Research Article

Motivational design for inclusive digital learning: Women college engineering students’ motivation for online STEM learning

Jung Sun Sung 1 * , Wen-Hao David Huang 1
More Detail
1 College of Education, University of Illinois Urbana-Champaign, Champaign, IL, USA* Corresponding Author
Contemporary Educational Technology, 16(1), January 2024, ep489, https://doi.org/10.30935/cedtech/14047
Published: 01 January 2024
OPEN ACCESS   1400 Views   730 Downloads
Download Full Text (PDF)

ABSTRACT

This study identifies women college engineering students’ perception of online Science, technology, engineering, and mathematics (STEM) learning and factors that influence their learning motivation during the COVID-19 period. By conducting interviews with ten women engineering students and applying attention, relevance, confidence, and satisfaction (ARCS) model, this study aims to answer two questions: (1) How did women college engineering students perceive their experience with online STEM learning during the pandemic? (2) What category/categories based on ARCS motivational design model primarily account for women college engineering students’ learning motivation with online STEM learning during the pandemic?
The results show that the online learning format influenced women college engineering students’ perception regarding their academic plans, learning styles, learning environments, peer learning, and learning satisfaction. The most influential categories from ARCS model were ‘confidence’ and ‘attention’. Findings suggest that the online STEM learning format influenced women college engineering students’ learning motivation. The online format led to (1) low expectations for attention category when analyzed using ARCS model, (2) anticipation of more self-control, and (3) a desire for more peer interactions in their online STEM learning.
As students would have new expectations for the role of online learning due to their experience during the pandemic, assessing women students’ emerging motivational needs for STEM online learning is critical in developing a more inclusive instructional system design process in the future.
Keywords: motivation, motivational design, STEM, women engineering, online learning, inclusive design

CITATION (APA)

Sung, J. S., & Huang, W.-H. D. (2024). Motivational design for inclusive digital learning: Women college engineering students’ motivation for online STEM learning. Contemporary Educational Technology, 16(1), ep489. https://doi.org/10.30935/cedtech/14047

REFERENCES

  1. Abdool, A., Ringis, D., Maharajh, A., Sirju, L., & Abdool, H. (2017). DataRPG: Improving student motivation in data science through gaming elements. In Proceedings of the 2017 IEEE Frontiers in Education Conference (pp. 1-5). IEEE. https://doi.org/10.1109/FIE.2017.8190442
  2. Amina, T. (2021). Online education and women’s empowerment. Oxford Research Encyclopedia of Education. https://doi.org/10.1093/acrefore/9780190264093.013.1592
  3. Anfara, V. A. (2002). Qualitative analysis on stage: Making the research process more public. Educational Researcher: A Publication of the American Educational Research Association, 31(7), 28-38. https://doi.org/10.3102/0013189X031007028
  4. Angiello, R. S. (2002). Enrollment and success of Hispanic students in online courses. U.S. Department of Education, Office of Educational Research and Improvement Educational Resources Information Center. https://eric.ed.gov/?id=ED469358
  5. Arora, A. S., & Sharma, A. (2019). Integrating the arcs model with instruction for enhanced learning. Journal of Engineering Education Transformations, 32(3), 31-35.
  6. Asgari, S., Trajkovic, J., Rahmani, M., Zhang, W., Lo, R. C., & Sciortino, A. (2021). An observational study of engineering online education during the COVID-19 pandemic. PLoS ONE, 16(4), e0250041. https://doi.org/10.1371/journal.pone.0250041
  7. Assarroudi, A., Heshmati Nabavi, F., Armat, M. R., Ebadi, A., & Vaismoradi, M. (2018). Directed qualitative content analysis: The description and elaboration of its underpinning methods and data analysis process. Journal of Research in Nursing, 23(1), 42-55. https://doi.org/10.1177/1744987117741667
  8. Bacher-Hicks, A., Goodman, J., & Mulhern, C. (2021). Inequality in household adaptation to schooling shocks: COVID-19-induced online learning engagement in real time. Journal of Public Economics, 193, 104345. https://doi.org/10.1016/j.jpubeco.2020.104345
  9. Brunelli, E., & Macirella, R. (2021). Exploring the critical points of teaching STEM subjects in the time of COVID-19: The experience of the course “microscopy techniques for forensic biology.” F1000Research, 10, 89. https://doi.org/10.12688/f1000research.28455.1
  10. Chee, K. H. (2005). Gender differences in the academic ethic and academic achievement. College Student Journal, 39(3), 604e618.
  11. Chen, B., Bastedo, K., & Howard, W. (2018). Exploring design elements for online STEM courses: Active learning, engagement & assessment design. Online Learning, 22(2), 59-75.
  12. Cintron, L., Chang, Y., Cohoon, J., Tychonievich, L., Halsey, B., Yi, D., & Schmitt, G. (2019, October). Exploring underrepresented student motivation and perceptions of collaborative learning-enhanced CS undergraduate introductory courses. In 2019 IEEE Frontiers in Education Conference (FIE) (pp. 1-6). IEEE. https://doi.org/10.1109/FIE43999.2019.9028463
  13. Conway, K. (2009). Exploring persistence of immigrant and native students in an urban community college. The Review of Higher Education, 32(3), 321-352. https://doi.org/10.1353/rhe.0.0059
  14. Cromley, J., & Kunze, A. (2021). Motivational resilience during COVID-19 across at-risk undergraduates. Journal of Microbiology & Biology Education, 22(1), 22.1.46. https://doi.org/10.1128/jmbe.v22i1.2271
  15. Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5-22. https://doi.org/10.1177/0047239520934018
  16. Dick, W. (1996). The Dick and Carey model: Will it survive the decade? Educational Technology Research and Development, 44(3), 55-63. https://doi.org/10.1007/BF02300425
  17. Flowers, L. O., White, E. N., Raynor, J. E., & Bhattacharya, S. (2012). African American students’ participation in online distance education in STEM disciplines. SAGE Open, 2(2). https://doi.org/10.1177/2158244012443544
  18. Glesne, C. (2016). Becoming qualitative researchers: An introduction. Pearson.
  19. Hackling, M., Murcia, K., West, J., & Anderson, K. (2014). Optimizing STEM education in WA schools. ECU Publications Post. https://ro.ecu.edu.au/ecuworkspost2013/6935
  20. Halsne, A., & Gatta, L. (2002). Online versus traditionally-delivered instruction: A descriptive study of learner characteristics in a community college setting. Online Journal of Distance Learning Administration, 5(1).
  21. Hardre, P. L. (2005). A case for instructional system design as a professional development tool-of-choice for university teaching assistants. Innovative Higher Education, 30(3), 163-175. https://doi.org/10.1007/s10755-005-6301-8
  22. Hartnett, M. (2016). The importance of motivation in online learning. Springer. https://doi.org/10.1007/978-981-10-0700-2_2
  23. Hess, A. N. (2015). Motivational design in information literacy instruction. Communications in Information Literacy, 9(1), 44-59. https://doi.org/10.15760/comminfolit.2015.9.1.175
  24. Hilts, A., Part, R., & Bernacki, M. L. (2018). The roles of social influences on student competence, relatedness, achievement, and retention in STEM. Science Education, 102(4), 744-770. https://doi.org/10.1002/sce.21449
  25. Hsieh, H.-F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277-1288. https://doi.org/10.1177/1049732305276687
  26. Hu, C. C., Yeh, H. C., & Chen, N. S. (2020). Enhancing STEM competence by making electronic musical pencil for non-engineering students. Computers & Education, 150, 103840. https://doi.org/10.1016/j.compedu.2020.103840
  27. Huang, W. H. D. (2013). Online learning engagement system (OLES) design framework for postsecondary online learning environments: A synthesis on affordances from game-based learning, social media-enabled learning, and open learning. In V. Wang (Ed.), Handbook of research on teaching and learning in K-20 education. IGI Global. https://doi.org/10.4018/978-1-4666-4249-2.ch011
  28. Jiang, S., Eccles, J. S., Xu, D., Warschauer, M., & Schenke, K. (2018). Cross-national comparison of gender differences in the enrollment in and completion of science, technology, engineering, and mathematics massive open online courses. PLoS ONE, 13(9), e0202463. https://doi.org/10.1371/journal.pone.0202463
  29. Kaldheim, H. K. A., Fossum, M., Munday, J., Creutzfeldt, J., & Slettebø, Å. (2021). Use of interprofessional simulation-based learning to develop perioperative nursing students’ self-efficacy in responding to acute situations. International Journal of Educational Research, 109, 101801. https://doi.org/10.1016/j.ijer.2021.101801
  30. Kaupp, R. (2012). Online penalty: The impact of online instruction on the Latino-White achievement gap. Journal of Applied Research in the Community College, 19(2), 3-11.
  31. Kearsley, G., & Shneiderman, B. (1998). Engagement theory: A framework for technology-based teaching and learning. Educational Technology, 38, 20-23. https://doi.org/10.1007/BF02299671
  32. Keller, J. M. (1983). Motivational design of instruction. In C. M. Reigeluth (Ed)., Instructional system design theories and models: An overview of their current status. Lawrence Erlbaum Associates.
  33. Keller, J. M. (1987). Development and use of the ARCS model of instructional design. Journal of Instructional Development, 10, 2-10. https://doi.org/10.1007/BF02905780
  34. Keller, J. M. (2000). How to integrate learner motivation planning into lesson planning: The ARCS model approach [Paper presentation]. The VII Semanario.
  35. Keller, J. M. (2008). An integrative theory of motivation, volition, and performance. Technology, Instruction, Cognition, and Learning, 6(2), 79-104.
  36. Keller, J. M. (2010). Motivational design for learning and performance: The ARCS model approach. Springer. https://doi.org/10.1007/978-1-4419-1250-3
  37. Kibiswa, N. K. (2019). Directed qualitative content analysis (DQlCA): A tool for conflict analysis. The Qualitative Report, 24(8), 2059-2079. https://doi.org/10.46743/2160-3715/2019.3778
  38. Li, K., & Keller, J. M. (2018). Use of the ARCS model in education: A literature review. Computers and Education, 122, 54-62. https://doi.org/10.1016/j.compedu.2018.03.019
  39. Li, K., & Moore, D. R. (2018). Motivating students in massive open online courses (MOOCs) using the attention, relevance, confidence, satisfaction (ARCS) model. Journal of Formative Design in Learning, 2, 102-113. https://doi.org/10.1007/s41686-018-0021-9
  40. MacPhail, C., Khoza, N., Abler, L., & Ranganathan, M. (2016). Process guidelines for establishing intercoder reliability in qualitative studies. Qualitative Research, 16(2), 198-212. https://doi.org/10.1177/1468794115577012
  41. Maj, S. P. (2020). Cognitive load optimization–A statistical evaluation for three STEM disciplines. In Proceedings of the 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (pp. 414-421). IEEE. https://doi.org/10.1109/TALE48869.2020.9368430
  42. Maxwell, J. A. (2008). The value of a realist understanding of causality for qualitative research. In N. K. Dezin (Ed.), Qualitative research and the politics of evidence (pp.163-181). Left Coast Press.
  43. Maxwell, J. A. (2011). A realist approach for qualitative research. SAGE.
  44. Maxwell, J. A. (2012). Qualitative research design: An interactive approach. SAGE.
  45. McEntee, C. (2020). STEM supports 67% of U.S. jobs. Eos. https://doi.org/10.1029/2020EO139416
  46. Means, B., & Neisler, J. (2020). Suddenly online: A national survey of undergraduates during the COVID-19 pandemic. Digital Promise. https://doi.org/10.51388/20.500.12265/98
  47. Mossberger, K., Tolbert, C. J., & Mcneal, R. S. (2007). Digital citizenship: The Internet, society, and participation. MIT Press. https://doi.org/10.7551/mitpress/7428.001.0001
  48. Museus, S., Palmer, R., Davis, R., & Maramba, D. (2011). Special issue: Racial and ethnic minority students’ success in STEM education. ASHE Higher Education Report, 36, 1-140.
  49. National Center for Education Statistics. (2022). Table 318.45. Number and percentage distribution of science, technology, engineering, and mathematics (STEM) degrees/certificates conferred by postsecondary institutions, by race/ethnicity, level of degree/certificate, and sex of student: 2010-11 through 2019-20. National Center for Education Statistics. https://nces.ed.gov/programs/digest/d21/tables/dt21_318.45.asp
  50. National Science Foundation. (2023). Diversity and STEM: Women, minorities, and persons with disabilities. National Center for Education Statistics. https://ncses.nsf.gov/pubs/nsf23315/
  51. Okey, J. R., & Santiago, R. S. (1991). Integrating instructional and motivational design. Performance Improvement Quarterly, 4(2), 11-21. https://doi.org/10.1111/j.1937-8327.1991.tb00500.x
  52. Park, J. H., & Choi, H. J. (2009). Factors influencing adult learners’ decision to drop out or persist in online learning. Journal of Educational Technology & Society, 12(4), 207-217.
  53. Reginaldo, A. L., & Ching, D. A. (2021). Online learning expectations among engineering students: Analyzing pre-determined factors in the implementation of flexible learning. International Journal of Educational Management and Development Studies, 2(4), 24-43. https://doi.org/10.53378/352076
  54. Rockinson-Szapkiw, A. J., Sharpe, K., & Wendt, J. (2022). Promoting self-efficacy, mentoring competencies, and persistence in STEM: A case study evaluating racial and ethnic minority women’s learning experiences in a virtual STEM peer mentor training. Journal of Science Education and Technology, 31(3), 386-402. https://doi.org/10.1007/s10956-022-09962-3
  55. Roy, J. (2019). Engineering by the numbers. American Society for Engineering Education. https://ira.asee.org/by-the-numbers/
  56. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. The American Psychologist, 55, 68-78. https://doi.org/10.1037/0003-066X.55.1.68
  57. Seaman, J., Allen, I. E., & Ralph, N. (2021). Teaching online: STEM education in the time of COVID-19. Bay View Analytics.
  58. Seels, B. B., & Richey, R. C. (1994). Instructional technology: The definition and domains of the field. Association for Educational Communications & Technology.
  59. Simon, R. M., Wagner, A., & Killion, B. (2017). Gender and choosing a STEM major in college: Femininity, masculinity, chilly climate, and occupational values. Journal of Research in Science Teaching, 54(3), 299-323. https://doi.org/10.1002/tea.21345
  60. Smith, P. L., & Ragan, T. J. (2005). Instructional system design. John Wiley & Sons.
  61. St. Rose, A. (2010). STEM major choice and the gender pay gap. On Campus with Women, 39(1).
  62. Sung, J. S., & Huang, W. D. (2022). Motivational design for inclusive digital learning innovation: A systematic literature review. The Journal of Applied Instructional Design, 11(2). https://doi.org/10.59668/377.8287
  63. United States Census Bureau. (2021). Women are nearly half pf U.S. workforce but only 27% of STEM workers. United States Census Bureau. https://www.census.gov/library/stories/2021/01/women-making-gains-in-stem-occupations-but-still-underrepresented.html
  64. USA FACTS. (2020). How many women graduate with STEM degrees? USA FACTS. https://usafacts.org/articles/women-stem-degrees/
  65. Visser, J., & Keller, J. M. (1990). The clinical use of motivational messages: An inquiry into the validity of the ARCS model of motivational design. Instructional Science, 19(6), 467-500. https://doi.org/10.1007/BF00119391
  66. Vojinovic, O., Simic, V., Milentijevic, I., & Ciric, V. (2020). Tiered assignments in lab programming sessions: Exploring objective effects on students’ motivation and performance. IEEE Transactions on Education, 63(3), 164-172. https://doi.org/10.1109/TE.2019.2961647
  67. Vovides, Y., & Lemus, L. R. (2019). Optimizing Instructional system design methods in higher education. IGI Global. https://doi.org/10.4018/978-1-5225-4975-8
  68. Waits, T., & Lewis, L. (2003). Distance education at degree-granting postsecondary institutions: 2000-2001 (NCES 2003-017). U.S. Department of Education, National Center for Education Statistics. https://doi.org/10.1037/e492152006-015
  69. Walsh, B. A., Woodliff, T. A., Lucero, J., Harvey, S., Burnham, M. M., Bowser, T. L., Aguirre, M., & Zeh, D. W. (2021). Historically underrepresented graduate students’ experiences during the COVID-19 pandemic. Family Relations, 70(4), 955-972. https://doi.org/10.1111/fare.12574
  70. Wang, M., Wu, B., Kirschner, P. A., & Spector, J. M. (2018). Using cognitive mapping to foster deeper learning with complex problems in a computer-based environment. Computers in Human Behavior, 87, 450-458. https://doi.org/10.1016/j.chb.2018.01.024
  71. Willis, J. (1995). A recursive, reflective instructional system design model based on constructivist-interpretivist theory. Educational Technology, 35(6), 5-23.
  72. Willis, J. (2000). The maturing of constructivist instructional system design: Some basic principles that can guide practice. Educational Technology, 40(1), 5-16.
  73. Wladis, C., Hachey, A. C., & Conway, K. M. (2015). The representation of minority, female, and non-traditional STEM majors in the online environment at community colleges: A nationally representative study. Community College Review, 43(1), 89-114. https://doi.org/10.1177/0091552114555904
  74. Yang, D. (2017). Instructional strategies and course design for teaching statistics online: Perspectives from online students. International Journal of STEM Education, 4(1), 34. https://doi.org/10.1186/s40594-017-0096-x
  75. Yoo, S. J., & Huang, W. D. (2013). Engaging online adult learners in higher education: Motivational factors impacted by gender, age, and prior experiences. Journal of Continuing Higher Education, 61(3), 151-164. https://doi.org/10.1080/07377363.2013.836823
  76. Zaccoletti, S., Camacho, A., Correia, N., Aguiar, C., Mason, L., Alves, R. A., & Daniel, J. R. (2020). Parents’ perceptions of student academic motivation during the COVID-19 lockdown: A cross-country comparison. Frontiers in Psychology, 11, 592670. https://doi.org/10.3389/fpsyg.2020.592670