A scholar-practitioner model (S-P) was the basis of my graduate training. Whether by training or by chance, my personal philosophy values the same basic principles. My research themes each demonstrate my belief in the importance of translating research into the practice of everyday education in its many forms.
My research interests involve the use of technology to facilitate and maximize learning and emanate from a socio-cognitive conceptual framework (Bandura, 1986). I value knowledge and view the goal of education as serving self-regulation as both a means and an end (Schraw, Crippen, & Hartley, 2006). I am heavily influenced by conceptual change theory (Duit, 2003) and the notion of scaffolded knowledge integration (Linn, Bell, & Davis, 2005).
Technology includes a myriad of hardware and software and can be a separate entity from a traditional classroom, or used within everyday instruction. Regardless of whether the technology is used as a tool in a more traditional model or as a stand-alone instructor in a Web-based or distance-learning environment, it has the potential to significantly impact the acquisition of knowledge and development of understanding.
I favor a descriptive approach to research emphasizing a design theory paradigm where development and analysis cycles rely heavily on retrospection (Cobb, Confrey, diSessa, Lehrer, & Schauble, 2003). I would classify my work as use-inspired basic research (Stokes, 1997) focused on powerful learning environments, scaffolded learning with electronic knowledge representations, and large scale theory to practice.
Figure 1: Quadrant Model of Scientific Research (Stokes, 1997)
Powerful Learning Environments
The Internet and its technologies have great promise for instructional purposes. My interests include the research and development of dynamic, database driven systems for delivering Web-based science learning materials (Crippen, Brooks, & Abuloum, 2000; Crippen & Earl, 2004). These materials include assessment techniques grounded in learning science that use instructional design strategies to target the development of expertise and self-regulated learning (Brooks, Schraw, & Crippen, 2005; Schraw, Brooks, & Crippen, 2005). A powerful learning environment (PLE) is the theoretical model and organizing framework used in designing these systems (DeCorte, Vershaffel, Entwistle, & VanMerrienboer, 2003).
Traditionally, assessment was used to determine raw student mastery of material. Instructors arbitrarily set passing marks, constructed tests, and adjusted raw results to determine which students had achieved mastery. Current theories of assessment go far beyond this paradigm. Techniques such as Knowledge Space, Rule Space, and Pathfinder Associative Networks allow the elucidation of cognitive structure from assessment results. Mathematical modeling techniques can be applied to calibrated item responses to produce maps describing how students construct the knowledge of a particular domain. These longitudinal maps are very useful for researchers, teachers, and instructional designers. Such maps coupled with server-side trace analysis of student behavior create powerful behavioral profiles (Winne, 2006). In a dynamic environment, an analysis of these data can be positively fed back into the user experience as learning scaffolds.
Exam-PLE is a worked example PLE that scaffolds the development of fundamental strategies for well-structured problem solving. This is the first known project to use the Web to deliver meaningful example-based learning. Our research has shown that: a) students make extensive use of worked examples and self-explanation prompts b) students self-report that both strategies are helpful for improving both their learning and performance and c) use of the learning system correlates significantly with performance (Crippen & Earl, 2004, 2007). Recent work intends to understand the predictive power of achievement goal theory for student self-regulated learning behavior and interaction effects with feedback (Crippen, Biesinger, Muis, & Orgill, under review; Crippen, Biesinger, & Orgill, 2007).
Haptically Enhanced STEM Materials - Humans, from their earliest days, use touch to “seek out and acquire information”(McLinden & McCall, 2002). This haptic perceptual system is distinctive from the senses of sight and hearing, which historically have been more commonly used in formal education. Haptic technologies provide a human computer interaction in the form of tactile sensory feedback such as surface texture, vibration, pliability, weight, and hardness/softness. These interactions offer the opportunity to increase the accessibility of science materials to the broadest possible audience and to change the immersive dynamic of instructional material. Recent work includes R&D with haptic assistive technology focused on the following themes: Increased understanding of how spatial thinking can be fostered for persons with specific disabilities through the use of haptic systems and the potential impact of such a system for persons with disabilities in the areas of STEM education and transition to work in STEM-related professions.
Scaffolding Learning with Electronic Representations
Jonassen (2003) has suggested that certain computer software can be implemented as cognitive tools to aid students in thinking during problem solving. This would include, for example, students using semantic networking software to represent their understanding of complex phenomenon before, during, and after instruction. Using technology as a cognitive tool requires a unique instructional model that uses current technologies as the technical foundation of a new and dramatically enhanced literacy referred to by diSessa as "computational literacy" (diSessa, 2001). The learning power of a cognitive tool is not in the software, but in the modeling strategies used by the learner (Gilbert & Boulter, 2003). The representational capacity of the technology is coupled with sense-making strategies to create a powerful learning environment.
One area of profound importance to science education and a key component of authentic inquiry, is understanding how to help students and teachers construct mental representations of scientific phenomena and the role that metacognition plays in this process (Beeth, 1998; Gunstone & Mitchell, 1998). Understanding and constructing mental models is an essential component of science learning. Having science students use technology to create both static and dynamic conceptual models is thought to broaden and strengthen their mental models and support critical thinking (Stratford, 1997).
Electronic concept mapping is an example of such a process and involves creating external knowledge representations that illustrate the meaningful relationships among ideas. Concept mapping is a metacognitive process of making thinking explicit. The act of using continuous, temporal concept making as part of a self-expression and reflection process is an adaptive self-regulation strategy that facilitates the conceptual change process (Kern & Crippen, 2008). Recent research has involved developing a protocol and set of standards-based instructional activities for concept mapping that, when implemented, serve to improve the self-regulatory skills of students, scaffold conceptual change, and improve problem solving skill. As a matter of practice, these activities are used in on-going teacher professional development and subsequent K12 science classrooms where the data are reported back and used for theory building (Crippen, in preparation)
Large Scale Theory to Practice
The research and design procedures for powerful learning environments and scaffolded learning with electronic representations are being combined to develop high-quality professional development for local science teachers (Crippen, Ebert, Asay, & Sibley, under review). Our professional development model includes the innovative use of real-time video conferencing, technology infused science inquiry experiences, and classroom-based action research. Research related to all components of this model is ongoing. Results are demonstrating important changes in knowledge, dispositions, skills, and behaviors for both teachers and their students (Bailey, Crippen, Sangueza, Colin, & Ebert, 2008).
References
Bailey, J. M., Crippen, K. J., Sangueza, C., Colin, C., & Ebert, E. (2008). Project PASS: Researching Outcomes of a Long-Term Model for Science Teacher Professional Development. Paper presented at the Association of Science Teacher Educators, St. Louis, MO.
Bandura, A. (1986). Social Foundations of Thought and Action. Englewood Cliffs, NJ: Prentice-Hall.
Beeth, M. E. (1998). Facilitating conceptual change learning: The need for teachers to support metacognition. Journal of Science Teacher Education, 9, 49-61.
Brooks, D. W., Schraw, G. P., & Crippen, K. J. (2005). Performance-related feedback: The hallmark of good instruction. Journal of Chemical Education, 82(4), 641-644.
Cobb, P., Confrey, J., diSessa, A., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9-13.
Crippen, K. J. (in preparation). A Concept Mapping Task For Measuring Changes In Teacher Science Content Knowledge.
Crippen, K. J., Asay, L., Ebert, E., Orgill, M., Thomas, M., Bailey, J. M., et al. (2007, 3/30). Project PASS Action Research Poster Session. Paper presented at the NationalScience Teachers Conference (NSTA), St. Louis, MO.
Crippen, K. J., Biesinger, K. D., Muis, K. R., & Orgill, M. (under review). Scaffolding motivation through the use of worked examples. Journal of Interactive Learning Research.
Crippen, K. J., Biesinger, K. D., & Orgill, M. (2007, 4/6). Achievement goal orientation as a predictor for learning in an online environment for undergraduate chemistry. Paper presented at the Proceedings of the National Association of Research in Science Teaching (NARST) Annual Meeting, New Orleans, LA.
Crippen, K. J., Brooks, D. W., & Abuloum, A. (2000). A web-site supporting the AP descriptive chemistry question. Journal of Chemical Education, 77(8), 1087-1088.
Crippen, K. J., & Earl, B. L. (2004). Considering the Efficacy of Web-based Worked Examples In Introductory Chemistry. Journal of Computers in Mathematics and Science Teaching, 23(2), 151-167.
Crippen, K. J., & Earl, B. L. (2007). The impact of Web-based worked examples and self-explanation on performance, problem solving, and self-efficacy. Computers & Education, 49(3), 809-821.
Crippen, K. J., Ebert, E. K., Asay, L. D., & Sibley, B. (under review). Project PASS: A High Quality Professional Development Program for Secondary Science Teachers in Southern Nevada. The Science Educator.
DeCorte, E., Vershaffel, L., Entwistle, N., & VanMerrienboer, J. J. G. (Eds.). (2003). Powerful Learning Environments: Unravelling basic components and dimensions: Pergamon: Elsevier.
diSessa, A. (2001). Changing Minds: Computers, Learning, and Literacy: MIT Press.
Duit, R. (2003). Conceptual change: a powerful framework for improving science teaching and learning. International Journal of Science Education, 25(6), 671-688
Ebert, E. K., & Crippen, K. J. (under review). Evaluating Professional Development as Conceptual Change with a Cognitive-Affective Model. Science Education.
Gilbert, J. K., & Boulter, C. J. (2003). Learning science through models and modelling. In B. J. Fraser & K. G. Tobin (Eds.), International Handbook of Science Education (pp. 53-66). Boston, MA: Kluwer.
Gunstone, R., & Mitchell, I. J. (1998). Metacognition and conceptual change. In J. L. Mintzes, J. H. Wandersee & J. D. Noval (Eds.), Teaching for science education: A human constructivist view. San Diego, CA: Academic Press.
Jonassen, D. H. (2003). Using cognitive tools to represent problems. Journal of Research on Technology in Education, 35(3), 362-381.
Kern, C., & Crippen, K. J. (under review). Mapping for Conceptual Change. The Science Teacher.
Linn, M. C., Bell, P., & Davis, E. A. (2005). Internet Environments for Science Education: Lawrence Erlbaum.
Schraw, G., Brooks, D. W., & Crippen, K. J. (2005). Improving chemistry teaching using an interactive compensatory model of learning. Journal of Chemical Education, 82(4),637-640.
Schraw, G., Crippen, K. J., & Hartley, K. D. (2006). Promoting self-regulation in science education: Metacognition as part of a broader perspective on learning. Research in Science Education, 36(1-2), 111-139.
Stokes, D. E. (1997). Pasteur's Quadrant: Basic Science and Technological Innovation. Washington, DC: Brookings Institution Press.
Stratford, S. J. (1997). A review of computer-based model research in precollege science classrooms. Journal of Computers in Mathematics and Science Teaching, 16(1), 3-23.
Winne, P. H. (2006). How software technologies can improve research on learning and bolster school reform. Educational Psychologist, 41(1), 5-17.
