Abstract
It was investigated how domain-specific knowledge, fluid intelligence, vocational interest and work-related self-efficacy predicted domain-specific problem-solving performance in the field of office work. The participants included 100 German VET (vocational education and training) students nearing the end of a 3-year apprenticeship program as an industrial clerk (n = 63) which usually leads to a position in office work, lower or middle management, or a similar apprenticeship program to become IT-systems management assistants (n = 37). The participants worked on three computer-based problem scenarios dealing with operative controlling, a relevant domain to both training occupations, and completed further assessments to measure the variables listed above. Theoretical considerations, prior research and domain analyses suggested that industrial clerks would have greater domain-specific problem-solving competence (H1a) and domain-specific knowledge (H1b) than IT-systems management assistants and that domain-specific knowledge would be the strongest predictor of problem-solving competence (H2: “knowledge-is-power” hypothesis); all hypotheses were confirmed. Hypothesis 3, the “Elshout-Raaheim hypothesis,” predicts that fluid intelligence and problem-solving competence are most strongly correlated in the context of intermediate levels of task-related content knowledge, however the highest correlation was found in the group with low domain-specific knowledge. The findings suggest that intelligence plays a minor role in later stages of competence development whereas typical problem situations in later stages particularly require prior knowledge. The relationship of intelligence, knowledge and problem solving as well as limitations of the study, particularly weaknesses in the measurement of non-cognitive dispositions, are discussed.
Similar content being viewed by others
Notes
This research was funded by the German Federal Ministry of Education and Research (BMBF) under Grant No. 01DB1119-23: ‘Modeling and assessing domain-specific problem-solving competence of industrial clerks’.
The English occupational titles may suggest that IT systems management assistant is a higher status role than industrial clerk, but this is not the case; entry requirements, duration of training and curricular demands for the two programs are similar. In particular, IT systems management assistants are also concerned with operative controlling, however, curriculum analysis indicated that they would be less proficient in this domain as compared to industrial clerks because they spend less time on these contents.
References
Abele, A., Stief, M., & Andrä, M. (2000). Zur ökonomischen Erfassung beruflicher Selbstwirksamkeitserwartungen – Neukonstruktion einer BSW-Skala [On the economic measurement of occupational self-efficay—reconstruction of a BSW scale]. Zeitschrift für Arbeits- und Organisationspsychologie, 44(3), 145–151.
Abele, S., Greiff, S., Gschwendtner, T., Wüstenberg, S., Nickolaus, R., Nitzschke, A., & Funke, J. (2012). Dynamische Problemlösekompetenz – Ein bedeutsamer Prädiktor von Problemlöseleistungen in technischen Anforderungskontexten? [Dynamic problem solving—an important predictor of problem-solving performance in technical domains?]. Zeitschrift für Erziehungswissenschaft, 15, 363–391.
Ackerman, P. L. (2007). New developments in understanding skilled performance. Current Directions in Psychological Science. doi:10.1111/j.1467-8721.2007.00511.x.
Ackerman, P. L., & Beier, M. E. (2006). Methods for studying the structure of expertise: Psychometric approaches. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (pp. 147–165). New York: Cambridge University Press.
Artistico, D., Cervone, D., & Pezzuti, L. (2003). Perceived self-efficacy and everyday problem solving among young and older adults. Psychology and Aging, 18(1), 68–79.
Backhaus, K., Erichson, B., Plinke, W., & Weiber, R. (2016). Multivariate Analysemethoden [Multivariate analysis] (14th ed.). Heidelberg: Springer.
Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215.
Beckmann, J. F., & Guthke, J. (1995). Complex problem solving, intelligence, and learning ability. In P. A. Frensch & J. Funke (Eds.), Complex problem solving. The European perspective (pp. 177–200). Hillsdale: Lawrence Erlbaum.
Benjamin, R., Chun, M., Hardison, C, Hong, E., Jackson, C., Kugelmass, H., Nemeth, A., & Shavelson, R. J. (Monograph, 2009). Returning to learning in an age of assessment: Introducing the rationale of the collegiate learning assessment. Retrieved from http://www.plu.edu/assessment/wp-content/uploads/sites/168/2014/11/returning-to-learning-document.pdf
Bennett, R. E., Jenkins, F., Persky, H., & Weiss, A. (2003). Assessing complex problem solving performances. Assessment in Education, 10(3), 347–359.
Bilalić, M., McLeod, P., & Gobet, F. (2007). Does chess need intelligence?—a study with young chess players. Intelligence, 35, 457–470.
Bong, M. (2013). Self-efficacy. In J. Hattie & E. M. Anderman (Eds.), International guide to student achievement (pp. 64–66). New York: Routledge.
Bortz, J., & Schuster, C. (2010). Statistik für Human- und Sozialwissenschaftler [Statistics in human and social sciences] (7th ed.). Heidelberg: Springer.
Brand-Gruwel, S., Wopereis, I., & Walraven, A. (2009). A descriptive model of information problem solving while using internet. Computers & Education, 53, 1207–1217.
Bransford, J. D., & Stein, B. S. (Eds.). (1993). The ideal problem solver. A guide for improving thinking, learning and creativity (2nd ed.). New York: Freeman.
Brosius, F. (2008). SPSS 16. Heidelberg: mitp.
Brunswik, E. (1956). Perception and the representative design of psychological experiments. Berkeley: University of California Press.
Cattell, R. B. (1971). Abilities: their structure, growth, and action. Boston: Houghton Mifflin.
Cheng, Y. Y., Wang, W. C., & Ho, Y. H. (2008). Multidimensional Rasch analysis of a psychological test with multiple subtests—a statistical solution for the bandwidth-fidelity dilemma. Educational and Psychological Measurement, 69(3), 369–388.
Chronicle, E. P., MacGregor, J. N., & Ormerod, T. C. (2004). What makes an insight problem? The roles of heuristics, goal conception, and solution recoding in knowledge-lean problems. Journal of Experimental Psychology, 30(1), 14–27.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: Erlbaum.
Cronbach, L. J., & Gleser, G. C. (1965). Psychological tests and personnel decisions. Oxford: University of Illinois Press.
Dermitzaki, I., Leondari, A., & Goudas, M. (2009). Relations between young students’ strategic behaviours, domain-specific self-concept, and performance in a problem-solving situation. Learning and Instruction, 19, 144–157.
Dörner, D. (1987). Denken und Wollen. Ein systemtheoretischer Ansatz [Cognition and volition. A system-theoretical approach]. In H. Heckhausen, P. M. Gollwitzer, & F. E. Weinert (Eds.), Jenseits des Rubikon [Beyond the Rubicon] (pp. 238–250). Berlin: Springer.
Dörner, D. (1996). The logic of failure: Recognizing and avoiding error in complex situations. New York: Perseus.
Dörner, D., & Wearing, A. (1995). Complex problem solving: Toward a (computer-simulated) theory. In P. A. Frensch & J. Funke (Eds.), Complex problem solving. The European perspective (pp. 65–99). Hillsdale: Lawrence Erlbaum.
Duncker, K. (1945). On problem solving. Psychological Monographs. doi:10.1037/h0093599.
Dye, D. A., Reck, M., & McDaniel, M. A. (1993). The validity of job knowledge measures. International Journal of Selection and Assessment, 1(3), 153–157.
Eigenmann, R., Siegfried, C., Kögler, K., & Egloffstein, M. (2015). Aufgaben angehender Industriekaufleute im Controlling: Ansätze zur Modellierung des Gegenstandsbereichs [Prospective industrial clerks’ tasks in controlling: approaches to domain modelling]. Zeitschrift für Berufs- und Wirtschaftspädagogik, 111(3), 417–436.
Elshout, J. J. (1987). Problem solving and education. In E. DeCorte, H. Lodewijks, R. Parmentier, & P. Span (Eds.), Learning and instruction (pp. 259–273). Oxford: Pergamon.
Eysenck, M. W. (1994). Intelligence. In M. W. Eysenck (Ed.), The Blackwell dictionary of cognitive psychology (pp. 192–193). Oxford: Blackwell.
Federal Institute for Vocational Education and Training (BIBB). (2016). DATENBLATT 71302010 Industriekaufmann/-kauffrau [Fact sheet 71302010 Industrial clerks]. Retrieved from http://www2.bibb.de/bibbtools/tools/dazubi/data/Z/B/30/71302010.pdf
Fischer, A., & Neubert, J. C. (2015). The multiple faces of complex problems: a model of problem solving competency and its implications for training and assessment. Journal of Dynamic Decision Making, 1, 1–14. doi:10.1037/10315-004.
Fleck, J. I. (2008). Working memory demands in insight versus analytic problem solving. European Journal of Cognitive Psychology, 20(1), 139–176.
Frensch, P. A., & Funke, J. (1995). Definitions, traditions, and a general framework for understanding complex problem solving. In P. A. Frensch & J. Funke (Eds.), Complex problem solving. The European perspective (pp. 3–25). Hillsdale: Lawrence Erlbaum Associates.
Funke, J. (2003). Problemlösendes Denken [Problem-solving thinking]. Stuttgart: W. Kohlhammer.
Glaser, R., & Chi, M. T. H. (1988). Overview. In M. T. H. Chi, R. Glaser, & M. J. Farr (Eds.), The nature of expertise (pp. 15–28). Hillsdale: Lawrence Erlbaum.
Goode, N., & Beckmann, J. (2010). You need to know: there is a causal relationship between structural knowledge and control performance in complex problem solving tasks. Intelligence, 38(3), 345–352.
Gottfredson, L. S. (1997). Why g matters: the complexity of everyday life. Intelligence, 24(1), 79–132.
Greiff, S., Wüstenberg, S., Monlár, G., Fischer, A., Funke, J., & Caspó, B. (2013). Complex problem solving in educational contexts – something beyond g: concept, assessment, measurement invariance, and construct validity. Journal of Educational Psychology, 105(2), 364–379.
Hambrick, D. Z., & Engle, R. W. (2002). Effects of domain knowledge, working memory capacity and age on cognitive performance: an investigation of the knowledge-is-power hypothesis. Cognitive Psychology, 44, 339–387.
Hambrick, D. Z., & Engle, R. W. (2003). The role of working memory in problem solving. In J. E. Davidson & R. J. Sternberg (Eds.), The psychology of problem solving (pp. 176–206). London: Cambridge Press.
Herl, H. E., O’Neil, H. F. Jr., Chung, G. K., Bianchi, C., Wang, S., Mayer, R. A., et al. (1999). Final report for validation of problem-solving measures. Technical report No. 501 at the Center for the Study of Evaluation (CSE), National Center for Research on Evaluation, Standards, and Student Testing (CRESST), Graduate School of Education & Information Studies, University of California, Los Angeles, CA.
Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127.
Hoffman, B. (2010). ‘I think I can, but I’m afraid to try’: the role of self-efficacy beliefs and mathematics anxiety in mathematics problem-solving efficiency. Learning and Individual Differences, 20, 276–283.
Jonassen, D. H., & Hung, W. (2012). Problem solving. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp. 2680–2683). New York: Springer.
Kanfer, R., & Ackerman, P. L. (2005). Work competence: a person-oriented perspective. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 336–353). New York: The Guilford Press.
Köller, O., Baumert, J., & Schnabel, K. (2001). Does interest matter? The relationship between academic interest and achievement in mathematics. Journal for Research in Mathematics Education, 32(5), 448–470.
Leutner, D. (2002). The fuzzy relationship of intelligence and problem solving in computer simulations. Computers in Human Behavior, 18, 685–697.
Leutner, D., Funke, J., Klieme, E., & Wirth, J. (2005). Problemlösekompetenz als fächerübergreifende Kompetenz [Problem-solving competence as cross-curricular competence]. In E. Klieme, D. Leutner, & J. Wirth (Eds.), Problemlösekompetenz von Schülerinnen und Schülern. Diagnostische Ansätze, theoretische Grundlagen und empirische Befunde der deutschen PISA-2000-Studie [Students’ problem-solving competence. Diagnostic approaches, theoretical foundations and empirical results of the German PISA study 2000] (pp. 11–19). Wiesbaden: VS Verlag
Lipshitz, R., & Bar-Ilan, O. (1996). How problems are solved. Reconsidering the phase theorem. Organizational Behavior and Human Decision Processes, 65(1), 48–60.
Mayer, R. E. (1994). Problem solving. In M. W. Eysenck (Ed.), The Blackwell dictionary of cognitive psychology (pp. 284–288). Oxford: Blackwell.
Neubauer, A. C. (2012). Intelligence, learning, and neural plasticity. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp. 1593–1597). New York: Springer.
Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs: Prentice-Hall.
Nokes, T. J., Schunn, C. D., & Chi, M. T. H. (2011). Problem solving and human expertise. In V. Grøver Aukrust (Ed.), Learning and cognition in education (pp. 104–111). Oxford: Elsevier.
Palumbo, M. V., Miller, C. E., Shalin, V. L., & Steele-Johnson, D. (2005). The impact of job knowledge in the cognitive ability-performance relationship. Applied H.R.M. Research, 10(1), 13–20.
Pekrun, R. (2006). The control-value theory of achievement emotions: assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18, 315–341.
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33–40.
Raaheim, K. (1988). Intelligence and task novelty. In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence (Vol. 4, pp. 73–97). Hillsdale: Lawrence Erlbaum.
Rausch, A., & Wuttke, E. (2016). Development of a multi-faceted model of domain-specific problem-solving competence and its acceptance by different stakeholders in the business domain. Unterrichtswissenschaft, 44(2), 164–189.
Rausch, A., Schley, T., & Warwas, J. (2015). Problem solving in everyday office work—a diary study on differences between apprentices and skilled employees. International Journal of Lifelong Education, 34, 448–467. doi:10.1080/02601370.2015.1060023.
Rausch, A., Seifried, J., Wuttke, E., Kögler, K., & Brandt, S. (2016). Reliability and validity of a computer-based assessment of cognitive and non-cognitive facets of problem-solving competence in the business domain. Empirical Research in Vocational Education and Training, 8(9). doi:10.1186/s40461-016-0035-y
Reither, F., & Stäudel, T. (1985). Thinking and Action. In M. Frese & J. Sabini (Eds.), Goal directed behavior: The concept of action in psychology (pp. 110–122). Hillsdale: Lawrence Erlbaum.
Rigas, G., Carling, E., & Brehmer, B. (2002). Reliability and validity of performance measures in microworlds. Intelligence, 30, 463–480.
Rost, J. (2004). Testtheorie – Testkonstruktion [Test theory – test construction] (2nd ed.). Bern: Hans Huber.
Rychen, D. S., & Salganik, L. H. (2003). A holistic model of competence. In D. S. Rychen & L. H. Salganik (Eds.), Key competencies for a successful life and well-functioning society (pp. 41–62). Göttingen: Hogrefe & Huber.
Schendera, C. F. G. (2008). Regressionsanalyse mit SPSS [Regression analysis with SPSS]. München: Oldenbourg.
Schiefele, U., Krapp, A., Wild, K.-P., & Winteler, A. (1993). Der ‘Fragebogen zum Studieninteresse’ (FSI) [The ‘questionnaire on students’ interest’ (FSI)]. Diagnostica, 39(4), 335–351.
Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124(2), 262–274.
Schoenfeld, A. H. (1992). Learning to think mathematically: Problem solving, metacognition, sense-making in mathematics. In D. Grouws (Ed.), Handbook of research on mathematics teaching and learning (pp. 334–370). New York: Macmillan.
Schoenfeld, A. H. (2013). Reflections on problem solving theory and practice. The Mathematics Educator, 10(1-2), 9.
Scholz, U., Dona, B. G., Shonali, S., & Schwarzer, R. (2002). Is general self-efficacy a universal construct? Psychometric findings from 25 countries. European Journal of Psychological Assessment, 18(3), 242–251.
Sembill, D., Rausch, A., & Kögler, K. (2013). In K. Beck & O. Zlatkin-Troitschanskaia (Eds.), From diagnostics to learning success: Proceedings in vocational education and training (pp. 199–212). Rotterdam: Sense. doi:10.1007/978-94-6209-191-7_15.
Shavelson, R. J. (2010). On the measurement of competency. Empirical Research in Vocational Education and Training, 2(1), 41–63.
Stajkovic, A. D., & Luthans, F. (1998). Self-efficacy and work-related performance: a meta-analysis. Psychological Bulletin, 124(2), 240–261.
Sternberg, R. J. (2005). Intelligence, competence, and expertise. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 15–30). New York: The Guilford Press.
Süss, H.-M. (1996). Intelligenz, Wissen und Problemlösen. Kognitive Voraussetzungen für erfolgreiches Handeln bei computersimulierten Problemen [Intelligence, knowledge, and problem solving. Cognitive prerequisites of successful action in computer-simulated problems]. Goettingen: Hogrefe.
Süss, H.-M., Kersting, M., & Oberauer, K. (1991). Intelligenz und Wissen als Prädiktoren für Leistungen bei computersimulierten komplexen Problemen [Intelligence and knowledge as predictors of performance in computer-simulated complex problems]. Diagnostica, 37(4), 334–352.
Taconis, R. (2013). Problem solving. In J. Hattie & E. C. Anderman (Eds.), International guide to student achievement (pp. 379–381). New York: Routledge.
Van Gog, T. (2012). Expertise. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp. 1238–1240). New York: Springer. doi:10.1007/978-1-4419-1428-6.
Van Iddekinge, C. H., Putka, D. J., & Campbell, J. P. (2011). Reconsidering vocational interests for personnel selection: the validity of an interest-based selection test in relation to job knowledge, job performance, and continuance intentions. Journal of Applied Psychology, 96(1), 13–33.
Verschaffel, L., Dooren, W. V., & De Smedt, B. (2012). Mathematical learning. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp. 2107–2110). New York: Springer.
Weinert, F. E. (2001). Concept of competence: a conceptual clarification. In D. S. Rychen & L. H. Salganik (Eds.), Defining and selecting key competencies (pp. 45–65). Seattle: Hogrefe and Huber.
Weiss, R. H. (2006). CFT 20-R (4th ed.). Göttingen: Hogrefe.
Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology. doi:10.1006/ceps.1999.1015.
Wiley, J. (1998). Expertise as mental set: the effects of domain knowledge in creative problem solving. Memory & Cognition, 26(4), 716–730.
Wittmann, W. W., & Süß, H.-M. (1999). Investigating the paths between working memory, intelligence, knowledge, and complex problem-solving performances via Brunswik symmetry. In P. L. Ackerman, P. C. Kyllonen, & R. D. Roberts (Eds.), Learning and individual differences: Process, trait, and content determinants (pp. 77–104). Washington, DC: American Psychological Association.
Woolfolk, A. (2005). Educational psychology (9th ed.). Boston: Pearson.
Wuttke, E., Seifried, J., Brandt, S., Rausch, A., Sembill, D., Martens, T., & Wolf, K. (2015). Modellierung und Messung domänenspezifischer Problemlösekompetenz bei angehenden Industriekaufleuten—Entwicklung eines Testinstruments und erste Befunde zu kognitiven Kompetenzfacetten [Modeling and measuring domain-specific problem-solving competence of prospective industrial clerks—development of an instrument and first results regarding cognitive facets of competence]. Zeitschrift für Berufs- und Wirtschaftspädagogik, 111(2), 189–207.
Zimmerman, B. J., & Campillo, M. (2003). Motivating self-regulated problem solvers. In J. E. Davidson & R. J. Sternberg (Eds.), The psychology of problem solving (pp. 233–262). Cambridge: University Press.
Acknowledgments
I would like to thank the DomPL-IK research group (in alphabetical order): Steffen Brandt, Marc Egloffstein, Rebecca Eigenmann, Kristina Kögler, Thomas Schley, Christin Siegfried, Jürgen Seifried, Detlef Sembill, and Eveline Wuttke
Furthermore, my sincere thanks go to the reviewers for providing many valuable comments.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Rausch, A. Dispositional Predictors of Problem Solving in the Field of Office Work. Vocations and Learning 10, 177–199 (2017). https://doi.org/10.1007/s12186-016-9165-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12186-016-9165-4