Principle 13: Skills in specific contexts gradually transfer and generalize to new contexts.
In most cases, applying skills learned in one context to another is time-intensive. It requires intentional effort to build generalized skills.
Note: Unconstrained Kids unpacks, translates, and integrates academic research and data about skill type and the science of skill building to support the improvement of PK-12 reading, writing, and mathematics. This post is part of a series that describes 14 key principles of skill building I identified from Dynamic Skill Theory and the Science of Learning and Development. (Especially DST.) Like everything on this Substack, this post is a work-in-progress. I will make updates as needed. Citations are included at the end. Questions, comments, and suggestions are welcome.
First posted: June 4, 2025
Last updated: April 18, 2026
Key Takeaway
Skill is the capacity to think and act in an organized way in a specific context (Immordino-Yang and Fischer, 2010). The transfer of skills learned in one context to other contexts is a major challenge in education. In most cases, skill transfer (generalization) is time-intensive and requires intentional effort. Skills learned in one setting can be applied in another setting (Osher et al., 2020). But it is highly unlikely that mere exposure will do the trick. This is because skills are context sensitive. Change the context, change the skill. (See Principle 3.) Shortcuts to transfer, however, might be possible through the intentional leverage of existing skills, frameworks, and concepts (prior knowledge).
“When a person borrows someone else’s bicycle and rides it or rides his familiar bicycle on an unfamiliar type of terrain (e.g. across a grassy field instead of on a road or sidewalk), he must adapt his skill to the context of the new bicycle or terrain. He cannot immediately ride skillfully by using the skill he possesses from before. When a woman attempts to perform analysis of variance with a different computer program or when she tries to analyze the data in a study with an unfamiliar design, she has to work to adapt her skill. It can take days or weeks of hard work to generalize her skill to the new context.” (Fischer et al., 1993)
The transfer of knowledge and learning from one setting or domain to another is a well-established challenge in education (Barnett & Ceci, 2002). This process is referred to as the generalization of skills.
Near transfer involves the application of skills or knowledge learned in one context (task, activity, domain) to a similar context. Far transfer involves the use of knowledge or skills very different from the original context, task, or activity (Fischer & Bidell, 2006).
Education researchers and psychologists readily find evidence of near transfer from programs and interventions. But evidence of far transfer is much harder to find:
“Contrary to the common expectation that knowledge generalizes readily, researchers have had great difficulty finding generalization beyond very similar tasks. What occurs readily is near generalization, in which a student performs a task that differs modestly from the one where she learned the skill…Educational assessments indicate that students who perform well in a class typically generalize their knowledge only to material similar to what was explicitly taught in the class. They do not show far generalization to distinctly different material. The generalization about generalization is that near generalization is to be expected, but far generalization to very different tasks and situations does not usually occur.” (Fischer & Immordino-Yang, 2002)
Researchers have struggled to find evidence of far transfer of skills from interventions or activities developed in one domain to another. Take, for example, the popular belief that playing chess can improve math skills. While not specifically focusing on math facts, procedures, or concepts, the idea is that chess helps critical thinking, problem solving, and pattern recognition. Each of these skills are involved in math.
Unfortunately, experimental studies repeatedly fail to find a relationship between playing chess and math outcomes (Sala & Gobet, 2017; Blanch, 2022). Researchers similarly fail to find far transfer of skills from playing video games (Sala et al., 2018), playing brain games (Simons et al., 2016), participating in music training (Sala & Gobet, 2020), and cognitive training (e.g. working memory) (Gobet & Sala, 2023). Most of these interventions that have been studied, however, were not explicitly designed to promote skill transfer from one domain to another (Zelazo and Carlson, 2020).
Skill transfer obviously happens all the time. The math skills we learn in specific tasks in early elementary school become generalized and used in many settings throughout our adult lives – such as modifying a recipe, calculating the savings on a discounted purchase item, or deciding how much paint to buy for a home improvement project.
So, how does skill transfer occur? Better yet, how can we help it happen for children to support their success in school subjects like math and reading? Fischer and Immordino-Yang (2002) say there are two fundamental approaches:
“For the structures to become generalized, either students must 1) learn them extensively over long periods involving diverse tasks, or 2) instruction must be grounded in common frameworks and concepts that are part of students’ prior knowledge base.” (Fischer & Immordino-Yang, 2002)
Author’s Note: I initially outlined these two approaches proposed by Fischer and Immordino-Yang in the first draft of this post in 2025. Since then, I’ve uncovered three additional approaches proposed by other researchers. I list all four approaches in this updated post. I have less time today than I did in 2025 to write at length. I plan to expand more fully on this topic in the book I’m writing about skill type and the science of skill building.
Approach One: Build skills over long periods with diverse tasks
Skills are repeatedly rebuilt with variations in tasks, context, and support. Even minor changes can cause a skill to collapse and require reconstruction. The skill needs to be built and rebuilt with variations so it can be sustained in different contexts and under different states (emotion, energy, etc.) (Fischer, 2008). “Through this slow process, people gradually build a more general skill” (Fischer & Bidell, 2006).
Building new, generalizable skills in subjects such as reading and math usually takes a long time. Developing expertise in a broad area often requires 5 to 10 years of learning. Skill proficiency in a specific task or smaller domain can be achieved in weeks or months – but it still takes time (Fischer & Yan, 2002; Fischer & Bidell, 2006).
Skills are built for participation in specific mental and physical tasks and contexts. (See Principle 3.) Over time they can and will gradually extend from specific contexts to new contexts through real practice in real contexts (Fischer & Bidell, 2006; Cantor et al., 2019).
There is recent research that supports this approach. Researchers used a “spiral” supplemental literacy curriculum for three academic years (Grades 1 through 3) to build vocabulary, science content knowledge, and comprehension skills. The curriculum progressed from simple to complex concepts. The concepts were cyclically reintroduced with each encounter building upon previous lessons.
The researchers found evidence of far transfer of skills taught in the curriculum on state reading and math tests in Grade 3 (Kim et al., 2024). 1This impact was not a one-time occurrence. The researchers found a continued positive impact (far transfer) on state reading and math tests a year later in Grade 4.
Time is an important factor. In this experimental study, there was no evidence of far transfer of taught vocabulary and science concepts on reading comprehension after two years of intervention. It took a full three years of intervention for this impact to appear (Kim et al., 2024).
Researchers similarly found evidence for far transfer in dialogic argumentation skills (debate) after three years. Middle school students who participated in a twice weekly dialogic argumentation intervention showed evidence of enhanced argumentation skills on topics outside of what was covered in the program (Crowell and Kuhn, 2014). The intervention also showed positive far transfer on argumentative writing skills (Kuhn and Crowell, 2011).2
Approach Two: Leverage common or familiar frameworks and concepts (prior knowledge)
“Fortunately, a broader view gives a less bleak portrait. The key is to examine frameworks and concepts that students commonly use instead of those they learn in [school]. Typically, students who cannot generalize the frameworks and concepts that they learn in school can easily generalize their own favored frameworks and concepts.” (Fischer & Immordino-Yang, 2002)
The notion of leveraging children’s prior knowledge in their learning is not a new idea. What is novel is using prior knowledge – children’s “own favored frameworks and concepts” – to create a “shortcut” in the ordinary time-intensive mechanism of skill construction, collapse, and reconstruction to generalize skills. (See Principle 10.)
Children have already gone through this process of skill building with their own “favored skills” and knowledge. Providing experiences, opportunities, and instruction based upon familiar ways of thinking and/or existing knowledge can accelerate the process of generalization of new skills. The alternative – extensive, long-term practice in diverse tasks – takes more time for generalized skill development than the typical intervention, program, curriculum module, or school course allows (Fischer & Immordino-Yang, 2002).
A positive example of this approach is the work of Robbie Case and colleagues on the mental number line (Case et al., 1996). The mental number line represents numbers in a sequential order. Each number has a specific place and relationship to other numbers. The mental number line helps children develop “number sense,” the understanding that numbers are both discrete units and part of a continuous system. This fundamental understanding supports later math achievement.
Case and colleagues developed an approach to teaching the mental number line to Pre-K and early elementary children that explicitly builds upon skills they already developed for understanding quantity and for counting. The intervention delivered positive results after as few as ten weeks. Children showed evidence of far transfer in unrelated tasks such as musical scales (which are based in numbers) and distributing gifts at birthday parties.
Approach Three: Use adaptive tasks to support skill building
“It has recently been argued that training needs to be continually novel, complex, and diverse and be motivating to maximize transfer to other domains…Indeed, several studies found support for transfer effects of training in adults as long as the training was adaptive to the performance level of the participants (i.e. the task becomes more difficult when performance is improving).” (Crone et al., 2018)
Researchers question whether the reason interventions often fail to show transfer of developed skills to other domains is because the skills failed to develop in the first place. Success in the task can be attributed less to improved skill and more to use of an effective strategy for the task being trained (Moreau & Conway, 2014; Diamond & Ling, 2016; Crone et al., 2018).
Researchers argue that far-transfer from skill building activities depends on what is actually being trained. They cite studies in which training successfully supported the far transfer of targeted executive skills in children and adolescents (Crone et al., 2018). The through line across these studies was the use of adaptive interventions that avoided repetition and introduced novelty, complexity, and diversity in the tasks involved.
“A large body of research suggests that [executive function] skills can be cultivated through scaffolded practice. As with other neurocognitive skills, the repeated engagement and use of [executive function] skills in problem solving at increasing levels of challenge strengthens those skills and increases the efficiency of the corresponding neural circuitry.” (Zelazo and Carlson, 2020)
Approach Four: Build skills in specific contexts
“[T]transfer of domain-general cognitive training to non-trained tasks (i.e. transfer of [executive function] training to reading tasks) is most effective when training shares elements with target tasks, reducing the demands on students to generalize training routines to outcomes of interest.” (Cartwright & Palian, 2024)
Another path to skill development is to target building specific skills in the context in which we seek improvement. In other words, if we want to strengthen a child’s ability to mentally switch between different aspects of reading, we should build her cognitive flexibility in the context of reading tasks (Sala & Gobet, 2020b; Cartwright & Palian, 2024). Researchers argue that context-free training of cognitive flexibility that involves domain-general tasks, like switching between colors and shapes, does not involve the precise types of control required for successful reading (Cartwright & Palian, 2024). They contend this is why these general types of interventions have failed to demonstrate successful transfer to academic outcomes like reading and math.
Expanding into a fuller definition of context, other researchers have called for promoting far transfer of executive skills not only for specific academic domains such as reading or math. They’ve also suggested considering skill building within the physical context of the classroom to promote skill transfer to academic outcomes (Guzenhauser & Nückles, 2021).
Approach Five: Add a reflective component to promote transfer
[R]esearch on learning shows clearly that to promote the far transfer of any skill, it is important to train that skill in a wide range of contexts. Newer evidence also indicates the importance not only of practicing [executive function] skills in a variety of contexts but also of encouraging children to reflect upon them metacognitively so they learn the purpose and utility of '[executive function] skills, as well as how and when to apply those skills in new situations.” (Zelazo & Carlson, 2020).
Reflection is the mental act of “stepping back” from a situation to notice, engage with, and consider one’s own thoughts. In chldren, this looks like “notic[ing] challenges, paus[ing], consider[ing] their options, put[ting] things in context before responding, and monitor[ing] their ongoing efforts” (Zelazo & Carlson, 2020).
As mentioned above, time demands prevent me from providing a fuller description of this approach. Interested readers should look up the citation for Zelazo and Carlson (2020) below. These researchers have deep experience and expertise in executive skill assessment and intervention. I find the studies cited in this paper very compelling. It also reminds me of a comment I heard from Hugh Catts at Florida State University extolling the importance of metacognition on reading.
But wait, there’s more
Works Cited
Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn?: A taxonomy for far transfer. Psychological Bulletin, 128(4), 612.
Blanch, A. (2022). Chess Instruction Improves Cognitive Abilities and Academic Performance: Real Effects or Wishful Thinking? Educational Psychology Review, 34(3), 1371–1398.
Cantor, P., Osher, D., Berg, J., Steyer, L., & Rose, T. (2019). Malleability, plasticity, and individuality: How children learn and develop in context. Applied Developmental Science, 23(4), 307–337.
Case, R., Okamoto, Y., Griffin, S., McKeough, A., Bleiker, C., Henderson, B., Stephenson, K. M., Siegler, R. S., & Keating, D. P. (1996). The role of central conceptual structures in the development of children’s thought. Monographs of the Society for Research in Child Development, 61(1/2), i–295.
Crone, E. A., Peters, S., & Steinbeis, N. (2018). Executive function development in adolescence. In S. A. Wiebe & J. Karbach (Eds.), Executive function: Development across the life span (pp. 44–58). Routledge.
Crowell, A., & Kuhn, D. (2014). Developing dialogic argumentation skills: A 3-year intervention study. Journal of Cognition and Development, 15(2), 363–381.
Diamond, A., & Ling, D. S. (2016). Conclusions about interventions, programs, and approaches for improving executive functions that appear justified and those that, despite much hype, do not. Developmental Cognitive Neuroscience, 18, 34–48.
Fischer, K. W. (2008). Dynamic cycles of cognitive and brain development: Measuring growth in mind, brain, and education. In A. M. Battro, K. W. Fischer, & P. Léna (Eds.), The educated brain (pp. 127–150). Cambridge University Press.
Fischer, K. W., & Bidell, T. R. (2006). Dynamic development of action and thought. In R. M. Lerner & W. Damon (Eds.), Handbook of child psychology: Theoretical models of human development (6th ed., pp. 313–399). John Wiley & Sons, Inc.
Fischer, K. W., Bullock, D. H., Rotenberg, E. J., & Raya, P. (1993). The dynamics of competence: How context contributes directly to skill. In R. Wozniak & K. Fischer (Eds.), Development in context: Acting and thinking in specific environments. Lawrence Erlbaum Associates.
Fischer, K. W., & Immordino-Yang, M. H. (2002). Cognitive development and education: From dynamic general structure to specific learning and teaching. In E. Lagemann (Ed.), Traditions of scholarship in education. Spencer Foundation.
Fischer, K. W., & Yan, Z. (2002). Darwin’s construction of the theory of evolution: Microdevelopment of explanations of variation and change in species. In N. Granott & J. Parziale (Eds.), Microdevelopment: Transition processes in development and learning. Cambridge University Press.
Gobet, F., & Sala, G. (2023). Cognitive Training: A Field in Search of a Phenomenon. Perspectives on Psychological Science, 18(1), 125–141.
Gunzenhauser, C., & Nückles, M. (2021). Training Executive Functions to Improve Academic Achievement: Tackling Avenues to Far Transfer. Frontiers in Psychology, 12.
Immordino-Yang, M. H., & Fischer, K. W. (2010). Neuroscience bases of learning. In V. G. Aukrust (Ed.), International encyclopedia of education (3rd Edition, pp. 310–316). Elsevier.
Kim, J. S., Gilbert, J. B., Relyea, J. E., Rich, P., Scherer, E., Burkhauser, M. A., & Tvedt, J. N. (2024). Time to transfer: Long-term effects of a sustained and spiraled content literacy intervention in the elementary grades. Developmental Psychology.
Kuhn, D., & Crowell, A. (2011). Dialogic argumentation as a vehicle for developing young adolescents’ thinking. Psychological Science, 22(4), 545–552.
Moreau, D., & Conway, A. R. A. (2014). The case for an ecological approach to cognitive training. Trends in Cognitive Sciences, 18(7), 334–336.
Osher, D., Cantor, P., Berg, J., Steyer, L., & Rose, T. (2020). Drivers of human development: How relationships and context shape learning and development. Applied Developmental Science, 6–36.
Sala, G., & Gobet, F. (2017). Does chess instruction improve mathematical problem-solving ability? Two experimental studies with an active control group. Learning Behavior, 45(4), 414–421.
Sala, G., & Gobet, F. (2020a). Cognitive and academic benefits of music training with children: A multilevel meta-analysis. Memory & Cognition, 48(8), 1429–1441.
Sala, G., & Gobet, F. (2020b). Working memory training in typically developing children: A multilevel meta-analysis. Psychonomic Bulletin & Review, 27(3), 423–434.
Sala, G., Tatlidil, K. S., & Gobet, F. (2018). Video game training does not enhance cognitive ability: a comprehensive meta-analytic investigation. Psychological Bulletin, 144(2), 111–139.
Simons, D. J., Boot, W. R., Charness, N., Gathercole, S. E., Chabris, C. F., Hambrick, D. Z., & Stine-Morrow, E. A. L. (2016). Do “brain-training” programs work? Psychological Science in the Public Interest, 17(3), 103–186.
Zelazo, P. D., & Carlson, S. M. (2020). The neurodevelopment of executive function skills: Implications for academic achievement gaps. Psychology & Neuroscience, 13(3), 273–298.
This was a large study with 30 elementary schools randomly assigned to treatment and control groups. A total of 2,780 students were included in the study.
This was a much smaller study. The 2014 study (Crowell and Kuhn) included 79 students randomly assigned to one of three treatment conditions. The 2011 study (Kuhn and Crowell) included 71 students randomly assigned to one of three treatment conditions.