Ed Psych 1

Hmelo-Silver, C. (2000). Knowledge Recycling: Crisscrossing the Landscape of Educational Psychology in a Problem-Based Learning Course for Preservice Teachers. Journal on Excellence in College Teaching, 11, 41-56.

Running head: KNOWLEDGE RECYCLING

Knowledge Recycling: Crisscrossing the Landscape of Educational Psychology in

a Problem-Based Course for Preservice Teachers

Cindy E. Hmelo-Silver

Rutgers, The State University of New Jersey

Correspondence to:

Cindy Hmelo-Silver

Department of Educational Psychology

Graduate School of Education

Rutgers, The State University of New Jersey

10 Seminary Place

New Brunswick, NJ 08901-1183

Email:

August 2001

In press

Journal o f Excellence in College Teaching
Abstract

Many current educational reform movements recommend an increased emphasis on flexible thinking and learning that integrate content knowledge with rich problem-solving contexts. Teacher education is no exception. Educational psychology courses emphasize the psychological theories, principles, and concepts that prospective teachers need to know. The goal of these courses is for teachers to be able to apply psychological knowledge to classroom situations. This argues for situating learning in educational problems. This paper describes a problem-based learning approach to teaching educational psychology. Analysis of the classroom implementation shows that students revisit concepts across multiple problems, grounded in instructional contexts, crisscrossing the landscape of educational psychology. This suggests that a problem-based approach helps students construct usable knowledge that they are able to apply to a variety of problem situations.


Knowledge Recycling: Crisscrossing the Landscape of Educational Psychology in

A Problem-Based Course for Preservice Teachers

Many current educational reform movements recommend an increased emphasis on flexible thinking and learning that integrate content knowledge with rich problem solving contexts. Teacher education is no exception (Anderson et al., 1995). Educational psychology courses emphasize the psychological theories, principles and concepts that prospective teachers need to know (Anderson et al., 1995). The goal of these courses is for teachers to be able to apply psychology to classroom situations. This argues for situating learning in educational problems. Problem-based learning (PBL), with its dual emphasis on strategies and content, is well suited to helping students construct usable knowledge because it situates learning in real-world problems (Barrows, 1988; Savery & Duffy, 1995).

In this paper, I describe the design and implementation of a problem-based course to teach educational psychology. I will briefly describe cognitive flexibility theory and how it can serve as a conceptual framework for thinking about student learning in a PBL classroom. Finally, I will present a content analysis of student artifacts created during the implementation of this course.

Developing Cognitive Flexibility through PBL

Cognitive Flexibility Theory (CFT) is a theory of instructional design for complex domains in which learners need to develop advanced understanding and real-world problem solving capability (Spiro, Feltovich, Jacobson, & Coulson, 1992). Appropriate domains for the application of CFT include professional-level education, such as medical and teacher education. A central argument of CFT is that many instructional approaches fail because they represent complex subject matter in an overly simplified and well-structured manner. They argue that to understand complex domains, instructional designs are required that afford greater cognitive flexibility:

This includes the ability to represent knowledge from different conceptual and case perspectives and then, when knowledge must later be used, the ability to construct from those different conceptual and case representations a knowledge ensemble tailored to the needs of the understanding or problem-solving situation at hand (Spiro et al., p.58).

An ill-structured domain is one in which each case of knowledge application involves the simultaneous application of multiple conceptual structures and the pattern of concept application varies across cases. It follows then that individuals must be able to construct new understandings from diverse sources of knowledge (Spiro et al., 1992). For this on-the-fly knowledge assembly to occur, learners need to revisit the same concepts from different contexts and for different purposes. Spiro et al (1992) use the metaphor of a crisscrossed landscape to describe a CFT approach to instructional design. PBL meets these requirements as students learn through solving complex problems and reflecting on their experiences.

PBL in Educational Psychology

Recent research has demonstrated that teaching is an ill-structured and complex domain (Borko & Putnam, 1996). Teachers must continually solve problems and integrate diverse sources of knowledge (Shulman, 1987). Principles of educational psychology can serve as tools to help teachers in their problem solving, provided that they understand how they are applicable. Teachers need to be able to use psychological perspectives on teaching as they confront the complex and situated nature of teaching problems (Anderson et al., 1995; Blumenfeld, Hicks, & Krajcik, 1996). By situating learning in a series of educational problems, prospective teachers can begin to construct the flexible knowledge and reasoning processes that CFT suggests are needed.

In the PBL educational psychology course described in the paper, problems were used as the major vehicle for student learning. The benefits of PBL are not merely in the production of a problem solution but in thinking through a problem, considering alternative ideas, and justifying decisions. Students develop self-directed learning skills as they identify what they need to know to solve a problem and research those issues. These processes help make the preservice teachers’ thinking explicit and open for discussion, refinement, and revision within their groups.

Course Organization

This course was taken primarily by undergraduate students who were seeking to enter the teacher education program or by graduate students seeking their initial teacher certification. The class met for two 80 min sessions per week. The students used a commercially available textbook as a resource although no specific readings were assigned from the textbook. The student groups worked through eight problems over the course of the semester (see Table 1 for a list of problems). At the beginning of the class, the students were oriented to PBL and they received a handout on the role of the facilitator. This handout contained a chart that indicated what the facilitator might be doing at the each stage of the PBL process (see Hmelo & Ferrari, 1997 for example). Students were told that the facilitator role was to help maintain the group dynamics and push other students to explain their thinking.

The class consisted of 35 students initially divided into seven groups.[1] The student groups were organized by the instructor to assure that a variety of teaching areas were represented in each group (e.g., elementary, foreign language, mathematics, etc.) and that more advanced students were distributed evenly among the groups. In typical PBL, students work in small groups of 5-7 students with a facilitator (Barrows, 1988; Hmelo & Ferrari, 1997). The facilitator serves to guide the learning process rather than providing information. In this undergraduate class, there were 35 students so the traditional PBL model had to be modified. The class used a combination of large group and small group discussions. The small group discussion provided opportunities for the students to clarify their ideas as they made collaborative decisions. It also allowed the students to divide the cognitive load and contribute different areas of expertise. As students worked in their groups, the students rotated the role of the facilitator. In addition, the course instructor served as a wandering facilitator, rotating among the different groups.

The large group discussions allowed the groups to share the learning issues that each group generated as well as providing an opportunity for students to critique each other’s ideas about problem solutions. The large group discussions were conducted at the end of each class session. The course instructor facilitated the large group discussions, challenging students to explain why they thought particular facts, ideas, and learning issues were important in the problem. This served to make students’ thinking visible as well as to model facilitation skills. Through the large group discussions, learning issues often diffused to other groups as noted below.

The students received a problem in one class. The initial phase of the students’ work involved identifying the facts in the problem, generating ideas about the cause of the problem and the solution, identifying learning issues, and planning the actions that they were going to take. The students kept structured whiteboards in which they recorded the facts, ideas, learning issues, and action plans. Large post-it notes (2.5 feet by 2 feet) were used for this purpose. An example of a whiteboard is shown in Table 2. This provided an external memory aid that helped students keep track of where they had been and where they were going. It also communicated a problem solving process to help students deal with the complexity of the problems they were working on.

Learning issues are ideas that students need to better understand in order to solve the problem that they are working on. Students divided up the learning issues and researched them independently between classes. At subsequent sessions, students revisited the problem with the information that they had gathered in their research. In addition, I gave a 20 min mini-lecture at the conclusion of each problem to go over important learning issues. The mini-lecture was designed to serve three purposes. First, it provided an opportunity for students to get feedback on the learning issues that they generated. Second, it was a chance for students to fill in gaps in their understanding. Schwartz and Bransford (1998) demonstrated that students get more out of a lecture after they have been grappling with a problem. Third, it added to the students’ comfort level by providing some familiar structure. Early in the course, the students found the mini-lectures useful but as the course progressed, they indicated that the lectures were not particularly helpful and they would have preferred to spend the time in group work or class discussion. The groups also presented their work in class and got feedback on their ideas. The final written products were turned in at the class following the conclusion of the problem. The students were assessed based on their group papers, two problem-based exams, and reflective learning logs, which they completed for each problem. The two exams consisted of two problems that were 1-2 pages each. The students made a brief first attempt at the problem and generated learning issues in class and then completed the problem at home.

Problem Design

To construct the problems, a curriculum matrix was developed to cover the topics normally addressed in a traditional educational psychology course. Problems were initially chosen based on the kinds of problems teachers need to solve, such as diagnosing students’ misconceptions, designing and improving instruction. Additional problems that were more policy-related were designed to help cover some of the content that was not covered in this initial set of problems developed. The students completed six of the problems over two classes each and the remaining two problems (problems 4 and 8) over four class sessions each. Problems 1 and 4 had students deal with district-wide issues whereas the other problems dealt with issues at a school or classroom level. Each problem began by presenting a scenario. Most of the problems also had additional data that the students needed to sort through, not all of which were relevant. For example, problem 5 presented as:

You are a student teacher in a second grade science classroom. You have been teaching a unit on classifying plants and animals and Joe, a very quiet student, still does not understand the differences between different kinds of animals. How can you help Joe understand?

The students also received additional data in the form of a list of what Joe classifies as a fish and what he classifies as an animal.

Knowledge Recycling, Diffusing, And Differentiating

In designing this course, I hypothesized that students would apply multiple concepts to each problem and apply each concept to multiple problems. Moreover, I also expected that students would move from undifferentiated understanding to a more differentiated understanding of key ideas in educational psychology. A content analysis was conducted based on the whiteboards that the groups had turned in and the group papers. Several of the whiteboards were not available for analysis.[2] The coding scheme was developed empirically based on the topics that are generally covered in an educational psychology course but guided by CFT. Peripheral, nonpsychological issues, such as “What is Title I” were not coded. Although assessment, classroom management, and developmental psychology are covered in other courses, they were coded because they involve psychological issues.

This analysis focuses on how students recycled, diffused, and differentiated their knowledge. The Merriam-Webster Collegiate Dictionary has several definitions for the word “recycle.” These include passing again through a series of changes or treatments, to process in order to regain material for use; to adapt to a new use; to bring back; to return to an original condition so that an operation can begin again. All these definitions suggest that recycling involves the use, reuse, or adaptation of something of value. Diffusion refers to the extent to which ideas are spread from one group to others in the classroom. Differentiation denotes the extent to which students’ ideas (a) develop from simple to complex and (b) include knowledge about how information is applicable in different situations. Examining the students’ construction of flexible understanding in this manner is an application of CFT (Spiro et al., 1992)

A total of 91 concept categories were identified and these were grouped among 14 superordinate categories. Table 3 indicates whether an idea appeared on any whiteboards as a learning issue and in any of the group papers. In general, there were fewer concepts listed in the learning issues than appear in the papers. These results show that students indeed covered a range of ideas and that there was some recycling and diffusion of knowledge. The students were recycling their knowledge as indicated by their use of 12 of the 14 concepts in more than one problem. Groups used concepts that they had researched in one problem and applied it, often more flexibly, to a later problem (see example below). It is also interesting to note that students connected their ideas to instructional methods in all the problems.

The learning issues were often diffused to other groups through the large class discussion. To illustrate this concretely, I provide an example from the information processing (IP) topics. Table 4 shows the number of groups that used IP concepts on their whiteboards. These concepts only appeared in one group when they were first relevant in problem 4. After the students shared their whiteboards in the whole class discussion, the concepts appeared in all the papers in one subcategory or another. Knowledge representation was listed as a learning issue by one group in Problem 4 but it appeared in 5 of the group papers. It was also raised as a learning issue in 4 out of 6 groups in Problem 8, demonstrating that knowledge diffused around the class. As shown in Table 4, metacognition first appeared as a learning issue is Problem 2 and reappeared in Problems 4,5, and 7 as well as in the papers for Problems 4, 6,7, and 8. Moreover, the students differentiated their ideas about IP, going from listing a single IP concept on the whiteboards (in problem 1) to including additional related concepts in subsequent problems. For example, in Problem 4, the students listed 5 different IP ideas in their learning issues whereas 12 were discussed in their papers.