Carnegie Mellon University

CoBox is an outreach and co-design component of the Player-Programmed-Parnter Games research project

Design of the CoBox and Player-Programmed-Partner Games is informed by research in Informal Education, Cultural Probes, Game Design, and Computer Science, Collaborative Robotics. Here are some featured papers and articles.

Informal Education

  • Afterschol Alliance. (2016). Growing computer science education in afterschool: Opportunities and challenges. http://afterschoolalliance.org/documents/Growing_Computer_Science_Education_2016.pdf.
  • Cole, M. (1996). A multilevel methodology for cultural psychology. Cultural psychology: A once and future discipline, 286-325.
  • Crocco, M. S., & Costigan, A. T. (2007). The Narrowing of Curriculum and Pedagogy in the Age of Accountability Urban Educators Speak Out. Urban Education, 42(6), 512–535. https://doi.org/10.1177/0042085907304964
  •  Gelman, B., Beckley, C., Johri, A., Domeniconi, C., & Yang, S. (n.d.). Online Urbanism: Interest-based Subcultures as Drivers of Informal Learning in an Online Community. In Proceedings of the Third (2016) ACM Conference on learning @ scale (pp. 21–30). ACM. https://doi.org/10.1145/2876034.2876052.
  • Hendricks, C., Alemdar, M., & Williams, T. (2012, June). The Impact of Participation in Vex Robotics Competition on Middle and High School, Students’ Interest in Pursuing STEM Studies and STEM Related Careers. Paper presented at 119th American Society for Engineering Education Annual Conference & Exposition, San Antonio.

Cultural Probes

  • Holland, D. (2001). Identity and agency in cultural worlds. Harvard University Press.

Game Design & Games-Based Learning

  • Aleven, V., Myers, E., Easterday, M., & Ogan, A. (2010, April). Toward a framework for the analysis and design of educational games. In Digital Game and Intelligent Toy Enhanced Learning (DIGITEL), 2010 Third IEEE International Conference on (pp. 69-76). IEEE.
  • Annetta, L. A. (2010). The “I's” have it: A framework for serious educational game design. Review of General Psychology, 14(2), 105.
  • Bos, N. D., Shami, N. S., & Naab, S. (2006). A globalization simulation to teach corporate social responsibility: Design features and analysis of student reasoning. Simulation & Gaming, 37(1), 56-72.

  • Harpstead, E. (2017). Projective Replay Analysis: A Reflective Approach for Aligning Educational Games to their Goals. Carnegie Mellon University. Retrieved from http://reports-archive.adm.cs.cmu.edu/anon/anon/hcii/CMU-HCII-17-107.pdf.

  • Harpstead, E., MacLellan, C. J., Aleven, V., & Myers, B. A. (2015). Replay Analysis in Open-Ended Educational Games. In C. S. Loh, Y. Sheng, & D. Ifenthaler (Eds.), Serious Games Analytics (pp. 381–399). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-05834-4_17.

  • Harpstead, E., MacLellan, C. J., Aleven, V., & Myers, B. A. (2014). Using extracted features to inform alignment-driven design ideas in an educational game. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems - CHI ’14 (pp. 3329–3338). New York, New York, USA: ACM Press. https://doi.org/10.1145/2556288.2557393.

  • Harpstead, E., MacLellan, C. J., Koedinger, K. R., Aleven, V., Dow, S. P., & Myers, B. A. (2013). Investigating the Solution Space of an Open-Ended Educational Game Using Conceptual Feature Extraction. In Proceedings of the 6th International Conference on Educational Data Mining - EDM 2013 (pp. 51–58).

  • Clark, D. B., Tanner-Smith, E. E., & Killingsworth, S. S. (2016). Digital games, design, and learning: A systematic review and meta-analysis. Review of educational research, 86(1), 79-122.

  • Gee, J. P. (2005). Pleasure, learning, video games, and life: The projective stance. E-Learning and Digital Media, 2(3), 211-223.

  • Gee, J. P., & Hayes, E. (2012). Nurturing affinity spaces and game-based learning. Games, learning, and society: Learning and meaning in the digital age, 123, 1-40.

Computer Science

Collaborative Robotics

  • Gerber, L. C., Calasanz-Kaiser, A., Hyman, L., Voitiuk, K., Patil, U., & Riedel-Kruse, I. H. (2017). Liquid-handling Lego robots and experiments for STEM education and research. PLoS biology15(3), e2001413. Gee, J. P., & Hayes, E. (2012). Nurturing affinity spaces and game-based learning. Games, learning, and society: Learning and meaning in the digital age, 123, 1-40.
  • Johnson, R. C. (2012). 'Co-robots' help boost human productivity. Electronic Engineering Times, (1626), 25-29. Retrieved February 13, 2018, from https://www.eetimes.com/.

Motivation and Engagement

  • Ben-Eliyahu, A., Moore, D., Dorph, R., & Schunn, C. D. (2018). Investigating the multidimensionality of engagement: Affective, behavioral, and cognitive engagement across science activities and contexts. Contemporary Educational Psychology, 53, 87-105.
  • Boyle, E. A., Connolly, T. M., Hainey, T., & Boyle, J. M. (2012). Engagement in digital entertainment games: A systematic review. Computers in human behavior, 28(3), 771-780.
  • Dede, C. (2009). Immersive interfaces for engagement and learning. Science, 323(5910), 66-69.
  •  Collins, A., Joseph, D., & Bielaczyc, K. (2004). Design research: Theoretical and methodological issues. The Journal of the learning sciences, 13(1), 15-42.
  • DiSalvo, B., Guzdial, M., Bruckman, A., & McKlin, T. (2014). Saving face while geeking out: Video game testing as a justification for learning computer science. Journal of the Learning Sciences, 23(3), 272-315.
  • Egenfeldt-Nielsen, S. (2007). Third generation educational use of computer games. Journal of Educational Multimedia and Hypermedia, 16(3), 263-281. Waynesville, NC USA:
  • Gao, Y., Gerling, K. M., Mandryk, R. L., & Stanley, K. G. (2014, October). Decreasing sedentary behaviours in pre-adolescents using casual exergames at school. In Proceedings of the first ACM SIGCHI annual symposium on Computer-human interaction in play (pp. 97-106). ACM.
  •  Garris, R., Ahlers, R., & Driskell, J. E. (2002). Games, motivation, and learning: A research and practice model. Simulation & gaming, 33(4), 441-467.
  • Habgood, M. J., & Ainsworth, S. E. (2011). Motivating children to learn effectively: Exploring the value of intrinsic integration in educational games. The Journal of the Learning Sciences, 20(2), 169-206.
  • Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational psychologist, 41(2), 111-127.

Additional Research 

  • Kafai, Y. B., Fields, D. A., Roque, R., Burke, W. Q., & Monroy-Hernández, A. (in press). Collaborative agency in youth online and offline creative production in Scratch. Research and Practice in Technology Enhanced Learning.
  • Kapp, K. M. (2012). The gamification of learning and instruction: game-based methods and strategies for training and education. John Wiley & Sons.
  • Ke, Fengfeng & Xie, Kui & Xie, Ying. (2015). Game-based learning engagement: A theory- and data-driven exploration. British Journal of Educational Technology. 10.1111/bjet.12314.
  • Ketelhut, D. J. (2007). The impact of student self-efficacy on scientific inquiry skills: An exploratory investigation in River City, a multi-user virtual environment. Journal of science education and technology, 16(1), 99-111.
  • Kier, M. W., Blanchard, M. R., Osborne, J. W., & Albert, J. L. (2014). The development of the STEM career interest survey (STEM-CIS). Research in Science Education, 44(3), 461-481.
  • Klopfer, E., Osterweil, S., & Salen, K. (2009). Moving learning games forward. Cambridge, MA: The Education Arcade.
  • Lee, I., Martin, F., Denner, J., Coulter, B., Allan, W., Erickson, J., & Werner, L. (2011). Computational thinking for youth in practice. ACM Inroads, 2(1), 32-37.
  • Lee, M., & Ko, A. (n.d.). Comparing the Effectiveness of Online Learning Approaches on CS1 Learning Outcomes. In Proceedings of the eleventh annual International Conference on international computing education research (pp. 237–246). ACM. https://doi.org/10.1145/2787622.2787709.
  • Lerner, R., Bornstein, M., Leventhal, T., & Lerner, R. (2015). Handbook of child psychology and developmental science. Volume 4, Ecological settings and processes (7th ed.). Hoboken, New Jersey: Wiley.
  • Lightbot, Inc. (2008). Lightbot[Mobile game]. http://lightbot.com/
  • Linnenbrink-Garcia, L., Durik, A. M., Conley, A. M., Barron, K. E., Tauer, J. M., Karabenick, S. A., & Harackiewicz, J. M. (2010). Measuring situational interest in academic domains. Educational and psychological measurement, 70(4), 647-671.
  • Loh, C. S., Sheng, Y., & Ifenthaler, D. (2015). Serious Games Analytics: Theoretical Framework. In Serious Games Analytics (pp. 3–29). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-05834-4_l.
  • London, R. A., Pastor, M., Servon, L. J., Rosner, R., & Wallace, A. (2009). The Role of Community Technology Centers in Promoting Youth Development. Youth & Society,42(2), 199-228. doi:10.1177/0044118x09351278.
  • Lomas, J. D., Koedinger, K., Patel, N., Shodhan, S., Poonwala, N., & Forlizzi, J. L. (2017, May). Is Difficulty Overrated?: The Effects of Choice, Novelty and Suspense on Intrinsic Motivation in Educational Games. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 1028-1039). ACM.
  • MacLellan, C. J., Harpstead, E., Patel, R., & Koedinger, K. R. (2016). The Apprentice Learner Architecture: Closing the loop between learning theory and educational data. In Proceedings of the 9th International Conference on Educational Data Mining - EDM ’16 (pp. 151–158). Retrieved from http://www.educationaldatamining.org/EDM2016/proceedings/paper_118.pdf.
  • Malliarakis, C., Satratzemi, M., & Xinogalos, S. (2014). Designing Educational Games for Computer Programming: A Holistic Framework. Electronic Journal of e-Learning, 12(3), 281-298.
  • Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., . . . S. S. (2017). JOBS LOST, JOBS GAINED: WORKFORCE TRANSITIONS IN A TIME OF AUTOMATION. McKinsey Global Institute. Retrieved January 5, 2078, from https://www.mckinsey.com.
  • Marsh, H. W. (1987). The big-fish-little-pond effect on academic self-concept. Journal of educational psychology, 79(3), 280.
  • Melchior, A., Burack, C., Hoover, M., & Marcus, J. (2017). FIRST Longitudinal Study: Findings at 36 Month Follow-Up (Year 4 Report). Center for Youth and Communities, Brandeis Univ., Apr.FIRST. (n.d.). Retrieved November 2, 2018, from https://www.firstinspires.org/
  • Melchior, A., Fay, C., Leavitt, T., (2005) More than Robots: An Evaluation of the FIRST Robotics Competition Participant and Institutional Impacts. (pp 34-38). The Ford Foundation April, 2005.
  • Miller, D. P., Nourbakhsh, I. R., & Siegwart, R. (2008). Robots for education. In Springer handbook of robotics (pp. 1283-1301). Springer, Berlin, Heidelberg.
  • Mislevy, R. J., Oranje, A., Bauer, M. I., Davier, A. von, Hao, J., Corrigan, S., & John, M. (2014). Pyschometric Considerations in Game-Based Assessments. Glasslab. Retrieved from https://www.envisionexperience.com/~/media/files/blog/glasslab-psychometrics.pdf.
  • Moreno, R., & Mayer, R. E. (2005). Role of guidance, reflection, and interactivity in an agent-based multimedia game. Journal of educational psychology, 97(1), 117.
  • Murayama, K., & Elliot, A. J. (2012). The competition–performance relation: A meta-analytic review and test of the opposing processes model of competition and performance. Psychological bulletin, 138(6), 1035.
  • National Research Council (2009). Learning Science in Informal Environments: People, Places, and Pursuits Washington, D. C.: The National Academies Press http://www.nap.edu/openbook.php?record_id=12190.
  • Nanavati, A., Dias, M. B., & Steinfeld, A. (2018, April). Speak Up: A Multi-Year Deployment of Games to Motivate Speech Therapy in India. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (p. 318). ACM.
  • Neuman, S. B., & Celano, D. (2006). The knowledge gap: Implications of leveling the playing field for low‐income and middle‐income children. Reading Research Quarterly, 41(2), 176-201.
  • Nicolopoulou, A. (1993). Play, Cognitive Development, and the Social World: Piaget, Vygotsky, and Beyond. Human Development, 36(1), 1–23. https://doi.org/10.1159/000277285.
  • Oh, Y. J., Jia, Y., Lorentson, M., & LaBanca, F. (2013). Development of the educational and career interest scale in science, technology, and mathematics for high school students. Journal of science Education and Technology, 22(5), 780-790.
  • Nanavati, A., Dias, M. B., & Steinfeld, A. (2018, April). Speak Up: A Multi-Year Deployment of Games to Motivate Speech Therapy in India. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (p. 318). ACM.
  • National Research Council. (2011). Learning science through computer games and simulations. National Academies Press.
  • National Research Council. (2011). Successful K-12 STEM education: Identifying effective approaches in science, technology, engineering, and mathematics. National Academies Press.
  • Parsons, S., & Sklar, E. (2004, March). Teaching AI using LEGO mindstorms. In AAAI Spring Symposium.
  • Pareto, L., Haake, M., Lindström, P., Sjödén, B., & Gulz, A. (2012). A teachable-agent-based game affording collaboration and competition: Evaluating math comprehension and motivation. Educational Technology Research and Development, 60(5), 723-751.
  • Pawar, U. S., Pal, J., & Toyama, K. (2006, May). Multiple mice for computers in education in developing countries. In Information and Communication Technologies and Development, 2006. ICTD'06. International Conference on (pp. 64-71). IEEE.
  • Piaget, J. (2013). Play, dreams and imitation in childhood. Routledge.
  • Revelle, W., Condon, D. M., Wilt, J., French, J. A., Brown, A., & Elleman, L. G. (2016). Web and phone based data collection using planned missing designs. Handbook of Online Research Methods. Thousand Oaks, CA: Sage Publications.
  • Rideout, V., & Katz, V. S. (2016). Opportunity for All? Technology and Learning in Lower-Income Families. In Joan Ganz Cooney Center at Sesame Workshop. Joan Ganz Cooney Center at Sesame Workshop. 1900 Broadway, New York, NY 10023.
  • Robelen, E. W. (2010). US gets poor grades in nurturing STEM diversity. Education Week.
  • Scassellati, B., & Tsui, K. M. (2016). Co-Robots: Humans and Robots Operating as Partners. Handbook of Science and Technology Convergence, 427-439. doi:10.1007/978-3-319-07052-0_27.
  • SCE Japan Studio & Team Ico (2001). Ico[Playstation 2 game]. Tokyo, Japan: Sony Computer Entertainment.
  • Schiefele, U. (2009). Situational and individual interest. Handbook of motivation at school, 197-222.
  • Searle, K. A., & Kafai, Y. B. (2015, April). Culturally responsive making with American Indian girls: Bridging the identity gap in crafting and computing with electronic textiles. In Proceedings of the Third Conference on GenderIT (pp. 9-16). ACM.
  • Shaffer, D. W. (2006). How computer games help children learn. Macmillan.
  • Solomon, C. J., & Papert, S. (1976, June). A case study of a young child doing Turtle Graphics in LOGO. In Proceedings of the June 7-10, 1976, national computer conference and exposition (pp. 1049-1056). ACM.
  • Thomas, J. O., Rankin, Y., Minor, R., & Sun, L. (2017). Exploring the difficulties African-American middle school girls face enacting computational algorithmic thinking over three years while designing games for social change. Computer Supported Cooperative Work (CSCW), 26(4-6), 389-421.
  • To, A., Fath, E., Zhang, E., Ali, S., Kildunne, C., Fan, A., ... Kaufman, G. (2016). Tandem Transformational Game Design: A Game Design Process Case Study. In Proceedings of the International Academic Conference on Meaningful Play.
  • Vishwanath, A., Kam, M., & Kumar, N. (2017, June). Examining low-cost virtual reality for learning in low-resource environments. In Proceedings of the 2017 Conference on Designing Interactive Systems (pp. 1277-1281). ACM.
  • Vygotsky, L. S. (2012). Thought and language. MIT press.
  • Wardaszko, M., & Podgórski, B. (2017). Mobile learning game effectiveness in cognitive learning by adults: A comparative study. Simulation & Gaming, 48(4), 435-454.
  • Whitecraft, M. A., & Williams, W. M. (2010). Why Aren’t More Women in Computer Science?. Making Software: What Really Works, and Why We Believe It, 221-238.
  • Wing, J. M. (2006). Computational thinking. Communications of the ACM49(3), 33-35.
  • Witherspoon, E. B., Schunn, C. D., Higashi, R. M., & Shoop, R. (2018). Attending to structural programming features predicts differences in learning and motivation. Journal of Computer Assisted Learning. https://s3.amazonaws.com/cs2n/research/attending-to-structural-programming-features-predicts-differences-in-learning-and-motivation-in-virtual-robotics-programming-curriculum.pdf.
  • Witherspoon, E., Higashi, R., Schunn, C., Shoop, R., Baehr, E. (October, 2017) Developing Computational Thinking through a Virtual Robotics Programming Curriculum. ACM Transactions on Computing Education, Vol. 18, No. 1, Article 4. https://s3.amazonaws.com/cs2n/research/a4-witherspoon.pdf.
  • Yang, S., Domeniconi, C., Revelle, M., Sweeney, M., Gelman, B., Beckley, C., & Johri, A. (n.d.). Uncovering Trajectories of Informal Learning in Large Online Communities of Creators. In Proceedings of the Second (2015) ACM Conference on learning @ scale (pp. 131–140). ACM. https://doi.org/10.1145/2724660.2724674.

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