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Algortmika i programowanie: od graficznych schematów blokowych do programu Flowgorithm

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EN
Algorthmics and programmig: from graphical flowcharts to Flowgorithm program
Konferencja
V Konferencja e-Technologie w Kształceniu Inżynierów eTEEE'2018 (V; 19.04-20.04.2018; Kraków, Polska)
Języki publikacji
PL
Abstrakty
PL
Artykuł przedstawia próbę odpowiedzi na pytanie czy podstawy algorytmiki i programowania na wydziałach innych niż informatyczne mogą być efektywniej nauczane przy użyciu programu Mathcad z grupy Computer Algebra Systems (CAS) i specjalistycznego oprogramowania służącego do tworzenia schematów blokowych. Problem jest istotny, gdyż obecnie studenci takich wydziałów sprzeciwiają się tego typu zajęciom twierdząc, że nie chcą być specjalistami komputerowymi. Pierwszą część pracy stanowi krytyczny przegląd literatury zagadnienia. W drugiej części artykułu przedstawiono program zajęć, w ramach których omawiane są zagadnienia programowania i algorytmika. Trzecia część zawiera wyniki dwóch ankiet. Końcowym uwagom towarzyszy powtórzenie otwartego pytania "jak zmotywować cyfrowych tubylców do nauki".
EN
The paper tries to answer the question – can basics of algorithms and programming at faculties other than computer sciences be taught more effectively using spreadsheets, computer algebra systems and particularly specialized flowchart software. Students nowadays are rather against algorithms and programming claiming that they do not want to be computer scientists. The first part of the pa-per gives a critical review of the literature of the subject. In the second part of the paper program of applied computer science course devoted to algorithms programming is presented. The third part shows results of two surveys based on surveys conducted by Konecki in Croatia and by Malik and Coldwell-Neilson in Oman. Final remarks are accompanied by repeating an open question raised four years ago – “how to motivate digital natives to learn”. Students are generally against programming. There are absolutely satisfied even by their poor knowledge of IT limited to some basic editing skills. Flowgorithm proved to be very effective lecture tool allowing to present algorithms and their results. During laboratories Flowgorithm was used mainly only when students were obliged to do this, which is the result of negative attitude to programming. Flowgorithm enabled to distinguish between programming (creating an algorithm) and coding (representing an algorithm in a particular programming language) and concentrate on algorithms and programming.
Słowa kluczowe
Rocznik
Tom
Strony
23--26
Opis fizyczny
Bibliogr. 39 poz., rys., wykr., tab.
Twórcy
  • Politechnika Warszawska, Wydział Inżynierii Lądowej tel.: 22 825 65 32
Bibliografia
  • 1. Wing, J.M.: Computational Thinking. Communications of the ACM. 49, 33–35 (2006).
  • 2. Wolfram, S.: How to Teach Computational Thinking, https://www.wired.com/2016/09/how-to-teach-computational-thinking/.
  • 3. Sleeman, D.: The Challenges of Teaching Computer Programming. Commun. ACM. 29, 840–841 (1986).
  • 4. Gomes, A., Mendes, A.J.: Learning to program-difficulties and solutions. In: International Conference on Engineering Education–ICEE (2007).
  • 5. Shneiderman, B., Mayer, R., McKay, D., Heller, P.: Experimental investigations of the utility of detailed flowcharts in programming. Communications of the ACM. 20, 373–381 (1977).
  • 6. Goktepe, M.: Design and Implementation of a Tool for Teaching Programming, (1988).
  • 7. Nickerson, J.V.: Visual Programming, (1994).
  • 8. Diehl, S. ed: Software Visualization. Springer Berlin Heidelberg (2002).
  • 9. Baldwin, L.P., Kuljis, J.: Learning programming using program visualization techniques. In: Proceedings of the 34th Annual Hawaii International Conference on System Sciences, 2001. p. 8 pp. IEEE, USA (2001).
  • 10. Gaddis, T.: Starting Out with Programming Logic and Design. Pearson, Boston (2015).
  • 11. Venit, S., Drake, E.: Prelude to Programming. Pearson, Boston (2014).
  • 12. Robins, A., Rountree, J., Rountree, N.: Learning and Teaching Programming: A Review and Discussion. Computer Scence Education. 13, 137–172 (2003).
  • 13. Hooshyar, D., Ahmad, R.B., Nasir, M.H.N.M., Shamshirband, S., Horng, S.-J.: Flowchart-based programming environments for improving comprehension and problem-solving skill of novice programmers: a survey. International Journal of Advanced Intelligence Paradigms. 7, 24–56 (2015).
  • 14. Carlisle, M.C., Wilson, T.A., Humphries, J.W., Hadfield, S.M.: RAPTOR: a visual programming environment for teaching algorithmic problem solving. ACM SIGCSE Bulletin. 37, 176–180 (2005).
  • 15. Carlisle, M.C.: Raptor: a visual programming environment for teaching object-oriented programming. Journal of Computing Sciences in Colleges. 24, 275–281 (2009).
  • 16. Thompson, M.: Evaluating the Use of Flowchart-based RAPTOR Programming in CS0. In: Proceedings of the 45 th Annual Midwest Instruction and Computing Symposium. University of Northern Iowa, Cedar Falls, Iowa (2012).
  • 17. Hundhausen, C.D., Douglas, S.A.: Low-fidelity algorithm visualization. Journal of Visual Languages & Computing. 13, 449–470 (2002).
  • 18. Hundhausen, C.D., Brown, J.L.: What You See Is What You Code: A radically dynamic algorithm visualization development model for novice learners. In: 2005 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC'05). pp. 163–170. IEEE (2005).
  • 19. Crews, T., Ziegler, U.: The flowchart interpreter for introductory programming courses. In: Frontiers in Education Conference, 1998. FIE ’98. 28th Annual. pp. 307–312 (1998).
  • 20. Kuen, K.C.: Learning Programming Concepts Using Flowcharting Software. In: Proceedings of the Global Chinese Conference on Computers in Education (GCCCE) 2011. , Hangzhou, China (2011).
  • 21. Dol, S.M.: Fe.g.: An Animated Flowchart with Example to Teach the Algorithm Based Courses in Engineering. In: 2015 IEEE Seventh International Conference on Technology for Education (T4E). pp. 49–52 (2015).
  • 22. Gajewski, R., Wlasak, L., Jaczewski, M.: IS (ICT) and CS in Civil Engineering Curricula: Case Study. In: Proceedings of the 2013 Federated Conference on Computer Science and Information Systems. pp. 717–720. IEEE, Krakow (2013).
  • 23. Gajewski, R.R., Jaczewski, M.: Flipped Computer Science Classes. In: Federated Conference on Computer Science and Information System. pp. 795–802. , Warsaw (2014).
  • 24. Gajewski, R.R., Jaczewski, M.: PTC Mathcad Prime 3.0 Obliczenia i programowanie. Wydawnictwo Naukowe PWN, Warszawa (2014).
  • 25. Azemi, A., Pauley, L.L.: Teaching the introductory computer programming course for engineers using Matlab. In: Frontiers in Education Conference, 2008. FIE 2008. 38th Annual. p. T3B–1–T3B–23. IEEE (2008).
  • 26. Azemi, A., Bodek, M., Chinn, G.: Teaching an introductory programming course using hybrid e-learning approach. In: Proceedings - Frontiers in Education Conference, FIE. pp. 1911–1913 (2013).
  • 27. Giannakos, M.N., Pappas, I.O., Jaccheri, L., Sampson, D.G.: Understanding student retention in computer science education: The role of environment, gains, barriers and usefulness. Education and Information Technologies. (2016).
  • 28. Rahmat, M., Shahrani, S., Latih, R., Yatim, N.F.M., Zainal, N.F.A., Rahman, R.A.: Major Problems in Basic Programming that Influence Student Performance. Procedia - Social and Behavioral Sciences. 59, 287–296 (2012).
  • 29. Zainal, N.F.A., Shahrani, S., Yatim, N.F.M., Rahman, R.A., Rahmat, M., Latih, R.: Students’ Perception and Motivation Towards Programming. Procedia - Social and Behavioral Sciences. 59, 277–286 (2012).
  • 30. Yadin, A.: Reducing the dropout rate in an introductory programming course. ACM Inroads. 2, 71 (2011).
  • 31. Ala-Mutka, K.: Problems in Learning and Teaching Programming - a literature study for developing visualizations in the Codewitz-Minerva project. Institute of Software Systems, Tampere University of Technology, Finland (2004).
  • 32. Pears, A., Seidman, S., Malmi, L., Mannila, L., Adams, E., Bennedsen, J., Devlin, M., Paterson, J.: A survey of literature on the teaching of introductory programming. ACM SIGCSE Bulletin. 39, 204–223 (2007).
  • 33. Konecki, M.: Problems in Programming Education and Means in Their Improvement. In: DAAAM International Scientific Book 2014. pp. 459–470 (2014).
  • 34. Konecki, M.: Algorithmic thinking as a prerequisite of improvements in introductory programming courses. Uporabna Informatika. 23, 162–169 (2015).
  • 35. Konecki, M., Petrlic, M.: Main problems of programming novices and the right course of action. In: Central European Conference on Information and Intelligent Systems. pp. 116–123. Faculty of Organization and Informatics Varazdin, Varazdin (2014).
  • 36. Malik, S.I., Coldwell-Neilson, J.: A model for teaching an introductory programming course using ADRI. Educ Inf Technol. 1–32 (2016).
  • 37. Wlasak, L., Jaczewski, M., Dubilis, T., Warda, T.: How to Motivate Digital Natives to Learn? In: WCCE 2013 10th IFIP World Conference on Computers in Education. pp. 78–79. IFIP, Torun (2013).
  • 38. OECD: Students, Computers and Learning. Making the connection. (2015).
  • 39. Bell, T., Witten, I.H., Fellows, M.: CS Unplugged. Computer Science Without a Computer. Creative Commons (2015).
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-54b4d4f1-082e-4d0a-b989-4d14e624a92d
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