identify the big ideas of the unit, then find a real-world situation where those ideas matter; wrap them in a narrative with roles and stakes.
2. Launch with a Problem Presentation: present the scenario dramatically and concretely — artifacts, news, a visitor — so learners perceive a genuine problem worth solving.
3. Support knowledge identification: have learners list what they know, need to know, and how they will find out; provide Assigned Readings and expert access rather than answers.
4. Facilitate collaborative solution development: use Coaching and Socratic Questioning to probe reasoning; keep Collaboration structured with roles and individual accountability.
5. Require Application of Knowledge: solutions must be artifacts or performances judged by real-world criteria (budget must balance, menu must be feasible).
6. Debrief: explicitly extract the target concepts from the story so learning outlives the make-believe frame; connect solutions back to the big ideas.

Related Strategies¶

• Case-Based Learning — the same authentic-problem core without the narrative role-play frame; cases are typically past-tense, make-believe PBL is present-tense
• Simulation — a more systematized version where the make-believe world has explicit rules and feedback loops
• Project-Based Learning — shares authentic products and sustained inquiry, but centers on creating an artifact rather than solving a posed problem
• Role-Play — the enactment technique that supplies the make-believe frame
• Culminate engineering learning tasks in action within real contexts through industry, government, and NGO partnerships

Examples¶

• Problem-based medical education (McMaster, Maastricht) — the canonical model: small groups diagnose simulated patients, identify learning needs, and research them between tutorials (Hmelo-Silver, 2004).
• A high school class designs a restaurant — budget, menu, and brand developed collaboratively, with math, economics, and writing embedded in the make-believe venture.
• Forensics mystery unit — learners arrive to find the teacher "kidnapped" and must collect fingerprints and analyze clues, embedding scientific method in a narrative.
• Model UN / climate summit simulations — learners take national roles and negotiate solutions to global warming, practicing argumentation under authentic constraints.

Key Sources¶

• Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16(3), 235–266. doi:10.1023/B:EDPR.0000034022.16470.f3
• Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86. doi:10.1207/s15326985ep4102_1
• Strobel, J., & van Barneveld, A. (2009). When is PBL more effective? A meta-synthesis of meta-analyses comparing PBL to conventional classrooms. Interdisciplinary Journal of Problem-Based Learning, 3(1), 44–58. doi:10.7771/1541-5015.1046
• Walker, A., & Leary, H. (2009). A problem based learning meta analysis: Differences across problem types, implementation types, disciplines, and assessment levels. Interdisciplinary Journal of Problem-Based Learning, 3(1), 12–43. doi:10.7771/1541-5015.1061
• Dolmans, D. H. J. M., De Grave, W., Wolfhagen, I. H. A. P., & van der Vleuten, C. P. M. (2005). Problem-based learning: Future challenges for educational practice and research. Medical Education, 39(7), 732–741. doi:10.1111/j.1365-2929.2005.02205.x