A walking tour of the pathway to wisdom

My previous blog the: “Introduction of the Higher Learning Stage of the Spiral Model of Learning.” (Psuf10, 2013) introduced my theory of the spiral model of learning. However, I realised that I need to give some background to make a coherent argument to propose such a theory. This blog will give the background; in addition it will explain what areas the next few blogs will address.

According to Biggs (1999) there are two basic kinds of learning: surface and deep learning (figure 1) (Marton & Säaljö, 1976; Wheeler 2013).


Figure 1.

Surface learning is when students learn basic facts and information about a topic, but they do not consider the relationship between them. In surface learning there is not much focus on context and further progression. The isolation of this information means students do not see the usefulness of the information, thus causing the information to be forgotten more readily (Rusbult, 1978). Deep learning is when information is given context to allow for higher level of thinking and observation.

According to Deep and Surface Approaches to Learning (2011) much of the current educational system is based on surface learning. It is more important for students, when given information, to be able to recall than understand the information and the context around it.  Burnett, Pillay & Dart (2003) stated there needs to be a shift towards deep learning to improve educational standards.

To back up their claim, Burnett, Pillay & Dart (2003), studied the effects of surface and deep learning on different aspects of learning. Their:

“Results indicated that the secondary school students in this study who use a Deep Approach liked and enjoyed learning new things and viewed learning as a product of experience … Additionally, students who adopted a Surface Approach to learning reported that they were not good at learning, and also adopted an Achieving Approach. … Finally, students who adopted an Achieving Approach also had a Deep Approach, liked learning new things, viewed learning as developing understanding and indirectly viewed learning as a product of experience and as developing social competence.”

Another finding was deep learning caused an increase in developing social competence and personal change to child’s behaviour towards education. These findings seem to provide valid evidence that deep learning is a better way of learning and creating more socially aware, and proactive students. The findings can be observed in figure 2.


Figure 2.


Figure 3.

As seen in figure 3 Wheeler (2013) combined the theory of deep and surface learning with theory of DIKW. Originally DIKW was a theory, which suggested the path through understanding was made up of 4 parts: data, information, knowledge and wisdom. According to (Bellinger, Castro & Mills,2004; Data, Information, Knowledge, and Wisdom, n.d.) their definitions are:

  • Data = fact and skills based information, which is needed for students to progress to higher stages of understanding.
  • Information = data which has been given its meaning by giving it a label or definition
  • Knowledge = information which has been given context by linking information through patterns, sequences and categories.
  • Wisdom = using accumulating knowledge to create well-educated decisions or judgements. According to Data, Information, Knowledge, and Wisdom (n.d.) wisdom is shared by everyone.

However, overtime this basic structure has been updated by multiple researchers (fig 3.). These categories are: Transformation (wheeler, 2013), Meta-Knowledge (Giarratano and Riley, 1998), Experience (Bellinger, Castro & Mills, 2004), Theory (Bellinger, Castro & Mills, 2004), Laconic and Vision (Carpenter and Cannady, 2004):

  • Transformation = evaluation and analysis of your current knowledge to see which is most appropriate for the stimulus. Different people will differ in their analysis and evaluations because of different knowledge bases, which were used to create their wisdoms. Such transformations cause the creation of opinions. These opinions can be novel, thus transformation is the source of creativity.
  • Meta-knowledge = the sum of your knowledge you have in this area. Thus it is knowledge of multiple interconnecting knowledge bases. As well as the cognitive explanations to link them together.
  • Experience = continuous use of knowledge in areas where the knowledge is relevant.
  • Theory = Using knowledge to create ideas to explain paradigms of knowledge. These theories can be novel to the individual; this is one of the first areas of creativity. This is one of the ways of creating new data for others
  • Laconic = this is when you start creating shortcuts in your knowledge. Your knowledge becomes more succinct, making it simpler to process. This allows you to view things as whole rather than a sum of its parts.
  • Vision = your view of the future, using wisdoms to mould the world into an image that an expert has created. This is where an expert moves their field of experience forward. This is one of the main ways of creating new information for others to learn.


Figure 4.

The stages can be observed in figure 4 (Eisenberg, 2012; Bellinger, Castro & Mills, 2012; Beck, 1999; Giarratano and Riley, 1998; Carpenter and Cannady, 2004; Bransford, Brown & Cocking, 2002; Wheeler, 2013)

As you can see through figure 3 and 4 the learning of “data” and “information” are the areas associated with the less useful parts of education : extrinsic and surface learning (psuf10, 2013; Eisenberger & Shanock, 2003). Thus the philosophy intrinsic within education should be to make students reach Meta knowledge. However for this to occur, engagement in the subject is essential, as seen in figure 5 (Bellinger, Castro & Mills, 2012). Engagement is what causes students to move through each of the stages.


Fig 5.

Here is an example of how this process can be explained through music:

  • Data is the basic forms of music e.g. Notes.
  • Information is putting certain notes together gives you chords.
  • Knowledge is the knowledge that putting certain chords together in this particular order gives you a song.
  • Transformation is creating an opinion on which genre of music you like the best by analysing and evaluating your knowledge of the genres you know. However, it should be noted that even within the specific genre there are different wisdoms, which allows for creativity e.g. creating your own play style or music.
  • Meta-knowledge is having a wide repeat of songs, which you can play as well as a deep understanding
  • Experience is the continuous playing of the songs
  • Theory is playing around with song rifts and trying to create rhythms lyrics and other creative thoughts around the area of music your playing.
  • Laconic is where you play songs so much that you do not remember the notes, the playing is almost automatic.
  • Wisdom is knowing certain songs when played in a specific style, are part of a specific genre of music.
  • Vision is creating your own style of music and play style of songs which can be picked up by people who are learning

Overall this blog was an introduction to the area of understanding and how it fits into a model of understanding (fig 5.). I hope you understand the idea of deep and surface learning and the differences between each stages of the DIKW. If you have any questions please comment underneath, I would love to hear your thoughts though feedback.

My next few blogs will consider the different kinds of competency-based learning and which kind of competency-based learning is most effective in allowing people to gain knowledge.


Biggs, J. (1999). Teaching for quality learning at university: what the student does. SRHE and Open University Press imprint.

Marton, F., & Säaljö, R. (1976). On qualitative differences in learning – It outcome as a function as a function of the learner’s conceptions of the. British Journal of educational Psychology,46(2), 115-127.

Deep and Surface Approaches to Learning. (2011). In Higher Education Academy Engineering Subject Centre. Retrieved from http://exchange.ac.uk/learning-and-teaching-theory-guide/deep-and-surface-approaches-learning.html

Burnett, P. C., Pillay, H., & Dart, B. C. (2003). The influences of conceptions of learning and learner self-concept on high school students’ approaches to learning. School Psychology International24(1), 54-66.

Data, Information, Knowledge, and Wisdom. (n.d.). In Oregon Technology in Education Council. Retrieved February 7, 2014, from http://otec.uoregon.edu/data-wisdom.htm

Eisenberg, M. (2012, March 30). Information Alchemy: Transforming Data and Information into Knowledge and Wisdom [Electronic mailing list message]. Retrieved from http://faculty.washington.edu/mbe/Eisenberg_Intro_to_Information%20Alchemy.pdf

Bellinger, G., Castro, D., & Mills, A. (2012). Data, Information, Knowledge and Wisdom. In http://www.systemswiki.org/. Retrieved February 8, 2014, from http://www.systemswiki.org/index.php?title=Data,_Information,_Knowledge_and_Wisdom

Eisenberger, R., & Shanock, L. (2003). Rewards, intrinsic motivation, and creativity: A case study of conceptual and methodological isolation. Creativity Research Journal15(2-3), 121-130.

Psuf10. (2013, November 29). Introduction of the higher learning stage of the spiral model of learning. [Web log post]. Retrieved from https://psuf10.wordpress.com/2013/11/29/introduction-of-the-higher-learning-stage-of-the-spiral-model-of-learning/

Wheeler, S. (2013). Learning Theories for the Digital Age [Online Slideshow]. Retrieved from http://www.slideshare.net/timbuckteeth

Rusbult, C. (1978). Transfers of Learning. In https://mywebspace.wisc.edu. Retrieved October 29, 2013, from https://mywebspace.wisc.edu/crusbult/web/design/tr.htm

Giarratano, J. C., & Riley, G. (1998). Expert systems: Principles and programming. PWS Publishing Co.

Cleveland, H. Information as a Resource. The Futurist, December (1982), 34-39.

Ackoff, R. From Data to Wisdom. Journal of Applied Systems Analysis, 16 (1989), 3-9.

Bellinger, G. Knowledge Management—Emerging Perspectives. Circa 1997. Last retrieved February 6, 2005: http://www.systems-thinking.org/kmgmt/kmgmt.htm

Tuomi, I. Data is More than Knowledge: Implications of the Reversed Knowledge Hierarchy for Knowledge Management and Organizational Memory. Journal of Management Information Systems, 16(3) (Winter 1999-2000), 103-17.

Carpenter, S. A. and Cannady, J.  Tool for Sharing and Assessing Models of Fusion-Based Space Transportation Systems.  Proceedings of the 40th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, (July 11-14, 2004), Fort Lauderdale, Florida.

Bransford, J. D., Brown, A. L., & Cocking, R. R. (2002). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press.