Beyond Experiential Learning: Why Higher Education Needs a Metacognitive Model of Learning Practice
Part of B Optimal’s series on learning practice:
Making Learning Practices Visible, Developable, and Actionable
David Kolb’s Experiential Learning Theory remains one of the most influential models of learning in higher education. Its central contribution is both elegant and enduring: learning is not simply the accumulation of experiences but the transformation of experience through reflection, conceptualization, and future application.
That insight fundamentally changed how educators think about learning.
Yet it also raises another question that Experiential Learning Theory was not designed to answer:
What are learners actually doing cognitively as they transform experience into learning?
This question shifts attention from the structure of the experience to the learner’s metacognitive regulation of thinking, action, and performance within that experience. It asks how learners understand what a task requires, decide how to approach it, judge whether their efforts are working, interpret what happens, revise their actions, evaluate the result, and carry learning into future situations.
That distinction represents a different unit of analysis and a different educational problem.
Different Questions, Different Purposes
Experiential Learning Theory explains how experience contributes to learning. A metacognitive model of learning practice examines the regulatory work learners perform as they understand tasks, direct their cognitive activity, respond to emerging conditions, evaluate performance, and carry learning into future situations.
These perspectives are closely related, but they are organized around different questions.
Kolb’s theory is organized around the transformation of experience through Concrete Experience, Reflective Observation, Abstract Conceptualization, and Active Experimentation. A learning-practice perspective is organized around what learners do as they attempt to understand a situation, act effectively within it, determine whether their approach is working, and use what they have learned elsewhere.
Rather than asking where a learner is within an experiential cycle, it asks:
How did the learner understand the task?
What approach did the learner choose, and why?
What evidence did the learner use to judge progress?
How did the learner interpret difficulty or feedback?
What prompted the learner to adjust?
What did the learner recognize as transferable?
This distinction matters because participation in the same educational activity does not produce the same learning.
Students may complete the same assignment, receive the same instructions, participate in the same discussion, and encounter similar feedback while developing very different levels of understanding and capability. Those differences may reflect prior knowledge, motivation, instructional conditions, access to resources, competing demands, and many other factors.
They may also reflect differences in how learners regulate their thinking, action, and performance throughout the experience. Research supports the importance of this regulatory dimension. A meta-analysis of 118 studies found a meaningful relationship between metacognition and academic performance, including after controlling for intelligence (Ohtani & Hisasaka, 2018).
Metacognitive regulation is therefore not the sole explanation for differences in learner outcomes. It is an important and often underexamined part of the explanation.
When this regulatory work remains invisible, educators can see the outcome without being able to identify the process that produced it. A student may appear unprepared, disengaged, disorganized, resistant to feedback, or unable to transfer learning, while the underlying difficulty lies in how the task was understood, how progress was judged, how feedback was interpreted, or whether the learner recognized that a different approach was available.
Without a more precise level of analysis, support often defaults to broad advice:
work harder
manage your time
study differently
reflect more deeply
ask for help
apply what you learned
Such advice may point in a useful direction, but it rarely identifies the specific form of learning practice that needs to change.
This has consequences beyond individual interactions. Faculty, advisors, tutors, coaches, and career-development professionals may all support the same learner while using different language and responding to difficulty in disconnected ways. Institutions may offer extensive support without a shared means of identifying what learners are doing, where their regulation is breaking down, or how capability is developing across contexts.
Making learning practices visible gives learners and practitioners a clearer way to understand what is happening, identify where support is needed, and determine what kind of development the situation requires.
From Experiences to Learning Practices
Research on metacognition commonly distinguishes between knowledge of cognition and regulation of cognition. Metacognitive knowledge concerns what learners know about cognition, strategies, and their own learning, while regulation concerns the processes through which learners plan, monitor, evaluate, and adjust learning activity. These dimensions are distinct but interdependent: what learners know about cognition can shape how they regulate learning, while experience regulating learning can further develop metacognitive knowledge (Schraw & Moshman, 1995; Schraw, 1998).
The Metacognitive Moves System operates primarily within this regulatory dimension. It is a task-linked metacognitive regulatory system that explains how learners manage, evaluate, and improve cognitive activity and performance within authentic tasks.
The system organizes this work through five phases: Planning, Monitoring, Adjustment, Evaluation, and Transfer. These phases provide a temporal orientation for understanding when different forms of metacognitive work tend to occur. They do not form a rigid sequence.
Within the five phases, the system distinguishes eight functional slices.
Planning
Planning includes three regulatory functions:
Task Framing — constructing an understanding of what the task requires, what success looks like, and what demands the work presents.
Strategy Selection — identifying and choosing approaches suited to the task rather than relying automatically on familiar habits.
Resource Allocation — distributing time, attention, effort, information, tools, and support according to the demands of the work.
Monitoring
Monitoring includes two distinct functions:
Monitoring — noticing progress, uncertainty, misunderstanding, and discrepancies between current performance and intended outcomes.
Interpretation — making sense of what those observations, feedback, and outcomes reveal about the source of progress or difficulty.
The distinction between these functions is especially important. Monitoring identifies what the learner is noticing. Interpretation determines what that information means.
A learner may accurately recognize that progress has stalled but interpret the cause incorrectly. The learner may conclude that greater effort is required when the strategy is ineffective, assume a lack of ability when the task was misunderstood, or blame limited time when attention was allocated to the wrong part of the work. Interpretation shapes what happens next. Accurate monitoring cannot produce an effective adjustment when the learner assigns the wrong meaning to what was noticed.
Adjustment
Adjustment — modifying strategy, effort, resources, or task understanding in response to what has been identified and interpreted.
Evaluation
Evaluation — examining both the outcome and the processes that produced it in order to generate usable insight for future learning.
Transfer
Transfer — recognizing, adapting, and applying learning, strategies, and regulatory insight across new tasks and contexts.
The five phases and eight slices provide two related forms of structure. The phases orient metacognitive work across task engagement. The slices distinguish the specific regulatory functions operating within that process.
Their organization is temporal without being linear. Monitoring and Interpretation may reveal that the learner misunderstood the task, prompting a return to Task Framing. Adjustment may require a different strategy or allocation of resources. Evaluation may expose weaknesses in the original plan. Transfer places prior learning into a new situation, where the learner must once again determine what the task requires and how to approach it. Learners move backward, forward, and repeatedly among these functions as conditions change.
Experienced learners often make these moves quickly and with limited conscious effort. Less experienced learners may not recognize that such decisions are available to them. They may interpret difficulty as evidence that they lack ability rather than as information that the task, strategy, resources, or approach needs to be reconsidered.
Making these practices visible expands the learner’s range of possible responses. Difficulty can be examined in terms of task understanding, strategy, resources, interpretation, or adjustment rather than treated as evidence of limited ability or effort.
The same architecture also helps learners understand successful performance. They can identify what worked, why it worked, which indicators helped them judge progress, and what should be carried into another situation. The model therefore supports both diagnosis and development by making the regulatory processes behind performance available for examination.
What This Looks Like in Practice
Consider a student completing a major research project. The student begins with a broad topic and assumes the primary task is to collect as much information as possible. After several days, the student has accumulated numerous sources but cannot form a coherent argument.
An experience-centered discussion might focus on what happened during the project and what the student learned from the difficulty.
A learning-practice discussion goes further.
The educator begins by examining how the student understood and framed the task. If the student treated the project as an information-gathering exercise rather than an argument-building task, that initial framing shaped every decision that followed. It influenced the strategy chosen, where time and attention were directed, and which signs counted as evidence of progress. Once the student recognizes that collecting more sources is not producing a clearer argument, the discussion can turn to how that difficulty was interpreted, what change in approach became necessary, and how the same pattern might be identified earlier in a future project.
The problem is no longer described only as “struggling with a research paper.” The learner and educator can identify the practices contributing to the struggle.
The student may discover that the initial Task Framing was too narrow, that Strategy Selection emphasized collection rather than synthesis, and that Resource Allocation directed too much time toward gathering sources. Monitoring focused on the number of sources collected rather than the quality of the emerging argument. Interpretation helps the student determine what those signals reveal, while Adjustment allows the student to revise the approach.
Reframing the task in this way illustrates a distinction Argyris and Schön (1978) drew between single-loop learning, in which a learner corrects behavior without revising the assumptions guiding it, and double-loop learning, in which those assumptions are also examined and revised.
A learner who works longer hours after discouraging feedback but never questions whether the task was framed correctly is making a single-loop adjustment: more effort, same frame. A learner who instead recognizes that “gather sources” should have been understood as “construct an argument” is engaging in double-loop learning.
Within a metacognitive model of learning practice, this distinction helps explain why some adjustments produce durable change while others simply intensify an already flawed approach. The difference lies not in effort alone, but in whether Task Framing itself becomes available for revision.
Naming these breakdowns creates a more precise opportunity for development. The student can learn to define the task differently, identify better indicators of progress, allocate effort more effectively, and carry those practices into future projects.
The same architecture can also be used when the project goes well. The student can examine which framing decisions clarified the work, why a strategy proved effective, what evidence indicated meaningful progress, how emerging problems were interpreted, and which practices should be adapted for another task.
Learning practices become easier to strengthen when learners and practitioners have tools that bring them into view. The framework provides the language and architecture; practical tools help learners apply and examine specific regulatory functions within real tasks.
B Optimal Learning Practice Programs help institutions put that architecture into practice. Each program applies relevant parts of the Metacognitive Moves System to a defined area of learning practice and combines learner-facing tools with professional learning and implementation support. This gives practitioners a shared basis for helping students move beyond broad directions such as “reflect more” or “think about your thinking” toward specific practices they can understand, use, and improve.
The experience still matters. Metacognitive analysis makes its learning potential easier to recognize, develop, and carry forward.
How This Relates to Self-Regulated Learning
A metacognitive model of learning practice overlaps with established theories of self-regulated learning. Zimmerman’s cyclical model, for example, describes self-regulation through forethought, performance, and self-reflection phases (Zimmerman, 2002). That scholarship provides an essential foundation for understanding how learners direct and regulate learning.
The Metacognitive Moves System works at a different level of resolution. It distinguishes the regulatory functions operating within those broader phases. Performance, for example, may require Strategy Selection, Resource Allocation, Monitoring, Interpretation, and Adjustment. Self-reflection may involve Interpretation, Evaluation, renewed Task Framing, and Transfer.
This distinction helps educators see that self-regulation is not a single general capability. A learner may notice that progress has stalled but misinterpret the cause. Another may identify the problem accurately but lack an alternative strategy. A third may adjust successfully without evaluating the process or recognizing its relevance elsewhere.
The model therefore builds on self-regulated learning scholarship by providing an applied architecture for identifying and developing the distinct regulatory functions involved in task performance.
Making Transfer an Explicit Object of Development
One of the clearest differences in analytical emphasis concerns transfer. Experiential Learning Theory recognizes that learning informs future experience through continued experimentation. A learning-practice perspective examines the regulatory work required to make that future application possible:
How does a learner recognize that previous learning may apply?
What must be adapted when the new situation differs?
How will the learner judge whether the transfer worked?
Transfer research shows that knowledge and strategies are not automatically applied simply because they would be useful in a new setting. Transfer depends on recognition, abstraction, interpretation, adaptation, and deliberate application (Bransford, Brown, & Cocking, 2000; Perkins & Salomon, 1992).
Transfer should therefore be treated as more than the hoped-for consequence of learning. It is a practice learners can develop. Educators can help students identify what may carry forward, compare the demands of different situations, adapt prior learning, and evaluate its effectiveness in the new context. Transfer then becomes something learners learn to do rather than something educators hope will happen.
Related reading: Starting From Strength: An Adult Learner’s Story
From Explaining Learning to Developing Learners
Higher education seeks to develop learners who can direct their own learning, respond productively to difficulty, adapt to unfamiliar conditions, and continue developing across contexts.
Meaningful experiences contribute to those outcomes, but experience does not perform the regulatory work on the learner’s behalf. Learners must still determine what a situation requires, select and revise an approach, interpret evidence, evaluate what happened, and recognize what can be carried forward.
A metacognitive model of learning practice makes that work available for development. It gives practitioners a more precise basis for support while allowing different educational environments to adapt the practices to their own purposes and relationships.
Its value is ultimately measured by what learners can do with that visibility.
Successful learning can occur without students understanding how they produced it or how to reproduce it elsewhere. When the regulatory work behind performance becomes visible, effective learning becomes something learners can recognize, strengthen, and carry forward.
References
This article is intended as a thought piece rather than a comprehensive review of the literature. The references below include the primary works that informed the ideas discussed and provide additional context for readers who wish to explore these topics further.
Argyris, C., & Schön, D. A. (1978). Organizational learning: A theory of action perspective. Addison-Wesley.
Ambrose, S. A., Bridges, M. W., DiPietro, M., Lovett, M. C., & Norman, M. K. (2010). How learning works: Seven research-based principles for smart teaching. Jossey-Bass.
Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience, and school. National Academies Press. https://doi.org/10.17226/9853
Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003-066X.34.10.906
Kolb, D. A. (2015). Experiential learning: Experience as the source of learning and development (2nd ed.). Pearson Education.
Kolb, A. Y., & Kolb, D. A. (2005). Learning styles and learning spaces: Enhancing experiential learning in higher education. Academy of Management Learning & Education, 4(2), 193–212. https://doi.org/10.5465/amle.2005.17268566
Ohtani, K., & Hisasaka, T. (2018). Beyond intelligence: A meta-analytic review of the relationship among metacognition, intelligence, and academic performance. Metacognition and Learning, 13, 179–212. https://doi.org/10.1007/s11409-018-9183-8
Perkins, D. N., & Salomon, G. (1992). Transfer of learning. In T. Husén & T. N. Postlethwaite (Eds.), The international encyclopedia of education (2nd ed.). Pergamon Press.
Schraw, G. (1998). Promoting general metacognitive awareness. Instructional Science, 26(1–2), 113–125. https://doi.org/10.1023/A:1003044231033
Schraw, G., & Moshman, D. (1995). Metacognitive theories. Educational Psychology Review, 7, 351–371. https://doi.org/10.1007/BF02212307
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/S15430421TIP4102_2
Author’s Note
This article reflects B Optimal Consulting’s ongoing work to make the practices of learning more visible, actionable, and transferable across higher education. While it builds on established scholarship in experiential learning, metacognition, self-regulated learning, and learning transfer, the synthesis and perspectives presented here represent the author’s continuing effort to advance the understanding of learning practice.