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Chapter 25 - Design-Based Research in Engineering Education

Current State and Next Steps

Published online by Cambridge University Press:  05 February 2015

By
Anthony E. Kelly
Edited by
Aditya Johri  and
Barbara M. Olds
Anthony E. Kelly
Affiliation:
George Mason University
Aditya Johri
Affiliation:
Virginia Polytechnic Institute and State University
Barbara M. Olds
Affiliation:
Colorado School of Mines
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Summary

Introduction

Engineering education research is looking to research methods in education and related social sciences as a source for new approaches (Adams et al., 2011; Borrego & Bernhard, 2011; Case & Light, 2011; Davison, 2010; Ganesh, 2011; Johri & Olds, 2011; Pears, Fincher, Adams, & Daniels, 2008; Streveler & Smith, 2010). Design-based research in education, the focus of this chapter, is a natural source for ideas because this emerging methodology draws on engineering practices for some of its key values and approaches (e.g., Brown, 1992; Hjalmarson & Lesh, 2008; Middleton, Gorard, Taylor, & Bannan-Ritland, 2008). Indeed, inspiration for one of the early stage models for design-based research in education (Bannan-Ritland, 2003) was proposed by Woodie Flowers, an MIT engineer, at a National Science Foundation (NSF)–funded workshop (Kelly & Lesh, 2001). Design-based research can contribute to engineering education research because it also draws on (1) a tradition of studies in mathematics and science education (e.g., Cobb, McClain, & Gravemeijer, 2003; Kelly, Baek, Lesh, & Bannon-Ritland, 2008) and (2) frameworks from diffusion of innovations (Zaritsky et al., 2003) and more recently (3) from educational data mining (e.g., Baker & Yacef, 2009).

In this chapter, I use one model for design-based research in education, the Integrative Learning Design (ILD) framework (Bannan-Ritland, 2003), and illuminate its use with examples from education and an engineering education study by Hundhausen, Agarwal, Zollars, and Carter (2011). I suggest next steps in design-based research for engineering education research, including the creation of a Design Exchange for Scholars that would actively integrate insights from educational and engineering education research and place a greater emphasis on learning analytics and educational data mining.

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Information
Cambridge Handbook of Engineering Education Research , pp. 497 - 518
Publisher: Cambridge University Press
Print publication year: 2014

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References

Adams, R., Evangelou, D., English, L., De Figueiredo, A., Mousoulides, N., Pawley, A. L., … Wilson, D. (2011). Multiple perspectives on engaging future engineers. Journal of Engineering Education, 100(1), 48–88.CrossRefGoogle Scholar
Aiken, L. S., West, S. G., & Millsap, R. E. (2008). Doctoral training in statistics, measure-ment, and methodology in psychology: Replication and extension of the Aiken, West, Sechrest, and Reno (1990) Survey of Ph.D. Programs in North America. American Psychologist, 63(1), 32–50.CrossRefGoogle Scholar
Aleven, V., Roll, I., McLaren, B. M., & Koedinger, K. R. (2010). Automated, unobtrusive, action-by-action assessment of self-regulation during learning with an intelligent tutoring system. Educational Psychologist, 45(4), 224–233.CrossRefGoogle Scholar
Ambrose, S. A., & Norman, M. (2006). Preparing engineering faculty as educators. The Bridge, 36(2), 25–32.Google Scholar
Atman, C. J., Kilgore, D., & McKenna, A. (2008). Characterizing design learning: A mixed-methods study of engineering designers’ use of language. Journal of Engineering Education, 97(3), 309–326.CrossRefGoogle Scholar
Baker, R. S. J. D., Goldstein, A. B., & Heffernan, N. T. (2011). Detecting learning moment-by-moment. International Journal of Artificial Intelligence in Education.
Baker, R. S. J. D., Gowda, S. M., & Corbett, A. T. (2011a). Towards predicting future transfer of learning. In Proceedings of 15th International Conference on Artificial Intelligence in Education, Auckland, NZ (pp. 23–30).CrossRefGoogle Scholar
Baker, R. S. J. D., Gowda, S. M., & Corbett, A. T. (2011b). Automatically detecting a student's preparation for future learning: Help use is key. In Proceedings of the 4th International Conference on Educational Data Mining, Eindhoven, The Netherlands (pp. 179–188).Google Scholar
Baker, R. S. J. D., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–17.Google Scholar
Bannan-Ritland, B. (2003). The role of design in research: The integrative learning design framework. Educational Researcher, 32(1), 21–24.CrossRefGoogle Scholar
Bannan-Ritland, B. (2008). Teacher design re-search: An emerging paradigm for teachers’ professional development. In Kelly, A. E., Lesh, R. A., & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 246–262). New York, NY: Routledge.Google Scholar
Bannan-Ritland, B., & Baek, J. Y. (2008). Investigating the act of design in design research: The road taken. In Kelly, A. E., Lesh, R. A., & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 299–319). New York, NY: Routledge.Google Scholar
Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. Journal of the Learning Sciences, 13(1), 1–14.CrossRefGoogle Scholar
Barr, R. E., Pandy, M. G., Petrosino, A. J., Roselli, R. J., Brophy, S., & Freeman, R. A. (2007). Challenge-based instruction: The VaNTH biomechanics learning modules. Advances in Engineering Education, 1(1), 1–30.Google Scholar
Barriga, A. Q., Doran, J. W., Newell, S. B., Morrison, E. M., Barbetti, V., & Robbins, B. E. (2002). Relationships between problem behaviors and academic achievement in adolescents: The unique role of attention problems. Journal of Emotional and Behavioral Disorders, 10(4), 233–240.CrossRefGoogle Scholar
Berliner, D. C. (2002). Educational research: The hardest science of all. Educational Researcher, 31(8), 18–20.CrossRefGoogle Scholar
Bhide, A. (2008). The venturesome economy: How innovation sustains prosperity in a more connected world. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Borner, K. (2012). Visual analytics in support of education. Keynote Presentation, LAK12, Vancouver. Retrieved from CrossRefGoogle Scholar
Borrego, M., & Bernhard, J. (2011). The emergence of engineering education research as an internationally connected field of inquiry. Journal of Engineering Education, 100(1), 14–47.CrossRefGoogle Scholar
Borrego, M., Froyd, J., & Hall, T. (2010). Diffusion of engineering education innovations: A survey or awareness and adoption rates in U.S. engineering departments. Journal of Engineering Education, 99(3), 185–207.CrossRefGoogle Scholar
Borrego, M., Newswander, C. B., McNair, L. D., McGinnis, S., & Paretti, M. C. (2009). Using concept maps to assess interdisciplinary integration of green engineering knowledge. Advances in Engineering Education, 2(1), 1–26.Google Scholar
Bransford, J. D., Stipek, D. J., Vye, N. J., Gomez, L. M., & Lam, D. (Eds.). (2009). The role of research in educational improvement. Cambridge, MA: Harvard Education Press.Google Scholar
Brazer, D. S., & Keller, R. (2008). A design research approach to investigating educational decision making. In Kelly, A. E., Lesh, R. A., & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 284–296). New York, NY: Routledge.Google Scholar
Bryk, A. (2009). Support a science of performance improvement. Phi Delta Kappan, 90(8), 592–595.CrossRefGoogle Scholar
Brown, A. L. (1992). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. The Journal of the Learning Sciences, 2(2), 141–178.CrossRefGoogle Scholar
Case, J., & Light, G. (2011). Emerging methodologies in engineering education research. Journal of Engineering Education, 100(1), 186–210.CrossRefGoogle Scholar
Cheville, A., & Bunting, C. (2011). Engineering students for the 21st century: Student development through the curriculum. Advances in Engineering Education, 2(4), 1–37.Google Scholar
Clements, D. H. (2008). Design experiments in curriculum research. In Kelly, A. E., Lesh, R. A. & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 410–422). New York, NY: Routledge.Google Scholar
Cobb, P., & Smith, T. (2008). The challenge of scale: Designing schools and districts as learning organizations for instructional improvement in mathematics. In Krainer, K. & Wood, T. (Eds.), International handbook of mathematics teacher education: Vol. 3. Participants in mathematics teacher education: Individuals, teams, communities and networks (pp. 231–254). Rotterdam, The Netherlands: Sense.Google Scholar
Cobb, P., Confrey, J., diSessa, A., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32, 9–13.CrossRefGoogle Scholar
Cobb, P., McClain, K., & Gravemeijer, K. (2003). Learning about statistical covariation. Cognition and Instruction, 21, 1–78.CrossRefGoogle Scholar
Collins, A., Joseph, D., & Bielaczyc, K. (2004). Design research: Theoretical and methodological issues. The Journal of the Learning Sciences, 13(1), 15–42.CrossRefGoogle Scholar
Constantine, L. L., & Lockwood, L. A. (1999). Software for use: A practical guide to the models and methods of usage-centered design. Boston, MA: Addison Wesley Professional.Google Scholar
Cooper, A. (1999). The inmates are running the asylum: Why high-technology products drive us crazy and how to restore the sanity. Indianapolis, IN: SAMS.CrossRefGoogle Scholar
Cordray, D. S., Harris, T. R., & Klein, S. (2009). A research synthesis of the effectiveness, replicability, and generality of the VaNTH challenge-based instructional modules in bioengineering. Journal of Engineering Education, 98(4), 335–348.CrossRefGoogle Scholar
Cronbach, L. J. (1975, February). Beyond the two disciplines of scientific psychology. American Psychologist, 30(2), 116–127.CrossRefGoogle Scholar
Cuban, L. (1986). Teachers and machines: The classroom use of technology since 1920. New York, NY: Teachers College Press.Google Scholar
Cuban, L. (2001). Oversold and underused: Computers in the classroom. Cambridge, MA: Harvard University Press.Google Scholar
Cuban, L. (2009). Hugging the middle: how teachers teach in an era of testing and accountability. New York, NY: Teachers College Press.Google Scholar
Cyberlearning and Workforce Development Task Force. (2011). National Science Foundation advisory task force on cyberlearning and workforce development: Final report. Arlington, VA: NSF.Google Scholar
Dahm, K., Riddell, W., Constans, E., Courtney, J., Harvey, R., & Von Lockette, P. (2009). Implementing and assessing the converging-diverging model of design in a sequence of sophomore projects. Advances in Engineering Education, 1(3), 1–16.Google Scholar
Dalrymple, O., Sears, D., & Evangelou, D. (2011). The motivational and transfer potential of disassemble/analyze/assemble activities. Journal of Engineering Education, 100(4), 741–759.CrossRefGoogle Scholar
Davenport, J. L., Yaron, D., Klahr, D., & Koedinger, K. (2008). When do diagrams enhance learning? A framework for designing relevant representations. Paper presented at 2008 International Conference of the Learning Sciences, Utrecht, The Netherlands.Google Scholar
Davison, R. C. (2010). Engineering curricula: Understanding the design space and exploiting the opportunities: Summary of a workshop. Washington, DC: The National Academies Press. Retrieved from Google Scholar
Dede, C. (2005). Why design-based research is both important and difficult. Educational Technology, 45(1), 5–8.Google Scholar
Delale, F., Liaw, B. M., Jiji, L. M., Voiculescu, I., & Yu, H. (2011). Infusion of emerging technologies and new teaching methods into the mechanical engineering curriculum at the City College of New York. Advances in Engineering Education, 2(4), 1–36.Google Scholar
Dinehart, D. W., & Gross, S. P. (2010). A service learning structural engineering capstone course and the assessment of technical and non-technical objectives. Advances in Engineering Education, 2(1), 1–19.Google Scholar
Eggermont, M., Brennan, R., & Freiheit, T. (2010). Improving a capstone design course through mindmapping. Advances in Engineering Education, 2(1), 1–26.Google Scholar
Forster, S., & Lavie, N. (2007). High perceptual load makes everybody equal: Eliminating individual differences in distractibility with load. Psychological Science, 18(5), 377–381.CrossRefGoogle ScholarPubMed
Froyd, J. (2005). The Engineering Education Coalitions program. Educating the engineer of 2020: Adapting engineering education to the new century. Washington, DC: National Academies Press. Retrieved from
Ganesh, T. G. (2011). Design-based research: A framework for designing novel teaching and learning experiences in middle school engineering education. In 41st ASEE/IEEE Frontiers in Education (FIE) 2011 (Paper no. 1534, pp. 1–7). Rapid City, SD: ASEE/IEEE.Google Scholar
Garcia, O. N., Varnasi, M. R., Acevedo, M. R., & Guturu, P. (2011). An innovative project and design oriented electrical engineering curriculum at the University of North Texas. Advances in Engineering Education, 2(4), 1–34.Google Scholar
Gery, G. (2003). Ten years later: A new introduction to attributes & behaviors and the state of performance-centered systems. In Dickelman, G. J. (Ed.), EPSS Revisited: A Lifecycle for Developing Performance-Centered Systems (pp. 1–3). Silver Spring, MD: Society for Performance Improvement.Google Scholar
Hamilton, E., Lesh, R., Lester, F., & Brilleslyper, M. (2008). Model-eliciting activities (MEAs) as a bridge between engineering education research and mathematics education research. Advances in Engineering Education, 1(2), 1–25.Google Scholar
Hattie, J. A. C. (2009). Visible learning: A synthesis of over 800 meta-analyses related to achievement. New York, NY: Routledge.Google Scholar
Hayden, N. J., Rizzo, D. M., Dewoolkar, M. M., Neumann, M. D., Lathem, S., & Sadek, A. (2011). Incorporating a systems approach into civil and environmental engineering curricula: Effect on course redesign, and student and faculty attitudes. Advances in Engineering Education, 2(4), 1–27.Google Scholar
Howie, S. J., & Plomp, T. (Eds.). (2006). Contexts of learning mathematics and science: Lessons learned from TIMSS. London: Routledge.Google Scholar
Hjalmarson, M. A., & Lesh, R. A. (2008). Engineering and design research: Intersections for education research and design. In Kelly, A. E., Lesh, R. A., & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 96–110). New York, NY: Routledge.Google Scholar
Hundhausen, C., Agarwal, P., Zollars, R., & Carter, A. (2011). The design and experimental evaluation of a scaffolded software environment to improve engineering students’ disciplinary problem-solving skills. Journal of Engineering Education, 100(3), 574–603.CrossRefGoogle Scholar
Impelluso, T. J. (2009). Leveraging cognitive load theory, scaffolding, and distance technologies to enhance computer programming for non-majors. Advances in Engineering Education, 1(4), 1–19.Google Scholar
James, W. (1962). Talks to teachers on psychology and to students on some of life's ideals. New York, NY: Henry Holt 1899 reprint. Mineola, NY: Dover.Google Scholar
Johri, A., & Olds, B. (2011). Situated engineering learning: Bridging engineering education esearch and the learning sciences. Journal of Engineering Education, 100(1), 151–185.CrossRefGoogle Scholar
Kali, Y. (2008). The Design Principles Database as means for promoting design-based research. In Kelly, A. E., Lesh, R. A. & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 423–438). New York, NY: Routledge.Google Scholar
Kali, Y., Levin-Peled, R., & Dori, Y. (2009). The role of design-principles in designing courses that promote collaborative learning in higher-education. Computers in Human Behavior, 5, 1067–1078.CrossRefGoogle Scholar
Kali, Y., Levin-Peled, R., Ronen-Fuhrmann, T., & Hans, M. (2009). The Design Principles Database: A multipurpose tool for the educational technology community. Design Principles & Practices: An International Journal, 3(1), 55–65.CrossRefGoogle Scholar
Kelly, A. E. (2004). Design research in education: Yes, but is it methodological? Journal of the Learning Sciences, 13(1), 115–128.CrossRefGoogle Scholar
Kelly, A. E. (2009). When is design research appropriate? In Plomb, T. & Nieveen, N. (Eds.), Introduction to educational design research (pp. 73–88), Retrieved from
Kelly, A. E. (2011). Can cognitive neuroscience ground a science of learning? Theme issue on educational neuroscience. Educational Philosophy and Theory, 43(1), 17–23.CrossRefGoogle Scholar
Kelly, A. E. (2012). Developing validity and reliability criteria for assessments in innovation and design research studies. In Yun Dai, D. (Ed.), Design research on learning and thinking in educational settings: Enhancing intellectual growth and functioning. New York, NY: Taylor & Francis.Google Scholar
Kelly, A. E., Baek, J. Y., Lesh, R. A., & Bannan-Ritland, B. (2008). Enabling innovations in education and systematizing their impact. In Kelly, A. E., Lesh, R. A., & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 3–18). New York, NY: Routledge.Google Scholar
Kelly, A. E., & Lesh, R.. (2001). Understanding and explicating the design experiment. National Science Foundation Award No. 0107008.
Kelly, A. E., & Yin, R. (2007). Strengthening structured abstracts for educational research. Educational Researcher, 36(3), 133–138.CrossRefGoogle Scholar
Lesh, R. A., Kelly, A. E., & Yoon, C. (2008). Multitiered design experiments in mathematics, science, and technology education. In Kelly, A. E., Lesh, R. A., & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 131–148). New York, NY: Routledge.Google Scholar
Lobato, J. (2006). Alternative perspectives on the transfer of learning: History, issues, and challenges for future research. Journal of the Learning Sciences, 15(4), 431–449.CrossRefGoogle Scholar
Lobato, J. (2008). Research methods for alternative approaches to transfer: Implications for design experiments. In Kelly, A. E., Lesh, R. A., & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 167–194). New York, NY: Routledge.Google Scholar
Mantri, A., Dutt, S., Gupta, J. P., & Chitkara, M. (2009). Using PBL to deliver course in digital electronics. Advances in Engineering Education, 1(4), 1–17.Google Scholar
Maroulis, S., Guimera, R., Petry, H., Stringer, M. J., Gomez, L. M., Amaral, L. A. N., & Wilensky, U. (2010). Complex systems view of educational policy research. Science, 330(6000), 38–39.CrossRefGoogle ScholarPubMed
Martinez, M. E., Peterson, M. W., Bodner, M., Coulson, A., Vuong, S., Hu, W., … Shaw, G. L. (2008). Music training and mathematics achievement: A multiyear iterative project designed to enhance students’ learning. In Kelly, A. E., Lesh, R. A., & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 396–409). New York, NY: Routledge.Google Scholar
Maxwell, J. A. (2012). A realist approach for qualitative research. Thousand Oaks, CA: SAGE.Google Scholar
Mayer, R. E. (2003). The promise of multimedia learning: Using the same instructional design methods across different media. Learning and Instruction, 13(2), 125–139.CrossRefGoogle Scholar
McCandliss, B. D., Kalchman, M., & Bryant, P. (2003). Design experiments and laboratory approaches to learning: Steps toward collaborative exchange. Educational Researcher, 32(1), 14–16.CrossRefGoogle Scholar
McDonald, S.-K., Keesler, V., Kauffman, N., & Schneider, B. (2006). Scaling-up exemplary interventions. Educational Researcher, 35(3), 15–24.CrossRefGoogle Scholar
Middleton, J., Gorard, S., Taylor, C., & Bannan-Ritland, B. (2008). The “compleat” design experiment: From soup to nuts. In Kelly, A. E., Lesh, R. A. & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 21–46). New York, NY: Routledge.Google Scholar
Mosteller, F., & Boruch, R. F. (Eds.). (2002). Evidence matters: Randomized trials in education research. Washington, DC: Brookings Institution Press.Google Scholar
National Academy of Engineering, Committee on the Engineer of 2020, Phase II, Committee on Engineering Education (2005). Educating the engineer of 2020: Adapting engineering education to the new century. Washington, DC: The National Academies Press.Google Scholar
National Academy of Engineering (NAE). (2013). Educating engineers: Preparing 21st Century leaders in the context of new modes of learning. Washington, DC: The National Academies Press.Google Scholar
National Research Council (NRC). (2000). How people learn: Brain, mind, experience, and school (expanded edition). Washington, DC: The National Academies Press.Google Scholar
National Research Council (NRC). (2005a). How students learn: Mathematics in the classroom. Donovan, M. S. & Bransford, J. D. (Eds.). Washington, DC: The National Academies Press. Retrieved from Google Scholar
National Research Council (NRC). (2005b). How students learn: Science in the classroom. Donovan, M. S. & Bransford, J. D. (Eds.). Washington, DC: The National Academies Press.Google Scholar
Nelson, H. G., & Stolterman, E. (2003). The design way: Intentional change in an unpredictable world: Foundations and fundamentals of design competence. Englewood Cliffs, NJ: Education Technology Publications.Google Scholar
Nitko, A. J., & Brookhart, S. M. (2006). Educational assessment of students (5th ed.). New York, NY: Pearson.Google Scholar
Osmundson, E., Herman, J., Ringstaff, C., Dai, Y., & Timms, M. (2012). Measuring fidelity of implementation – Methodological and conceptual issues and challenges. (CRESST Report 811). Los Angeles, CA: University of California, National Center for Research on Evaluation, Standards, and Student Testing (CRESST).Google Scholar
Pears, A., Fincher, S. A., Adams, R., & Daniels, M. (2008). Stepping stones: Capacity building in engineering education. In Proceedings of the Annual ASEE/IEEE Frontiers in Education Conference, Saratoga, NY.Google Scholar
Penuel, W., Fishman, B., Cheng, B. H., & Sabelli, N. (2011). Organizing research and development at the intersection of learning, implementation, and design. Educational Researcher, 40, 331–337.CrossRefGoogle Scholar
Plomb, T., & Nieveen, N. (Eds.). (2010). Introduction to educational design research. The Netherlands : Netzodruk, Enschede.Google Scholar
Purzer, S. (2011). The relationship between team discourse, self-efficacy, and individual achievement: A sequential mixed-methods study. Journal of Engineering Education, 100(4), 655–679.CrossRefGoogle Scholar
Rasmussen, C., & Stephan, M. (2008). A methodology for documenting collective activity. In Kelly, A. E., Lesh, R. A., & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 195–216). New York, NY: Routledge.Google Scholar
Reese, D. D., Seward, R. J., Tabachnick, B. G., Hitt, B., Harrison, A., & McFarland, L. (2012). Timed report measures learning through game-based embedded assessment. In Ifenthaler, D., Eseryel, D. & Ge, X. (Eds.), Assessment in game-based learning: Foundations, innovations, and perspectives. New York, NY: Springer.Google Scholar
Reeves, T. C. (2011). Can educational research be both rigorous and relevant? Educational Designer, 1(4). Retrieved from Google Scholar
Reinking, D., & Bradley, B. A. (2008). On formative and design experiments. New York, NY: Teachers College Press.Google Scholar
Rodrigo, M. M. T., & Baker, R. S. J. D. (2011). Comparing learners’ affect while using an intelligent tutor and an educational game. Research and Practice in Technology Enhanced Learning, 6(1), 43–66.Google Scholar
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York, NY: Free Press.Google Scholar
Roll, I., Aleven, V., & Koedinger, K. R. (2011). Metacognitive practice makes perfect: Improving students’ self-assessment skills with an intelligent tutoring system. In Biswas, G. (Ed.), Proceedings of the international conference on artificial intelligence in education (pp. 288–295). Berlin, Germany: Springer-Verlag.CrossRefGoogle Scholar
Roll, I., Aleven, V., McLaren, B. M., & Koedinger, K. R. (2011). Improving students’ help-seeking skills using metacognitive feedback in an intelligent tutoring system. Learning and Instruction, 21, 267–280.CrossRefGoogle Scholar
Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R. S. J. D. (Eds.). (2012). Handbook of educational data mining. Boca Raton, FL: CRC Press.Google Scholar
Roschelle, J., Tatar, D., & Kaput, J. (2008). Getting to scale with innovations that deeply structure how students come to know mathematics. In Kelly, A. E., Lesh, R. A., & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 369–395). New York, NY: Routledge.Google Scholar
Roschelle, J., Shechtman, N., Tatar, D., Hegedus, S., Hopkins, B., Empson, S., Knudsen, J., & Gallagher, L. (2010). Integration of technology, curriculum, and professional development for advancing middle school mathematics: Three large-scale studies. American Educational Research Journal, 47(4), 833–878.CrossRefGoogle Scholar
Sabelli, S., Lemke, J., Cheng, B., & Richey, M. (2012). Education as a complex adaptive system: Report of progress. Retrieved from
Sao Pedro, M. A., Baker, R. S. J .D., Gobert, J., Montalvo, O., & Nakama, A. (2011). Leveraging machine-learned detectors of systematic inquiry behavior to estimate and predict transfer of inquiry skill. In User modeling and user-adapted interaction. Retrieved from
Sao Pedro, M. A., Gobert, J., & Baker, R. S. J. D. (2012). The development and transfer of data collection inquiry skills across physical science microworlds. Paper presented at the American Educational Research Association Conference. Retrieved from
Sandoval, W. A., & Bell, P. L. (2004). Design-based research methods for studying learning in context: Introduction. Educational Psychologist, 39(4), 199–201.CrossRefGoogle Scholar
Scardamalia, M., Bransford, J., Kozma, B., & Quellmalz, E. (2012). New assessments and environments for knowledge building. In Griffin, P., McGaw, B., & Care, E. (Eds.), Assessment and teaching of 21st century skills (pp. 231–300). New York, NY: Springer.CrossRefGoogle Scholar
Schickore, J., & Steinle, F. (Eds), (2010). Revisiting discovery and justification: Historical and philosophical perspectives on the context distinction. Dordrecht, The Netherlands: Springer.Google Scholar
Schneider, B., & McDonald, S.-K. (Eds.). (2006a). Scale-up in practice. Lanham, MD: Rowman & Littlefield.Google Scholar
Schneider, B., & McDonald, S.-K. (Eds.). (2006b). Scale-up in principle. Lanham, MD: Rowman & Littlefield.Google Scholar
Schwartz, D. L., Chang, J., & Martin, L. (2008). Instrumentation and innovation in design experiments: Taking the turn to efficiency. In A. E. Kelly, Lesh, R. A., & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 47–67). New York, NY: Routledge.Google Scholar
Seely, B. E. (2005). Patterns in the history of engineering education reform: A brief essay, for National Academy of Engineering. Engineer of 2020: National Education Summit (pp. 114–130). Washington, DC: The National Academies Press.Google Scholar
Shadish, W. R., & Cook, D. T. (2009). The renaissance of field experimentation in evaluating interventions. Annual Review of Psychology, 60, 607–629.CrossRefGoogle ScholarPubMed
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin.Google Scholar
Shavelson, R. J., Phillips, D. C., Towne, L. T., & Feuer, M. J. (2003). On the science of education design studies. Educational Researcher, 32(1), 25–28.CrossRefGoogle Scholar
Sloane, F. C. (2008a). Randomized trials in mathematics education: Recalibrating the proposed high watermark. Educational Researcher, 37(9), 624–630.CrossRefGoogle Scholar
Sloane, F. C. (2008b). Multilevel models in design research: A case from mathematics education. In Kelly, A. E., Lesh, R. A., & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, mathematics and engineering (pp. 459–476). New York, NY: Routledge.Google Scholar
Sloane, F. C., & Gorard, S. (2003). Exploring modeling aspects of design experiments. Educational Researcher, 32(1), 29–31.CrossRefGoogle Scholar
Sloane, F. C., Helding, B., & Kelly, A. E. (2008). Longitudinal analysis and interrupted time series designs: Opportunities for the practice of design research: A case from mathematics education. In Kelly, A. E., Lesh, R. A., & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, mathematics and engineering (pp. 449–458). New York, NY: Routledge.Google Scholar
Sloane, F. C., & Kelly, A. E. (2008). Design research and the study of change: Conceptualizing individual growth in designed settings. In Kelly, A. E., Lesh, R. A., & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, mathematics and engineering (pp. 441–448). New York, NY: Routledge.Google Scholar
Stephens, R., & Richey, M. (2011). Accelerating STEM capacity: A complex adaptive system perspective. Journal of Engineering Education, 100(3), 417–423.CrossRefGoogle Scholar
Stokes, D. (1997). Pasteur's quadrant: Basic science and technological innovation. Washington, DC: Brookings Institution Press.Google Scholar
Streveler, R., & Smith, K. (2010). From the margins to the mainstream: The emerging landscape of engineering education research. Journal of Engineering Education, 99(4), 285–287.CrossRefGoogle Scholar
Sullivan, J. F. (2006). Broadening engineering's participation: A call for K-16 engineering education. The Bridge, 36(2), 17–24.Google Scholar
Tadmore, Z. (2006). Redefining engineering disciplines for the twenty-first century. The Bridge, 36(2), 33–37.Google Scholar
Taraban, R., Craig, C., & Anderson, E. (2011). Using paper-and-pencil solutions to assess problem solving skills. Journal of Engineering Education, 100(3), 498–519.CrossRef
Thomas, M. K., Barab, S. A., & Tuzun, H. (2009). Developing critical implementations of technology-rich innovations: A cross-case study of the implementation of Quest Atlantis. Journal of Educational Computing Research, 41(2), 125–153.CrossRefGoogle Scholar
Toulmin, S. (1998). The idol of stability. Presented at the Tanner Lectures on Human Values. Retrieved from
Toulmin, S. (2001). Return to reason. Cambridge, MA: Harvard University Press.Google Scholar
Tyack, D., & Cuban, L. (1995). Tinkering toward utopia. Cambridge, MA: Harvard University Press.Google Scholar
van den Akker, J. (1999). Principles and methods of development research. In van den Akker, J., Nieveen, N., Branch, R. M., Gustafson, K. L., & Plomp, T. (Eds.), Design methodology and developmental research in education and training (pp. 1–14). Dordrecht, The Netherlands: Kluwer Academic.Google Scholar
van den Akker, J., Gravemeijer, K., McKenney, S., & Nieveen, N. (2006). Educational design research. London: Routledge.Google Scholar
Vendlinski, T. P., Chung, G. K. W. K., Binning, K. R., & Buschang, R. E. (2011). Teaching rational number addition using video games: The effects of instructional variation. (CRESST Report 808). Los Angeles, CA: University of California, National Center for Research on Evaluation, Standards, and Student Testing (CRESST).Google Scholar
Vest, C. (2006). Educating engineers for 2020 and beyond. The Bridge, 36(2), 38–44.Google Scholar
Weiss, I. R., Miller, B. A., Heck, D. J., & Cress, K. (2004). Handbook for enhancing strategic leadership in the math and science partnerships. Chapel Hill, NC: Horizon Re-search Inc.Google Scholar
Wolf, J., & Le Vasan, M. (2008). Toward assessment of teachers’ receptivity to change in Singapore: A case study. In Kelly, A. E., Lesh, R. A., & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 265–283). New York, NY: Routledge.Google Scholar
Yin, R. K. (2009). Case study research: Design and methods (4th ed.). Thousand Oaks, CA: SAGE.Google Scholar
Yun Dai, D. (Ed.) (2012). Design research on learning and thinking in educational settings: Enhancing intellectual growth and functioning. New York, NY: Taylor & Francis.Google Scholar
Zaritsky, R., Kelly, A. E., Flowers, W., Rogers, E., & O'Neill, P. (2003). Clinical design sciences: A view from sister design efforts. Educational Researcher, 32(1), 32–34.CrossRefGoogle Scholar
Zawojewski, J., Chamberlin, M., Hjalmarson, M. A., & Lewis, C. (2008). Developing design studies in mathematics education professional development: Studying teachers’ interpretive systems. In Kelly, A. E., Lesh, R. A., & Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 219–245). New York, NY: Routledge.Google Scholar

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