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home link Nauka i badania Serie wydawnicze i czasopisma Seria „E-learning” 11. E-learning and STEM Education – Articles 24

COMPUTATIONAL THINKING: MOTIVATION TO LEARN IN TERTIARY EDUCATION

full-text article

DOI: 10.34916/el.2019.11.25

R. Robert Gajewski Warsaw University of Technology (Poland)

Abstract: The paper presents an educational case study – investigation of motivation towards learning computing and computational thinking in tertiary education. In the first part of the paper background of the study is presented – why it was necessary to try to measure motivation. The second part describes the three motivation surveys known in the literature – Motivated Strategies for Learning Questionnaire (MSLQ), Academic Motivation Scale (AMS) and Model of Academic Motivation Inventory MUSIC. The next part describes a survey in which the Model of Academic Motivation Inventory was used. Statistical results of MUSIC Inventory are presented and answers to one of the five open-ended questions are discussed. Preliminary cluster analysis is performed which is the part of ongoing research. Final remarks include an open question – is it possible to increase students’ motivation and, if it is, how to do this?

Keywords: motivation, learning, computing, computational thinking

REFERENCES

  • Artino Jr, A. R. (2005). Review of the Motivated Strategies for Learning Questionnaire. (Reports – Research; Tests/Questionnaires No. ERIC Number: ED499083; p. 24). Retrieved from https://eric.ed.gov/?id=ED499083
  • Broussard, S. C., & Garrison, M. E. B. (2004). The Relationship Between Classroom Motivation and Academic Achievement in Elementary-School-Aged Children. Family and Consumer Sciences Research Journal, 33(2), 106–120. https://doi.org/10.1177/1077727X04269573
  • Chittum, J. R., & Jones, B. D. (2017). Identifying Pre-High School Students’ Science Class Motivation Profiles to Increase Their Science Identification and Persistence. Journal of Educational Psychology, 109(8), 1163–1187. https://doi.org/10.1037/edu0000176
  • Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning: A systematic and critical review. Learning and Skills Research Centre.
  • Cokley, K. (2000). Examining the validity of the Academic Motivation Scale by comparing scale construction to self-determination theory. Psychological Reports, 86(2), 560–564. https://doi.org/10.2466/PR0.86.2.560-564
  • Cokley, K. O., Bernard, N., Cunningham, D., & Motoike, J. (2001). A psychometric investigation of the academic motivation scale using a United States sample. Measurement and Evaluation in Counseling and Development, 34(2), 109–119.
  • Cook, D. A., Thompson, W. G., & Thomas, K. G. (2011). The Motivated Strategies for Learning Questionnaire: Score validity among medicine residents. Medical Education, 45(12), 1230–1240. https://doi.org/10.1111/j.1365-2923.2011.04077.x
  • Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555
  • Dale, N., & Lewis, J. (2015). Computer Science Illuminated (6th ed.). Jones & Bartlett Learning.
  • Douven, I. (2017). A Bayesian perspective on Likert scales and central tendency. Psychonomic Bulletin & Review, 1–9. https://doi.org/10.3758/s13423-017-1344-2
  • Duncan, T. G., & McKeachie, W. J. (2005). The making of the motivated strategies for learning questionnaire. Educational Psychologist, 40(2), 117–128.
  • Eccles, J. S. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievements and Achievement Motives (pp. 75–146). Retrieved from http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/ 12327
  • Fairchild, A. J., Horst, S. J., Finney, S. J., & Barron, K. E. (2005). Evaluating existing and new validity evidence for the Academic Motivation Scale. Contemporary Educational Psychology, 30(3), 331–358. https://doi.org/10.1016/j.cedpsych.2004.11.001
  • Gajewski, R. R. (1994). An object oriented approach to finite element programming. In B. H. V. Topping & M. Papadrakakis (Eds.), Artificial Intelligence and Object Oriented Approaches for Structural Engineering (pp. 107–113). Athens, Greece: Civil Comp Press.
  • Gajewski, R. R. (2016a). IT in Educational Management: Can it Support Solution of e-Cheating Problem? In T. Brinda, N. Mavengere, I. Haukijarvi, C. Lewin, & D. Passey (Eds.), Stakeholders and Information Technology in Education (SAITE 2016) (pp. 104–113). Springer International Publishing.
  • Gajewski, R. R. (2016b). Pitfalls of E-education: From multimedia to digital dementia? In M. Ganzha, L. Maciaszek, M. Paprzycki (Eds) Proceedings of the 2016 Federated Conference on Computer Science and Information Systems,. ACSIS, Vol. 8, pages 913–920 (2016) DOI: http://dx.doi.org/10.15439/2016F356
  • Gajewski, R. R. (2018). Algorithms, Programming, Flowcharts and Flowgorithm. In E. Smyrnova-Trybulska (Ed.), E-Learning and Smart Learning Environment for the Preparation of New Generation Specialists (pp. 393–408). Katowice, Poland: Univ Silesia.
  • Gajewski, R. R., & Jaczewski, M. (2014). Flipped Computer Science Classes. In M. P. M. Ganzha, L. Maciaszek (Eds), Proceedings of the 2014 Federated Conference on Computer Science and Information Systems (pp. 795–802.). https://doi.org/10.15439/2014F300
  • Gajewski, R. R., & Lompies, P. (1996). Object-oriented implementation of bandwidth, profile and wavefront reduction algorithms. In B. H. V. Topping (ed.), Advances in Computational Structures Technology (pp. 115–119). Budapest, Hungary: Civil Comp Press.
  • Gajewski, R. R., Wlasak, L., & Jaczewski, M. (2013). IS (ICT) and CS in Civil Engineering Curricula: Case Study. In M. Ganzha, M. Paprzycki, & L. Maciaszek (eds.), Proceedings of the 2013 Federated Conference on Computer Science and Information Systems (pp. 717–720.). Kraków, Poland: IEEE.
  • Guay, F., Chanal, J., Ratelle, C. F., Marsh, H. W., Larose, S., & Boivin, M. (2010). Intrinsic, identified, and controlled types of motivation for school subjects in young elementary school children. The British Journal of Educational Psychology, 80(4), 711–735. https://doi.org/10.1348/000709910X499084
  • Guilford, J. P. (1965). Fundamental statistics in psychology and education. New York: McGraw-Hill.
  • Guttman, L. (1945). A basis for analyzing test-retest reliability. Psychometrika, 10(4), 255–282. https://doi.org/10.1007/BF02288892
  • Haslofça, F., & Korkmaz, N. H. (2016). Reliability and validity of academic motivation scale for sports high school students’. SHS Web of Conferences, 26. EDP Sciences.
  • Hegarty, N. (2010). Application of the academic motivation scale to graduate school students. The Journal of Human Resource and Adult Learning, 6(2), 48–55.
  • Hoffman, B. (2015). Motivation for learning and performance. Academic Press.
  • Ilker, E., Arslam, Y., & Demirhan, G. (2014). A Validity and Reliability Study of the Motivated Strategies for Learning Questionnaire. Educational Sciences: Theory and Practice, 14(3), 829–833.
  • Jones, B. D. (2009). Motivating students to engage in learning: The MUSIC model of academic motivation. International Journal of Teaching and Learning in Higher Education, 21(2), 272–285.
  • Jones, B. D. (2010). An examination of motivation model components in face-to-face and online instruction. Electronic Journal of Research in Educational Psychology, 8(3), 915–944.
  • Jones, B. D. (2017). Motivating Students by Design: Practical Strategies for Professors (2 edition). CreateSpace Independent Publishing Platform.
  • Jones, B. D., Li, M., & Cruz, J. M. (2017). A Cross-Cultural Validation of the MUSIC® Model of Academic Motivation Inventory: Evidence from Chinese-and Spanish-Speaking University Students. International Journal of Educational Psychology, 6(1), 25–44.
  • Jones, B. D., Sahbaz, S., Schram, A. B., & Chittum, J. R. (2017). Using psychological constructs from the MUSIC Model of Motivation to predict students’ science identification and career goals: Results from the U.S. and Iceland. International Journal of Science Education, 39(8), 1089–1108. https://doi.org/10.1080/09500693.2017.1319093
  • Jones, B. D., & Sigmon, M. L. (2016). Validation Evidence for the Elementary School Version of the MUSIC® Model of Academic Motivation Inventory (Pruebas de validación para el Modelo MUSIC® de Inventario de Motivación Educativa para Escuela Primaria). Electronic Journal of Research in Educational Psychology, 14(1), 155–174. https://doi.org/10.14204/ejrep.38.15081
  • Jones, B. D., & Skaggs, G. (2016). Measuring students’ motivation: Validity evidence for the MUSIC Model of Academic Motivation Inventory. International Journal for the Scholarship of Teaching and Learning, 10(1), 7.
  • Jones, B. D., Watson, J. M., Rakes, L., & Akalin, S. (2012). Factors that impact students’ motivation in an online course: Using the MUSIC Model of Academic Motivation. Journal of Teaching and Learning with Technology, 1(1), 42–58.
  • Kusurkar, R., Croiset, G., Kruitwagen, C., & ten Cate, O. (2011). Validity evidence for the measurement of the strength of motivation for medical school. Advances in Health Sciences Education, 16(2), 183–195.
  • Lai, E. R. (2011). Motivation: A Literature Review (p. 44) [Research Report]. Pearsons.
  • Likert, R. (1932). A Technique for the Measurement of Attitudes. Archives of Psychology, (140), 1–55.
  • Macqueen, J. (1967). Some methods for classification and analysis of multivariate observations.  Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics, 281-297, Berkeley, California: University of California Press.
  • Olkkonen, M., McCarthy, P. F., & Allred, S. R. (2014). The central tendency bias in color perception: Effects of internal and external noise. Journal of Vision, 14(11). https://doi.org/10.1167/14.11.5
  • Pace, A. C., Ham, A.-J. L., Poole, T. M., & Wahaib, K. L. (2016). Validation of the MUSIC® Model of Academic Motivation Inventory for use with student pharmacists. Currents in Pharmacy Teaching and Learning, 8(5), 589–597. https://doi.org/10.1016/j.cptl.2016.06.001
  • Pintrich, P. R. (1988). A process-oriented view of student motivation and cognition. New Directions for Institutional Research, 1988(57), 65–79. https://doi.org/10.1002/ir.37019885707
  • Pintrich, P. R. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). (No. NCRIPTAL-91-B-004; p. 76). Ann Arbor, MI: National Center for Research to Improve Postsecondary Teaching and Learning.
  • 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.
  • Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1993). Reliability and Predictive Validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801–813. https://doi.org/10.1177/ 0013164493053003024
  • Riley, D., & Hunt, K. A. (2014). Computational Thinking for the Modern Problem Solver (1st ed.). Boca Raton, FL: Chapman and Hall/CRC.
  • Stoffa, R., Kush, J. C., & Heo, M. (2011). Using the Motivated Strategies for Learning Questionnaire and the Strategy Inventory for Language Learning in Assessing Motivation and Learning Strategies of Generation 1.5 Korean Immigrant Students. Education Research International, 2011(Article ID 491276), 8. https://doi.org/10.1155/2011/491276
  • Taylor, R. T. (2012). Review of the motivated strategies for learning questionnaire (MSLQ) using reliability generalization techniques to assess scale reliability (PgD Thesis). Auburn Unibersity, Auburn, Alabama.
  • Vallerand, R. J., Blais, M. R., Briere, N. M., & Pelletier, L. G. (1989). Construction et validation de l’Echelle en Motivation eb Education (EME). Canadian Journal of Behavioral Sciences, 21, 323–349.
  • Vallerand, R. J., Pelletier, L. G., Blais, M. R., Briere, N. M., Senecal, C., & Vallieres, E. F. (1992). The Academic Motivation Scale: A Measure of Intrinsic, Extrinsic, and Amotivation in Education. Educational and Psychological Measurement, 52(4), 1003–1017. https://doi.org/10.1177/0013164492052004025
  • Vallerand, R. J., Pelletier, L. G., Blais, M. R., Brière, N. M., Senecal, C., & Vallieres, E. F. (1993). On the assessment of intrinsic, extrinsic, and amotivation in education: Evidence on the concurrent and construct validity of the Academic Motivation Scale. Educational and Psychological Measurement, 53(1), 159–172.
  • Wang, P. S. (2015). From Computing to Computational Thinking. Boca Raton, FL: Chapman and Hall/CRC.
  • Zhang, B., Li, Y. M., Li, J., Li, Y., & Zhang, H. (2016). The Revision and Validation of the Academic Motivation Scale in China. Journal of Psychoeducational Assessment, 34(1), 15–27.
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