Building Mental Models
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[edit] Introduction
Several types of online learning involve the building of mental models--learners' internal conceptualizations of a system or paradigm, phenomena, or equipment. Mental models include implicit and explicit knowledge, internalized and externalized information, and subconscious and conscious beliefs. Mental models refer to operationalized templates that provide "meaning and form" (Riggins & Slaughter, 2006, p. 4) to a situation. They offer an "analogue of the world." People act on their theories-in-use vs. their espoused theories (Argyris & Schon). Mental models structure knowledge in a way that "integrates ideas, assumptions, relationships, insights, facts, and misconceptions that together shape the way an individual views and interacts with reality" (Steiger & Steiger, 2007, p. 1). These models help people abstractly reason about the particular knowledge domain: its objects, groupings, interrelationships, sequences, processes and behaviors (Hai-Jew, 2008, Slide 3).
Naive mental models tend to be elusive and poorly formed. They are incomplete. They are not realistically structured. They are difficult to articulate. They may be invalid. (Nardi & Zarmer, 1991, p. 487).
Subject matter experts, by contrast, maintain "conceptual models" of the particular domain field. Experts tend to establish a context for the task, classify problems based on underlying principles and concepts, and bring a greater sophistication to decision-making. (Alexander & Judy, 1988, p. 382)
In e-learning, mental models may be created through automated simulations. They may be built up through constructivist approaches, which encourage interactions between people. Cognitivistic approaches focus on the awareness and mental functions of hte human mind, particularly the Dual Channel Model (auditory-verbal and visual-pictorial), with ways to deliver information while maintaining healthy cognitive loads (Mayer & Moreno, 1998).
[edit] Some Types of Mental Models
Mental models may describe or represent a particular situation. It may predict or anticipate a particular development into the future. It may define how something should work ideally--as in a piece of equipment. It may be speculative and propose an untestable thesis. (As an example, there may be mental models at extreme scales, beyond human sensory perception.)
Some information systems use multi-variate information from sensors, cameras, people and other sources, to create a live visualization--for real-time and dynamic mental modeling of a situation.
Some visualizations are effective for design and prototyping such as virtual teaming (Thomas & Bostrom, 2007). Mental model visualizations may enhance the definition of relationships between informational objects (Ahmad, et al., June 2007).
[edit] The Viability of Mental Models
Mental models are deemed "viable" based on various factors: their accuracy and comprehensiveness; their logical alignment to the real world; their predictability; the efficacy of the model used in conjunction with other tested models; the expressibility or communicability of the model; the timeliness and updatability of the model; the soundness of the premises; the technological portability; the information richness; the aesthetics of the presentation; the originality of the model; the malleability of the model, and the legality of the materials.
[edit] Building Mental Models
Naive mental models are helpful in building a more sophisticated sense of a particular learning domain. Learners need to be able to articulate their own mental models first, no matter how accurate or inaccurate.
Instructors identify threshold concepts--core ideas that provide a broad base of comprehension for more advanced concepts. The differences between a learner's naive mental model and the sophistication of a conceptual model then leads to cognitive dissonance. This dissonance then results in changes to internal mental models.
Effective learning situations to change mental models must involve ways to test learner assumptions. There should be an avoidance of "negative transfer" or unintended learning or inaccuracies. Misconceptions should be surfaced, so they may be addressed in the learning experience. Learners need "discovery methods" to build mental models (Moreno, 2004, p. 99).
[edit] Designing a Mental Model
A mental model learning situation may be built online following the basic steps.
"1. Identify a learning domain. Select a portion (or the whole) to model. 2. Define the foundational realities. 3. Define the learning objectives and outcomes. 4. Define the relevant terminology and nomenclature. 5. Define the range of possible variables and measures. 6. Define relevant processes within the model. 7. Prototype and build the mental model while considering and adhering to mental modeling standards. 8. Build learning scenarios. 9. Build test scenarios, and test with novices and experts" (Hai-Jew, 2008, p. 12).
Setting up the learning may involve pre-learning, definitions of decision supports, and the facilitation of human interactions.
After the mental model learning (and assessments), there may be individualized feedback and group debriefing.
[edit] See Also
[edit] References
Ahmad, F., de la Chica, S., Butcher, K., Tumner, T. & Martin, J.H. (2007). Towards automatic conceptual personalization tools. ACM. 452 – 461.
Alexander, P.A. & Judy, J.E. (1988). The interaction of domain-specific and strategic knowledge in academic performance. Review of Educational Research: Vol. 58, No. 4, 375-404.
Hai-Jew, S. (2008). Building mental models with visuals for e-learning. MERLOT: Minneapolis, Minnesota, USA. Slideshow. Retrieved Feb. 15, 2009, at http://conference.merlot.org/2008/Program2008.html#Sunday.
Moreno, R. (2004). Decreasing cognitive load for novice students: Effects of explanatory versus corrective feedback in discovery-based multimedia. Instructional Science: Vol. 32. Kluwer Academic Publishers. 99 – 113.
Nardi, B.A. & Zarmer, C.L. (1991). Beyond models and metaphors: Visual formalisms in user interface design. IEEE. 487.
Riggins, F.J. & Slaughter, K.T. (2006). The role of collective mental models in IOS adoption: Opening the black box of rationality in RFID deployment. 4.
Steiger, N.M. & Steiger, D.M. (2007). Knowledge management in decision making: Instance-based cognitive mapping. Proceedings of the 40th Hawaii International Conference on System Sciences. 1- 2.
Thomas, D.M. & Bostrom, R.P. (2007). The role of a shared mental model of collaboration technology in facilitating knowledge work in virtual teams. Proceedings of the 40th Hawaii International Conference on System Sciences 2007. IEEE. 1 – 8.
Ware, C. (2004). Information Visualization. 2nd Ed. San Francisco: Elsevier, Morgan Kaufmann.
Wells, J.D. & Fuerst, W.L. (2000). Domain-oriented interface metaphors: Designing Web interfaces for effective customer interaction. IEEE. 1.
