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Making Apps: An Approach to Recruiting Youth to Computer Science

Published: 12 November 2020 Publication History
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  • Abstract

    In response to the need to broaden participation in computer science, we designed a summer camp to teach middle-school-aged youth to code apps with MIT App Inventor. For the past four summers, we have observed significant gains in youth's interest and self-efficacy in computer science, after attending our camps. The majority of these youth, however, were youth from our local community. To provide equal access across the state and secure more diversity, we were interested in examining the effect of the camp on a broader population of youth. Thus, we partnered with an outreach program to reach and test our camps on youth from low-income high-poverty areas in the Intermountain West. During the summer of 2019, we conducted two sets of camps: locally advertised app camps that attracted youth from our local community and a second set of camps as part of a larger outreach program for youth from low-income high-poverty areas. The camps for both populations followed the same design of personnel, camp activities, structure, and curriculum. However, the background of the participants was slightly different. Using survey data, we found that the local sample experienced significant gains in both self-efficacy and interest, while the outreach group only reported significant gains in self-efficacy after attending the camp. However, the qualitative data collected from the outreach participants indicated that they had a positive experience both with the camp and their mentors. In this article, we discuss the camp design and findings in relation to strategies for broadening participation in Computer Science education.

    References

    [1]
    Mohammed Al-Bow, Debra Austin, Jeffrey Edgington, Rafael Fajardo, Joshua Fishburn, Carlos Lara, Scott Leutenegger, and Susan Meyer. 2009. Using game creation for teaching computer programming to high school students and teachers. ACM SIGCSE Bull. 41, 3 (2009), 104--108.
    [2]
    Amnah Alshahrani, Isla Ross, and Murray I. Wood. 2018. Using social cognitive career theory to understand why students choose to study computer science. In Proceedings of the ACM Conference on International Computing Education Research (ICER’18). Association for Computing Machinery, New York, NY, 205--214.
    [3]
    Chulakorn Aritajati, Mary Beth Rosson, Joslenne Pena, Dana Cinque, and Ana Segura. 2015. A socio-cognitive analysis of summer camp outcomes and experiences. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education. ACM, 581--586.
    [4]
    Catherine Ashcraft, Elizabeth Eger, and Michelle Friend. 2012. Girls in iT: The facts. National Center for Women 8 IT. Boulder, CO. Retrieved from http://www.bgwomeninict.org/language/en/uploads/files/documents__0/documents__4f2236edd9c4aefc792678bbb3c58e63.pdf.
    [5]
    Flávio S. Azevedo. 2013. The tailored practice of hobbies and its implication for the design of interest-driven learning environments. J. Learn. Sci. 22, 3 (2013), 462--510.
    [6]
    Lecia J. Baker, Eric Snow, Kathy Garvin-Doxas, and Tim Weston. 2006. Recruiting middle school girls into IT: Data on girls’ perceptions and experiences from a mixed-demographic group. In Women and Information Technology: Research on Underrepresentation., J. McGrath Cohoon and William Aspray (Eds.). MIT Press, Cambridge, MA.
    [7]
    Yael M. Bamberger. 2014. Encouraging girls into science and technology with feminine role model: Does this work? J. Sci. Educ. Technol. 23, 4 (2014), 549--561.
    [8]
    Albert Bandura. 1977. Self-efficacy: Toward a unifying theory of behavioral change. Psychol. Rev. 84, 2 (1977), 191.
    [9]
    Albert Bandura. 1986. Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice Hall, Englewood Cliffs, NJ.
    [10]
    Albert Bandura. 1997. Self-efficacy: The Exercise of Control. Macmillan, New York, NY. Retrieved from https://doi.org/10.1007/SpringerReference_223312.
    [11]
    Sylvia Beyer. 2008. Gender differences and intra-gender differences amongst management information systems students. J. Info. Syst. Edu. 19, 3 (2008), 301.
    [12]
    Sylvia Beyer. 2014. Why are women underrepresented in Computer Science? Gender differences in stereotypes, self-efficacy, values, and interests and predictors of future CS course-taking and grades. Comput. Sci. Edu. 24, 2–3 (2014), 153--192.
    [13]
    Sylvia Beyer and Susan Haller. 2006. Gender differences and intragender differences in computer science students: Are female cs majors more similar to male cs majors or female nonmajors? JWM 12, 4 (2006).
    [14]
    Sylvia Beyer, Kristina Rynes, Julie Perrault, Kelly Hay, and Susan Haller. 2003. Gender differences in computer science students. In Proceedings of the 34th Technical Symposium on Computer Science Education (SIGCSE’03). ACM, Reno, NV, 49--53.
    [15]
    Jennifer M. Blaney and Jane G. Stout. 2017. Examining the relationship between introductory computing course experiences, self-efficacy, and belonging among first-generation college women. In Proceedings of the ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE’17). Association for Computing Machinery, New York, NY, 69--74.
    [16]
    Jennifer Burg, V. Paúl Pauca, William Turkett, Errin Fulp, Samuel S. Cho, Peter Santago, Daniel Cañas, and H. Donald Gage. 2015. Engaging non-traditional students in computer science through socially-inspired learning and sustained mentoring. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (SIGCSE’15). ACM, New York, NY, 639--644.
    [17]
    Jody Clarke-Midura, Victor R. Lee, Jessica F. Shumway, and Megan M. Hamilton. 2019. The building blocks of coding: A comparison of early childhood coding toys. Info. Learn. Sci. (2019).
    [18]
    Jody Clarke-Midura, Frederick J. Poole, Katarina Pantic, Chongning Sun, and Vicki Allan. 2018. How mother and father support affect youths’ interest in computer science. In Proceedings of the 2018 ACM Conference on International Computing Education Research (ICER’18). ACM, New York, NY, 215--222.
    [19]
    Jody Clarke-Midura, Frederick Poole, Katarina Pantic, Megan Hamilton, Chongning Sun, and Vicki Allan. 2018. How near peer mentoring affects middle school mentees. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education (SIGCSE’18). ACM, New York, NY, 664--669.
    [20]
    Jody Clarke-Midura, Chongning Sun, Megan Marie Hamilton, Katarina Pantic, Frederick Poole, and Vicki Allan. 2018. Near-peer mentoring as a way to foster self-efficacy in informal computer science environments. In Roundtable, Toronto, Canada.
    [21]
    Jody Clarke-Midura, Chongning Sun, Katarina Pantic, Frederick Poole, and Vicki Allan. 2019. Using informed design in informal computer science programs to increase youths’ interest, self-efficacy, and perceptions of parental support. ACM Trans. Comput. Educ. 19, 4 (2019), 37:1–37:24.
    [22]
    College Board. 2018. AP Program Participation and Performance Data 2017. Retrieved from https://research.collegeboard.org/programs/ap/data/participation/ap-2017.
    [23]
    Computing Research Association. 2016. Generation CS: Report on CS enrollment. CRA. Retrieved from https://cra.org/data/generation-cs/.
    [24]
    Edward L. Deci, Haleh Eghrari, Brian C. Patrick, and Dean R. Leone. 1994. Facilitating Internalization: The self-determination theory perspective. J. Personal. 62, 1 (1994), 119--142.
    [25]
    Jennifer Dempsey, Richard T. Snodgrass, Isabel Kishi, and Allison Titcomb. 2015. The emerging role of self-perception in student intentions. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (SIGCSE’15). Association for Computing Machinery, New York, NY, 108--113.
    [26]
    Jill Denner, Linda Werner, L. O'Connor, and J. Glassman. 2014. Community college men and women: A test of three widely held beliefs about who pursues computer science. Commun. Coll. Rev. 42, (2014), 342--362.
    [27]
    Anita DeWitt, Julia Fay, Madeleine Goldman, Eleanor Nicolson, Linda Oyolu, Lukas Resch, Jovan Martinez Saldaña, Soulideth Sounalath, Tyler Williams, and Kathryn Yetter. 2017. Arts coding for social good: A pilot project for middle-school outreach. In Proceedings of the ACM SIGCSE Technical Symposium on Computer Science Education. ACM, 159--164.
    [28]
    Daryl D'Souza, Margaret Hamilton, James Harland, Peter Muir, Charles Thevathayan, and Cecily Walker. 2008. Transforming learning of programming: A mentoring project. In Proceedings of the 10th Conference on Australasian Computing Education—Volume 78 (ACE’08). Australian Computer Society, Inc., Darlinghurst, Australia, Australia, 75--84. Retrieved from http://dl.acm.org/citation.cfm?id=1379249.1379256.
    [29]
    Wendy DuBow and L. James-Hawkins. 2016. What influences female interest and persistence in computing?: Preliminary findings from a multiyear study. Comput. Sci. Engineer. 18, 2 (2016), 58--67.
    [30]
    Jacquelynne S. Eccles and Allan Wigfield. 1995. In the mind of the actor: The structure of adolescents’ achievement task values and expectancy-related beliefs. Pers. Soc. Psychol. Bull. 21, 3 (1995), 215--225.
    [31]
    Elizabeth Fennema and Julia A. Sherman. 1976. Fennema-sherman mathematics attitudes scales: Instruments designed to measure attitudes toward the learning of mathematics by females and males. J. Res. Math. Edu. 7, 5 (1976), 324--326.
    [32]
    Deborah Fields, Lisa Quirke, Tori Horton, Jason Maughan, Xavier Velasquez, Janell Amely, and Katarina Pantic. 2016. Working toward equity in a constructionist scratch camp lessons learned in applying a studio design model. Bangkok, Thailand.
    [33]
    Allan Fisher, Jane Margolis, and Faye Miller. 1997. Undergraduate women in computer science: Experience, motivation and culture. In ACM SIGCSE Bulletin. ACM, 106--110.
    [34]
    Michelle Friend. 2015. Middle school girls’ envisioned future in computing. Comput. Sci. Edu. 25, 2 (2015), 152--173.
    [35]
    Gerald C. Gannod, Janet E. Burge, Victoria McIe, Maureen Doyle, and Karen C. Davis. 2014. Increasing awareness of computer science in high school girls. In Proceedings of the IEEE Frontiers in Education Conference (FIE’14). 1--8.
    [36]
    T. St Georgiev. 2019. Students’ viewpoint about using MIT app inventor in education. In Proceedings of the 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO’19). 611--616.
    [37]
    Sandy Graham and Celine Latulipe. 2003. CS girls rock: Sparking interest in computer science and debunking the stereotypes. In ACM SIGCSE Bulletin. ACM, 322--326.
    [38]
    Kenneth E. Graves and Leigh Ann DeLyser. 2017. Interested in class, but not in the hallway: A latent class analysis (LCA) of CS4All student surveys. In Proceedings of the ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE’17). Association for Computing Machinery, New York, NY, 243--248.
    [39]
    Shuchi Grover and Roy Pea. 2013. Computational thinking in K–12: A review of the state of the field. Education. Res. 42, 1 (2013), 38--43.
    [40]
    Denise Gürer and Tracy Camp. 2002. An ACM-W literature review on women in computing. SIGCSE Bull. 34, 2 (2002), 121--127.
    [41]
    Denise Gürer and Tracy Camp. 2003. Investigating the incredible shrinking pipeline for women in computer science. Retrieved from /paper/Investigating-the-Incredible-Shrinking-Pipeline-for-Denise-Camp/548a891c28203afc0c92092443f81daadd2219f1.
    [42]
    Cathy Hall, Jeremy Dickerson, David Batts, Paul Kauffmann, and Michael Bosse. 2011. Are we missing opportunities to encourage interest in STEM fields? J. Technol. Edu. 23, 1 (2011).
    [43]
    Judith M. Harackiewicz, Kenneth E. Barron, John M. Tauer, Suzanne M. Carter, and Andrew J. Elliot. 2000. Short-term and long-term consequences of achievement goals: Predicting interest and performance over time. J. Edu. Psychol. 92, 2 (2000), 316--330.
    [44]
    Judith M. Harackiewicz, Christopher S. Rozek, Chris S. Hulleman, and Janet S. Hyde. 2012. Helping parents to motivate adolescents in mathematics and science: An experimental test of a utility-value intervention. Psychol. Sci. 23, 8 (2012), 899--906.
    [45]
    Judith M. Harackiewicz, Jessi L. Smith, and Stacy J. Priniski. 2016. Interest matters: The importance of promoting interest in education. Retrieved from https://journals.sagepub.com/doi/full/10.1177/2372732216655542.
    [46]
    Suzanne Hidi and K. Ann Renninger. 2006. The four-phase model of interest development. Education. Psychol. 41, 2 (2006), 111--127.
    [47]
    John L. Holland. 1973. Making Vocational Choices: A Theory of Careers. Engelwood Cliffs.
    [48]
    John L. Holland. 1997. Making Vocational Choices: A Theory of Vocational Personalities and Work Environments, 3rd ed. Psychological Assessment Resources, Odessa, FL.
    [49]
    Vincent Hoogerheide, Sofie M. M. Loyens, and Tamara van Gog. 2016. Learning from video modeling examples: Does gender matter? Instr. Sci. 44, 1 (2016), 69--86.
    [50]
    Caitlin Hulsey, Toni B. Pence, and Larry F. Hodges. 2014. Camp CyberGirls: Using a virtual world to introduce computing concepts to middle school girls. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education. ACM, 331--336.
    [51]
    Susan Jamieson. 2004. Likert scales: how to (ab) use them. Med. Edu. 38, 12 (2004), 1217--1218.
    [52]
    Yasmin Kafai, Jean Griffin, Quinn Burke, Michelle Slattery, Deborah Fields, Rita Powell, Michele Grab, Susan Davidson, and Joseph Sun. 2013. A cascading mentoring pedagogy in a CS service-learning course to broaden participation and perceptions. In Proceeding of the 44th ACM Technical Symposium on Computer Science Education (SIGCSE’13). ACM, Denver, Colorado, 101--106.
    [53]
    Shereen Khoja, Camille Wainwright, Juliet Brosing, and Jeffrey Barlow. 2012. Changing girls’ attitudes towards computer science. J. Comput. Sci. Coll. 28, 1 (2012), 210--216.
    [54]
    Päivi Kinnunen and Beth Simon. 2011. CS majors’ self-efficacy perceptions in CS1: results in light of social cognitive theory. In Proceedings of the 7th International Workshop on Computing Education Research (ICER’11). Association for Computing Machinery, New York, NY, 19--26.
    [55]
    Antti-Jussi Lakanen and Tommi Kärkkäinen. 2019. Identifying pathways to computer science: The long-term impact of short-term game programming outreach interventions. ACM Trans. Comput. Educ. 19, 3 (2019), 20:1–20:30.
    [56]
    Kathleen J. Lehman, Linda J. Sax, and Hilary B. Zimmerman. 2016. Women planning to major in computer science: Who are they and what makes them unique? Comput. Sci. Edu. 26, 4 (2016), 277--298.
    [57]
    Robert W. Lent, Steven D. Brown, and Gail Hackett. 1994. Toward a unifying social cognitive theory of career and academic interest, choice, and performance. J. Vocation. Behav. 45, 1 (1994), 79--122.
    [58]
    Robert W. Lent, Antonio M. Lopez, Frederick G. Lopez, and Hung-Bin Sheu. 2008. Social cognitive career theory and the prediction of interests and choice goals in the computing disciplines. J. Vocation. Behav. 73, 1 (2008), 52--62.
    [59]
    Guan-Yu Lin. 2016. Self-efficacy beliefs and their sources in undergraduate computing disciplines: An examination of gender and persistence. J. Education. Comput. Res. 53, 4 (2016), 540--561.
    [60]
    Hilary M. Lips and Linda Temple. 1990. Majoring in computer science: Causal models for women and men. Res. High Edu. 31, 1 (1990), 99--113.
    [61]
    Alex Lishinski and Joshua Rosenberg. 2020. Accruing interest: What experiences contribute to students developing a sustained interest in computer science over time? In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (SIGCSE’20). Association for Computing Machinery, New York, NY, 1414.
    [62]
    Penelope Lockwood. 2006. “Someone like me can be successful”: Do college students need same-gender role models? Psychol. Women Quart. 30, 1 (2006), 36--46.
    [63]
    Andy Luse, Julie A. Rursch, and Doug Jacobson. 2014. Utilizing structural equation modeling and social cognitive career theory to identify factors in choice of IT as a major. Trans. Comput. Edu. 14, 3 (2014), 19:1–19:19.
    [64]
    Pruthikrai Mahatanankoon. 2018. Exploring the antecedents to computer programming self-efficacy. In Proceedings of the 10th International Conference on Advances in Information Technology (IAIT’18). Association for Computing Machinery, New York, NY, 1--6.
    [65]
    David M. Marx and Jasmin S. Roman. 2002. Female role models: Protecting women's math test performance. Pers. Soc. Psychol. Bull. 28, 9 (2002), 1183--1193.
    [66]
    Allison Master, Sapna Cheryan, and Andrew N. Meltzoff. 2016. Computing whether she belongs: Stereotypes undermine girls’ interest and sense of belonging in computer science. J. Education. Psychol. 108, 3 (2016), 424--437.
    [67]
    Microsoft. 2018. Closing the STEM Gap: Why STEM Classes and Careers Still Lack Girls and What We Can Do About It. Retrieved from https://youthrex.com/report/closing-the-stem-gap-why-stem-classes-and-careers-still-lack-girls-and-what-we-can-do-about-it/.
    [68]
    Iwona Miliszewska, Gayle Barker, Fiona Henderson, and Ewa Sztendur. 2006. The issue of gender equity in computer science – what students say. J. Info. Technol. Edu.: Res. 5, 1 (2006), 107--120.
    [69]
    Ralph Morelli, Nina Limardo, Trishan de Lanerolle, and Elizabeth Tamotsu. 2011. Can Android app inventor bring computational thinking to K-12? Retrieved from https://www.researchgate.net/publication/228442759_Can_Android_App_Inventor_Bring_Computational_Thinking_to_K-12.
    [70]
    Engineering National Academies of Sciences. 2017. Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments.
    [71]
    National Science Foundation. 2017. Women, minorities, and persons with disabilities in science and engineering. Retrieved from https://www.nsf.gov/statistics/2017/nsf17310/.
    [72]
    National Science Foundation. 2018. CISE - Broadening Participation in Computing (BPC) | NSF - National Science Foundation. Retrieved from https://www.nsf.gov/cise/bpc/.
    [73]
    Lijun Ni, Farzeen Harunani, and Fred Martin. 2017. Empowering middle school students to create data-based social apps. J. Comput. Sci. Colleges 32, 6 (2017), 88--100.
    [74]
    Lijun Ni, Mark Sherman, Diane Schilder, and Fred Martin. 2016. Computing with a community focus: An app inventor summer camp for middle school students. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education. ACM, 690--690.
    [75]
    Katarina Pantic, Deborah A. Fields, and Lisa Quirke. 2016. Studying situated learning in a constructionist programming camp: A multimethod microgenetic analysis of one girl's learning pathway. In Proceedings of the The 15th International Conference on Interaction Design and Children (IDC’16). ACM, New York, NY, 428--439.
    [76]
    Stamatios Papadakis and Vasileios Orfanakis. 2018. Comparing novice programing environments for use in secondary education: App inventor for android vs. Alice. Int. J. Technol. Enhanced Learn. 10, 1/2 (2018), 44.
    [77]
    Michael Quinn Patton. 2002. Designing qualitative studies. Qualitat. Res. Eval. Methods 3, (2002), 230--246.
    [78]
    Markeya S. Peteranetz, Shiyuan Wang, Duane F. Shell, Abraham E. Flanigan, and Leen-Kiat Soh. 2018. Examining the impact of computational creativity exercises on college computer science students’ learning, achievement, self-efficacy, and creativity. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education (SIGCSE’18). Association for Computing Machinery, New York, NY, 155--160.
    [79]
    Nichole Pinkard, Sheena Erete, Caitlin K. Martin, and Maxine McKinney de Royston. 2017. Digital youth divas: Exploring narrative-driven curriculum to spark middle school girls’ interest in computational activities. J. Learn. Sci. 26, 3 (2017), 477--516.
    [80]
    Lori Pollock, Kathleen McCoy, Sandra Carberry, Namratha Hundigopal, and Xiaoxin You. 2004. Increasing high school girls’ self confidence and awareness of CS through a positive summer experience. In ACM SIGCSE Bulletin. ACM, 185--189.
    [81]
    Vennila Ramalingam, Deborah LaBelle, and Susan Wiedenbeck. 2004. Self-efficacy and mental models in learning to program. In Proceedings of the 9th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (ITiCSE’04). Association for Computing Machinery, New York, NY, 171--175.
    [82]
    K. Ann. Renninger and Suzanne Hidi. 2011. Revisiting the conceptualization, measurement, and generation of interest. Education. Psychol. 46, 3 (2011), 168--184.
    [83]
    Jean E. Rhodes, Ranjini Reddy, Jean B. Grossman, and Judy Maxine Lee. 2002. Volunteer mentoring relationships with minority youth: An analysis of same- versus cross-race matches1. J. Appl. Soc. Psychol. 32, 10 (2002), 2114--2133.
    [84]
    Ricarose Roque, Yasmin Kafai, and Deborah Fields. 2012. From tools to communities: Designs to support online creative collaboration in scratch. In Proceedings of the Conference on Interaction Design and Children (IDC’12). ACM, New York, NY, 220--223.
    [85]
    Mary Beth Rosson, John M. Carroll, and Hansa Sinha. 2011. Orientation of undergraduates toward careers in the computer and information sciences: Gender, self-efficacy and social support. Trans. Comput. Edu. 11, 3 (2011), 14:1–14:23.
    [86]
    Krishnendu Roy. 2012. App inventor for android: report from a summer camp. In Proceedings of the 43rd ACM Technical Symposium on Computer Science Education. ACM, 283--288.
    [87]
    Miguel Angel Rubio, Rocio Romero-Zaliz, Carolina Mañoso, and Angel P. de Madrid. 2015. Closing the gender gap in an introductory programming course. Comput. Edu. 82, (2015), 409--420.
    [88]
    Richard M. Ryan. 1982. Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory. J. Personal. Soc. Psychol. 43, 3 (1982), 450--461.
    [89]
    Richard M. Ryan and Edward L. Deci. 2000. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Amer. Psychol. 55, 1 (2000), 68--78.
    [90]
    Mihaela Sabin, Rosabel Deloge, Adrienne Smith, and Wendy DuBow. 2017. Summer learning experience for girls in grades 7–9 boosts confidence and interest in computing careers. J. Comput. Sci. Colleges 32, 6 (2017), 79--87.
    [91]
    Philip M. Sadler, Gerhard Sonnert, Zahra Hazari, and Robert Tai. 2012. Stability and volatility of STEM career interest in high school: A gender study. Sci. Edu. 96, 3 (2012), 411--427.
    [92]
    Milagros Sáinz and Jacquelynne Eccles. 2012. Self-concept of computer and math ability: Gender implications across time and within ICT studies. J. Vocation. Behav. 80, 2 (2012), 486--499.
    [93]
    Johnny Saldaña. 2015. The Coding Manual for Qualitative Researchers. SAGE.
    [94]
    Linda J. Sax, Kathleen J. Lehman, Jerry A. Jacobs, M. Allison Kanny, Gloria Lim, Laura Monje-Paulson, and Hilary B. Zimmerman. 2017. Anatomy of an enduring gender gap: The evolution of women's participation in computer science. J. Higher Edu. 88, 2 (2017), 258--293.
    [95]
    Pasqueline Dantas Scaico, Ruy José G. B. de Queiroz, and José Jorge Lima Dias. 2017. Analyzing how interest in learning programming changes during a CS0 course: A qualitative study with brazilian undergraduates. In Proceedings of the ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE’17). Association for Computing Machinery, New York, NY, 16--21.
    [96]
    Kusum Singh, Katherine R. Allen, Rebecca Scheckler, and Lisa Darlington. 2007. Women in computer-related majors: A critical synthesis of research and theory from 1994 to 2005. Rev. Education. Res. 77, 4 (2007), 500--533.
    [97]
    Renée Spencer. 2007. “It's Not What I Expected”: A qualitative study of youth mentoring relationship failures. J. Adolesc. Res. 22, 4 (2007), 331--354.
    [98]
    Courtney Starrett, Marguerite Doman, Chlotia Garrison, and Merry Sleigh. 2015. Computational bead design: A pilot summer camp in computer aided design and 3D printing for middle school girls. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education. ACM, 587--590.
    [99]
    Chongning Sun and Jody Clarke-Midura. Testing the efficacy of a near-peer mentoring model for recruiting youth into computer science. In review.
    [100]
    Edna Tan and Angela Calabrese Barton. 2018. Towards critical justice: Exploring intersectionality in community-based STEM-rich making with youth from non-dominant communities. Equity Excell. Edu. 51, 1 (2018), 48--61.
    [101]
    Cathy van Tuijl and Juliette H. Walma van der Molen. 2016. Study choice and career development in STEM fields: An overview and integration of the research. Int. J. Technol. Des. Edu. 26, 2 (2016), 159--183.
    [102]
    Timothy Urness and Eric D. Manley. 2013. Generating interest in computer science through middle-school android summer camps. J. Comput. Sci. Colleges 28, 5 (2013), 211--217.
    [103]
    Marie E. Vachovsky, Grace Wu, Sorathan Chaturapruek, Olga Russakovsky, Richard Sommer, and Li Fei-Fei. 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. ACM, 303--308.
    [104]
    Roli Varma. 2002. Women in information technology: A case study of undergraduate students in a minority-serving institution. Bull. Sci. Technol. Soc. 22, 4 (2002), 274--282.
    [105]
    Tamar Vilner and Ela Zur. 2006. Once she makes it, she is there: Gender differences in computer science study. In Proceedings of the 11th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (ITICSE’06). ACM, New York, NY, 227--231.
    [106]
    Anna Vitores and Adriana Gil-Juárez. 2016. The trouble with ‘women in computing’: A critical examination of the deployment of research on the gender gap in computer science. J. Gender Studies 25, 6 (2016), 666--680.
    [107]
    Jayce R. Warner, Carol L. Fletcher, and Lisa S. Garbrecht. 2019. Better data, better progress: Methods for measuring inequities in computer science education. In Proceedings of the American Educational Research Association (AERA’19).
    [108]
    Heidi C. Webb and Mary Beth Rosson. 2011. Exploring careers while learning Alice 3D: A summer camp for middle school girls. In Proceedings of the 42nd ACM Technical Symposium on Computer Science Education. ACM, 377--382.
    [109]
    Brenda Cantwell Wilson. 2002. A study of factors promoting success in computer science including gender differences. Comput. Sci. Edu. 12, 1–2 (2002), 141--164.

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      cover image ACM Transactions on Computing Education
      ACM Transactions on Computing Education  Volume 20, Issue 4
      December 2020
      146 pages
      EISSN:1946-6226
      DOI:10.1145/3428081
      Issue’s Table of Contents
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      Publication History

      Published: 12 November 2020
      Accepted: 01 September 2020
      Revised: 01 June 2020
      Received: 01 February 2020
      Published in TOCE Volume 20, Issue 4

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      Author Tags

      1. Middle school youth
      2. access
      3. diversity
      4. high school youth
      5. interest
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      7. self-efficacy

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      • (2022)"It is the Future": Exploring Parent Perspectives of CS EducationProceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 110.1145/3478431.3499411(258-264)Online publication date: 22-Feb-2022

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