Science for Education Today, 2019, vol. 9, no. 2, pp. 140–155
© Mezhennaya N. M., 2019
UDC: 
378.14

On the perception of the ‘Microsoft Excel’ software program by engineering students

Mezhennaya N. M. 1 (Moscow, Russian Federation)
1 Bauman Moscow State Technical University
Full text (Open Access) — Downloaded 3973
Abstract: 

Introduction. The author investigated how engineering students perceive the Microsoft Excel spreadsheet software program. The aim of this research is to identify gender differences in the popularity, perception, and usage scenarios of Microsoft Excel among engineering students.
Materials and Methods. At the first stage of the research, the author used statistical methods for the collection and processing of empirical data. A questionnaire was conducted with students of Bauman Moscow State Technical University. At the second stage of the research the author used the following quantitative and qualitative methods of statistical analysis of the obtained data: descriptive statistical methods, contingency table analysis, Mann-Whitney test, etc. Using statistical analysis, gender differences in the perception and use of Microsoft Excel were identified, and were justified with the help of comparative analysis.
Results. The research established that the Microsoft Excel software program is highly appreciated by all indicators (suitability for solving various types of tasks, use on a stationary computer or mobile devices, simplicity of the interface) by all students regardless of the gender. The research revealed that the evaluation of Microsoft Excel in the group of young women is higher in all indicators than in the group of young men, and women attitude to the program is better in general. The results of the study confirmed that Microsoft Excel can be used for successful teaching students in large classes.
Conclusions. The main features in the perception of the Microsoft Excel software program by engineering students are identified.

Keywords: 

Gender differences; Microsoft Excel; Spreadsheets; Engineering education; Perception of program product; Training in large classes.

URL WoS/RSCI: https://www.webofscience.com/wos/rsci/full-record/RSCI:38191470

Prominence Percentile SciVal: 83.211 Spreadsheets | End-User Development | End-User Software Engineering

https://www.scopus.com/record/display.uri?eid=2-s2.0-85065996999&origin=...

On the perception of the 'Microsoft Excel' software program by engineering students

For citation:
Mezhennaya N. M. On the perception of the ‘Microsoft Excel’ software program by engineering students. Science for Education Today, 2019, vol. 9, no. 2, pp. 140–155. DOI: http://dx.doi.org/10.15293/2658-6762.1902.10
References: 
  1. de Barba P. G., Kennedy G. E., Ainley M. D. The role of students’ motivation and participation in predicting performance in a MOOC. Journal of Computer Assisted Learning, 2016, vol. 32, issue 3, pp. 218–231. DOI: https://doi.org/10.1111/jcal.12130
  2. Ivaniushina V., Alexandrov D., Musabirov I. The structure of students' motivation: Expectancies and values in taking data science course. Voprosy obrazovaniya / Educational Studies. Moscow, 2016, no. 4, pp. 229–250. (In Russian) DOI: https://doi.org/10.17323/1814-9545-2016-4-229-250 URL: https://elibrary.ru/item.asp?id=27633332
  3. Buteau C., Jarvis D. H., Lavicza Z. On the integration of computer algebra systems (CAS) by Canadian mathematicians: Results of a national survey. Canadian Journal of Science, Mathematics and Technology Education, 2014, vol. 14, issue 1, pp. 35–57. DOI: https://doi.org/10.1080/14926156.2014.874614
  4. Mezhennaya N. M., Pugachev O. V. On the results of using interactive education methods in teaching Probability Theory. Problems of Education in the 21st Century, 2018, vol. 76, no. 5, pp. 678–692. URL: http://www.scientiasocialis.lt/pec/node/1165
  5. Ivanov O. A., Ivanova V. V., Saltan A. A. Discrete mathematics course supported by CAS MATHEMATICA. International Journal of Mathematical Education in Science and Technology, 2017, vol. 48, issue 6, pp. 953–963. DOI: https://doi.org/10.1080/0020739X.2017.1319979
  6. Harrison T. R., Lee H. S. iPads in the mathematics classroom: Developing criteria for selecting appropriate learning apps. International Journal of Education in Mathematics, Science and Technology (IJEMST), 2018, vol. 6, issue 2, pp. 155–172. DOI: https://doi.org/10.18404/ijemst.408939
  7. Jacinto H., Carreira S. Mathematical problem solving with technology: The techno-mathematical fluency of a student-with-GeoGebra. International Journal of Science and Mathematics Education, 2017, vol. 15, issue 6, pp. 1115–1136. DOI: https://doi.org/10.1007/s10763-016-9728-8
  8. Albano G., Dello Iacono U. GeoGebra in e-learning environments: A possible integration in mathematics and beyond. Journal of Ambient Intelligence and Humanized Computing, 2018, pp. 1–13. DOI: https://doi.org/10.1007/s12652-018-1111-x
  9. Cretchley P., Harman C., Ellerton N., Fogarty G. MATLAB in early undergraduate mathematics: An investigation into the effects of scientific software on learning. Mathematics Education Research Journal, 2000, vol. 12, issue 3, pp. 219–233. DOI: https://doi.org/10.1007/BF03217086
  10. Durán M. J., Gallardo S., Toral S. L., Martínez-Torres R., Barrero F. J. A learning methodology using Matlab/Simulink for undergraduate electrical engineering courses attending to learner satisfaction outcomes. International Journal of Technology and Design Education, 2007, vol. 17, issue 1, pp. 55–73. DOI: https://doi.org/10.1007/s10798-006-9007-z
  11. Broley L., Caron F., Saint-Aubin Y. Levels of programming in mathematical research and university mathematics education. International Journal of Research in Undergraduate Mathematics Education, 2018, vol. 4, issue 1, pp. 38–55. DOI: https://doi.org/10.1007/s40753-017-0066-1
  12. Gorbacheva A. N., Smirnova A. N., Potekhin N. V. Solution of tasks for modeling in Microsoft Excel. Informatics and Education, 2008, no. 3, pp. 34–40. (In Russian) URL: https://elibrary.ru/item.asp?id=9947985
  13. Beare R. A system to exploit the spreadsheet ‘Excel’ for enhancing learning in science. Research in Science Education, 1991, vol. 21, issue 1, pp. 20–29. DOI: https://doi.org/10.1007/BF02360453
  14. Nash J. C. Teaching statistics with Excel 2007 and other spreadsheets. Computational Statistics and Data Analysis, 2008, vol. 52, issue 10, pp. 4602–4606. DOI: https://doi.org/10.1016/j.csda.2008.03.008
  15. Bryanceva O. V. Application of information technology for teaching mathematical methods of information processing. Saratov State Law Academy Bulletin, 2013, no. 2, pp. 219–223. (In Russian) URL: https://elibrary.ru/item.asp?id=21023876
  16. Rusakov A. A., Rusakova V. N., Savateeva E. S. Some methodical features of training to application methods of mathematical statistics to processing of results of experiments in "Ms Excel". Pedagogical Informatics, 2016, no. 1, pp. 69–76. (In Russian) URL: https://elibrary.ru/item.asp?id=25900353
  17. Tabach M., Friedlander A. Understanding equivalence of symbolic expressions in a spreadsheet-based environment. International Journal of Computers for Mathematical Learning, 2008, vol. 13, issue 1, pp. 27–46. DOI: https://doi.org/10.1007/s10758-008-9125-7
  18. Ainley J., Bills L., Wilson K. Designing spreadsheet-based tasks for purposeful Algebra. International Journal of Computers for Mathematical Learning, 2005, vol. 10, issue 3, pp. 191–215. DOI: https://doi.org/10.1007/s10758-005-8420-9
  19. Fowler M. Using Excel to simulate pendulum motion and maybe understand Calculus a little better. Science and Education, 2004, vol. 13, issue 7-8, pp. 791–796. DOI: https://doi.org/10.1007/s11191-004-6731-1
  20. Erokhin S. V., Sadikova A. R., Zhdankina J. S., Korzhuev A. V., Semenov S. V. Moodle E-learning platform as a resource for improving the quality of technical education. Novosibirsk State Pedagogical University Bulletin, 2018, no. 6, pp. 138–154. (In Russian) DOI: http://dx.doi.org/10.15293/2226-3365.1806.09
  21. Ibrahim D. Using the Excel spreadsheet in teaching science subjects. Procedia – Social and Behavioral Sciences, 2009, vol. 1, issue 1, pp. 309–312. DOI: https://doi.org/10.1016/j.sbspro.2009.01.058
  22. Martín J. D. EQMIN, a Microsoft Excel spreadsheet to perform thermodynamic calculations: A didactic approach. Computers and Geosciences, 1996, vol. 22, issue 6, pp. 639–650. DOI: https://doi.org/10.1016/0098-3004(96)00006-4
  23. Briones L., Escola J. M. Application of the Microsoft Excel Solver tool in the solution of optimization problems of heat exchanger network systems. Education for Chemical Engineers, 2019, vol. 26, pp. 41–47. DOI: https://doi.org/10.1016/j.ece.2018.10.003
  24. Lam N. T. K. A new approach to the teaching of structural mechanics. Procedia Engineering, 2011, vol. 14, pp. 695–703. DOI: https://doi.org/10.1016/j.proeng.2011.07.089
  25. Malone K. L., Schunn C. D., Schuchardt A. M. Improving conceptual understanding and representation skills through Excel-based modeling. Journal of Science Education and Technology, 2018, vol. 27, issue 1, pp. 30–44. DOI: https://doi.org/10.1007/s10956-017-9706-0
  26. Davidovitch N., Yavich R. The impact of mobile tablet use on students’ perception of learning processes. Problems of Education in the 21st Century, 2018, vol. 76, no. 1, pp. 29–42. URL: http://www.scientiasocialis.lt/pec/node/1105
  27. Zeldin A. L., Pajares F. Against the odds: Self-efficacy beliefs of women in mathematical, scientific, and technological careers. American Educational Research Journal, 2000, vol. 37, issue 1, pp. 215–246. DOI: https://doi.org/10.3102/00028312037001215
  28. Peng Y., Hong E., Mason E. Motivational and cognitive test-taking strategies and their influence on test performance in mathematics. Educational Research and Evaluation, 2014, vol. 20, issue 5, pp. 366–385. DOI: https://doi.org/10.1080/13803611.2014.966115
Date of the publication 30.04.2019