Becoming an Expert: An Overview of Higher-Order Learning

Figure of Albert Einstein

What is an expert, and how do we become one?

In this article, we will explore the characteristics of an expert and their knowledge, how SOLO and Bloom’s revised taxonomy can help us identify higher-order knowledge structures and thinking processes, the challenges of higher-order thinking, and how we teach these skills in our program.

The characteristics of an expert

Experts are individuals who possess a deep understanding of a subject, allowing them to solve complex problems and make informed decisions in their field. Expertise is not just about having a large amount of knowledge, but also the ability to apply that knowledge in novel and complex situations. They are able to think critically, solve problems, and make decisions based on their understanding of the subject matter.

Expert knowledge is organized into structures that allow experts to think and reason more efficiently. These structures are often based on extensive experience in the field and are not readily accessible to novices. However, expert-level knowledge can be gained much more rapidly with the appropriate learning strategies.

Defining “expert knowledge”

SOLO (Structure of Observed Learning Outcomes) is a taxonomy that helps us identify the nature of expert higher-order knowledge structures. It describes a progression of levels of understanding, from simple recall of information to the ability to think abstractly and make connections between ideas. The levels of understanding are:

  1. Prestructural: The learner has no understanding of the topic and responds inappropriately or provides irrelevant information.
  2. Unistructural: The learner has a basic understanding of the topic and can identify one relevant aspect of it.
  3. Multistructural: The learner has a good understanding of the topic and can identify multiple relevant aspects of it.
  4. Relational: The learner can make connections between different aspects of the topic, demonstrating a deep understanding of how they relate to each other.
  5. Extended Abstract: The learner can generalise and transfer their understanding of the topic to new situations, demonstrating a high level of abstraction and creativity.

Ref: Rembach, Lauren & Dison, Laura. (2016). Transforming taxonomies into rubrics: Using SOLO in Social Science and Inclusive Education. Perspectives in Education. 34. 68-83. 10.18820/2519593X/pie.v34i1.6.

Experts have knowledge that is extended abstract. This is considered a higher-order knowledge structure.

Bloom’s revised taxonomy

Bloom’s revised taxonomy helps us identify which thinking processes are needed to achieve higher-order knowledge structures. It describes a progression of cognitive skills, from simple recall of information to the ability to analyze, evaluate, and create.


If SOLO is a house, Bloom’s is the way we build the house. To build an expert-quality house, we must use expert-quality methods.

  1. Remembering: The learner can recall information from memory.
  2. Understanding: The learner can comprehend the meaning of the information.
  3. Applying: The learner can use the information to solve a problem or complete a task.
  4. Analyzing: The learner can break down complex information into its component parts and identify relationships between them.
  5. Evaluating: The learner can make judgments about the value or quality of the information or ideas.
  6. Creating: The learner can combine information or ideas to create something new.

One common misconception around Bloom’s revised taxonomy is that one cannot engage in higher-order learning without first engaging in lower-order learning. The assumption is that learners must first memorize and understand basic information before moving on to more complex thinking processes like analysing, evaluating, and creating. However, this is not necessarily true, and in fact, the idea of starting with lower-order learning can be counterproductive.

Lower-order learning causes information to be forgotten more quickly than higher-order learning. In real-world practice, when there are multiple things to learn at a high volume, lower-order learning is much less efficient as learners must spend extra time and effort to relearn information frequently.

Challenges of higher-order thinking

Learners often struggle with the higher cognitive load of higher-order thinking. Unfortunately, learners can interpret the effort and difficulty associated with effective learning as a sign that the methods are ineffective and choose not to use them. This is known as the misinterpreted-effort hypothesis. In short, learners sabotage themselves…

Overcoming these challenges has been the mission of iCanStudy. Through our program, we teach the skills of effective self-regulated higher-order learning by teaching metacognition, self-reflection, and the ability to adjust learning strategies towards cognitively optimal techniques.


Becoming an expert is not easy. It requires a deep understanding of a subject, the ability to apply that knowledge in complex situations, and the organization of knowledge into structures that allow for efficient thinking and reasoning. SOLO and Bloom’s revised taxonomy provide frameworks for identifying higher-order knowledge structures and thinking processes. However, learners may struggle with the cognitive load of higher-order thinking, choosing to use less effective strategies. To overcome this, learners must be taught to improve their metacognition and self-regulated learning strategies.


Biggs, J. B., & Collis, K. F. (1982). Evaluating the Quality of Learning: The SOLO Taxonomy (Structure of the Observed Learning Outcome). Academic Press.

Kirk-Johnson, A., Galla, B. M., & Fraundorf, S. H. (2019). Perceiving effort as poor learning: The misinterpreted-effort hypothesis of how experienced effort and perceived learning relate to study strategy choice. Cognitive Psychology, 115, 101237. 

Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory into practice, 41(4), 212-218.

Mislevy, R. J., & Haertel, G. D. (2006). Implications of cognitive psychology for educational measurement. In R. L. Brennan (Ed.), Educational Measurement (4th ed., pp. 207-260). American Council on Education/Praeger Series on Higher Education.

Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of Self-Regulation (pp. 451-502). Academic Press.

Schunk, D. H., & Zimmerman, B. J. (Eds.). (2008). Motivation and Self-Regulated Learning: Theory, Research, and Applications. Routledge.

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