Cutting Edge > Develop Program-Wide Abilities > Metacognition > Workshop 08 > Participants and their Contributions > Laura Wenk
Author Profile

Teaching for Intentional Learning: How learning goals, learning skills, and feedback affect learning


Laura Wenk, Hampshire College

I teach courses on cognition and instruction, educational research, and curriculum and instruction at Hampshire College, and have begun collaborating with Darby Dyar at Mount Holyoke College on a project to use visualizations in teaching mineralogy and geology.

I have specific skills I teach explicitly in my courses. My courses are inquiry-based and require a range of inquiry skills, skills associated with reading primary research literature (Wenk, Tronsky, & McNeal, 2005), and specific skills associated with any number of disciplines in which my students have interest. Because of the domain in which I teach (teaching and learning), my employment of a metacognitive approach to teaching is, itself, a little "meta" at times. When I teach about metacognition, I teach about teaching mental processes as the focus of instruction while I am making the mental processes required to do so the focus of my instruction. Sometimes it makes my head spin. More seriously, my overarching tactic, as with many who use a metacognitive approach, is to engage in co-construction of knowledge with my students, whereby I model strategies, scaffold my students' practice of these strategies, give them feedback, and then turn over control of these operations to them. The explicit discussion of the strategies allows for consideration of other situations in which to use the strategies, and equally important, when not to. Such conditionalizing of the procedural knowledge aids in transfer of use (Bransford & Schwartz, 1999).

In this essay I will first talk about a set of ideas I teach in my course, "How People Learn" that my students and I find interconnect to build a powerful explanation of why some students are more engaged learners than others, and what we can do to improve learning for more students. Then I will take a preliminary look at the work Darby and I have begun on visualizations. It is this latter work that I most hope to improve through my participation in this workshop. Hopefully, I can tie the two pieces of the essay together.

Intentional Learning and Learning as Improvement

The ideas we explore early on in "How People Learn" are about students' implicit theories of intelligence, their goals, their behaviors as learners, their motivation, the intentionality they employ as they learn, and the specific strategies they gain when they are intentional learners. Dweck and Legget (1988) teach us that students believe either that intelligence is a fixed entity (you have it or you do not) or that it is improved incrementally. The former set of learners often develop performance goals, whereby they are most concerned with demonstrating they are intelligent by doing well on an assignment. When met with a challenge they might demonstrate maladaptive behaviors such as avoidance or helplessness. It is safer to have an excuse for not doing something than to do poorly and show you are not smart. The incremental theorists generally develop learning goals, enjoy a challenge and work even harder when faced with a challenge. They see the opportunity to advance their learning when things are more complex than they first imagined.

Bereiter and Scardemalia (1989) show us that learning goals are necessary for intentional learning. To become successful intentional, effortful, learners, students must have an orientation to learning that goes beyond knowing facts, performing on tests, or completing assignments. They must understand learning as problem solving, have learning goals, and have metacognitive strategies. Of course, not all students develop learning goals on their own. In all cases, maybe particularly so when students believe intelligence is fixed, teachers must teach an appropriate stance about knowledge and model cognitive goals and planning. Some ways to do this are for teachers to think aloud and to teach metacognitive strategies in the contexts in which they are used. When students are entity theorists (intelligence is fixed), they need help focusing on their progress (Dweck & Legget, 1988). More focus on formative assessment and the use of activities that build on earlier successes might be useful.

In "How People Learn" we also read a series of articles that demonstrate metacognitive approaches to teaching and/or the mental processes used by experts in different disciplines. For example, in reading comprehension (Brown, 1980), history (Wineburg, 1991), physics (White & Frederiksen, 1988), and literacy (Palincsar & Klenk, 1992). After analyzing these articles, students understand that metacognitive skills are both self-regulatory skills used in planning and monitoring (developing goals and strategies, evaluating progress), and self-knowledge and improvement expertise (awareness of one's knowledge and knowledge gaps, and awareness of when to use specific strategies and when to modify them).

By exploring these ideas, my students come to more reasoned understandings of the roles of students and teachers in learning. Students must be aware of their cognitive strategies. Teachers' assignments must engage students in transforming ideas to create new knowledge, rather than in simply summarizing or learning facts. Learners' roles must be expanded so they can practice strategies, get feedback on their process skills, and hopefully internalize them. Students should be engaged in consequential tasks that require the thinking strategies associated with expertise in the field and that help them make connections among ideas. Hopefully, my students will go on to teach in some setting where they will not repeat some of the school-based assignments that subvert the development of learning goals and of metacognitive strategies.

Metacognition in the Geosciences

My work with Darby Dyar on visualizations in teaching mineralogy and geology is an opportunity to explore another set of disciplinary specific metacognitive strategies. Geology and mineralogy expertise includes thinking that is common to all scientists (e.g., how to reason from evidence). As in any field, there are specific learning goals, some of which may be difficult to master. Many of the learning challenges in the geosciences are of abstraction, which require both external and internal visualization (e.g. the ability to coordinate a range of 3-D structural views to dynamic processes that occur in all planes). Thinking in these domains taxes one' ability to represent shapes and patterns over space and time, and not only requires memory of visual patterns and concepts, but also involves visual imagination. These are all capacities of our visual system, but in learning, the use and practice of these capacities must be applied to specific content. Spatial thinking in geosciences spans a huge range of scales, from the atomic (e.g., the crystalline structure of minerals) to the global (e.g., atmospheric circulation patterns). Some of the specific spatial reasoning tasks that geologists and mineralogists engage in include: a) recognizing, describing, and classifying the shape of an object, b) describing the position and orientation of objects, c) making and using maps, d) envisioning processes in three dimensions, and e) using spatial-thinking strategies to think about non-spatial phenomena (Kastens & Ishikawa, 2006). In addition, geoscientists must synthesize representations about 3D structures from memory and predict transformations in the physical world over long periods of time. Teaching in the geosciences must support student learning with regards to this complex, abstract thinking. The use of static visualizations in addition to text has been commonplace, and the addition of animated visualizations has been increasing with the development of new technologies.

Visualization

The term visualization traditionally refers to activities of the visual imagination, but has been extended to refer to the creation and use of activities involving external images intended to bootstrap visual learning, which is how the term will be used here. Visualizations can be 2D or 3D, detailed or simple, true to actual form or highly abstracted, static or animated. In order to make use of them, learners must have a rudimentary knowledge of the concepts that the visualizations represent; they must understand how the components of the system are represented in the visual display, and they must understand how the display maps onto the real world. While apparent to the expert, these understandings about visualizations do not necessarily occur naturally for all learners. For this, and perhaps other reasons, the use of visualizations in teaching does not immediately improve learning for all students. There are some findings that point to the cognitive load involved in interpreting visual representations (Jones, Jordan, and Stillings, 2002) and to the difficulty of creating animated visualizations that remain true to all aspects of a complex system (Tversky, Morrison, & Betrancourt, 2002).

There are differences in learners in terms of their initial spatial abilities, further making sound tests of the use of visualizations difficult, but perhaps making a metacognitive approach to learning in a field requiring spatial visualization especially important. There are substantial research findings that males outperform females on spatial visualization tasks (Kimura, 1999; Voyer, Voyer, & Bryden, 1995; Linn & Petersen 1985). It is thought to be the greatest sex difference in cognitive function (Kimura 1999). Yet Newcombe et al. (2005) see these sex differences in the performance of spatial tasks only in children of middle and high socioeconomic status (SES), and not in children of low SES, suggesting it is sensitive to educational environment. A failure to teach the strategies used in spatial tasks could be contributing to sex differences in particular fields.

Interactive Animation

The literature on the usefulness of visualizations in learning is complex and there are few claims that do not require qualification; studies often confound interactivity or the making of predictions with learning from an animation, both of which improve learning in and of themselves (Tversky et al., 2002). Animations cannot be looked at as a unitary teaching tool. The types of concepts and systems portrayed in each animation are further confounds (e.g. whether the concept includes change over time, spatial relations, interaction of moving parts, etc.). In some studies the animations conveyed more material than the conditions in which there were no animations. The ability to convey much information might be reason enough to use animations, and faculty might have preferences about their use that speak to the difficulties of talking about certain kinds of processes.

What does seem to be true, in general, is that a graphic is more useful in learning when it abstracts or points out the most salient qualities of the objects or situation represented. Animations can be used to highlight significant aspects of the visualization and show relationships between and among parts of a whole. Animations are most successful in supporting visual learning that requires re-orientation in space and for real-time changes. Re-orientation in space is a task necessary for understanding mineral structure.

Interactive animations allow the learner to stop and manipulate the animation so as to pose questions and test ideas, other strategies that for many students, must be taught. The use of animations is time consuming. This coupled with the additional work of making sense of animations might be why students do not universally like them (Tversky et al., 2002). Even if students don't choose to spend time making sense of animations of complex systems, it is entirely possible that the animations allow faculty to present ideas that are difficult to represent otherwise.

As textbooks more and more include costly interactive animations, we must not expect that they are necessarily used and understood outside of the classroom. It will take work to understand how they are used and whether they improve learning. As with other learning tools, a geoscience professor must be aware of learning requirements of interactive animations and teach the strategies for interpreting them, just as she or he must be aware of and point out the limitations of such displays (e.g. the limitation of computer screens for angles that are not 90 degrees, etc.). For many visual tasks, a 2-D representation is even more limited.

What does this have to do with intentionality and effort?

As with any educational tool, there is a danger of the medium's becoming the message and of students' engaging with the surface features of an assignment. While students might look more engaged when working with an interactive animation, in order to put effort into the aspects of the task that will more likely result in learning, they must have learning goals for themselves, ask themselves questions about what they are seeing, understand what they will learn by engaging in the task, and understand where they might be confused. Of course, that means they must work through the challenging thinking skills needed for success in the geosciences rather than simply care about getting the assignments done.

References

Bereiter, C. & Scardamalia, M. (1989). Intentional learning as a goal of instruction. In Knowing, Learning, and Instruction, L. Resnick, (Ed.), 361-392. NJ: Lawrence Erlbaum and Associates, Pub.

Bransford, J. D. & Schwartz, D. L. (1999). Rethinking transfer: A Simple proposal with multiple implications. Review of Research in Education. 24, 61-100.

Brown, A. L. (1980) Metacognitive development and reading. In Theoretical Issues in Reading Comprehension: Perspectives from Cognitive Psychology, Linguistics, Artificial Intelligence, and Education, R. J. Spiro, B. C. Bruce, and W. F. Brewer, eds. Hillsdale, NJ: Erlbaum.

Dweck, C. and Legget, E. (1988). A Social-cognitive approach to motivation and personality. Psychological Review, 95(2) 256-273.

Jones, L., Jordan, K., and Stillings, N. A. (2002). Molecular visualization in science education. Report from the Molecular Visualization In Science Education Workshop sponsored by the NSF. NCSA Access Center, Arlington, VA January 12‐14, 2001. http://pro3.chem.pitt.edu/workshop/index.html.

Kastens, K. and Ishikawa, T. (2006). Spatial thinking in the geosciences and cognitive sciences: A cross‐disciplinary look at the intersection of the two fields. In Earth and Mind: How Geologists Think and Learn about the Earth. C. A. Manduca and D. W. Mogk eds. Geological Society of America.

Kimura, D. (1999). Sex and cognition. Cambridge, MA: MIT Press.

Levine, S. C., Vasilyeva, M., Lourenco, S. F., Newcombe, N. S., and Huttenlocher, J. (2005). Socioeconomic status modifies the sex difference in spatial skill. Psychological Science, 16 (11) pp. 841‐845.

Linn, M. C., & Petersen, A. C. (1985). Emergence and characterization of sex differences in spatial ability: A meta‐analysis. Child Development, 56, 1479–1498.

Palincsar A. S., & Klenk, L. (1992). Fostering literacy learning in supportive contexts. Journal of Learning Disabilities, 25(4), 211-225.

Tversky, B., Morrison, J.B., Betrancourt, M. (2002). Animation: Can it facilitate? International Journal of Human‐Computer Studies, 57(4), 247‐262.

Voyer, D., Voyer, S., & Bryden, M. P. (1995). Magnitude of sex differences in spatial abilities: A meta-analysis and consideration of critical variables. Psychological Bulletin, 117, 250–270.

Wenk, L., Tronsky, L., McNeal, A. (2005, April). Improving college students' primary literature skills: Using primary literature in first year science courses. Paper presented at the meeting of the American Educational Research Association, Montreal, Quebec, Canada.

White, B. Y. , and J. R. Frederiksen (1998). Inquiry, modeling and metacognition: Making science accessible to all students. Cognition and Instruction 16(1): 3–117.

Wineburg, S. (1991). Historical problem solving: A Study of the cognitive processes used in the evaluation of documentary and pictorial evidence. Journal of Educational Psychology, 83(1), 73-87.


« John Ottenhoff       Cathy Summa »