Abstract
This study examined how practicing instructional designers manage cognitive load in a standardized scenario as they select and implement instructional strategies, message design, content sequencing, delivery medium, and technology within various domains with learners at different levels of expertise. The study employed a quasi-experimental, mixed methods design to gain insight into how practicing instructional designers perceive their awareness of strategies to manage cognitive load and implement those strategies within a standardized design scenario. The results of the study indicated that both novice and expert practitioners frequently used several strategies to manage extraneous load (worked examples, completion tasks, and dual modality) as prescribed by theory, as well as the simple-to-complex presentation strategy to manage intrinsic load. While participants frequently acknowledged differences in the levels of learner expertise within the instructional scenario, few employed strategies prescribed to address the expertise reversal effect as outlined by theory. Based on the results of this study, we present a framework to assist designers with managing for cognitive load in their everyday design practices.
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References
Biggs, J., & Tang, C. (1999). Teaching for quality learning at university. Maidenhead, Berkshire: Open University Press.
Blayney, P., Kalyuga, S., & Sweller, J. (2010). Interactions between the isolated-interactive elements effect and levels of learner expertise: Experimental evidence from an accountancy class. Instructional Science, 38(3), 277–287.
Blayney, P., Kalyuga, S., & Sweller, J. (2015). The impact of complexity on the expertise reversal effect: Experimental evidence from testing accounting students. Educational Psychology, 36(10), 1868–1885.
Chandler, P., & Sweller, J. (1991). Cognitive load theory and the format of instruction. Cognition and Instruction, 8(4), 293–332.
Chang, T. W., Hsu, J. M., & Yu, P. T. (2011). A comparison of single- and dual-screen environment in programming language: Cognitive loads and learning effects. Educational Technology & Society, 14(2), 188–200.
Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145–182.
Chi, M. T. H., Glaser, R., & Farr, M. J. (2014). The nature of expertise. New York: Psychology Press.
Christensen, T. K., & Osguthorpe, R. T. (2004). How do instructional-design practitioners make instructional-strategy decisions? Performance Improvement Quarterly, 17(3), 45–65.
Creswell, J. (2015). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (5th ed.). New York: Pearson.
de Jong, T. (2010). Cognitive load theory, educational research, and instructional design: Some food for thought. Instructional Science, 38(2), 105–134.
Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406.
Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data. Cambridge, MA: MIT Press.
Gray, C. M., Dagli, C., Demiral-Uzan, M., Ergulec, F., Tan, V., Altuwaijri, A. A., … Boling, E. (2015). Judgment and instructional design: How ID practitioners work in practice. Performance Improvement Quarterly, 28(3), 25–49
Honebein, P. C., & Honebein, C. H. (2014). The influence of cognitive domain content levels and gender on designer judgments regarding useful instructional methods. Educational Technology Research and Development, 62(1), 53–69.
Hoogveld, A. W. M., Paas, F., & Jochems, W. M. G. (2005). Training higher education teachers for instructional design of competency-based education: Product-oriented versus process-oriented worked examples. Teaching and Teacher Education, 21(3), 287–297.
Jonassen, D. H. (1988). Integrating learning strategies into courseware to facilitate deeper processing. In D. H. Jonassen (Ed.), Instructional designs for microcomputer courseware (pp. 151–182). Hillsdale, NJ: Erlbaum.
Jonassen, D. H. (2000). Toward a design theory of problem solving. Educational Technology Research and Development, 48(4), 63–85.
Jonassen, D., Prevish, T., Christy, D., & Stavrulaki, E. (2006). Learning to solve problems on the web: Aggregate planning in a business management course. Distance Education, 20(1), 49–63.
Kalyuga, S. (2006). Rapid cognitive assessment of learners’ knowledge structures. Learning and Instruction, 16(1), 1–11.
Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19(4), 509–539.
Kalyuga, S., Chandler, P., & Sweller, J. (2000). Incorporating learner experience into the design of multimedia instruction. Journal of Educational Psychology, 92(1), 126–136.
Kalyuga, S., & Sweller, J. (2005). Rapid dynamic assessment of expertise to improve the efficiency of adaptive e-learning. Educational Technology Research and Development, 53(3), 83–93.
Keller, J. M. (2010). Motivational design for learning and performance: The ARCS model approach. New York: Springer.
Kerr, S. T. (1983). Inside the black box: Making design decisions for instruction. British Journal of Educational Technology, 14(1), 45–58.
Kirschner, P., Carr, C., & van Merriënboer, J. (2002). How expert designers design. Performance Improvement Quarterly, 15(4), 86–104.
Kyun, S., Kalyuga, S., & Sweller, J. (2013). The effect of worked examples when learning to write essays in English literature. The Journal of Experimental Education, 81(3), 385–408.
Leung, C. F. (2000). Assessment for learning: Using SOLO taxonomy to measure design performance of design and technology students. International Journal of Technology and Design Education, 10(1), 149–161.
Lim, J., Reiser, R. A., & Olina, Z. (2009). The effects of part-task and whole-task instructional approaches on acquisition and transfer of a complex cognitive skill. Educational Technology Research and Development, 57(1), 61–77.
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Newbury Park, CA: Sage.
Margulieux, L. E., & Catrambone, R. (2016). Improving problem solving with subgoal labels in expository text and worked examples. Learning and Instruction, 42(1), 58–71.
Moreno, R. (2010). Cognitive load theory: More food for thought. Instructional Science, 38(2), 135–141.
Mousavi, S. Y., Low, R., & Sweller, J. (1995). Reducing cognitive load by mixing auditory and visual presentation modes. Journal of Educational Psychology, 87(2), 319–334.
Mulder, Y. G., Lazonder, A. W., & de Jong, T. (2014). Using heuristic worked examples to promote inquiry-based learning. Learning and Instruction, 29(1), 56–64.
Nievelstein, F., Van Gog, T., Van Dijck, G., & Boshuizen, H. P. A. (2013). The worked example and expertise reversal effect in less structured tasks: Learning to reason about legal cases. Contemporary Educational Psychology, 38(2), 118–125.
Oksa, A., Kalyuga, S., & Chandler, P. (2010). Expertise reversal effect in using explanatory notes for readers of Shakespearean text. Instructional Science, 38(3), 217–236.
Owens, P., & Sweller, J. (2008). Cognitive load theory and music instruction. Educational Psychology, 28(1), 29–45.
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(4), 429–434.
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.
Paas, F. G. W. C., & van Merriënboer, J. J. G. (1994). Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-load approach. Journal of Educational Psychology, 86(1), 122–133.
Pieters, J. M., & Bergman, R. (1995). The empirical basis of designing instruction. Performance Improvement Quarterly, 8(3), 118–129.
Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12(1), 61–86.
Reisslein, J., Atkinson, R. K., Seeling, P., & Reisslein, M. (2006). Encountering the expertise reversal effect with a computer-based environment on electrical circuit analysis. Learning and Instruction, 16(2), 92–103.
Renkl, A., Atkinson, R. K., Maier, U. H., & Staley, R. (2002). From example study to problem solving: Smooth transitions help learning. The Journal of Experimental Education, 70(4), 293–315.
Rourke, A., & Sweller, J. (2009). The worked-example effect using ill-defined problems: Learning to recognize designers’ styles. Learning and Instruction, 19(2), 185–199.
Rowland, G. (1992). What do instructional designers actually do? An initial investigation of expert practice. Performance Improvement Quarterly, 5(2), 65–86.
Schworm, S., & Renkl, A. (2006). Computer-supported example-based learning: When instructional explanations reduce self-explanations. Computers & Education, 46(4), 426–445.
Sentz, J. A., & Watson, G. S. (2017, November). From theory to practice: Are instructional designers using strategies to manage cognitive load? Paper presented at Association for Educational Communications & Technology International Convention, Jacksonville, FL.
Si, J., Kim, D., & Na, C. (2014). Adaptive instruction to learner expertise with bimodal process-oriented worked-out examples. Educational Technology & Society, 17(1), 259–271.
Stark, R., Kopp, V., & Fischer, M. R. (2011). Case-based learning with worked examples in complex domains: Two experimental studies in undergraduate medical education. Learning and Instruction, 21(1), 22–33.
Sugar, W. A., & Luterbach, K. J. (2016). Using critical incidents of instructional design and multimedia production activities to investigate instructional designers’ current practices and roles. Educational Technology Research and Development, 64(2), 285–312.
Sweller, J., & Chandler, P. (1994). Why some material is difficult to learn. Cognition and Instruction, 12(3), 185–233.
Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2(1), 59–89.
Sweller, J., & Levine, M. (1982). Effects of goal specificity on means-ends analysis and learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 8(5), 463–474.
Sweller, J., Mawer, R., & Howe, W. (1982). The consequences of history-cued and means-end strategies in problem solving. American Journal of Psychology, 95(3), 455–484.
Sweller, J., Mawer, R., & Ward, M. (1983). Development of expertise in mathematical problem solving. Journal of Experimental Psychology: General, 112(4), 639–661.
Tarmizi, R. A., & Sweller, J. (1988). Guidance during mathematical problem solving. Journal of Educational Psychology, 80(4), 424–436.
Van Merriënboer, J. J. G. (1990). Strategies for programming instruction in high school: Program completion vs. program generation. Journal of Educational Computing Research, 6(3), 265–285.
Van Merriënboer, J. J. G., & Sweller, J. (2010). Cognitive load theory in health professional education: Design principles and strategies. Medical Education, 44(1), 85–93.
Wedman, J., & Tessmer, M. (1993). Instructional designers’ decisions and priorities: A survey of design practice. Performance Improvement Quarterly, 6(2), 43–57.
Weston, C., & Cranton, P. A. (1986). Selecting instructional strategies. The Journal of Higher Education, 57(3), 259–288.
Williams, D. D., South, J. B., Yanchar, S. C., Wilson, B. G., & Allen, S. (2011). How do instructional designers evaluate? A qualitative study of evaluation in practice. Educational Technology Research and Development, 59(6), 885–907.
Willig, C. (2008). Introducing qualitative research in psychology: Adventures in theory and method (2nd ed.). Maidenhead: McGraw Hill/Open University Press.
Winer, L. R., & Vázquez-Abad, J. (1995). The present and future of ID practice. Performance Improvement Quarterly, 8(3), 55–67.
Wright, G., & Ayton, P. (1987). Eliciting and modeling expert knowledge. Decision Support Systems, 3(1), 13–26.
Yanchar, S. C., South, J. B., Williams, D. D., Allen, S., & Wilson, B. G. (2010). Struggling with theory? A qualitative investigation of conceptual tool use in instructional design. Educational Technology Research and Development, 58(1), 39–60.
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Appendices
Appendix A: Cognitive load studies and prescribed strategies (well-structured domains)
Authors (Year) | Context | Domain | Cognitive load effects | Prescribed strategies |
---|---|---|---|---|
Sweller et al. (1983) | K-12 and higher education | Kinematics and geometry | Learners who studied with reduced goal specificity were more efficient | Goal-free tasks during acquisition rather than conventional problem solving |
Sweller and Cooper (1985) | K-12 and higher education | Algebra | Learners who studies worked examples took less time and made fewer errors | Worked examples rather than solution generation as learners become familiar with subject |
Tarmizi and Sweller (1988) | K-12 education | Geometry | Learners who used integrated diagrams and text took less time to solve and made fewer errors | Integrate multiple sources of related information into a single element |
Chi et al. (1989) | Higher education | Physics | Students who generated explanations of solutions had higher problem-solving scores | Prompt learners to produce self-explanations while studying worked examples and completion tasks |
Jelsma and van Merriënboer (1989) | Higher education | General problem solving | Participants who used a random practice schedule took less time and made fewer errors | Present series of random tasks containing high contextual interference |
van Merriënboer (1990) | K-12 education | Computer programming | Learners who studied completion problems had higher completion rates and percentage of correct feature use | Have learners complete larger portions of a solution until they are prepared to generate solutions |
Chandler and Sweller (1991) | K-12 and technical education | Engineering and biology | Shorter instruction time and higher test scores when students used integrated instructions | Eliminate redundant information if material can be understood from a single element |
Paas (1992) | Technical education | Statistics | Lower mental effort ratings and time on task for students using completion problems | Use completion tasks to allow learner to finish partial problem solutions |
Paas and van Merriënboer (1994) | Technical education | Geometry | Better test performance, lower perceived mental effort and time on task for learners studying examples with high variability | Present a series of tasks that differ in surface features as they would in realistic situations |
Sweller and Chandler (1994) | K-12 and technical education | Computer software and electrical testing | Lower time for instruction and testing, higher test scores for learners who studied with only a manual rather than a manual and equipment | Examine material for number of interacting elements to determine complexity relative to learner expertise |
Mousavi et al. (1995) | K-12 education | Geometry | Less time spent studying and solving problems and better performance for learners who used dual-modality worked examples | Supplement visual information with a second mode of delivery (audio explanations) |
Kalyuga et al. (1998) | Technical education | Electrical circuits | Learners with less expertise had lower mental effort ratings and higher performance scores with integrated diagrams and text; reverse effect for learners with more expertise | Replace worked examples including fully integrated information with visual-only or text-only examples as learners develop expertise |
Kalyuga et al. (2000) | Technical education | Manufacturing | Students with less expertise had lower task difficulty ratings and higher performance test scores when using diagrams with auditory text; reverse effect for learners with more expertise | Replace dual modality materials with visual-only materials (no supplemental audio information) as learners gain expertise |
Pollock et al. (2002) | Technical education | Electrical circuits | Lower subjective mental load and higher performance scores for learners who used isolated task elements first and interacting elements second | Replace conventional problem solving tasks with a strategy of gradually moving from simple, isolated tasks to tasks of full complexity |
Renkl et al. (2002) | Higher education | Probability | Learners who studied with faded worked examples had a lower number of errors and better performance in near transfer | Start learners with a larger amount of guidance and progressively fade guidance over time as they develop expertise (scaffolding) |
Appendix B: Cognitive load studies and prescribed strategies (ill-structured domains)
Authors (year) | Context | Domain | Cognitive load effects | Prescribed strategies |
---|---|---|---|---|
Reisslein, Atkinson, Seeling, and Reisslein (2006) | Higher education | Engineering | Learners with low expertise had better performance scores when moving from examples to conventional problems | Fade instructional guidance over time as learners develop expertise |
Schworm and Renkl (2006) | Higher education | Instructional design | Higher post-test scores for learners who used self-explanations | Prompt learners to produce self-explanations as they study worked examples and completion problems |
Owens and Sweller (2008) | K-12 education | Music | More correct solutions during acquisition and higher post-test scores for learners using worked examples with spatial integration and simultaneous presentation | Integrate related information to reduce split attention |
Rourke and Sweller (2009) | Higher education | Design history | Learners performed better after studying worked examples rather than problem solving | Use worked examples rather than conventional problem solving as learners become familiar with material |
Oksa et al. (2010) | K-12 and adult education | Literary studies | Lower mental load ratings and better test performance for learners who studied worked examples; reverse effect for learners with more expertise | Fade instructional guidance over time as learners develop expertise |
Stark et al. (2011) | Higher education | Medicine | Lower cognitive load scores and better performance for learners using worked examples with elaborated feedback | Prompt learners to produce self-explanations as they study worked examples and completion problems |
Kyun et al. (2013) | Higher education | English literature | Lower mental effort ratings and higher performance for learners with less expertise studying worked examples | Move learners through a progression of tasks from worked examples to completion problems to solution generation |
Nievelstein et al. (2013) | Higher education | Legal cases | Lower mental effort ratings and better learning outcomes for learners using worked examples | Use a high degree of task variability when presenting worked examples |
Mulder et al. (2014) | K-12 education | Physics | Improved inquiry behavior and higher quality models during learning phase for students using worked examples | Gradually move the learner from tasks of low complexity to tasks of high complexity |
Si et al. (2014) | Higher education | Computer programming | Higher efficiency for learners studying with adaptive instruction rather than fixed instruction | Have learners complete larger portions of a solution until they are prepared to generate solutions |
Jung and Suzuki (2015) | Higher education | Japanese language learning | Better learning outcomes and higher student satisfaction for learners who used less comprehensive worked example templates | Use less detailed worked examples in instances where creative and independent thinking are intended outcomes |
Margulieux and Catrambone (2016) | Higher education | Computer programming | Lower time on task and better performance for learners using worked examples with labeled sub-goals | Examine material for number of interacting elements to determine complexity relative to learner expertise |
Appendix C: Instructional designer use of theory studies and findings
Authors (year) | Experience | Settings | Study goals | Findings on theory use |
---|---|---|---|---|
Rowland (1992) | 7–20 years of experience (experts), one or no projects (novices) | Academic (2), business (1), government (1) | Problem understanding and solution generation | Significant differences in problem interpretation, representation, solution generation, solutions, use of internal resources, external resources, decision making; prescriptions without experience base for novices are not effective |
Wedman and Tessmer (1993) | A few months to 25 years (mean of 6 years) | 30 from same training and development group, 43 from business and government | Identify frequency of 11 ID activities and reasons why certain activities are excluded from projects | Design activities occur on an irregular basis in practice, almost no designers use all ID model design activities, various reasons for omitting design activities (decisions already made, lack of time, etc.); models need to be more practical |
Winer and Vázquez-Abad (1995) | 1–35 years of experience (mean of 13 years) | Business and industry | Identify frequency of 11 ID activities and reasons why certain activities are excluded from projects | Selecting instructional strategies and media formats seen as most necessary activities, emphasis on prototyping rather than classic ID life cycle (with extensive analysis) for projects; many contextual pressures, instructional methods may be considered inputs to the ID process rather than outcomes, use of heuristic knowledge rather than prescriptive models |
Pieters and Bergman (1995) | 2–6 years of professional experience | Education and training | Indicate characteristics of the design process in practice | Many deviations from general ISD models, practical context leads to less time for activities than needed, iterative processes are common; experts use contextual knowledge, social variables influence the process, evaluation and implementation activities do not get the time they deserve |
Kirschner et al. (2002) | Expert designers (years not mentioned) | Industry and education | Identify important design principles and complete a design task through a series of actions to create a course | Designers in competency based education agree that design should be based on the needs of learners rather than the content, university designers feel alternative solutions are important and focus on the instructional blueprint, business designers are more client oriented and concerned about process buy-in |
Christensen and Osguthorpe (2004) | Didn’t ask number of years of experience, 69% had master’s degree, 30% had doctorate | Industry, higher education, K12, military, adult education | Select frequency of their use of 12 ID theories and strategies; list useful ID and learning theories, rate information sources for learning about theories and strategies, list useful information sources | Little over half use theory when making strategy decisions, those who use theory use a variety of theories based on design situation, over 80% indicate interactions with others are most common means of making strategy decisions and learning about new theories; range of theories used such as Gagne and Merrill, as well as learning theories such as constructivism and information processing theory |
Yanchar et al. (2010) | 4 had master’s degrees in ID, 3 were informally trained with non-ID master’s degrees, 1 had a PhD in the natural sciences | 1 in large design organization, 2 in small design organization, 1 in a university ID center, 2 as in-house designers in a laboratory organization, 1 as in-house designer in a technology corporation | Describe practical involvement in design process, uses and views of formal theories | Largely considered theory as potentially helpful for generating ideas of making sense of situations but didn’t endorse all aspects of a particular theory, situational limitations prevented them from implementing theory in practice more than they do, opposed to rigid use of theory without reflection on its application, importance of using theory to help shape and inform their intuition as they continue to gain experience within their craft |
Honebein and Honebein (2014) | Didn’t ask number of years of experience; were seeking an ID certificate, master’s degree, or PhD degree | Corporations, consulting firms, colleges, K12 | Indicate usefulness of methods for each content level (186 unique ratings) on a scale from 1 (least useful) to 5 (most useful) | Level of cognitive domain content had a statistically significant effect on judgments of IDs regarding the appropriateness of specific instructional methods, methods judged by practitioners aligned with those of experts; statistically significant effect of designer gender on judgments of methods, although effect size was small and didn’t support their hypothesis |
Gray et al. (2015) | 2–11 years of experience in ID | 1 from in-house consultancy at a university, 7 from a commercial ID firm near the university | Practitioners conducted their everyday activities on various projects | IDs made an average of 35 judgments during observation (16 per h), wide variety of types of judgments regardless of design activity or project phase; judgments of practitioners tend to be clustered closely together based on the activity rather than occurring discretely, judgements depend on position, design firm culture, and type of client |
Sugar and Luterbach (2016) | 7–20 years of experience (experts), one or no projects (novices) | Academic (2), business (1), government (1) | Problem understanding and solution generation | 6 effective practices (collaboration, providing resources, social presence, differentiated instruction, examples, creating instructional materials), 4 extraordinary (using theory in practice, managing complex projects, organizing content, matching methods to learners and content), 6 ineffective (dealing with inadequate technology, not collaborating, not using an instructional design process, selecting inappropriate strategies, not supporting interaction, mismatching methods to learners and content) |
Appendix D: Instructional designer decision making questionnaire
Please complete this questionnaire to the best of your ability and respond to each of the questions as accurately as possible. The data gathered from the responses will be used to examine how instructional designers make decisions in practice.
The questionnaire will take approximately 15 min to complete. Please return it to jsent003@odu.edu prior to your appointment time for the instructional design scenario activity. You may use the same email address should you have any issues or questions. Thank you for your time.
Please answer the following questions about yourself.
For each of the following, please indicate the degree to which you agree with the statement:
For each of the following, please indicate how often you currently use the strategy in your instructional design work:
Appendix E: Instructional design scenario
Please review the instructional scenario below and design a solution that addresses the needs of the learners to the best of your ability. There is no correct or preferred approach to the scenario, so please do not worry about whether your approach is the “right” one. Please speak aloud as you are making your design decisions so that the researchers can follow the process you are using. The data gathered from the responses will be used to examine how instructional designers make decisions in practice.
The scenario will take approximately 30 min to complete. All of the information you will need is contained within the scenario, but please feel free to ask the researcher if you need clarification on any of the information. Thank you for your time.
Scenario
You are an instructional designer working for your current or most recent organization (K-12, higher education, industry, government, etc.), and you need to cover the creation of spreadsheets as part of the regular course of your instructional duties. The creation and use of spreadsheets is considered a basic competency within your area of practice that learners need to master in order to work with data in their particular settings. You have been asked to incorporate instruction related to the basic creation of spreadsheets within the Microsoft Excel software package as part of regular training/education activities for your learners.
Needs assessment
Your supervisor has asked you to create an instructional module on the creation of basic spreadsheets within Microsoft Excel that will enable all learners to establish a consistent level of competency inputting and manipulating data. The instruction needs to be basic enough that learners are able to complete it with only a fundamental understanding of the mathematical operations involved in creating a spreadsheet, and the instruction needs to be flexible enough to be used by learners independently at their own pace.
Learner analysis
Regardless of your particular practice setting, all learners are able to read English at an 8th grade level or higher, have basic proficiency in the use of computers and mathematical formulas, and are physically able to perform the tasks involved.
The majority of the learners (16 students in a class of 20) have little experience using Microsoft Excel and should be considered novices with respect to the creation and use of spreadsheet applications. Robert, shown below, is one of these learners:
Robert is a third-year undergraduate student who is majoring in studio art. He has used computers throughout his K-12 and college education but has not done much work with Microsoft Office applications other than basic word processing. He has experience viewing budget data in Excel spreadsheets during his time in the Art Club in high school, but he has not created a spreadsheet from scratch or manipulated the data in an existing spreadsheet. Robert has taken typical mathematics courses prior to enrolling in college, including 2 years of algebra. He is considering a minor in business due to his interest in starting his own art studio, so Robert is motivated to learn and apply the information from the unit to his area of study.
There are, however, a few learners (4 students in a class of 20) who have an intermediate understanding of Microsoft Excel and the creation of basic spreadsheets. Karen, shown below, is one of these learners:
Karen is a first-year undergraduate student who is majoring in business administration. She has used computers throughout her K-12 education and has some experience with each of the Microsoft Office applications. She has not taken any formal coursework in Excel, but she has a working knowledge of the basic functionality involved in creating a spreadsheet from tutorials within the program itself to put together simple spreadsheets for high school classes. Karen has taken business mathematics and algebra courses prior to enrolling in college. She anticipates taking a few accounting courses later in college as part of her major, so she is motivated to build upon her existing knowledge by learning the information. As with the other learners with more expertise, Karen is still required to take the instruction and meet the objectives.
Environmental analysis
The instruction may be delivered by any means of delivery deemed appropriate, provided that the learners are able to progress through the material at their own pace. The learners have access to computers in a lab at your organization/institution, and all computers are equipped with Microsoft Office and an Internet connection. Written materials can also be made available to the learners if you determine they are needed for the instruction. An instructor station and a projector are located at the front of the lab if you find a need to use those. Learners will be given access to the desks and computers during class/working hours as needed to complete the instruction.
Task analysis
A task analysis of basic spreadsheet creation revealed the following steps:
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Determine a practical need for a spreadsheet application.
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Sketch out the structure of the spreadsheet.
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Determine the calculations that will be needed to manipulate the data.
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Open microsoft excel.
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Create column headings appropriate to the application.
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Create row headings appropriate for the data.
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Input the data in the appropriate cells.
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Format cells as appropriate for the types of data included.
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Use basic math symbols (= , +, −, *,/) to create formulas as appropriate.
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Use a function to calculate totals (SUM) as appropriate.
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Use a function to calculate averages (AVERAGE) as appropriate.
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Use a function to find the highest value (MAX) in a range of numbers.
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Use a function to find the lowest value (MIN) in a range of numbers.
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Use a function to determine how many numbers (COUNT) are in a range of cells.
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Copy a function across multiple spreadsheet cells.
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Create a basic chart that displays the information graphically in a useful manner.
Instructional objectives
Upon completion of the instruction:
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1.
The learners will create a spreadsheet application that addresses a real-world problem of either personal or professional significance.
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2.
The learners will structure the spreadsheet in a logical manner that lends itself to solving the problem.
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3.
The learners will create column and row headings that sufficiently explain the data.
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4.
The learners will input data as appropriate for the spreadsheet structure created.
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5.
The learners will format the cells as appropriate for the type(s) of data involved.
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6.
The learners will use three or more math symbols to create formulas to manipulate the data.
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7.
The learners will use at least three functions to manipulate the data in the process of solving the problem.
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8.
The learners will create a basic chart that presents the data graphically in order to solve the practical problem they have identified.
You have approximately 30 min to design and explain your solution to this instructional scenario. Please describe aloud to the researcher the steps you are taking throughout the process and the reasons you are making those decisions. This study is primarily concerned with the decision making process you use and the reasons you are taking specific steps to design a solution.
Appendix F: Instructional design scenario observation sheet
Overall SOLO rating (circle):
0 | Respondent did not apply any strategies to manage cognitive load. (pre-structural) |
1 | Respondent primarily considered a single source of cognitive load. (uni-structural) |
2 | Respondent considered multiple sources of cognitive load. (multi-structural) |
3 | Respondent considered the interaction of multiple sources of cognitive load. (relational) |
4 | Respondent considered cognitive load holistically and displayed a comprehensive understanding of its implications. (extended abstract) |
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Appendix G: Debriefing interview protocol
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Sentz, J., Stefaniak, J., Baaki, J. et al. How do instructional designers manage learners’ cognitive load? An examination of awareness and application of strategies. Education Tech Research Dev 67, 199–245 (2019). https://doi.org/10.1007/s11423-018-09640-5
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DOI: https://doi.org/10.1007/s11423-018-09640-5