Difference between revisions of "Emergece Project (Chi)"

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We hypothesize that emergence is the key structure that underlies this class of process concepts, and that misconceptions arise because most students know nothing about emergence. To test our hypothesis, we developed a brief, self-contained domain-general instructional module that we refer to as emergent schema training. It focuses on the idea of emergence, which is related to notions of complex dynamic systems (Goldstone, 2006), to see if it can help students achieve greater learning, deeper understanding, and more successful transfer to other concepts. Our domain-general approach is relevant to a variety of concepts across science domains as well as to concepts in domains other than science, such as economics, political science, and history. Thus, the structure of emergence is an ideal test bed for robust learning not only in terms of long-term retention, but also for accelerated learning of new concepts.
  
 
=== Explanation ===
 
=== Explanation ===

Revision as of 16:52, 11 January 2008

Learning about Emergence and Heat Transfer

Michelene T. H. Chi

Summary Table

Study 1

PIs Michelene T. H. Chi
Other Contributers
Study Start Date May 2008
Study End Date October 2008
LearnLab Site
LearnLab Course Chemistry
Number of Students
Total Participant Hours TBD
DataShop N/A




Abstract

The majority of learning studied within PSLC has been of the enrichment kind (Carey, 1991). Enrichment occurs when a student has missing knowledge components, and learning consists of adding knowledge components. Many PSLC studies focus on optimal methods and conditions for adding knowledge components that result in robust learning. However, there is a completely different kind of learning that few PSLC studies have addressed -— learning of the conceptual change kind. Conceptual change occurs when a student’s existing ideas conflict with to-be-learned material, usually in science domains (Vosniadou, 2004). It is customary to assume that the existing knowledge is incorrect or misconceived and that the to-be-learned information is correct by some normative standard. Although conceptual change can perhaps be modeled using canonical PSLC mechanisms of knowledge component construction, strengthening, and feature refinement, such a model would fail to explain why these mechanisms do not work when existing knowledge conflicts with new knowledge. Nor would it suggest how to alter the situation so that these mechanisms will work. The failure to achieve conceptual change in the face of robust misconceptions has been a problem for decades. The proposed study would extend the learning framework of PSLC by incorporating new information about conceptual change.

The purpose of the proposed study is to test an intervention designed to promote deep understanding of science processes about which many students hold robust misconceptions. The intervention, based on our previous research, involves introducing an unfamiliar type of process, called emergent, and differentiating it from a familiar type of process, called direct. This introduction and differentiation, called schema training, will occur in the context of a diffusion lesson in a college-level chemistry course. Our study will determine whether students who receive the schema training understand subsequent instruction on heat transfer better than students who do not receive the training.

Background & Significance

For years, researchers have been investigating student ideas about science concepts and how they interfere with learning. Some of these ideas, often referred to as misconceptions, are extremely robust, stable and resistant to instruction. Thousands of studies have documented specific misconceptions about a variety of science concepts and phenomena, beginning with a book by Novak (1977) and a review by Driver and Easley (1978), both published almost three decades ago. By 2004, there were over 6000 publications describing student ideas and instructional attempts to change them (Duit, 2004). Obviously, we have not yet solved the daunting problem of how to build conceptual understanding in the presence of robust misconceptions, a process sometimes referred to as radical conceptual change (Carey, 1985).

Our lab has been investigating the underlying structure of a class of science process concepts that are frequently misconceived across various science domains. Based on insights from that analysis, we hypothesize that emergence is the key structure that underlies this class of process concepts, and that misconceptions arise because most students know nothing about emergence. To test our hypothesis, we developed a brief, self-contained domain-general instructional module that we refer to as emergent schema training. It focuses on the idea of emergence, which is related to notions of complex dynamic systems (Goldstone, 2006), to see if it can help students achieve greater learning, deeper understanding, and more successful transfer to other concepts. Our domain-general approach is relevant to a variety of concepts across science domains as well as to concepts in domains other than science, such as economics, political science, and history. Thus, the structure of emergence is an ideal test bed for robust learning not only in terms of long-term retention, but also for accelerated learning of new concepts.

The long-term objective of our emergent work is to demonstrate that schema training can help students achieve radical conceptual change and, more importantly, transfer deep understanding to a variety of important concepts. Toward that end, we developed an emergent schema-training module for middle- and high-school level students. In 2005, we tested the module with eighth- and ninth graders, obtaining promising results. (See Prior Work below for details.)

The objective of the research proposed in this project plan is to replicate the 2005 study with college-level students in vivo, for reasons that are especially germane to the conception of LearnLabs. That is, we achieved only modest far transfer in our 2005 study primarily because the students did not possess the prerequisite knowledge needed for learning the transfer concept. A LearnLab context will completely avoid this problem. Our study will parallel and collaborate with an NSF-funded study being conducted with engineering students at the Colorado School of Mines, Purdue University and three other sites, by Ron Miller (a chemical engineer), Ruth Streveler (an educational psychologist), and Jim Slotta (an educational technologist). They have obtained NSF funding to test a modified version of our schema training method for helping college students develop accurate mental models of engineering processes. Our proposed project will develop materials for college students, and they will then use those materials to determine whether emergent schema training can help engineering students understand small-scale dynamic processes, such as microfluidics. A summary of the project is attached as an ancillary document. Taken together, these two studies will provide a rigorous test of our hypothesis about emergence and the value of schema training.

Prior Work

Through four years of funding from the Spencer Foundation, we conducted theoretical analyses of misconceptions and concluded that there are three types of conceptual change, based on the grain size of knowledge components: belief revision, mental model transformation, and categorical shift (Chi, in press). We contend that science misconceptions that are extremely robust require a categorical shift in order to build understanding that is equally robust. An example of a category shift is to reassign the concept whale from the category fish to the category mammal. This kind of shift is not difficult to achieve because most students are familiar with the category mammal. However, to understand many science processes, students need to shift their misconceived understanding from a familiar category, direct process, to an unfamiliar category, emergent process. Because the concept of an emergent process is often missing from student knowledge bases, we need to help them build understanding of the category before they can achieve a successful category shift. Once the emergence category is in place, appropriate science processes can be assimilated or added, using the PSLC mechanisms of construction, strengthening, and feature refinement.

Briefly, a direct (or indirect) process is one that usually has a causal agent or group that produces some outcome in a sequential and dependent sort of way. We will describe an everyday example, a less familiar example, and a scientific example.

Direct example 1. In the familiar process of a baseball game, the final outcome might be explained as being due to the excellent work of the pitcher (thus attributing the outcome to a single or multiple agents), or to some local events within the game (such as home runs). Moreover, the behavior of certain local events within the game corresponds to or aligns with the outcome. For example, a team with many home runs in a game is more likely to win. That is, home runs are positive local events and they align with the positive outcome of winning the game.

Direct example 2. A slightly less familiar example is seeing multiple airplanes flying in a V-formation. This V-pattern is intentional, created by the lead pilot (a single agent) telling the other pilots where to fly.

Direct example 3. A direct process from biology is cell division, which proceeds through a sequence of three stages. The first, interphase, is a period of cell growth. This is followed by mitosis, the division of the cell nucleus, and then cytokinesis, the division of the cytoplasm of a parent cell into two daughter cells. Such a process has a definite sequence, in which some events cannot occur until others are completed.

In contrast, emergent processes have neither a causal agent or agents nor an identifiable sequence of stages. Rather, the outcome results from the collective and simultaneous interactions of all local events. Let’s consider three examples here as well: an everyday example, a less familiar example, and a scientific example.

Emergent example 1. The process of a crowd forming a bottleneck at a door, as when the school bell rings and students hurry to get through the narrow classroom door, is an everyday example of an emergent process. Although there is an external trigger (the school bell), the outcome of forming the bottleneck cannot be attributed to any single agent or group of agents, and the process is not sequential. Instead, all the students simultaneously rush toward the door at about the same pace.

Emergent example 2. A slightly less familiar example is migrating geese flying in a V-formation. In contrast to the airplane example, the V-pattern is not intentionally formed, such as by the lead goose telling other geese where to fly. Instead, all the geese are doing the same thing, flying slightly behind another goose because instinctually they seek the area of least resistance. When all the geese do the same thing at the same time, a V-pattern emerges.

Emergent example 3. An example from biology is the diffusion of oxygen from the lungs to the blood vessels. This process is caused by all the oxygen and carbon dioxide molecules moving and colliding randomly. Because there are more oxygen molecules in the lungs than in the blood, more oxygen molecules tend to move from the lungs to the blood than from the blood to the lungs. The reverse is true for carbon dioxide molecules. Since all molecules move and collide randomly, both kinds of molecules move in both directions. Some oxygen molecules move from the blood to the lungs, and some carbon dioxide molecules move from the lungs to the blood. Despite local variations, however, the majority of oxygen molecules move from the lungs to the blood, and the majority of carbon dioxide molecules move in the opposite direction, creating an unintentional uni-directional flow.

These two sets of examples illustrate general differences between direct and emergent processes. A more detailed description that explicitly specifies a set of ten distinguishing features is provided in Chi (2005), and see also Table 2 at the end of this project plan. Explicit specification of these features is essential for purposes of instruction.

Our prior funding allowed us to develop two equal-length sets of instructional materials for middle school science, one for an experimental group and one for a control group. Each set consisted of one general module and two concept-specific modules. The general module for the experimental group introduced the emergence schema, describing everyday examples of emergent and direct processes and identifying explicit features for recognizing and explaining each type. The general module for the control group described the nature of science, using text taken directly from Science for All Americans, published by Project 2061 (AAAS, 1989). Pre- and post assessments of this module measured long-term retention because the posttest was administered several days after learning.

For both groups, the first concept-specific module was about diffusion. The diffusion modules for both the experimental and control groups included a text description and computer animations of diffusion. The text for the control group was a composite of passages taken from standard grade-appropriate textbooks. The same text was used for the experimental condition, with a small number of passages ablated and replaced by descriptions instantiating diffusion as an emergent process, so that they were about the same length.

The animations of the diffusion module consisted of dynamic simulations at both the macro (outwardly visible) and micro (molecular) levels. Although it has been found that in general, static diagrams are as supportive as animations when the to-be-learned materials can be visualized by students (Hegarty, Kriz, & Cate, 2004), in studying emergent processes, however, dynamic simulations are more helpful, since the emergence of the macro-level pattern from the micro-level interactions is not usually visualizable. Although both the experimental and control diffusion modules included the same simulations, the two modules differed in how the simulations were implemented. For the experimental group, the simulation was embedded with emergent “mapping prompts,” which are queries that focused on specific emergent properties depicted in the simulations. In essence, emergent mapping prompts helped students map diffusion to emergence. For the control group, the simulations were embedded with “attention prompts,” which were queries that focused student’s attention on similar aspects of the simulation, but without explicit mapping to emergence. The two modules contained the same number of questions. Pre- and post assessment of the diffusion module measured transfer.

The second concept-specific module, identical for both groups, was about heat transfer. Like the diffusion module, the heat module included animations of heat transfer at both the macro and micro levels. Descriptive text and questions were taken directly from a popular high school textbook, Conceptual Physical Science (Hewitt, 1987), without any modifications. Pre- and post assessment of the heat module measured preparation for future learning.

The modules were used in a lab study with 41 eighth and ninth graders, who were randomly assigned to either the experimental or control condition. The study was carried out over four consecutive days after classes in a private school. All instructional and assessment materials were delivered on-line, and students worked individually at their own laptops. Results from the diffusion assessment showed that not only did both groups gain significantly from pre- to posttest, the gains of the experimental group far exceeded those of the control group (ANCOVA, F(1,38) = 9.391, p = .004, with effect sizes of 1.4 for the control group and 1.6 for the experimental group). On the heat transfer assessment, experimental students gained significantly whereas control students did not. However, the gain of the emergent group did not significantly exceed the gain of the control group, when adjusted for pre-test. We believe that preparation for future learning was modest because students did not have sufficient prerequisite knowledge about concepts (such as energy) leading up to the study of heat transfer. Thus, the experimental group may have had difficulty mapping heat transfer to emergence. A LearnLab study can easily correct this shortcoming.

Glossary

Research questions

Hypotheses

We hypothesize that emergence is the key structure that underlies this class of process concepts, and that misconceptions arise because most students know nothing about emergence. To test our hypothesis, we developed a brief, self-contained domain-general instructional module that we refer to as emergent schema training. It focuses on the idea of emergence, which is related to notions of complex dynamic systems (Goldstone, 2006), to see if it can help students achieve greater learning, deeper understanding, and more successful transfer to other concepts. Our domain-general approach is relevant to a variety of concepts across science domains as well as to concepts in domains other than science, such as economics, political science, and history. Thus, the structure of emergence is an ideal test bed for robust learning not only in terms of long-term retention, but also for accelerated learning of new concepts.

Explanation

Further Information

Connections

Annotated Bibliography

References

Future Plans

  1. To be added