Relate Math to Learner Strengths¶

Strategy · All strategies
Evidence · 5 claims (4 for, 1 against) · 18 studies (10 causal, 4 review, 3 quant-synthesis, 1 theoretical), q1–q4 · 3 of 18 report an effect size

Description¶

This strategy connects mathematical concepts to learners' existing interests, hobbies, and areas of competence — sports statistics, music, design, gaming, finance — so that new math content arrives anchored in something the learner already knows and cares about. It is carried out by learning about learners, then selecting or adapting examples, problem contexts, and application tasks that draw on those strengths.

Design Implications¶

Connecting content to personal interest raises situational interest, which supports engagement and can develop into sustained individual interest over time Personal interest improves learning when the relevant context is genuinely familiar. [+M]. Personalized problem contexts have produced measurable learning gains in algebra when the contexts match students' actual interests, not just surface-level "real world" framing (Walkington, 2013) [+M]. The mechanism is partly motivational (relevance, Belonging) and partly cognitive: familiar contexts reduce the extraneous load of interpreting a problem, freeing working memory for the mathematics itself Chunking reduces working memory load. [+S].

Context¶

Requirements¶

• Systematic knowledge of learners' interests and strengths (interest surveys, Check-Ins, conversations with families)
• A repertoire of authentic contexts per math topic, so connections are genuine rather than decorative Decorative illustrations do not improve learning. [-S] — a basketball skin on a routine worksheet problem does not create relevance
• Willingness to let learners bring their own contexts (Choice Boards, learner-generated problems)

Constraints¶

• Superficial personalization (renaming variables with a learner's hobby) can distract rather than help; the context must actually shape the reasoning required [~M]
• Infeasible to individualize contexts for every learner in a large class; group-level interest profiles or adaptive platforms are practical compromises
• Over-reliance on a single strength can narrow learners' view of mathematics as "only useful for X," limiting transfer to unfamiliar contexts [-W]
• Learners with low interest in the chosen domain gain little; interest matching must be based on real data about the learner, not instructor assumptions

Implementation Variability¶

• Instructor-curated contexts: teacher maintains a bank of interest-tagged problems (e.g., percentages via sports salaries, ratios via cooking, geometry via skateboarding ramps)
• Learner-authored problems: students write and swap problems set in their own domains, doubling as Application and assessment evidence
• Adaptive personalization at scale: platforms like ASSISTments and Khan Academy-style systems that select context variants by interest profile (Walkington, 2013) [+M]
• Project-based framing: extended projects (designing a basketball court, budgeting a concert) rather than single problems

Target Learners¶

• Learners with low math self-efficacy who see themselves as "not a math person"; relevance framing supports motivation Autonomy supports intrinsic motivation. [+S]
• Learners whose strengths lie outside academic domains (athletics, trades, arts) and who rarely see those strengths legitimized in math class
• Less effective for learners who already experience math as meaningful — the added context can be redundant overhead [~W]

Target Learning Goals¶

• Motivation and engagement: increasing willingness to attempt and persist at math tasks
• Conceptual understanding: grounding abstract procedures in familiar structures (ratios in music, functions in game mechanics)
• Transfer and application: using math to model situations learners actually encounter Authentic audiences improve student work. [+M]

Instructions¶

1. Survey strengths and interests at the start of a term using a short inventory or Check-Ins; record per-learner and per-class profiles.
2. Map the curriculum to identify topics where authentic interest-based contexts exist; discard topics where only fake contexts are available.
3. Activate prior knowledge by opening each topic with a context from learners' domain and having them articulate what they already know about it.
4. Teach the math through the context, then strip the context: move from situated problems to abstract representation so learners see the general structure, not just the story (Analogies support this mapping).
5. Return via Application: close with a task where learners apply the math back in their own or a peer's interest domain.
6. Rotate contexts across the class's interest profile so no learner is always on the margins.

Related Strategies¶

• Activating Prior Knowledge — interest-based contexts work by activating what learners already know
• Authentic Audiences and Purposes — gives interest-based math work a real recipient and stakes

Related Elements¶

• Application — the apply-back step that cements the connection
• Analogies — maps the familiar domain onto the mathematical structure
• Choice Boards — lets learners select contexts aligned to their strengths
• Belonging — seeing one's identity valued in math class counters stereotype threat and disengagement

Patterns That Use This Strategy¶

• Cognitively Guided Instruction (CGI) for Math — builds problems from learners' own thinking and informal knowledge
• Anchored Instruction — situates math in a rich, meaningful context (e.g., The Adventures of Jasper Woodbury)

Examples¶

• Walkington's personalized algebra studies — algebra problems adapted to individual students' out-of-school interests (sports, music, gaming) improved performance and learning relative to standard contexts (Walkington, 2013).
• Percentages via basketball — a unit on shooting percentages, salary caps, and box-score analytics for a sports-oriented class, culminating in students computing their own season statistics.
• Geometry via design — connecting transformations and area to skatepark or room-design projects for learners interested in architecture and art.
• The Adventures of Jasper Woodbury (Vanderbilt) — anchored video adventures requiring authentic mathematical problem solving in realistic contexts.

Key Sources¶

• Walkington, C. (2013). Using adaptive learning technologies to personalize instruction to student interests: The impact of relevant contexts on performance and learning outcomes. Journal of Educational Psychology, 105(4), 932–945. doi:10.1037/a0031882
• Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127. doi:10.1207/s15326985ep4102_4
• Cognition and Technology Group at Vanderbilt (1992). The Jasper experiment: An exploration of issues in learning and instructional design. Educational Technology Research and Development, 40(1), 65–80. doi:10.1007/BF02296707
• Boaler, J. (2016). Mathematical Mindsets: Unleashing Students' Potential through Creative Math, Inspiring Messages and Innovative Teaching. Jossey-Bass.