Abstract
During the middle school years, students frequently show significant declines in motivation toward school in general and mathematics in particular. One way in which researchers have sought to spark students’ interests and build their sense of competence in mathematics and in STEM more generally is through the use of game-based learning environments. Yet evidence regarding the motivational effectiveness of this approach is mixed. Here, we evaluate the impact of three brief game-based technology activities on students’ short-term motivation in math. A total number of 16,789 fifth to eighth grade students and their teachers in one large school district were randomly assigned to three different game-based technology activities, each representing a different framework for motivation and engagement and all designed around an exemplary lesson related to algebraic reasoning. We investigated the relationship between specific game-based technology activities that embody various motivational constructs and students’ engagement in mathematics and perceived competence in pursuing STEM careers. Results indicate that the effect of each game-based technology activities on students’ motivation was quite modest. However, these effects were modified by students’ grade level and not by their demographic variables. In addition, teacher-level variables did not have an effect on student outcomes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Little’s (1988) Missing Completely at Random (MCAR) test confirmed that these data were not missing completely at random (χ 2 (1576) = 7162.88, p < .001). In particular, students with missing data were more likely to be male, African-American or Hispanic/Latino, with ELL status, and from schools with a high percentage of free or reduced lunch. For a more in-depth discussion of the impact of this missing data on our results, see Star et al. (2014).
- 2.
A delayed posttest was also administered, 2 months after the end of the intervention. However, due to large amounts of missing data, delayed posttest results were not easily interpretable and thus are not included in the present analysis.
References
Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. Washington, DC: United States Department of Education.
Archambault, I., Eccles, J. S., & Vida, M. N. (2010). Ability self-concepts and subjective value in literacy: Joint trajectories from Grades 1 through 12. Journal of Educational Psychology, 102, 804–816.
Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W.H. Freeman.
Bandura, A. (2006). Guide for constructing self-efficacy scales. In F. Pajares & T. Urdan (Eds.), Self-efficacy beliefs of adolescents (pp. 307–337). Greenwich, CT: Information Age.
Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and intervention. Child Development, 78, 246–263.
Britner, S. L., & Pajares, F. (2001). Self-efficacy beliefs, race, and gender in middle school science. Journal of Women and Minorities in Science and Engineering, 7, 271–285.
Council, N. R. (2011). Learning science through computer games and simulations. Washington, DC: National Academy Press.
Donohoe, C., Topping, K., & Hannah, E. (2012). The impact of an online intervention (Brainology) on the mindset and resiliency of secondary school pupils: A preliminary mixed methods study. Educational Psychology, 32, 641–655.
Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41, 1040–1048.
Dweck, C. S. (1999). Self-theories: Their role in motivation, personality, and development. Philadelphia, PA: Psychology Press.
Dweck, C. S., & Leggett, E. L. (1988). A social cognitive approach to motivation and personality. Psychological Review, 95, 256–273.
Eccles (Parsons), J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motivation (pp. 75–146). San Francisco, CA: W. H. Freeman.
Eccles, J. S., Barber, B. L., Stone, M., & Hunt, J. (2003). Extracurricular activities and adolescent development. Journal of Social Issues, 59, 865–889.
Eccles, J. S., Midgley, C., & Adler, T. (1984). Grade-related changes in the school environment: Effects on achievement motivation. In J. Nicholls (Ed.), Advances in motivation and achievement: The development of achievement motivation (Vol. 3, pp. 283–331). Greenwich, CT: JAI Press.
Gee, J. P. (2003). What video games have to teach us about learning and literacy. New York, NY: Palgrave MacMillan.
Good, C., Rattan, A., & Dweck, C. S. (2012). Why do women opt out? Sense of belonging and women’s representation in mathematics. Journal of Personality and Social Psychology, 102, 700–717.
Grant, H., & Dweck, C. S. (2003). Clarifying achievement goals and their impact. Journal of Personality and Social Psychology, 85, 541–553.
Gwilliam, L. R., & Betz, N. E. (2001). Validity of measures of math- and science-related self-efficacy for African Americans and European Americans. Journal of Career Assessment, 9, 261–281.
Hickey, D. T., Moore, A. L., & Pellegrino, J. W. (2001). The motivational and academic consequences of elementary mathematics environments: Do constructivist innovations and reforms make a difference? American Educational Research Journal, 38, 611–652.
Holzberger, D., Philipp, A., & Kunter, M. (2013). How teachers’ self-efficacy is related to instructional quality: A longitudinal analysis. Journal of Educational Psychology, 105(3), 774–786. doi:10.1037/a0032198.
Hong, Y. Y., Chiu, C. Y., Dweck, C. S., Lin, D. M. S., & Wan, W. (1999). Implicit theories, attributions, and coping: A meaning system approach. Journal of Personality and Social Psychology, 77, 588–599.
Kaput, J. (1999). Teaching and learn in a new algebra. In E. Fennema & T. Romberg (Eds.), Mathematics classrooms that promote understanding (pp. 133–155). Mahwah, NJ: Erlbaum.
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.
Ketelhut, D. J., Nelson, B. C., Clarke, J. E., & Dede, C. (2010). A multi-user virtual environment for building and assessing higher order inquiry skills in science. British Journal of Educational Technology, 41(1), 56–68.
Kieran, C. (1992). The learning and teaching of school algebra. In D. Grouws (Ed.), Handbook of research on mathematics teaching and learning (pp. 390–419). New York, NY: Simon & Schuster.
Klassen, R. M., Tze, V. M. C., Betts, S. M., & Gordon, K. A. (2011). Teacher efficacy research 1998–2009: Signs of progress or unfulfilled promise? Educational Psychology Review, 23, 21–43.
Lau, S., & Roeser, R. W. (2002). Cognitive abilities and motivational processes in high school students’ situational engagement and achievement in science. Educational Assessment, 8, 139–162.
Lent, R. W., Brown, S. D., Sheu, H.-B., Schmidt, J., Brenner, B. R., Gloster, C.,…Treistman, D. (2005). Social cognitive predictors of academic interest and goals in engineering: Utility for women and students at historically black universities. Journal of Counseling Psychology, 52, 84–92. doi:10.1037/0022-0167.52.1.84.
Lent, R. W., Brown, S. D., & Larkin, K. C. (1984). Relation of self-efficacy expectations to academic achievement and persistence. Journal of Counseling Psychology, 31, 356–362.
Lent, R. W., Lopez, A. M., Lopez, F. G., & Sheu, H. (2008). Social cognitive career theory and the prediction of interests and choice goals in the computing disciplines. Journal of Vocational Behavior, 73, 52–62.
Lent, R. W., Paixão, M. P., da Silva, J. T., & Leitão, L. M. (2010). Predicting occupational interests and choice aspirations in Portuguese high school students: A test of social cognitive career theory. Journal of Vocational Behavior, 76, 244–251.
Little, R. J. A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83(404), 1198–1201.
Liu, M., Hsieh, P., Cho, Y., & Schallert, D. L. (2006). Middle school students’ self-efficacy, attitudes, and achievement in a computer-enhanced problem-based learning environment. Journal of Interactive Learning Research, 17, 225–242.
Moos, D. C., & Marroquin, E. (2010). Multimedia, hypermedia, and hypertext: Motivation considered and reconsidered. Computers in Human Behavior, 26, 265–276.
National Academy of Sciences. (2011). Expanding underrepresented minority participation: America’s science and technology talent at the crossroads. Washington, DC: National Academies Press.
Raudenbush, S., & Bryk, A. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
Squire, K. D. (2003). Video games in education. International Journal of Intelligent Games & Simulation, 2, 49–62.
Star, J. R., Chen, J., Taylor, M., Durkin, K., Dede, C., & Chao, T. (2014). Evaluating technology-based strategies for enhancing motivation in mathematics. International Journal of STEM Education, 1:7. doi:10.1186/2196-7822-1-7. http://www.stemeducationjournal.com/content/1/1/7
Star, J. R., & Rittle-Johnson, B. (2009). Making algebra work: Instructional strategies that deepen student understanding, within and between representations. ERS Spectrum, 27(2), 11–18.
U.S. Department of Education. (2010). Transforming American education: Learning powered by technology (National Educational Technology Plan 2010). Washington, DC: Office of Educational Technology, U.S. Department of Education. Retrieved from http://www.ed.gov/technology/netp-2010
Watt, H. M. G., Eccles, J. S., & Durik, A. M. (2006). The leaky mathematics pipeline for girls: A motivational analysis of high school enrolments in Australia and the USA. Equal Opportunities International, 25, 642–659. doi:10.1108/02610150610719119.
Acknowledgments
The research was supported by a grant from the National Science Foundation (DRL #0929575) to Chris Dede and Jon R. Star. The ideas in this chapter are those of the authors and do not represent official positions of the National Science Foundation.
Portions of this chapter were adapted from: Star, J. R., Chen, J., Taylor, M., Durkin, K., Dede, C., & Chao, T. (2014). Evaluating technology-based strategies for enhancing motivation in mathematics. International Journal of STEM Education, 1:7. doi: 10.1186/2196-7822-1-7. http://www.stemeducationjournal.com/content/1/1/7
Thanks to Adam Seldow, Greg Jastrzemski, and the faculty, administration, and students of Chesterfield County Public Schools for their enthusiastic participation in the project. Thanks to Stephanie Fitzgerald for her assistance with all aspects of the project, and to Kinga Petrovai, Bharat Battu, Kevin Reeves, Arielle Niemeyer, Joy Casad, Chad Desharnais, Maisy Suslavich, Lauren Schiller, and Amy Venditta for their assistance with data collection and analysis.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Star, J.R., Chen, J.A., Taylor, M.W., Durkin, K., Dede, C., Chao, T. (2015). Evaluating Game-Based Learning Environments for Enhancing Motivation in Mathematics. In: Torbeyns, J., Lehtinen, E., Elen, J. (eds) Describing and Studying Domain-Specific Serious Games. Advances in Game-Based Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-20276-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-20276-1_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20275-4
Online ISBN: 978-3-319-20276-1
eBook Packages: Humanities, Social Sciences and LawEducation (R0)