“SHOULD I BECOME A COMPUTER ENGINEER?” USING AN IMMERSIVE EXPERIENCE WITH UPPER SECONDARY STUDENTS TO SUPPORT FACULTY CHOICE
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Aug 2, 2022
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Abstract
Universities often carry out initiatives to assist upper secondary students in their choice of university faculties and courses. However, most of such initiatives are transmissive, and do not offer students hands-on experiences or opportunities for peer interaction. This paper instead presents an immersive, team-based experience on educational robotics offered to prospective students in Computer Engineering (N=88). Evaluation of the activity focused on: (1) improvements in students’ awareness when it comes to pick a faculty and a course; (2) improvements in basic Computer Sciences knowledge; (3) prospective students’ interactions and community-building. The results suggest that students' knowledge and skills are improved at the end of the experience and that this has a positive effect on their attitude towards the choice of a specific faculty and/or course. Student interactions proved to be more critical, as most teams displayed a low quality of social interactions.
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Articles - General topics
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References
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Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: a new look at an old issue. Psychological Methods, 12(2), 121–138. doi: 10.1037/1082-989X.12.2.121
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Ioannou, A., & Makridou, E. (2018). Exploring the potentials of educational robotics in the development of computational thinking: A summary of current research and practical proposal for future work. Education and Information Technologies, 23(6), 2531-2544.
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Kumar, D., & Meeden, L. (1998). A robot laboratory for teaching artificial intelligence. ACM SIGCSE Bulletin, 30(1), 341-344. doi: 10.1145/273133.274326
McWhorter, W. I., & O'Connor, B. C. (2009). Do LEGO® Mindstorms® motivate students in CS1? ACM SIGCSE Bulletin, 41(1), 438-442. doi: 10.1145/1508865.1509019
Metcalfe, J., & Finn, B. (2008). Evidence that judgments of learning are causally related to study choice. Psychonomic Bulletin & Review, 15(1), 174–9. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/18605499
Nugent, G., Barker, B., Grandgenett, N., & Adamchuk, V. I. (2010). Impact of robotics and geospatial technology interventions on youth stem learning and attitudes. Journal of Research on Technology in Education, 42(4), 391-408. doi: 10.1080/15391523.2010.10782557
Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66(4), 543–578. doi: 10.3102/00346543066004543
Passarelli, M., Dagnino, F. M., Earp, J., Manganello, F., Persico, D., Pozzi, F., ... Haggis, M. (2019). Educational games as a motivational tool: considerations on their potential and limitations. In International Conference on Computer Supported Education 2019 (pp. 330-337). Setúbal, PT: Scitepress.
Petre, M., & Price, B. (2004). Using robotics to motivate ‘back door’ learning. Education and Information Technologies, 9(2), 147-158. doi: 10.1023/B:EAIT.0000027927.78380.60
Pordelan, N., Sadeghi, A., Abedi, M. R., & Kaedi, M. (2018). How online career counseling changes career development: A life design paradigm. Education and Information Technologies, 23(6), 2655-2672.
Quinn, J. (2013). Drop-out and completion in higher education in Europe among students from under-represented groups. Cardiff, UK: Network of Experts on Social Aspects of Education and Training
R Core Team, (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at http://www.R-project.org
Robinson, C., Le Riche, P., & Jacklin, A. (2007). Students' views of support in higher education: a study of current practice and future directions. Higher Education Review, 40(1), 3. Retrieved from: http://sro.sussex.ac.uk/id/eprint/17882
Romito, M., Pilutti, S, & Contini, D. (2020). Why do students leave university? Qualitative research at an Italian higher education institution. European Journal of Education, 55(3), 456-470. https://doi.org/10.1111/ejed.12408
Rourke, L., Anderson, T. Garrison, D. R., & Archer, W. (2001). Assessing social presence in asynchronous, text-based computer conferencing. Journal of Distance Education, 14(3), 51-70. Retrieved from: http://hdl.handle.net/2149/732
Salomon, G., (1997). Distributed cognitions: Psychological and educational considerations. Vol. 11. Cambridge, UK: Cambridge University Press.
Serra, M. J., & DeMarree, K. G. (2016). Unskilled and unaware in the classroom: College students’ desired grades predict their biased grade predictions. Memory & Cognition, 44(7), 1127–1137. doi: 10.3758/s13421-016-0624-9
Sklar, E., Eguchi, A., & Johnson, J. (2002, June). RoboCupJunior: learning with educational robotics. In RoboCup 2002: Robot Soccer World Cup VI (pp. 238-253). Heidelberg, Berlin, DE: Springer.
Tremblay, A., & Ransijn, J. (2014). LMERConvenienceFunctions: A suite of functions to back-fit fixed effects and forward-fit random effects, as well as other miscellaneous functions. R package version 2.5. Retrieved from http://cran.r-project.org/package=LMERConvenienceFunctions
Turkle, S., & Papert, S. (1992). Epistemological pluralism and the revaluation of the concrete. Journal of Mathematical Behavior, 11(1), 3-33. Retrieved from https://www.learntechlib.org/p/146817/
Van Leeuwen, M. H. D., & Maas, I. (1991). Log-linear analysis of changes in mobility patterns. Historical Methods, 24(2), 66–79. doi: 10.1080/01615440.1991.9955294
Vygotskiĭ, L. S., Hanfmann, E., & Vakar, G. (2012). Thought and language. Cambridge, MA, US: MIT press.
Yu, X., & White, W., & Smith, S., & Gu, K., & Weinberg, J., & Karacal, C., & Engel, G. L. (2001, June). A Multidisciplinary Model For Using Robotics In Engineering Education. Paper presented at 2001 Annual Conference, Albuquerque, NM, US. doi: 10.18260/1-2--9577
Bednar, A. K., Cunningham, D., Duffy, T. M., & Perry, J. D. (1992). Theory into practice: How do we link?. In T. M. Duffy & D. H. Jonassen (Eds.). Constructivism and the technology of instruction: A conversation, pp. 17-34. Hillsdale, NJ, US: Lawrence Erlbaum Associates Publishers.
Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32-42. doi: 10.3102/0013189X018001032
Chambers, J. M., & Carbonaro, M. (2003). Designing, developing, and implementing a course on Lego robotics for technology teacher education. Journal of Technology and Teacher Education, 11(2), 209-241. Retrieved from https://www.learntechlib.org/primary/p/14607/
Cockburn, A., & Williams, L. (2000). The costs and benefits of pair programming, extreme programming and flexible processes in software engineering. Cagliari, IT: XP2000.
De Feo, A., & Pitzalis, M. (2018). Le fiere dell'orientamento. La scelta come dramma sociale e come mercato. Etnografia e Ricerca Qualitativa, 2, 251-276. doi: 10.3240/90881
Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: a new look at an old issue. Psychological Methods, 12(2), 121–138. doi: 10.1037/1082-989X.12.2.121
Fagin, B., & Merkle, L. (2003, February). Measuring the effectiveness of robots in teaching computer science. ACM SIGCSE Bulletin, 35(1), 307-311. Retrieved from http://delivery.acm.org/10.1145/620000/611994/p307-fagin.pdf
Gaines, D., & Balac, N. (2000). Using mobile robots to teach artificial intelligence research skills. In Proceedings of the 2000 ASEE Annual Conference, Session 2220. Retrieved from: http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=BD199E1D9B0D7C97245F3A7561791465?doi=10.1.1.35.9104&rep=rep1&type=pdf
Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. American Journal of Distance Education, 15(1), 7-23. doi: 10.1080/08923640109527071
Goodman, L. A. (1979). Simple models for the analysis of association in cross-classifications having ordered categories. Journal of the American Statistical Association, 74(367), 537-552. doi: 10.1080/01621459.1979.10481650
Hirst, A. J., Johnson, J., Petre, M., Price, B. A., & Richards, M. (2003). What is the best programming environment/language for teaching robotics using Lego Mindstorms? Artificial Life and Robotics, 7(3), 124-131. doi: 10.1007/BF02481160
Ioannou, A., & Makridou, E. (2018). Exploring the potentials of educational robotics in the development of computational thinking: A summary of current research and practical proposal for future work. Education and Information Technologies, 23(6), 2531-2544.
Kanuka, H., & Anderson, T. (1999). Using constructivism in technology-mediated learning: Constructing order out the chaos in the literature. International Journal of Radical Pedagogy, 1(2). Retrieved from: http://hdl.handle.net/2149/728
Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys (CSUR), 37(2), 83-137. doi: 10.1145/1089733.1089734
Kelleher, C., & Pausch, R. (2007). Using storytelling to motivate programming. Communications of the ACM, 50(7), 58-64. doi: 10.1145/1272516.1272540
Kinnunen, P., & Malmi, L. (2006, September). Why students drop out CS1 course?. In Proceedings of the Second International Workshop on Computing Education Research (pp. 97-108). ACM. doi: 10.1145/1151588.1151604
Kuznetsova, A., & Brockhoff, P. B., & Christensen, R. H. B. (2014). lmerTest: Tests in Linear Mixed Effects Models. R package version 2.0-20. Retrieved from http://cran.r-project.org/package=lmerTest
Lave, J., & Wenger, E. (1990). Situated learning: Legitimate peripheral participation. Cambridge, UK: Cambridge University Press.
Major, L., Kyriacou, T., & Brereton, O. P. (2012). Systematic literature review: Teaching novices programming using robots. IET Software, 6(6), 502-513. doi: 10.1049/ic.2011.0003
Kumar, D., & Meeden, L. (1998). A robot laboratory for teaching artificial intelligence. ACM SIGCSE Bulletin, 30(1), 341-344. doi: 10.1145/273133.274326
McWhorter, W. I., & O'Connor, B. C. (2009). Do LEGO® Mindstorms® motivate students in CS1? ACM SIGCSE Bulletin, 41(1), 438-442. doi: 10.1145/1508865.1509019
Metcalfe, J., & Finn, B. (2008). Evidence that judgments of learning are causally related to study choice. Psychonomic Bulletin & Review, 15(1), 174–9. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/18605499
Nugent, G., Barker, B., Grandgenett, N., & Adamchuk, V. I. (2010). Impact of robotics and geospatial technology interventions on youth stem learning and attitudes. Journal of Research on Technology in Education, 42(4), 391-408. doi: 10.1080/15391523.2010.10782557
Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66(4), 543–578. doi: 10.3102/00346543066004543
Passarelli, M., Dagnino, F. M., Earp, J., Manganello, F., Persico, D., Pozzi, F., ... Haggis, M. (2019). Educational games as a motivational tool: considerations on their potential and limitations. In International Conference on Computer Supported Education 2019 (pp. 330-337). Setúbal, PT: Scitepress.
Petre, M., & Price, B. (2004). Using robotics to motivate ‘back door’ learning. Education and Information Technologies, 9(2), 147-158. doi: 10.1023/B:EAIT.0000027927.78380.60
Pordelan, N., Sadeghi, A., Abedi, M. R., & Kaedi, M. (2018). How online career counseling changes career development: A life design paradigm. Education and Information Technologies, 23(6), 2655-2672.
Quinn, J. (2013). Drop-out and completion in higher education in Europe among students from under-represented groups. Cardiff, UK: Network of Experts on Social Aspects of Education and Training
R Core Team, (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at http://www.R-project.org
Robinson, C., Le Riche, P., & Jacklin, A. (2007). Students' views of support in higher education: a study of current practice and future directions. Higher Education Review, 40(1), 3. Retrieved from: http://sro.sussex.ac.uk/id/eprint/17882
Romito, M., Pilutti, S, & Contini, D. (2020). Why do students leave university? Qualitative research at an Italian higher education institution. European Journal of Education, 55(3), 456-470. https://doi.org/10.1111/ejed.12408
Rourke, L., Anderson, T. Garrison, D. R., & Archer, W. (2001). Assessing social presence in asynchronous, text-based computer conferencing. Journal of Distance Education, 14(3), 51-70. Retrieved from: http://hdl.handle.net/2149/732
Salomon, G., (1997). Distributed cognitions: Psychological and educational considerations. Vol. 11. Cambridge, UK: Cambridge University Press.
Serra, M. J., & DeMarree, K. G. (2016). Unskilled and unaware in the classroom: College students’ desired grades predict their biased grade predictions. Memory & Cognition, 44(7), 1127–1137. doi: 10.3758/s13421-016-0624-9
Sklar, E., Eguchi, A., & Johnson, J. (2002, June). RoboCupJunior: learning with educational robotics. In RoboCup 2002: Robot Soccer World Cup VI (pp. 238-253). Heidelberg, Berlin, DE: Springer.
Tremblay, A., & Ransijn, J. (2014). LMERConvenienceFunctions: A suite of functions to back-fit fixed effects and forward-fit random effects, as well as other miscellaneous functions. R package version 2.5. Retrieved from http://cran.r-project.org/package=LMERConvenienceFunctions
Turkle, S., & Papert, S. (1992). Epistemological pluralism and the revaluation of the concrete. Journal of Mathematical Behavior, 11(1), 3-33. Retrieved from https://www.learntechlib.org/p/146817/
Van Leeuwen, M. H. D., & Maas, I. (1991). Log-linear analysis of changes in mobility patterns. Historical Methods, 24(2), 66–79. doi: 10.1080/01615440.1991.9955294
Vygotskiĭ, L. S., Hanfmann, E., & Vakar, G. (2012). Thought and language. Cambridge, MA, US: MIT press.
Yu, X., & White, W., & Smith, S., & Gu, K., & Weinberg, J., & Karacal, C., & Engel, G. L. (2001, June). A Multidisciplinary Model For Using Robotics In Engineering Education. Paper presented at 2001 Annual Conference, Albuquerque, NM, US. doi: 10.18260/1-2--9577