Learner-Centric Approach¶

Strategy · All strategies
Evidence · 4 claims (4 for) · 10 studies (5 review, 3 quant-synthesis, 2 theoretical), q1–q4 · 3 of 10 report an effect size

Description¶

A learner-centric approach positions the learner, rather than the instructor or a fixed curriculum sequence, as the primary decision-maker in learning. Problems are presented as open-ended — there are several viable solution paths, each leading to different outcomes — and learners must analyze the problem, select and justify an approach, and live with the consequences. The instructor's role shifts from transmitter to facilitator, providing resources, Coaching, and feedback as learners construct understanding through Active Learning.

Design Implications¶

Learner-centric designs draw on constructivist and self-determination research: autonomy and relevance increase intrinsic motivation and engagement Autonomy supports intrinsic motivation. [+S], and active participation improves exam performance relative to lecture alone Active learning improves exam performance. [+S]. However, the approach works only when learners have sufficient prior knowledge to navigate open-endedness; for novices, unguided exploration imposes extraneous load and can leave misconceptions uncorrected Minimal-guidance instruction is less effective for novices than explicit instruction. [~S]. The design problem is therefore not whether to center the learner but how much structure to provide at each point.

Context¶

Requirements¶

• An open-ended problem with multiple defensible solution paths and meaningful consequences for each
• Access to resources learners can consult independently (reference materials, data, experts)
• A facilitator who monitors progress and intervenes strategically rather than lecturing continuously
• A supportive environment that permits productive failure and treats wrong turns as data

Constraints¶

• Novices without domain schemas flounder in fully open-ended tasks; guidance must be scaffolded in Minimal-guidance instruction is less effective for novices than explicit instruction. [-S]
• Open-ended problems take more instructional time and smaller facilitator-to-learner ratios than direct instruction
• Without structured debriefs, learners may complete activities without extracting transferable principles
• Assessment is harder: divergent solutions resist rubric standardization and can mask gaps in foundational knowledge

Implementation Variability¶

• Problem-based learning: open problem precedes instruction; learners identify what they need to learn
• Case-based formats: a bounded real case constrains the space while preserving choice (Case-Based Learning)
• Choice within structure: learners select topics, pathways, or deliverables inside a fixed framework — a middle path that preserves autonomy without abandoning sequencing
• Self-directed study: learners set their own goals and pace, appropriate mainly for experienced learners

Target Learners¶

• Learners with moderate-to-strong prior knowledge who can evaluate options without drowning in search Minimal-guidance instruction is less effective for novices than explicit instruction. [~M]
• Adult and professional learners whose experience supplies the context open problems require
• Learners with low self-efficacy benefit from early, achievable choices that build confidence Self-efficacy predicts academic persistence. [+M]
• Pure novices need more structure initially; learner-centricity should be faded in rather than started with

Target Learning Goals¶

• Problem-solving and adaptive expertise: choosing among approaches, not just executing one
• Transfer: applying knowledge to ill-structured, real-world situations
• Self-regulated learning: planning, monitoring, and evaluating one's own approach
• Motivation and engagement: tasks perceived as relevant and autonomous Task value increases motivation and engagement. [+M]

Instructions¶

1. Present an authentic open-ended problem with visible stakes and multiple viable outcomes (Case Studies)
2. Activate relevant prior knowledge so learners enter the problem with something to build on (Activation)
3. Have learners analyze the problem and commit to a solution path, articulating their reasoning (Articulation)
4. Support exploration with facilitator coaching and peer input rather than answers (Coaching, Collaboration)
5. Debrief outcomes — both successes and failures — to consolidate principles learners can transfer (Class Discussion)
6. Assess both the solution quality and the reasoning process (Assessment)

Related Strategies¶

• Contrast with instructor-centered sequencing: Direct Instruction — the structured alternative that better serves novices; the two are complements across a learning sequence, not rivals
• Flipped Classroom — frees contact time for learner-centric application by moving exposition outside class
• Cognitive Apprenticeship — a structured way to make learner exploration productive through modeling and fading
• Choose problems with multiple entry points to stimulate discussion of different solution approaches
• Teach AI through holistic, active, collaborative pedagogical strategies starting from real problem solving

Examples¶

• Problem-based learning in medical education (e.g., the Maastricht and McMaster curricula): small groups work through authentic patient cases, deciding what to investigate and which diagnoses to pursue, with a tutor facilitating rather than lecturing.
• Harvard Business School case method (Case-Based Learning): students must take a position on an open business problem and defend it against peers, with each defensible decision leading to different consequences.
• Project-based courses such as Buck Institute's PBLWorks model (https://www.pblworks.org): learners choose their approach to a real-world driving question with milestone checkpoints and rubrics.

Key Sources¶

• Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. PNAS, 111(23), 8410–8415. doi:10.1073/pnas.1319030111
• 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
• 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
• Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. doi:10.1037/0003-066X.55.1.68