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Adapting the Teaching of Computational Intelligence Techniques to Improve Learning Outcomes

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New Trends in Business Information Systems and Technology

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 294))

Abstract

In the Master of Science program Business Information Systems at a Swiss university, the authors have been teaching artificial intelligence (AI) methods, in particularly computational intelligence (CI) methods, for about ten years. AI and CI require the ability and readiness of a deeper understanding of algorithms, which can hardly be achieved with classical didactic concepts. Therefore, the focus is on assignments that lead the students to develop new algorithms or modify existing ones, or make them suitable for new areas of applications. This article discusses certain teaching concepts, their changes over time and experiences that have been made with a focus on improving students’ learning outcomes in understanding and applying special AI/CI methods such as neural networks and evolutionary algorithms.

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References

  1. Rothwell, J.: The Hidden STEM Economy. Brookings Institution. https://www.brookings.edu/wp-content/uploads/2016/06/TheHiddenSTEMEconomy610.pdf (2013). Accessed 8 Nov 2019

  2. Bybee, R.W.: The Case for STEM Education: Challenges and Opportunities. NSTA Press, the United States of America (2013)

    Google Scholar 

  3. Pan, Y.: Heading toward Artificial Intelligence 2.0. Engineering 2(4), 409–413 (2016)

    Google Scholar 

  4. Engelbrecht, A.P.: Computational Intelligence: An Introduction. Wiley, Wiltshire (2007)

    Book  Google Scholar 

  5. Feigenbaum, E.A.: Some challenges and grand challenges for computational intelligence. J. ACM 50(1), 32–40 (2003)

    Article  MathSciNet  Google Scholar 

  6. Kim, K.J., Cho, S.B.: Game AI competitions: an open pfor computational intelligence education [Educational Forum]. IEEE Comput. Intell. Mag. 8(3), 64–68 (2013)

    Article  Google Scholar 

  7. Venayagamoorthy, G.K.K.: A successful interdisciplinary course on co[m]putational intelligence. IEEE Comput. Intell. Mag. 4(1), 14–23 (2009)

    Article  Google Scholar 

  8. Samanta, B.: Computational intelligence: a tool for multidisciplinary education and research. In: Proceedings of the 2011 ASEE Northeast Section Annual Conference. University of Hartford (2011)

    Google Scholar 

  9. Samanta, B., Turner, J.G.: Development of a Mechatronics and Intelligent Systems Laboratory for Teaching and Research. ASEE Comput. Educ. (CoED) J. 4(1), 60 (2013)

    Google Scholar 

  10. Stachowicz, M.S.: Soft computing for advanced control applications: teacher experience. In: 10th Proceedings of the 2011 ASEE Northeast Section Annual Conference. University of Hartford (2011)

    Google Scholar 

  11. Minaie, A., Sanati-Mehrizy, P., Sanati-Mehrizy, A., Sanati-Mehrizy, R.: Computational intelligence course in undergraduate computer science and engineering curricula. In: Paper presented at the 120th ASEE Annual Conference & Exposition, Atlanta, 23 June 2013

    Google Scholar 

  12. Gerber, D.J., Flager, F.: Teaching design optioneering: a method for multidisciplinary design optimization. In: Congress on Computing in Civil Engineering. Miami, Florida, United States, 19–22 June 2011

    Google Scholar 

  13. Dos Santos Coelho, L., Sierakowski, C.A.: A software tool for teaching of particle swarm optimization fundamentals. Adv. Eng. Softw. 39(11), 877–887 (2008)

    Article  Google Scholar 

  14. Pustulka, E., Hanne, T., Wetzel, R., Adriaensen, B., Eggenschwiler, S., Kaba, E.: A game teaching population based optimization using teaching learning based optimization. In: Proceedings of 4th Gamification and Serious Game Symposium (GSGS´19). Neuchatel, 3–5 July 2019

    Google Scholar 

  15. Selwyn, N.: The use of computer technology in university teaching and learning: a critical perspective. J. Comput. Assist. Learn. 23(2), 83–94 (2007)

    Article  Google Scholar 

  16. Poulova, P., Klimova, B.: Education in computational sciences. Procedia Comput. Sci. 51, 1996–2005 (2015)

    Article  Google Scholar 

  17. Yasar, O., Landau, R.: Elements of computational science and engineering education. SIAM Rev. 45(4), 787–805 (2003)

    Article  MathSciNet  Google Scholar 

  18. Kyriacou, G.A.: A view on electrical & computer engineering education: challenges toward convergence of different disciplines. In: Paper presented at the SEFI 40th Annual Conference, Thessaloniki, Greece, 23–26 Sept 2012 (2012)

    Google Scholar 

  19. Sutherland, R., Balacheff, N.: Didactical complexity of computational environments for the learning of mathematics. View Electr. Comput. Eng. Educ. Chall. Toward Converg. Differ. Discipl. 4(1), 1–26 (1999)

    Google Scholar 

  20. University of Applied Sciences Northwestern Switzerland, School of Business. https://www.fhnw.ch (2019). Accessed 8 Nov 2019

  21. Hanne, T., Dornberger, R.: Computational Intelligence in Logistics and Supply Chain Management. Springer International Publishing, Cham (2017)

    Book  Google Scholar 

  22. Akabuilo, E., Dornberger, R., Hanne, T.: How advanced are advanced planning systems. In: The Proceedings of the International Symposium on Information Systems and Software Engineering: ISSE 2011, pp. 27–30 (2011)

    Google Scholar 

  23. Dornberger, R., Hanne, T., Frey, L.: The way to an open-source software for automated optimization and learning—OpenOpal. In: IEEE Congress on Evolutionary Computation. pp. 1–8. Barcelona, Spain, 18–23 July 2010 (2010)

    Google Scholar 

  24. Dornberger, R., Ernst, R., Frey, L., Hanne, T.: Solving optimization problems by metaheuristics using the OpenOpal-Framework—integration of travelling salesman problem and selected solvers. Work Report No. 27, University of Applied Sciences and Arts Northwestern Switzerland FHNW, School of Business (2012)

    Google Scholar 

  25. Allen, D.E., Donham, R.S., Bernhardt, S.A.: Problem-based learning. New Dir. Teach. Learn. (Special Issue: Evidence‐Based Teaching) 2011(128), 21–29 (2011)

    Google Scholar 

  26. Dornberger, R., Hanne, T.: Problem-Based Learning in teaching the module “optimization for business improvement.” In: The 8th International Conference on Education, Training and Informatics (ICETI 2017), p. 5 (2017)

    Google Scholar 

  27. Bloom, B.S., Engelhart, M.D., Furst, E.J., Hill, W.H., Krathwohl, D.R.: Taxonomy of Educational Objectives: The Classification of Educational Goals. Handbook I: Cognitive Domain. David McKay Company. Inc., New York (1972)

    Google Scholar 

  28. AACBS—Association to Advance Collegiate Schools of Business: About AACSB. https://www.aacsb.edu/about (2019). Accessed 29 Apr 2019

  29. AACBS—Association to Advance Collegiate Schools of Business: Accreditation Standard 8 (2013 Business Standards): Curricula Management and Assurance of Learning—An Interpretation. https://www.aacsb.edu/-/media/aacsb/publications/white-apers/aol_white_paper_standard_8.ashx?la=en (2019). Accessed 8 Sept 2019

  30. Dornberger, R., Hanne, T.: E-learning and problem-based learning in teaching information systems-changing the style of teaching in the information systems programs. In: Proceedings of the 2nd International Conference on Education, Training and Informatics: ICETI, pp. 27–30. 2011

    Google Scholar 

  31. Dornberger, R., Hanne, T.: Problem-based learning in teaching information systems - experiences in teaching computational intelligence. In: Proceedings of the 5th International Conference on Education, Training and Informatics (ICETI 2014). Orlando, Florida, USA, 4–7 Mar 2014

    Google Scholar 

  32. Affolter, K., Schweizer, D., Hanne T., Dornberger, R.: Index tracking with invasive weed optimization. In: 2014 International Conference on Soft Computing and Machine Intelligence (ISCMI), pp. 110–114. New Delhi, 26–27 Sept 2014

    Google Scholar 

  33. Nienhold, D., Schwab, K., Dornberger, R., Hanne, T.: Effects of weight initialization in a feedforward neural network for classification using a modified genetic algorithm. In: 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI), pp. 6–12. Bali, Indonesia, 7–9 Dec 2015

    Google Scholar 

  34. Schär, M., Brennenstuhl, M., Dornberger R.: Improving the fuzzy logic controller of a car racing competition with adjusted fuzzy sets. In: 4th International Symposium on Computational and Business Intelligence (ISCBI 2016), pp. 118–124. University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland, 5–7 Sept 2016

    Google Scholar 

  35. Graf, M., Poy, M., Bischof, S., Dornberger, R., Hanne, T.: Rescue path optimization using ant colony systems. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI 2017). Honolulu, USA, 27 Nov–1 Dec 2017

    Google Scholar 

  36. Pochon, Y., Dornberger, R., Zhong, V.J., Korkut, S.: Investigating the democracy behavior of swarm robots in the case of a best-of-n selection. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018), pp. 743–748. Bengaluru, India, 18–21 Nov 2018

    Google Scholar 

  37. Lehner, J.E., Simić, R., Dornberger, R., Hanne, T.: Optimization of multi-robot sumo fight simulation by a genetic algorithm to identify dominant robot capabilities. In: 2019 IEEE Congress on Evolutionary Computation, pp. 490–496 Wellington, New Zealand, 10–13 June 2019

    Google Scholar 

  38. Gordon, M.E., Fay, C.H.: The effects of grading and teaching practices on students’ perceptions of grading fairness. Coll. Teach. 58(3), 93–98 (2010)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Business, Institute for Information Systems, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Riggenbachstrasse 16, 4600, Olten, Switzerland

    Thomas Hanne

  2. School of Business, Institute for Information Systems, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Peter Merian-Strasse 86, 4002, Basel, Switzerland

    Rolf Dornberger

Authors
  1. Thomas Hanne
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  2. Rolf Dornberger
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Corresponding author

Correspondence to Thomas Hanne .

Editor information

Editors and Affiliations

  1. School of Business, Institute for Information Systems, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Basel, Switzerland

    Rolf Dornberger

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Hanne, T., Dornberger, R. (2021). Adapting the Teaching of Computational Intelligence Techniques to Improve Learning Outcomes. In: Dornberger, R. (eds) New Trends in Business Information Systems and Technology. Studies in Systems, Decision and Control, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-48332-6_8

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