Guided discovery learning

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1 Definitions

  • “Students discover knowledge without guidance, developing their own understanding. The role of instruction is merely to provide a suitable environment, which in software might be a microworld or simulation. Discovery learning, or instructionless learning, involves hypothesis formulation and testing (Goodyear et al. 1991, Shrager and Klahr 1986).” (Stephen Bostock), retrieved, 17:17, 15 September 2006 (MEST).

See also: computer simulation, SimQuest, science simulation

2 Why guided discovery ?

de Jong and van Joolingen (1998) mention the following problems that learners encounter in scientific discovery learning:

  • Generating hypotheses is difficult. For example, learners may not know what a hypothesis should look like. They have difficulty in modifying their hypotheses to afford the data they gather. Or they make inferences based on variables that remain unchanged between two experiments.
  • Designing experiments for deciding on the validity of a hypothesis is also a major challenge. For example, learners have a certain tendency to seek information confirming their hypothesis instead of trying to falsify their hypothesis. Or they design experiments which vary too many variables at once so that no conclusion can be drawn.
  • Self-regulation of the discovery learning process is a key issue which separates successful learners from unsuccessful learners. Successful discoverers tend to follow a plan going through their experiments, where unsuccessful learners use a more random strategy.
- Summary by R. Reichert (2005). Review of de Jong, Ton; van Joolingen, Wouter R. (1998)

“Generally, one can say that successful discovery learning is related to reasoning from hypotheses, to applying a systematic and planned discovery process (like systematic variation of variable values), and to the use of high quality heuristics for experimentation.” (de Jong, T. & van Joolingen, W. (1998, Preprint). Since this is not natural, it requires guiding.

Alfieri et al. (2011:1) conducted two meta-analyses allow concluding that unguided discovery learning does not benefit learners, whereas guided discovery learning leads to better results than explicit instruction. “The 1st examined the effects of unassisted discovery learning versus explicit instruction, and the 2nd examined the effects of enhanced and/or assisted discovery versus other types of instruction (e.g., explicit, unassisted discovery). Random effects analyses of 580 comparisons revealed that outcomes were favorable for explicit instruction when compared with unassisted discovery under most conditions (d = –0.38, 95% CI [−.44, −.31]). In contrast, analyses of 360 comparisons revealed that outcomes were favorable for enhanced discovery when compared with other forms of instruction (d = 0.30, 95% CI [.23, .36]). The findings suggest that unassisted discovery does not benefit learners, whereas feedback, worked examples, scaffolding, and elicited explanations do.”. In the conclusion, the authors formulate the following recommendation: “On the basis of the current analyses, optimal approaches should include at least one of the following: (a) guided tasks that have scaffolding in place to assist learners, (b) tasks requiring learners to explain their own ideas and ensuring that these ideas are accurate by providing timely feedback, or (c) tasks that provide worked examples of how to succeed in the task. Opportunities for constructive learning might not present themselves when learners are left unassisted.” (Alfieri et al. , 2011: 13)

3 Features of discovery learning

Guided discovery was developed by Dr. Charles E. Wales at the Center for Guided Design, West Virginia University (Leutner, 1993). Discovery learning is much older and other forms of structuredness do exist.

“Guided Discovery, is characterized by convergent thinking. The instructor devises a series of statements or questions that guide the learner, step by logical step, making a series of discoveries that leads to a single predetermined goal. In other words the instructor initiates a stimulus and the learner reacts by engaging in active inquiry thereby discovering the appropriate response. Mosston (1972:117) specifies ten cognitive operations that might take place as the learner engages in active inquiry: recognizing da analysing, synthesizing, comparing and contrasting, drawing conclusions, hypothesizing memorizing, inquiring, inventing, and discovering. By actively doing and consequence discovering facts or concepts, the learner will understand and therefore remember the subject matter. Mosston (1972:122) cautions that "discovery learning cannot take place if t answers are given." He also points out certain drawbacks of this teaching method: it precisely controls and manipulates learning behaviour and could therefore be abused, and is designed for individual rather than group use.” - The Discovery LearningConcept, retrieved, 17:17, 15 September 2006 (MEST)

According to Spencer (1999), key features of guided discovery learning are:

  • A context and frame for student learning through the provision of learning outcomes
  • Learners have responsibility for exploration of content necessary for understanding through self directed learning
  • Study guides are used to facilitate and guide self directed learning
  • Understanding is reinforced through application in problem oriented, task based, and work related experiences

Guided discovery learning designs can be enhanced with various computational tools. One of these is simulation. According to Reichert's (2005) summary of de Jong and Joolingen (1998) the following scaffolds should be included in the design of computer simulations for discovery learning.

  • Direct "just-in-time" access to the domain knowledge seems to have a positive effect on problem solving and on transfer of knowledge.
  • Support for hypotheses generation, for example by providing hypothesis construction tools, seems to have positive effects on the performance of learners.
  • Support for designing experiments by providing hints and advice seems to positively affect the learners' experimentation abilities (but does not seem to influence the learning outcome).
  • Support for making predictions e.g. by providing them a graphic tool to draw a curve that depicts the prediction.
  • Support for the regulation the learning process includes various measures:
    • Model progression, such as step-by-step model expansion (e. g. expanding the complexity of the model).
    • Planning support (e. g. using guiding questions, quests or even assignments).
    • Monitoring support (e. g. show what has already be done in the simulation)
    • Structuring the discovery process (e. g. providing students with a sequenced structure such as "set-up, do, reflect").

Extensive review of the litterature by de Jong and Jooling showed that generally speaking guided simulations lead to better results than non-guided ones. Compared to expository teaching, guided simulation may increase aspects of "deep learning", e.g. understanding of concepts and of course better train for the discovery process itself. See also some of the debate reported in the discovery learning article. In short: it is still open...

Guided discovery or similar principles exists within many frameworks, e.g. Laurillard's conversational framework

4 An example in vocational training

According to Allen (2002), in an example of discovery learning in action, DaimlerChrysler uses guided discovery learning principles for teaching maintenance engineers to troubleshoot automotive electrical systems. Below we summarize its most salient features described in this article.

Determining the source of faults is a very complex task. Maintenance engineers must use diagnostic aids and equipment together with a carefully thought-out strategy to pinpoint and solve the problem. Since workers cannot remember the configurations in all the vehicles, training cannot anymore be specific to any one system. Training focus must be on strategic thinking as well as specific facts, procedures, and concepts. Training must build flexible skills and adaptive thinking to allow for situation-to-situation variations in task sequencing. Therefore the learning systems empowers maintenance engineers to:

  • Plot their own course of problem-solving;
  • Perform simulated tests on circuits, with simulated diagnostic equipment used to report accurate measures;
  • Access reference information;
  • Order repairs and test results;
  • Proceed with repairs of vehicles returned by customers who have complaints about prior service; and
  • Get feedback on efficiency (completion time and completion costs of the job), incorrect assumptions and decisions, and how to approach diagnosis more effectively.

The risk-and-contingent outcome for the DaimlerChrysler discovery learning application gives the exercises a game-like quality. Learners are motivated to try the exercises repeatedly, in order to improve their performance scores. The full simulation makes the learning task realistic, and supports the transfer of learning to real, on-the-job performance.

5 Discussion

The question is not whether guided learning is better than unguided discovery. It ought to rephased as "how much guiding" is needed for a given "learning level" (and maybe learning goals and subject areas). See also some discussion in science simulation.

6 Links

7 References

  • Allen, Michael (2002), Discovery Learning: Repurposing An Old Paradigm, LTI Newsline, HTML, retrieved, 17:17, 15 September 2006 (MEST).
  • Aleven, V., Stahl, E., Schworm, S., Fischer, F., & Wallace, R. (2003). Help Seeking and Help Design in Interactive Learning Environments. Review of Educational Research, 73(7), 277-320.
  • de Jong, T. & van Joolingen, W. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2):179-201, 1998. Abstract/PDF Preprint
  • Faryniarz, J. V., & Lockwood, L. G. (1992). Effectiveness of microcomputer simulations in stimulating environmental problem solving by community college students. Journal of Research in Science Teaching, 29(5), 453-470. Abstract and PDF (Access restricted).
  • Feldon, David, F. Dispelling a Few Myths about Learning, UrbanEd PDF
  • Gokhale, Anu A. (1996), Effectiveness of Computer Simulation for Enhancing Higher Order Thinking, Journal of Industrial Teacher Education, Volume 33, Number 4. HTML.
  • Goodyear, P., Njoo, M, Hijne, H & van Berkum, J.J.A. 1991. Learning processes, learner attributes and simulations. Education and Computing (6) 263-304.
  • Kirschner, P., Sweller, J., & Clark, R. E. (2006). Why Unguided Learning Does Not Work: An Analysis of the Failure of Discovery Learning, Problem-Based Learning, Experiential Learning and Inquiry-Based Learning. Educational Psychologist Vol. 41, Iss. 2 PDF Preprint
  • Kirschner, P. A., Sweller, J., & Clark, R. E. (2006) Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. An other draft version of the above PDF
  • Leutner, Detlev, (1993), Guided Discovery Learning with Computer-Based Simulation Games: Effects of Adaptive and Non-Adaptive Instructional Support, Learning and Instruction, 3 (2) p113-32.
  • Lindström, B., Marton, F., Ottosson, T., & Laurillard, D. (1993). Computer simulations as a tool for developing intuitive and conceptual understanding in mechanics. Computers in Human Behavior, 9, 263-281.
  • Mosston, Muska (1972), Teaching: From Command to Discovery. Belmont, California: Wadsworth Publishing.
  • Mott Bradford W. , Scott W. McQuiggan, Sunyoung Lee, Seung Y. Lee, and James C. Lester, Narrative-Centered Environments for Guided Exploratory Learning, PDF
  • Reichert, Raimond (2005). Review of de Jong, Ton; van Joolingen, Wouter R. (1998) Scientific Discovery Learning with Computer Simulations of Conceptual Domains, elearning-reviews, [ HTML]
  • Shulman, L. and Keisler, E. (1966).Learning by Discovery: A Critical Appraisal. Rand McNally.
  • Shrager, J & Klahr, D. 1986. Instructionless learning about a complex device: the paradign and observations. Int. J. Man-Machine Studies. 25, 153-198.
  • Spencer, John A., (1999) Learner centred approaches in medical education, BMJ318:1280-1283 (open access)