educational technology in relation to human cognitive
architecture [e.g., cognitive load]).
Therefore, a more complete version of the model would be:
Figure 2. Expanded assessment model to account for
context.
However, this model captures only the assessment of
learners’ performances without incorporating the instructional side of the
learning experience. Given that the
design decisions made by teachers and other architects of learning environments
meditate learners’ access to and comprehension of knowledge, a full picture of
learning requires attention to instructional design as it links the knowledge
and perspectives of experts with those of learners.
Figure 3. Instructional design as mediation between
expert knowledge and student understanding.
My previous research has explored the interactions within
either the learner performance model or the instructional design model. For example, my examination of computer-based
training and simulations (Clark & Feldon, 2005; Clark, Feldon, Howard,
& Choi, 2006; Feldon, 2004; Feldon & Gilmore, 2006) and informal virtual
learning environments (e.g., Kafai, Feldon, Fields, Giang, & Quintero,
2007) reflects the convergence of the four circles in Figure 2. Specifically, my work with Yasmin Kafai on
young science learners’ interactions within the virtual world of Whyville was
instrumental in understanding the full role that context can play in the
interpretation of supposedly objective data (Feldon & Kafai, in
press). However, my research in
instructional design related to cognitive task analysis (e.g., Feldon &
Clark, 2006; Clark, Feldon, van Merriënboer, Yates, & Early, in press;
Yates & Feldon, 2008) and implications of expertise for instruction (e.g.,
Feldon, 2005, 2007b; Feldon, 2007c; Feldon, 2007d) examines only the mediated
relationship between expert knowledge and student learning materials
illustrated in Figure 3.
Consequently, as I move forward, I am looking for the
opportunity to engage with colleagues to identify the key interactions that
shape learning through a comprehensive model:
Figure 4. Comprehensive model of impactful educational
technology.
Meeting this challenge will require the integration of many
perspectives that have historically been considered opposite poles: experimental and design-based methodologies,
multiple grain sizes and units of analysis, and educational technology,
educational psychology (social and cognitive), and learning sciences.
Current Research
I am currently working on several projects to move toward a
full integration of all facets of the above model. My STEP grant from NSF (DUE #0653160) applies distance learning technologies to augment
students’ current learning experiences in undergraduate biology by delivering
just-in-time training based on cognitive task analyses of expert scientists’
research practices. In addition to
direct evidence of improved student achievement and retention within the
biology major, my colleagues and I also hope to find that our intervention
produces instructional affordances within the contextual model that lead to
more equitable outcomes across demographic groups through a decreased
dependency on informal support structures that are not universally accessible.
My NSF REESE grant (DRL #0723686)
examines the interplay between graduate students in STEM disciplines and the
contextual, task, student, and evaluation models. Our hypothesis is that graduate students who
engage in both inquiry-oriented teaching and lab research experiences during
their graduate training develop their research skills at a faster rate than
their counterparts who only participate in one or the other of those
activities. In this project, we are
exploring the students’ changing perspectives on their research, the role of
teaching, and the interactions between research and teaching as they evolve
during their graduate studies. Obviously,
the students’ understanding of these issues is strongly influenced by the
cultural contexts within which they work as instructors and researchers. Further, incorporating their personalized
roles and experiences into the assessment of their skills presents
opportunities to refine my thinking on appropriate and effective ways to shape
evidence models to account for cultural and contextual influences.
Outside the university setting, I
am also collaborating with Richard Clark to pilot and institutionalize the
first new instructional design system used by the U.S. Army in 30 years. In addition to testing the instructional
effectiveness of cognitive task analysis-based instruction in a professional
education setting, we are working to understand and influence the army’s
cultural understandings of learning, training, and assessment. These experiences are proving invaluable as I
work to conceptualize a broader understanding of contextually grounded
instructional design.
Future Directions
Although my current research projects will be in data
collection for the next several years, I am in the early stages of planning my
next project. In order to capture the
nuanced interactions between each of the elements in the model, I want to
leverage the data capture abilities of a massive multi-user virtual world to
study training and professional collaboration mediated in that context. Specifically, I am looking for a web-based
environment (likely in Second Life) where early-career professionals interact
with senior mentors as they develop both their technical skills and their
socialization into their profession. My
goal is to develop and validate design principles to guide the development of
affordances that can shape social learning interactions to maximize the
attainment of learning outcomes.
References
Clark, R. E., & Feldon, D.
F. (2005). Five common but questionable principles of
multimedia
learning. In R. Mayer (Ed.), The Cambridge
Handbook of Multimedia Learning (pp. 97-115). New
York: Cambridge
University Press.
Clark, R. E., Feldon, D. F.,
Howard, K., & Choi, S. (2006). Five critical issues for web-
based
instructional design research and practice.
In H. F. O’Neil, Jr., & R. S. Perez (Eds.), Web-based learning: Theory, Research and Practice (pp. 343-370). Mahwah, NJ: Lawrence
Erlbaum Associates.
Clark, R. E., Feldon, D., van
Merriënboer, J. J. G., Yates, K., and Early, S.
(2008).
Cognitive task
analysis. In J. M. Spector, M. D.
Merrill, J. J. G. van Merriënboer, & M. P. Driscoll (Eds.). Handbook of research on educational
communications and technology (3rd ed.) (pp. 577-593). New York: Routledge.
Feldon,
D. F. (2004). The
benefits of an efficiency metric in the evaluation of simulation
task performance. Presented at the Annual Meeting of the
American Education Research Association.
San Diego, California: April, 2004.
Feldon, D. F. (2005).
Challenges for defining and using
expertise in education.
Symposium
presented at the Annual Meeting of the American Educational Research
Association. Montreal, Canada:
April, 2005.
Feldon,
D. F. (2007a). Discussant
paper: Technology research on multimedia learning:
A session in honor
of William Winn. Presented at the
Annual Meeting of the American Education Research Association. Chicago, Illinois: April, 2007.
Feldon, D. F. (2007b).
Cognitive load in the classroom:
The double-edged sword of
automaticity. Educational
Psychologist, 42(3), 123-137.
Feldon, D. F. (2007c).
Experimental design and analysis
strategies: What experts do
but fail to report. Presented at the Annual Meeting of the
American Education Research Association.
Chicago, Illinois: April, 2007.
Feldon, D. F. (2007d).
Implications of research on expertise for
curriculum and
pedagogy. Educational Psychology Review, 19(2),
91-110
Feldon, D. F. (2007e).
Experimental design and analysis
strategies: What experts do
but fail to report. Presented at the Annual Meeting of the
American Education Research Association.
Chicago, Illinois: April, 2007.
Feldon, D. F., & Clark, R.
E. (2006). Instructional implications of cognitive task
analysis
as a method for improving the accuracy of experts’ self-report. In G. Clarebout & J. Elen (Eds.), Avoiding simplicity, confronting complexity:
Advances in studying and designing (computer-based) powerful learning
environments (pp. 109-116). Rotterdam, Netherlands:
Sense Publishers.
Feldon, D. F., & Gilmore,
J. (2006). Patterns in children’s online behavior and
scientific
problem-solving: A large-N
microgenetic study. In
G. Clarebout & J. Elen (Eds.), Avoiding
simplicity, confronting complexity: Advances in studying and designing
(computer-based) powerful learning environments (pp. 117-125). Rotterdam,
Netherlands:
Sense Publishers.
Feldon, D. F., & Kafai, Y.
B. (in press). Mixed methods for mixed reality: Overcoming
methodological
challenges to understand user activities in virtual worlds. Educational Technology Research and
Development.
Kafai, Y., Feldon, D., Fields, D.
A., Giang, M., & Quintero, M.
(2007). Life in the time
of Whypox:
A virtual epidemic as a community event.
In C. Steinfield, B. Pentland, M. Ackerman,
&. N Contractor (Eds.), Communities and Technologies 2007 (pp.
171-190). New York:
Springer.
Mislevy,
R., Steinberg, L. S., Breyer, F. J., Almond, R. G., & Johnson, L. (2002).
Making sense of data in complex assessments. Applied Measurement in Education, 15(4),
363-389.
Yates, K. A., & Feldon, D.
F. (2008). Towards a taxonomy of cognitive task
analysis
methods for instructional design: Interactions with cognition. Human Factors.
