Theoretical Perspectives for Developmental Education



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Students will encounter heavy emphasis on application if they pursue coursework in the physical sciences. This is the basis for having them use data from maps, charts, graphs, equations, and tables to make inferences and draw conclusions. This approach helps many students realize that they can do science. Successful experiences of this type may encourage some of them ultimately to consider a degree program in the sciences.

Applications that involve handling quantitative information enable students to think about and view the field, whether it is meteorology or physics, in a way that is similar to that of professionals in those fields. Having this perspective is extremely helpful to novices who wants to pursue one of those fields because they can more easily understand what the professionals are saying as well as understand course materials.

Science in Context

Two in-context notions are addressed in this chapter. What has been discussed so far is study skills in context. What follows is a discussion of content in context or learning about science content based upon experiences with the familiar. In GC we have experimented with the “Science in Context” notion since the mid-1960s to enable underprepared students to learn basic science within the context of something with which they are very familiar. Bloom (1956) makes this notion clear by stating that the abilities and skills needed for critical thinking and problem solving are drawn from one’s previous experiences. This requires some understanding of the new situation. It requires prior knowledge or methods that can be used, and it requires some ability to recognize the appropriate relationships between prior experience and the new situation. In GC 1111, the familiar provides the basis that can lead to the application and understanding of new concepts, principles, and terminology from physics, chemistry and biology. As an example, this allows them to infer the whys and hows of the weather. This notion is expressed in the statement: “Each of us knows a lot about the weather, but we know very little about meteorology.” Meteorology is mainly the physics of gases, fluid dynamics, energy transfer, and energy transformation. The study of the atmosphere and its weather and climate is loaded with a considerable amount of basic science.

Svinicki (1993-94) expands on the value of connections between prior knowledge and knowledge to be learned, positing that learning is easier and faster if there are more connections. More connections increase the comfort zone between the student and the new knowledge. Also, instruction is aided if all the students in a class have comparable prior knowledge. To some degree, this is true in a course in weather and climate where most of the students share a common understanding of what weather is even though they do not know what causes it. In a weather course, it is often appropriate to explain the science behind some atmospheric phenomenon if it just occurred, is happening, or will be happening soon. As in any course of study, students may have faulty or wrong knowledge. This is very true with how people may explain the hows and whys of weather. Svinicki suggests that it is important to correct those wrong notions when connections are being made between the known and the unknown.

Course Organization

Occasionally, an instructor, in order to better serve underprepared students, may change the course organization and order of topics so that it looks very different from a traditional course in the subject. This is difficult to do but may be necessary in order to help the students make better sense of the subject matter. The traditional small scale to planetary scale perspective of the weather and climate in the traditional course is not always appropriate for some students who would better understand the planetary to small scale instead. Some of the difficulties in changing the perspective include objections from other colleagues and professionals in the field who were traditionally trained as well as introductory textbooks and published lab manuals that may not be suitable materials for a nontraditional audience. Often these published materials assume a level of sophistication of student knowledge and quantitative ability that is above that of a particular student cohort. All of this usually results in the teacher writing new materials for the developmentally based course.

I designed GC 1111 into a course that I believed would be more appropriate for the underprepared student. Earlier discussion describes attempts to incorporate developmental support directly into a degree credit course. The content and topics in GC 1111 have been reorganized beginning with the planetary or global viewpoint and then proceeding toward the small scale, or local perspective. I have written an extensive set of study notes that serve as the primary study guide for the course. These notes provide a detailed guide to which pages students should read in a traditionally organized textbook.

I believe that many students need to see the whole picture first in order to better understand the parts. Zoller (2000) addresses some of the features that a new model for teaching higher order cognitive skills should include. One of the guiding constructs of such a model is the need for a holistic, systemic, and interdisciplinary approach. A benefit of this approach is that students are able to grasp and learn how to produce weather forecasts much earlier in the term than in the more traditional course. This is important because we usually are not too interested in past weather, or even current weather, but are much more concerned about what will happen in the future. Being able to predict weather motivates students to actually learn about the atmosphere in more depth. This inquiry-based process demonstrates to the students that they, in fact, can do science. This is a strong motivator.



Methods of Inquiry

A final strategy is based on concerns relating to difficulties some underprepared students encounter with mathematics, and quantitative work in general. During my experience in teaching introductory physical sciences and developmental mathematics, I have observed the frustration of those students who did not master arithmetic or algebra during the elementary and secondary school years. They are turned off by the prospect of having to take courses in those areas again in college. In some cases they had bad experiences for any of several possible reasons. The resulting negative attitude they have towards math, and in some cases, the quantitative demands of the physical sciences, is compounded because taking developmental math lengthens their stay in college and consumes financial aid.

Most often, standard algebra courses place considerable emphasis on the steps involved in simplifying expressions and solving equations with less emphasis on applications. It is discouraging to find that too many students do not understand what graphs, equations, and inequalities are. What do they mean? How do we read them? Where do they come from? In addition, they often do not know how to collect good data, nor do they know how to construct an appropriate graph. In many cases they do not understand the independent and dependent relationships between the variables in which the value of one variable influences the value of the other.

I have had the opportunity to experiment with having students do science in GC 1160: Physical Science Laboratory (no longer offered) and GC 1163: Physical Systems: Principles and Practices by carrying out the steps or processes of inquiry, or what some people call the scientific method or the methods of research. Students begin by observing some kind of physical phenomenon that is actually a result of the interaction of two variables. The phenomenon must be simple and easily understood by students. This allows them the opportunity to focus mostly on the inquiry process itself. Observations consist of taking measurements of changes of the two variables during the interaction between them. Graphs, equations, and inequalities are eventually developed from these simple measurements. The students follow precise procedures to collect the most accurate data. Next they perform error analysis on the data to understand the variability and approximate nature of their measurements. The analysis process continues by constructing a visual picture or graph of the relationship between the variables. Equations or inequalities are constructed from the data and the graph. Graphs, equations, and inequalities are models that define and represent the relationships between the variables. These models can then be used to predict other interactions without having to rerun the experiment. A frequent comment by students who have worked through the inquiry process is, “Oh, that’s what it is all about!” I believe that it is necessary for students to have hands-on experience of the observation and analysis processes in order for them to realize what graphs, equations, and inequalities are all about. Even though it takes additional time to complete those initial steps of the inquiry process, it helps students buy into the role and purpose of mathematics in their academic work.

The process outlined here is not restricted to the physical sciences. Obviously this can be a valuable learning activity and should be offered in other natural science, social science, business, and technology courses. The outcome of such an experience can be extremely valuable as students continue their education. The confidence and the insight they gain may encourage them to pursue degree options that they had thought were out of reach for them. They may confidently enroll in courses that have a heavy research component.

Conclusion

Strong intervention strategies should be available and used during the student’s first term of enrollment. Courses that are entry points for new freshmen should be structured in such a way as to identify those individuals who need help very early in the term. They all do not need intervention, nor do they all need the same kind of intervention. Some students may need help in both study-skill development and preparation in basic science content, while others may need help in one of those areas. They should be identifiable very early in their first physical science course so that they can make certain course corrections and ultimately be successful in the course. Success breeds success.

Intervention strategies must be cut back or curtailed in subsequent terms so that students do not get too dependent on them. They should be expected to study and compete on their own, with only a very limited safety net available, before they transfer. The developmental process must be designed to motivate students to develop their strengths and overcome their weaknesses to such a level that they are confident about their own ability to compete academically.

References

Astin, A. W., Parrott, S. A., Korn, W. S., & Sax, L. J. (1997). The American freshman: Thirty year trends. Los Angeles: Higher Education Research Institute, University of California-Los Angeles.

Bloom, B. S. (1956). Taxonomy of educational objectives - Handbook I: Cognitive domain. New York: David McKay.

Francisco, J. S., Trautmann, M., & Nicoll, G. (1998). Integrating a study skills workshop and pre-examination to improve students’ chemistry performance. Journal of College Science Teaching, 27, 273-278.

Gebelt, J. L., Parilis, G. M., Kramer, D. A., & Wilson, P. (1996). Retention at a large university: Combining skills with course content. Journal of Developmental Education, 20 (1), 2-4, 6, 8, 10.

Greene, E. (2000, July 28). An emulated general-education program finds itself under attack at home. The Chronicle of Higher Education, 96 (47), A16-A18.

Levin, M. E., & Levin, J. R. (1991). A critical examination of academic retention programs for at-risk minority college students. Journal of College Student Development, 32 (4), 323-334.

Mallow, J. V. (1986). Science anxiety: Fear of science and how to overcome it. Clearwater, FL: H & H.

Reisberg, L. (2000, September 8). Faced with enrollment crunch, many colleges shut the back door. The Chronicle of Higher Education, 97 (2), A65-A67.

Sax, L. J., Astin, A. W., Korn, W. S., & Mahoney, K. M. (1999). The American freshman national norms for 1999. Los Angeles: Higher Education Research Institute, University of California-Los Angeles.

Shea, J. H. (1993). Student expectations, faculty expectations. Journal of Geological Education, 41 (2), 102.

Svinicki, M. (1993-1994). What they don’t know can hurt them: The role of prior knowledge in learning. In M. Svinicki (Ed.), A Publication of the Professional and Organizational Development Network in Higher Education, 5 (4), 1-2. Austin, TX: Center for Teaching Effectiveness, University of Texas-Austin.

Tobin, K., Tippins, D. J., & Gallard, A. J. (1994). Research on instructional strategies for teaching science. In D. L. Gabel (Ed.), Handbook of research on science teaching and learning (pp. 45-93). New York: National Science Teachers Association, Macmillan.

Wambach, C., Brothen, T., & Dikel, T. N. (2000). Toward a developmental theory for developmental educators. Journal of Developmental Education, 24 (1), 2-4, 6, 8, 10, 29.

Zoller, U. (1997). The traditional-to-innovative switch in college science teaching: An illustrative, longitudinal case study on the reform trail. In M. Caprio (Ed.), From traditional approaches toward innovation (pp. 3-10). Arlington, VA: The Society for College Science Teachers.

Zoller, U. (2000). Teaching tomorrow’s college science courses – Are we getting it right? Journal of College Science Teaching, 29 (6), 409-414.



A Selectionist Approach to Developmental Education

Thomas Brothen, Professor

Psychology

Cathrine A. Wambach, Associate Professor

Psychology

Developmental students are typically defined as a special population. They are most often served by special courses rather than by mainstream courses taught with more effective and diverse pedagogies applicable to a wide range of students. We argue that the current approach tacitly assumes deficit and is a product of essentialistic thinking. We further argue that selectionism provides a more useful philosophical framework for developmental education. We make an analogy to evolutionary thinking to foster a view of developmental students as products of environments that have selected behaviors unhelpful in educational settings. We conclude that this selectionist focus on the environment shows more clearly how to structure effective developmental education environments.

At the National Association of Developmental Education (NADE) web site (www.umkc.edu/cad/nade/nadedocs) one can view the association’s goals and strategic plan. These documents provide a picture of what NADE considers important and how it will work to further developmental education. The definition of developmental education adopted by NADE (1995) appears in the preface of this monograph. This broad definition and goals statement, with its heavy emphasis on the individual, suggests that developmental education is any educational intervention targeted to the specific needs of individual learners and implies that nondevelopmental postsecondary education does not accommodate a wide range of learners.

We believe that the rigidity of conventional pedagogy used in college classes has led developmental educators to conclude that students who fall outside of the “usual” range of college students can be served best by “special” courses. The other possible solution, creating more effective and diverse pedagogy for a wider range of students, is much less tenable given the entrenchment of college faculty in their disciplines and the lack of funds needed to implement sweeping changes in college classrooms. Therefore, despite the broadness of the definition of developmental education, it has mostly been operationalized as courses in reading, writing, mathematics, and study skills. In fact, more than 85% of all educational institutions test and place students into developmental education courses (Lewis & Farris, 1996). Typically, when students enter postsecondary institutions they are classified as ready or not ready for the college level curriculum. If admitted, students who are judged not ready are assigned to courses where they will learn the skills necessary to be fully prepared for college work. These students are often described as having deficits or special needs that must be addressed before they can enter the institution’s mainstream.

Many assumptions are implicit in this model. It assumes that students’ past behavior in academic situations accurately predicts their future behavior. It assumes that students who have not been successful in academic situations have defects in their abilities, skills, or attitudes that explain their lack of success. It also defines preparedness as a fairly stable quality possessed by students that can be measured. In any case, students placed into developmental courses tend to pass them. For example, Lewis and Farris (1996) report that 79% of students taking developmental education courses in the U.S. succeed in them. Similarly, Boylan, Bonham, Claxton, and Bliss’ (1992, November) national study of developmental education outcomes showed positive results. What we do not know is the proportion of students who could succeed in nondevelopmental courses if the methods used to teach those courses addressed the needs of a broad range of learners. Unfortunately, research suggests that too many students who start college with developmental courses never reach the nondevelopmental curriculum.

Statistics compiled from community colleges in California (Little Hoover Commission, 2000) cast doubt on the extent to which current developmental programs prepare students for the ultimate goal of degree completion. Whereas 85% of these community colleges consider transfer as their primary mission, and 31% of entering students state their goal as transfer to a baccalaureate program, only 3% actually do transfer. In California 10.4% of all community college students enrolled in developmental education courses with 80% completing them successfully, but only 26% went on to take even one higher level course (Little Hoover Commission). Even more disturbing, a study of community college students’ patterns of success (Broughan, 2000) found that 57% of working class African Americans placed in multiple developmental courses failed to complete a single course for graduation credit. These outcomes are depressingly similar to what Richardson, Fisk, and Okun (1983) found in the Maricopa County Community College system two decades ago. They reported that few students who entered the developmental education program emerged from it successfully. If these data are representative of the U.S. at large, one could argue that developmental education is not achieving its goals. We think the primary problem is that developmental education is founded on a deficit model that labels students rather than instruction as the problem.

We have argued that the prediction-placement model (i.e., assess deficits in reading and writing and place students into skills courses) by which most of developmental education functions is problematic (Wambach & Brothen, 1990). The moderate, positive correlations between standardized tests, past grades, and future performance make grades and test scores useful in selective admissions situations where not everyone who wants to attend an institution can be accepted. If a college can only serve 1000 students, it seems reasonable to admit the 1000 deemed most likely to succeed. The decision not to admit a particular student does not mean that individual would not succeed if he or she had been admitted. It means that, given a surplus of applicants, the institution can decide to serve another applicant instead. In fact, in most situations where applicants are rejected, there is some probability the student would succeed if admitted. When students are accepted who fall below the usual admissions criteria, these students are often identified as “deficient” and in need of intervention. They are typically labeled as developmental students.

Some charged with the task of teaching or advising developmental students are finding the concept of “deficit” problematic (Higbee, 1996). Working from post-modernist perspectives, scholars in the field of basic writing have found the entire notion of who is prepared or not to be a complex and political one. For example, Faigley (1992) rejects categorization and stresses the importance of process in basic writing. Iris Young (1990) takes a political perspective and argues that rights and power are not essential “things” but exist in relation to social structures. Although not abandoning the task of teaching students academic forms of writing, they reject the notion that students who do not yet know the forms are somehow defective. Instead, they propose identifying the skills a student already possesses and building academic writing skills on this foundation. Students’ prior skills include proficiency with language forms and cultural knowledge not generally valued by higher education institutions. This is consistent with the approach we take here.

Yet the notion of deficit does not go away. In the common discourse of developmental education, students are often described as “developmental” or “underprepared” or “at risk.” Reading courses are said to make up for lack of ability or interest in reading, and traditionally structured remedial writing courses strive to improve the inability to write complete sentences. In this paper we will argue that the concept of deficit is a product of essentialistic thinking, the belief that we can know the “essence” of a person. We will propose that selectionism, the idea that useful qualities are selected by environments, provides a more helpful philosophical framework for developmental education.



Essentialism from Aristotle to
the Evolutionary Synthesis

Fuss (1989) points out that “Essentialism is classically defined as a belief in true essence—that which is most irreducible, unchanging, and therefore constitutive of a given person or thing” (p. 2). Aristotle’s “types” were an early systematization of such essentialist thinking (Sober, 1984). Aristotle characterized things and people as deviations if they were not identical to their type. These deviations were caused by interferences that kept the entity from exhibiting the qualities of its type. Thus, “student” is a type, and developmental students would be seen as deviations from it. Remediation, then, is necessary to restore the deviation to its normal state.

From a biological perspective, essentialism is “a belief that the variation of nature can be reduced to a limited number of basic classes, representing constant, sharply delimited types; typological thinking” (Mayr, 1997, p. 307). In developmental education, typological thinking is evidenced most often through division of students into types by virtue of the stable characteristics they are said to possess (e.g., deficits, skills, learning styles, etc.) and then either helping students overcome the deficit (e.g., a skills course) or finding educational interventions adapted to them (i.e., teaching compatible with their learning style). Fuss (1989) makes a distinction between real essences that “are discovered by close empirical observation” and nominal essences that are “produced specifically by language” (pp. 4-5). We believe that essentialist concepts in developmental education are of the second type because their empirical basis is weak. We will review briefly some pertinent literature and make some proposals that suggest a way out of what we see as a problem for developmental educators.

To understand the pervasiveness of essentialistic thinking in our enterprise, a historical perspective is helpful. Mayr’s (1997) history of biology provides parallels to the issues facing developmental education. He points out that Copernicus, Galileo, Kepler, Newton, Descartes, Leibniz, and others developed the basic principles of the scientific method still in use today. Their Christian perspective caused them to view the universe as an orderly “machine” whose universal laws could be divined with the new methods developed during what has come to be called the scientific revolution. The science of mechanics (i.e., movements of planets, etc.) conformed well to the machine metaphor, but it soon became apparent that the mechanistic approach was insufficient for the finer grained analysis necessary for more complex systems. The complexities at the atomic level in physics and the complexities of life in biology demonstrated the importance of random factors and functional relationships between variables.

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