Kennesaw State University

How do Students Develop Mastery?

Photo by Eneko

Adapted from How Learning Works: Seven Research-Based Principles for Smart Teaching

by Linda Stewart

For students to achieve mastery within a domain, whether narrowly or broadly conceived, they need to develop a set of key component skills, practice them for fluency, and know how to apply them in relevant contexts.

The fourth principle of learning is mastery. What does the research tell us about mastery and what are some of the related components? Teaching toward mastery, whether broadly or narrowly defined, requires an understanding of expertise, the component skills involved in mastering tasks or knowledge, the process of integrating new skills or knowledge, and the ability to apply component skills and knowledge to diverse contexts.

Expertise

Mastery is best illustrated through a four-stage developmental continuum (Sprague and Stuart 2000) from novice to expert, which focuses on two dimensions: competence and consciousness. In these four stages, students move from unconscious incompetence (unaware of what they do not know), through conscious incompetence and conscious competence to unconscious competence. At the fourth stage of unconscious competence, experts organize, access, and apply their knowledge very differently than novices. They organize it into large, conceptual “chunks” that allows them to access and apply their knowledge with facility (Chase & Simon, 1973b; Chase & Ericsson, 1982; Koedinger & Anderson, 1990).

This expertise has a downside in that instructors who are experts in their field may not always be able to simplify their instruction to novices in such a way that the novice learns from them. Experts organize their knowledge in such a way that it may be difficult for them to clearly deconstruct a sequence or set of skills for novice learners. They may skip steps with no conscious awareness of doing so, which makes it difficult for student learners to follow. This inability to realize what students need to learn is identified as the expert blind spot (Nickerson, 1999; Hinds, 1999; Nathan & Koedinger, 2000; Nathan & Petrosino, 2003). Some of the obstacles to effective teaching as a result of the expert blind spot include:

  • difficulty in breaking down a skill
  • taking shortcuts and skipping steps with no conscious awareness of doing so
  • being unrealistic about the time it will take students to complete a complex task
  • overestimating students’ abilities to apply new component skills to complex tasks 

Component Skills

Research on component skills suggests the most effective ways to teach complex skills is via targeted or focused practice in which fundamental skills are prioritized and reinforced through practice to improve student learning (Koedinger & Anderson, 1993). However, there are advantages to practicing the whole task so that students see how the parts fit into the whole in a context that is authentically complex. Many instructors alter their approach to include focused and varied practices. The complexity of the task, along with consideration of the course goals, should factor into decisions about how to build students’ component skills.

Integration

As they are acquiring component skills, students need instruction and practice in integrating these skills. Students who are integrating new combinations of skills will often struggle and may, in fact, demonstrate performance deficits. This apparent regression happens when students are struggling to perform multiple tasks simultaneously, which requires attention to processing too much information at one time.

This information-processing demand is also known as cognitive load. While experts have sufficient practice balancing a number of tasks in diverse situations, the same is not true for novices and they can quickly become overwhelmed. One way instructors can help students reduce their cognitive load is to allow students to focus on one skill at a time or to support an aspect of a complex task while the student is performing the entire task (Sweller & Cooper, 1985; Cooper & Sweller, 1987; Paas & van Merrienboer, 1994). 

Application

Students who are nearing the mastery phase of a domain will exhibit the ability to apply new skills, knowledge, strategies, or attitudes to diverse contexts. This ability is called transfer, which is a key goal in education: the ability for our students to apply what they learned to contexts beyond the classroom. However, context dependence or a superficial understanding of the principles or deep structures behind certain tasks can become barriers to achieving transfer. Instructional approaches to overcome those barriers include employing structured comparisons, for example. Research shows when students have to compare case studies, problems, or concepts, they have to recognize and identify the deep features that are both similar and different in each (Loewenstein, Thompson, & Gentner 2003). Because transfer does not happen naturally or easily, it is crucial that instructors teach for transfer, employing instructional strategies that provide enough practice in different contexts so that students will move toward mastery.

Strategies to Reinforce Component Skills

  • To overcome the expert blind spot, deconstruct the tasks into steps, sequences, skills, and knowledge that students require.
  • Given their learning and competency stages, graduate student teaching assistants may be helpful in deconstructing tasks for undergraduates.
  • Prioritize key elements of a task to help students focus their cognitive resources on the most significant aspects.
  • Assess students’ competence of component skills or and knowledge prior to teaching them.
  • Use rubrics to identify performance criteria.
  • Pair isolated practice for weak and missing skills with practice with component skill building.

 

Strategies to Build Fluency and Facilitate Integration and Transfer

  • When introducing activities to improve automaticity, be transparent about the rationale for the activity.
  • To minimize the cognitive load, reduce the complexity of the task and build slowly.
  • Create opportunities for students to apply a skill to a relevant situation or context.
  • Create opportunities for students to apply a skill to diverse contexts.
  • Use analogies that compare particular features of the task using case studies, scenarios, or problems.
  • Ask students to reflect upon the connections between skills and knowledge they make and apply to particular contexts.

References

Ambrose, S. A., et al. (2010). How learning works: 7 research-based principles for smart teaching. San Francisco: Jossey-Bass.

Chase, W. G., & Ericsson, K. A. (1982). Skill and working memory. In G. H. Bower (Ed.), The psychology of learning and motivation (Vol. 16, pp. 1-58). New York: Academic Press.

Chase, W. G., & Simon, H. A. (1973b). The mind’s eye in chess. In W. G. Chase (Ed.), Visual information processing. New York: Academic Press.

Cooper, G. & Sweller, J. (1987). The effects of schema acquisitions and rule automation on mathematical problem-solving transfer. Journal of Educational Psychology, 79- 347-362. 

Hinds, P. J. (1999). The curse of expertise: The effects of expertise and debiasing methods on predictions of novice performance.  Journal of Experimental Psychology: Applied, 5(2), 205-221. 

Lowenstein, J., Thompson, l., & Gentner, D. (2003). Analogical learning in negotiation teams: Comparing cases promotes learning and transfer. Academy of Management Learning and Education, 2 (2), 119-127.

Nathan, M. J., & Koedinger, K. R. (2000). An investigation of  teachers’  beliefs of students’ algebra development. Journal of Cognition and Instruction, 18(2), 209-237.

Nathan, M. J., & Petrosino, A. (2003). Expert blind spot among preservice teachers. American Educational Research Journal, 40(4), 905-928.

Nickerson, R. (1999). How we know—and sometimes misjudge—what others know: Imputing one’s own knowledge to others. Psychological Bulletin. 125(6), 737-759.

Koedinger, K. R., & Anderson, J. R. (1990). Abstract planning and perceptual chunks: Elements of expertise in geometry. Cognitive Science, 14 (4), 511-550.

Paas, F., & van Merrienboer, J. (1994). Variability of worked examples and transfer of geometrical problem solving skills: A cognitive-load approach. Journal of Educational Psychology, 86(122-133).

Sprague, J., & Stuart, D. (2000). The speaker’s handbook. Fort Worth, TX: Harcourt College Publishers. 

Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem-solving in learning algebra. Cognition and Instruction, 2, 59-89.