413
Views
2
CrossRef citations to date
0
Altmetric
Research Article

Testing the efficacy of a near-peer mentoring model for recruiting youth into computer science

&

References

  • Adams, J. C. (2007). Alice, middle schoolers & the Imaginary Worlds camps. In Proceedings of the 38th SIGCSE Technical Symposium on Computer Science Education (pp. 307–311). Covington, Kentucky, USA. DOI: https://doi.org/10.1145/1227504.1227418
  • Ashcraft, C., Eger, E., & Friend, M. (2012). Girls in IT: The facts. Boulder, CO: National Center for Women and Information Technology.
  • Bamberger, Y. M. (2014). Encouraging girls into science and technology with feminine role model: Does this work? Journal of Science Education and Technology, 23(4), 549–561.
  • Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215.
  • Bandura, A. (1981). Self-referent thought: A developmental analysis of self-efficacy. In J. H. Flavell & L. Ross (Eds.), Social cognitive development: frontiers and possible futures (pp. 200–239). Cambridge: Cambridge University Press.
  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall. doi:https://doi.org/10.1037/13273-005
  • Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W.H. Freeman and Company. doi:https://doi.org/10.1007/SpringerReference_223312
  • Baylor, A. L., & Kim, Y. (2004). Pedagogical agent design: The impact of agent realism, gender, ethnicity, and instructional role. In J. C. Lester, R. M. Vicari, & F. Paraguaçu (Eds.), Intelligent Tutoring Systems (pp. 592–603). Berlin, Heidelberg: Springer Berlin Heidelberg.
  • Braaksma, M. A. H., Rijlaarsdama, G., & van den Bergh, H. (2002). Observational learning and the effects of model-observer similarity. Journal of Educational Psychology, 94(2), 405–415.
  • Byars-Winston, A., Diestelmann, J., Savoy, J. N., & Hoyt, W. T. (2017). Unique effects and moderators of effects of sources on self-efficacy: A model-based meta-analysis. Journal of Counseling Psychology, 64(6), 645–658.
  • Carrico, C., & Tendhar, C. (2012). The use of the social cognitive career theory to predict engineering students’ motivation in the produced program. In ASEE Annual Conference and Exposition, Conference Proceedings, 119th ASEE Annual Conference and Exposition San Antonio, Texas (pp. 25.1354.1-25.1354.13). American Society for Engineering Education.
  • Clarke-Midura, J., Poole, F., Pantic, K., Hamilton, M., Sun, C., & Allan, V. (2018). How near peer mentoring affects middle school mentees. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education (pp. 664–669). Baltimore, Maryland, USA.
  • Clarke-Midura, J., Sun, C., Pantic, K., Poole, F., & Allan, V. (2019). Using informed design in informal computer science programs to increase youths’ interest, self-efficacy, and perceptions of parental support. ACM Transactions on Computing Education, 19(4), Article 37, 1–24.
  • Code.org. (2018). Promote computer science . Retrieved from https://code.org/promote
  • Core Team, R. (2013). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
  • Fennema, E., & Sherman, J. A. (1976). Fennema-Sherman mathematics attitudes scales: Instruments designed to measure attitudes toward the learning of mathematics by females and males. Journal for Research in Mathematics Education, 7(5), 324–326.
  • Finch, W. H., Bolin, J. E., & Kelley, K. (2014). Multilevel modeling using R. New York, NY: CRC Press.
  • Franklin, D., Conrad, P., Aldana, G., & Hough, S. (2011). Animal tlatoque: Attracting middle school students to computing through culturally-relevant themes. In Proceedings of the 42nd ACM Technical Symposium on Computer Science Education (pp. 453–458). Dallas, TX, USA. DOI: https://doi.org/10.1145/1953163.1953295
  • Google, & Gallup. (2015). Searching for computer science: Access and barriers in U.S. K-12 education 9 8 2018. Retrieved from https://services.google.com/fh/files/misc/searching-for-computer-science_report.pdf
  • Graham, S., & Latulipe, C. (2003). CS girls rock: Sparking interest in computer science and debunking the stereotypes. In Proceedings of the 34th SIGCSE Technical Symposium on Computer Science Education (pp. 322–326). Reno, Navada, USA. DOI: https://doi.org/10.1145/611892.611998
  • Groenendijk, T., Janssen, T., Rijlaarsdam, G., & van den Bergh, H. (2013). The effect of observational learning on students’ performance, processes, and motivation in two creative domains. British Journal of Educational Psychology, 83(1), 3–28.
  • Hoogerheide, V., Loyens, S. M. M., & van Gog, T. (2016). Learning from video modeling examples: Does gender matter? Instructional Science, 44(1), 69–86.
  • Hoogerheide, Vincent, Loyens, Sofie M. M., van Gog, Tamara 2016 Learning from video modeling examples: Does gender matter? Instructional Science 44 1 69–86
  • Hoogerheide, V., van Wermeskerken, M., Loyens, S. M. M., & van Gog, T. (2016). Learning from video modeling examples: Content kept equal, adults are more effective models than peers. Learning and Instruction, 44, 22–30.
  • Hoogerheide, Vincent, van Wermeskerken, Margot, Loyens, Sofie M.M., van Gog, Tamara 2016 Learning from video modeling examples: Content kept equal, adults are more effective models than peers Learning and Instruction 44 22–30
  • Hoogerheide, V., van Wermeskerken, M., van Nassau, H., & van Gog, T. (2017). Model-observer similarity and task-appropriateness in learning from video modeling examples: Do model and student gender affect test performance, self-efficacy, and perceived competence? Computers in Human Behavior, 1–8. https://doi.org/https://doi.org/10.1016/j.chb.2017.11.012
  • Hox, J. J. (2010). Multilevel analysis: Techniques and applications (2nd ed. ed.). New York, NY: Routledge.
  • Huang, X. (2017). Example-based learning: Effects of different types of examples on student performance, cognitive load and self-efficacy in a statistical learning task. Interactive Learning Environments, 25(3), 283–294.
  • Huang, Xiaoxia 2017 Example-based learning: Effects of different types of examples on student performance, cognitive load and self-efficacy in a statistical learning task Interactive Learning Environments 25 3 283–294
  • Khoja, S., Wainwright, C., Brosing, J., & Barlow, J. (2012). Changing girls’ attitudes towards computer science. Journal of Computing Sciences in Colleges, 28(1), 210–216. :
  • Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software, 82(13), 1–26.
  • Lent, R. W., Bin, S. H., Miller, M. J., Cusick, M. E., Penn, L. T., & Truong, N. N. (2018). Predictors of science, technology, engineering, and mathematics choice options: A meta-analytic path analysis of the social-cognitive choice model by gender and race/ethnicity. Journal of Counseling Psychology, 65(1), 17–35.
  • Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79–122.
  • Lin, G. Y. (2016). Self-efficacy beliefs and their sources in undergraduate computing disciplines: An examination of gender and persistence. Journal of Educational Computing Research, 53(4), 540–561.
  • Lockwood, P. (2006). “Someone like me can be successful”: Do college students need same-gender role models? Psychology of Women Quarterly, 30(1), 36–46.
  • Maloney, J., Peppler, K., Kafai, Y. B., Resnick, M., & Rusk, N. (2008). Programming by choice: Urban youth learning programming with scratch. In Proceedings of the 39th SIGCSE Technical Symposium on Computer Science Education (pp. 367–371). Portland, OR, USA. DOI: https://doi.org/10.1145/1352135.1352260
  • Marx, D. M., & Roman, J. S. (2002). Female role models: Protecting women’s math test performance. Personality and Social Psychology Bulletin, 28(9), 1183–1193.
  • McGrath, C. J., & Aspray, W. (2006). A critical review of the research on women’s participation in postsecondary computing education. In J. McGarth Cohoon & W. Aspray (Eds.), Women and Information Technology: Research on Underrepresentation (pp. 138–180). Cambridge, MA: The MIT Press. doi:https://doi.org/10.7551/mitpress/9780262033459.003.0005
  • Murphey, T., & Arao, H. (2001). Reported belief changes through near peer role modeling. The Electronic Journal for English as a Second Language, 5(3), 1–15. :
  • Murphey, T., & Murakami, K. (1998). Teacher facilitated near peer role modeling for awareness raising within the zone of proximal development. ACADEMIA Literature and Language 65, 1–29.
  • National Science Board. (2018). Science and engineering indicators 2018. Alexandria, VA: National Science Foundation.
  • National Science Foundation. (2018). Broadening participation in computing: Directorate for computer information science & engineering 6 4 2018. Retrieved from https://www.nsf.gov/cise/bpc/
  • Outlay, C. N., Platt, A. J., & Conroy, K. (2017). Getting IT together: A longitudinal look at linking girls’ interest in IT careers to lessons taught in middle school camps. ACM Transactions on Computing Education, 17(4), 1–17.
  • Pollock, L., McCoy, K., Carberry, S., Hundigopal, N., & You, X. (2004). Increasing high school girls’ self confidence and awareness of CS through a positive summer experience. In Proceedings of the 35th SIGCSE technical symposium on Computer science education (pp. 185–189). Norfolk, Virginia, USA. DOI: https://doi.org/10.1145/971300.971369
  • Rhodes, J. E., Reddy, R., Grossman, J. B., & Lee, J. M. (2002). Volunteer mentoring relationships with minority youth: An analysis of same- versus cross-race matches. Journal of Applied Social Psychology, 32(10), 2114–2133.
  • Roy, K. (2012). App Inventor for Android: Report from a summer camp. In Proceedings of the 43rd ACM Technical Symposium on Computer Science Education (pp. 283–288). Raleigh, North Carolina, USA. DOI: https://doi.org/10.1145/2157136.2157222
  • Sabin, M., Deloge, R., Smith, A., & Dubow, W. (2017). Summer learning experience for girls in grades 7-9 boosts confidence and interest in computing careers. Journal of Computing Sciences in Colleges, 32(6), 79–87.
  • Schunk, D. H. (1987). Peer models and children’s behavioral change. Review of Educational Research, 57(2), 149–174.
  • Schunk, D. H., & Hanson, A. R. (1985). Peer models: Influence on children’s self-efficacy and achievement. Journal of Educational Psychology, 77(3), 313–322.
  • Schunk, D. H., & Hanson, A. R. (1989). Influence of peer-model attributes on children’s beliefs and learning. Journal of Educational Psychology, 81(3), 431–434.
  • Schunk, D. H., Hanson, A. R., & Cox, P. D. (1987). Peer-model attributes and children’s achievement behaviors. Journal of Educational Psychology, 79(1), 54–61.
  • Selzler, A.-M., Rogers, W. M., Berry, T. R., & Stickland, M. K. (2020). Coping versus mastery modeling intervention to enhance self-efficacy for exercise in patients with COPD. Behavioral Medicine, 46(1), 63–74.
  • Sheu, H.-B., Lent, R. W., Miller, M. J., Penn, L. T., Cusick, M. E., & Truong, N. N. (2018). Sources of self-efficacy and outcome expectations in science, technology, engineering, and mathematics domains: A meta-analysis. Journal of Vocational Behavior, 109, 118–136.
  • Spencer, R. (2007). “It’s not what I expected” A qualitative study of youth mentoring relationship failures. Journal of Adolescent Research, 22(4), 331–354.
  • St-Jean, E., Radu-Lefebvre, M., & Mathieu, C. (2018). Can less be more? Mentoring functions, learning goal orientation, and novice entrepreneurs’ self-efficacy. International Journal of Entrepreneurial Behaviour and Research, 4(1), 2–21.
  • Usher, E. L., & Pajares, F. (2008). Sources of self-efficacy in school: Critical review of the literature and future directions. Review of Educational Research, 78(4), 751–796.
  • Vachovsky, M. E., Wu, G., Chaturapruek, S., Russakovsky, O., Sommer, R., & Li, F. (2016). Toward more gender diversity in CS through an artificial intelligence summer program for high school girls. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education (pp. 303–308). Memphis, Tennessee, USA. DOI: https://doi.org/10.1145/2839509.2844620
  • Zeldin, A. L., & Pajares, F. (2000). Against the odds: Self-efficacy beliefs of women in mathematical, scientific, and technological careers. American Educational Research Journal, 37(1), 215–246.
  • Zimmerman, B. J., & Kitsantas, A. (2002). Acquiring writing revision and self-regulatory skill through observation and emulation. Journal of Educational Psychology, 94(4), 660–668.
  • Zimmerman, B. J., & Ringle, J. (1981). Effects of model persistence and success on children’s problem solving. Journal of Educational Psychology, 73(4), 485–493.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.