Skip to main content
Log in

Chemistry problem solving instruction: a comparison of three computer-based formats for learning from hierarchical network problem representations

  • Published:
Instructional Science Aims and scope Submit manuscript

Abstract

Within the cognitive load theory framework, we designed and compared three alternative instructional solution formats that can be derived from a common static hierarchical network representation depicting problem structure. The interactive-solution format permitted students to search in self-controlled manner for solution steps, static-solution format displayed all solutions steps, and no-solution format did not have solution steps. When we matched instructional time across the formats, in relation to the complex molarity problems rather than the dilution problems, differential transfer performance existed between the static-solution or no-solution formats and the interactive-solution format, but not between the static-solution format and no-solution format. The manner in which learners interact with the static-solution and no-solution formats depends on their level of expertise in the chemistry domain. With considerable learner expertise, provision of solution steps may be redundant incurring extraneous cognitive load. Absence of the solution steps may not have left sufficient cognitive capacity for germane cognitive load as some beginning learners lacked the prior knowledge to deduce the solution steps. Searching for solution steps presumably incurred extraneous cognitive load which interfered with learning and hence, in the interactive-solution format, it outweighed the benefit of engaging in self-regulated interaction with the content. Hence, cognitive load theory is a promising tool to predict the mental load associated with learning from the three alternative computer-based instructional formats.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Graph 1

Similar content being viewed by others

References

  • Anderson, R. C., & Pearson, P. D. (1984). A schema-theoretic view of basic processes in reading comprehension. In P. D. Pearson, R. Barr, M. L. Kamil, & P. Mosenthal (Eds.), Handbook of reading research (Vol. 1, pp. 255–291). White Plains, NY: Longman.

    Google Scholar 

  • Atkinson, R. K., Derry, S. D., Renkl, A., & Wortham, D. W. (2000). Learning from examples: Instructional principles from the worked examples research. Review of Educational Research, 70, 181–214.

    Google Scholar 

  • Blessing, S. B., & Ross, B. H. (1996). Content effects in problem categorization and problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 792–810.

    Article  Google Scholar 

  • Borsook, T. (1991). Harnessing the power of interactivity for instruction. In M. R. Simonson & C. Hargrave (Eds.), Proceedings of the 1991 Convention of the Association for Educational Communications and Technology (pp. 103–117). Orlando, FL: Association for Educational Communications and Technology.

  • Catenazzi, N., & Sommaruga, L. (1999). The evaluation of the Hyper Apuntes interactive learning environment. Computers & Education, 32, 35–49.

    Article  Google Scholar 

  • Chandler, P., & Sweller, J. (1991). Cognitive load theory and the format of instruction. Cognition and Instruction, 8(4), 293–332.

    Article  Google Scholar 

  • Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representations of physics problems by experts and novices. Cognitive Science, 5, 121–152.

    Article  Google Scholar 

  • Cummins, D. D. (1992). Role of analogical reasoning in the induction of problem categories. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18, 1103–1124.

    Article  Google Scholar 

  • Dellarosa, D. (1985). Abstraction of problem-type schemata through problem comparison (Tech. Rep. No. 146). Boulder: University of Colorado, Institute of Cognitive Science.

  • El-Tigi, M., & Branch, R. (1997, May–June). Designing for interaction, learner control, and feedback during web-based learning. Educational Technology, 37(3), 23–29.

    Google Scholar 

  • Gentner, D. (1982). Are scientific analogies metaphors? In D. S. Miall (Ed.), Metaphor: Problems and perspectives (pp. 106–132). Brighton, England: Harvester Press.

    Google Scholar 

  • Gentner, D. (1983). Structure mapping: A theoretical framework for analogy. Cognitive Science, 7, 155–170.

    Article  Google Scholar 

  • Gerjets, P., Scheiter, K., & Catrambone, R. (2004). Designing instructional examples to reduce intrinsic cognitive load: Molar versus modular presentation of solution procedures. Instructional Science, 32(1), 33–58.

    Article  Google Scholar 

  • Halabi, A., Tuovinen, J. E., & Smyrnios, K. X. (2000). Using CBL to improve cognitive load and reduce feedback redundancy in accounting distance learning. Distance Education, 21(1), 162–182.

    Article  Google Scholar 

  • Kalyuga, S., Chandler, P., Sweller, J., & Tuovinen, J. E. (2001). When problem solving is superior to studying worked examples. Journal of Educational Psychology, 93(3), 579–588.

    Article  Google Scholar 

  • Larkin, J., McDermott, J., Simon, D., & Simon, H. (1980). Models of competence in solving physics problems. Cognitive Science, 4, 317–345.

    Article  Google Scholar 

  • Lawless, K., & Brown, S. (1997). Multimedia learning environments: Issues of learner control and navigation. Instructional Science, 25, 117–131.

    Article  Google Scholar 

  • Low, R., & Over, R. (1989). Detection of missing and irrelevant information within algebraic story problems. British Journal of Educational Psychology, 59, 296–305.

    Google Scholar 

  • Low, R., & Over, R. (1990). Text editing of algebraic word problems. Australian Journal of Psychology, 42, 63–73.

    Article  Google Scholar 

  • Merrill, D., Li, Z., & Jones, M. K. (1990). Second generation instructional design. Educational Technology, 30(2), 7–15.

    Google Scholar 

  • Nathan, M. J., Kintsch, W., & Young, E. (1992). A theory of algebra-word-problem comprehension and its implications for the design of learning environments. Cognition and Instruction, 9(4), 329–389.

    Article  Google Scholar 

  • Novick, L. R., & Holyoak, K. J. (1991). Mathematical problem solving by analogy. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17(3), 398–415.

    Article  Google Scholar 

  • Paas, F. G. W. C. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84, 429–434.

    Article  Google Scholar 

  • Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71.

    Article  Google Scholar 

  • Paas, F. G. W. C., & van Merrienboer, J. J. G. (1993). The efficiency of instructional conditions: An approach to combine mental effort and performance measures. Human Factors, 35(4), 737–743.

    Google Scholar 

  • Paas, F. G. W. C., & van Merrienboer, J. J. G. (1994). Variability of worked examples and transfer of geometrical problem solving skills: A cognitive load approach. Journal of Educational Psychology, 86, 122–133.

    Article  Google Scholar 

  • Phelps, H., & Reynolds, R. (1999). Formative evaluation of a web-based course in meteorology. Computers & Education, 32, 181–193.

    Article  Google Scholar 

  • Philips, R., Jenkin, N., Fyfe, G., & Fyfe, S. (1997). The user interface design of learner-centred interactive multimedia programs. http://cleo.murdoch.edu.au/tlc/phillips/Ed-Media97/Paper294.htmlC.C.

  • Reed, S. K. (1987). A structure-mapping model for word problems. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13(1), 124–139.

    Article  Google Scholar 

  • Reed, S. K. (1989). Constraints on the abstraction of solutions. Journal of Educational Psychology, 81(4), 532–540.

    Article  Google Scholar 

  • Reed, S. K., Dempster, A., & Ettinger, M. (1985). Usefulness of analogous solutions for solving algebra word problems. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11, 106–125.

    Article  Google Scholar 

  • Renkl, A., Atkinson, R. K., & Grobe, C. S. (2004). How fading worked example steps works—A cognitive load perspective. Instructional Science, 32(1–2), 59–82.

    Article  Google Scholar 

  • Ross, B. H. (1984). Remindings and their effects in learning a cognitive skill. Cognitive Psychology, 16, 371–416.

    Article  Google Scholar 

  • Ross, B. H., & Kennedy, P. T. (1990). Generalizing from the use of earlier examples in problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16(1), 42–55.

    Article  Google Scholar 

  • Schnotz, W., Bockheler, J., & Grzondziel, H. (1999). Individual and co-operative learning with interactive animated pictures. European Journal of Psychology of Education, 14, 245–265.

    Article  Google Scholar 

  • Silver, E. A. (1979). Student perceptions of relatedness among mathematical verbal problems. Journal for research in Mathematics Education, 10, 195–210.

    Article  Google Scholar 

  • Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.

    Article  Google Scholar 

  • Sweller, J., Chandler, P., Tierney, P., & Cooper, M. (1990). Cognitive load and selective attention as factors in the structuring of technical materials. Journal of Experimental Psychology: General, 119, 176–192.

    Article  Google Scholar 

  • Sweller, J., van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296.

    Article  Google Scholar 

  • Tarmizi, R., & Sweller, J. (1988). Guidance during mathematical problem solving. Journal of Educational Psychology, 80, 424–436.

    Article  Google Scholar 

  • Tuovinen, J. E., & Paas, F. (2004). Exploring Multidimensional approaches to the efficiency of instructional conditions. Instructional Science, 32, 133–152.

    Article  Google Scholar 

  • Tuovinen, J. E., & Sweller, J. (1999). A comparison of cognitive load associated with discovery learning and worked examples. Journal of Educational Psychology,91, 334–341.

    Article  Google Scholar 

  • Van Gog, T., Paas, F., & Van Merrienboer, J. J. G. (2004). Process-oriented worked examples: Improving transfer performance through enhanced understanding. Instructional Science, 32(1), 83–98.

    Article  Google Scholar 

  • Wagner, E. D. (1994). Interactivity: From agents to outcomes. In T. E. Cyrs (Ed.), Teaching and learning at a distance: What it takes to effectively design, deliver, and evaluate program. Francisco: Jossey-Bass Publishers.

    Google Scholar 

  • Ward, M., & Sweller, J. (1990). Structuring effective worked examples. Cognition and Instruction, 7, 1–39.

    Article  Google Scholar 

  • Weaver, C. A., & Kintsch, W. (1992). Enhancing students’ comprehension of the conceptual structure of algebra word problems. Journal of Educational Psychology, 84, 419–428.

    Article  Google Scholar 

  • Weller, H. G. (1988). Interactivity in microcomputer-based instruction: Its essential components and how it can be enhanced. Journal of Educational Technology Systems, 28(2), 23–27.

    Google Scholar 

Download references

Acknowledgment

This project was supported by a Seed Grant [231/00 (22)] from the Universiti Malaysia Sarawak.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Juhani Tuovinen.

Appendices

Appendix 1

Similar and transfer test problems

Similar test

Dilution problem: Exactly 2.0 liters water was added to 1.5 liters of 2.0 M HCl. What was the concentration of HCl in the resulting solution?

Molarity problem: A NaCl solution is 2.0 M. How many milliliters (ml) of this solution will contain 10 g of NaCl? (Na = 23, Cl = 35.5).

Transfer test

Dilution problem: Hydrochloric acid is a strong acid, which completely dissociates in aqueous solution. 1.0 ml of 1.0 M HCl is diluted to 1 liter with distilled water. About 100 ml of this is then further diluted to 1 l using distilled water. What is the concentration of the final solution?

Molarity problem: Phenobarbitone, C12H12N2O3, is commonly used in medicine as a long-acting sedative. A patient may take 5.0 ml of phenobarbitone as dose. If the concentration of the phenobarbitone is 0.017 M. What mass of the drug is present in one dose? (C = 12, O = 16, N = 14, H = 1).

Appendix 2

Pre–posttests solution strategies

Format

Students

Pre-test

Post-test

Similar

Similar

Dilution

Molarity

Dilution

Molarity

IS

a.

\( \frac{\hbox{M}}{\hbox{V}}=n \)

M1V1 = M2V2

 

b.

\( \frac{\hbox{mass}}{\hbox{RFM}} = \chi \)

  
   

\( \frac {\chi \,\times\,1000} {\hbox {M}} = {\hbox {v}}\)

M1V1 = M2V2

SS

a.

\( \chi\,=\,\frac{\hbox{mass}}{\hbox{RFM}} \)

M1V1 = M2V2

\( \frac{\hbox{mass}}{\hbox{RFM}} = \frac{\hbox{MV}}{1000} \)

 

\( {\hbox{M}} = \chi \times \frac{\hbox{V}}{1000} \)

  

b.

\( \frac{\hbox{V1M1}}{1000} = \chi \)

\( \frac{\hbox{M1V1}}{{{\hbox{V}}2}} = {\hbox{M}}2 \)

\( \frac{\hbox{V2M2}}{1000} = \chi \)

   

NS

a.

\( \frac{\hbox{V1M1}}{1000} = \chi \)

\( \frac{\hbox{M1V1}}{{{\hbox{V}}2}} = {\hbox{M2}} \)

\( \frac{\hbox{mass}}{\hbox{RFM}} = \frac{\hbox{MV}}{1000} \)

\( \frac{\hbox{V2M2}}{1000} = \chi \)

   

b.

\( \frac{\hbox{V1M1}}{1000} = \chi \)

M1V1 = M2V2

\( \frac{\hbox{mass}}{\hbox{RFM}} = \frac{\hbox{MV}}{1000} \)

\( \frac{\hbox{V2M2}}{1000} = \chi \)

   
  1. Note: IS = Interactive Solution, SS = Static Solution, NS = No Solution
  2. a and b are two students randomly chosen from each format
  3. ‘–’ represents non-attempted problems

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ngu, B.H., Mit, E., Shahbodin, F. et al. Chemistry problem solving instruction: a comparison of three computer-based formats for learning from hierarchical network problem representations. Instr Sci 37, 21–42 (2009). https://doi.org/10.1007/s11251-008-9072-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11251-008-9072-7

Keywords

Navigation