Group Learning Pedagogy and Value Analysis

Introduction

Terms that are synonymous with group learning include collaborative learning, cooperative learning, and group work.  This pedagogy has been shown to be superior over individual learning (Spinger, Stanne, & Donovan, 1999).  However, the literature does not distinguish one method of group selection as being superior over any other method. This quasi-experimental mixed method research project was designed to determine which of two selection methods is better: self-selection or random assignment. The subject of the group learning project was value analysis, also known as value engineering. Teaching students about value analysis seems to be a method of teaching students how to be creative.

Based on existing literature, the merits of group learning pedagogy will be described and discussed. The merits of value analysis (VA) and value engineering (VE) will also be described and discussed. The research methodology and results will be presented along with conclusions and recommendations for collaborative learning group selection.

Group Learning Pedagogy

Rau and Heyl (1990) assert, “Collaborative learning clearly establishes its superiority over individualistic and competitive modes of learning. Isolated students do not learn as much or as well as students who are embedded in a network of informal social relations” (p. 144; italics in original). Similarly, Springer et al. (1999) say, “The message is clear: What students learn is greatly influenced by how they learn, and many students learn best through active, collaborative, small-group work inside and outside the classroom” (p. 22).  In the field of construction management, teams and the ability to work in groups are the central component of a successful project and organization. Group work in the classroom is a way to empower students and facilitate participation in decision-making (Meyer, 1994), thereby preparing them for such experiences in the construction industry upon graduation.

Collaborative learning, cooperative learning, and group work are similar terms to describe “students working together in a group small enough that everyone can participate on a collective task that has been clearly assigned” (Cohen, 1994, p. 3). Each refers to a variety of instructional practices which encourage students to work together as they apply course material to answer questions, solve problems, or create a product (Smith & MacGregor, 1992). In cooperative learning environments, students work in a structured group to perform a well-defined task or to understand a particular concept with the purpose of every individual within the group developing his or her academic and social skills to the maximum. A student’s grade depends not only on how well he or she understands the material or completes the task, but also on how well other members of the group do the same (Bartlett, 1995).

Previous research on the effects of group work have shown that such environments give to students: socio-emotional benefits from interpersonal relationships; added psychological health by learning to see the perspective of others, taking on more positive attitudes toward peers, and developing higher self-esteem; the ability to probe more deeply and critically into course material; and often greater academic success and more positive attitudes about learning (Bartlett, 1995; Cohen, 1994; Johnson & Johnson, 1978; Johnson, Johnson, & Smith, 1998; Springer et al., 1999). For example, Rau and Heyl (1990) tested three hypotheses in their study: 1) that students would test better on questions from readings discussed in group meetings than on other questions; 2) that students in collaborative learning groups would become highly interconnected, more so than those not in these learning groups; and 3) that students would endorse the use of group work and speak to its positive aspects. All three hypotheses were supported in their study. 

Additionally, group work has been shown as a way to manage academic heterogeneity in classrooms with a wide range of achievement in academic skills (Cohen, 1994). Most models of group work as collaborative learning advocate the use of heterogeneous groups because of the hypothesized benefits to low-achieving students of receiving instruction from high-achieving students or because of the desire to increase trust and friendliness between members of different social groups (Cohen, 1994; Johnson, Johnson, & Holubec, 1986). Furthermore, achievement is enhanced when students working in a group are held individually accountable for their own learning (Slavin, 1983).  Slavin (1983) emphasizes individual accountability as strongly as group rewards saying, “Learning is enhanced by provision of group rewards if and only if group members are individually accountable to the group for their own learning” (p. 59).  The rationale for implementing group goals with individual accountability is that students will be encouraged to help one another to achieve by teaching each other and regularly assessing each other’s learning (Springer et al., 1999).

However, placing students in groups does not mean that they will cooperate and participate in group learning. The effects of group work depend on how the group is organized, what the tasks are, who participates, and how the group is held accountable (Blumenfeld, Marx, Soloway, & Krajcik, 1996).  One problem is failure to contribute by all members of the group or the “free-rider” problem (Bartlett, 1995; Blumenfeld et al., 1996). This leads to those “free-riding” (not learning as they are not participating) while those that are doing most of the work can often feel exploited and either reduce their own efforts or work on their own. Other anti-social behavior can occur when forceful students dominate discussions, pressure others to accept their perspective, or force conclusions on the group. Others may ridicule and exclude group members or discount their contributions leaving those rejected members to feel humiliated or withdraw from the group completely (Blumenfeld et al., 1996). 

There are management techniques instructors can employ to help produce interaction among group members and reduce these anti-social behaviors. These range from simple task instruction, in which students are told to help one another or to discuss and come to consensus, to detailed procedures concerning how and what is to be discussed (Cohen, 1994). Procedures characterizing cooperative learning include communicating a common goal to group members; offering rewards for achieving the group’s goal; assigning interrelated and complementary roles and tasks to individuals within each group; holding each individual accountable for his or her own learning, providing team-building activities or elaborating on social skills needed for effective group work; and discussing ways in which the group’s work could be accomplished more effectively (Springer et al., 1999).

Aside from these management techniques, as previously stated, heterogeneity seems to be the most critical factor to improved individual performance in cooperative learning groups (Blumenfeld et al., 1996; Cohen, 1994; Johnson et al., 1986; Rau & Heyl, 1990). Generally, this means that groups are more successful when members are drawn from high and middle or middle and low achievement levels or where students are all in the middle. There is considerable support for the beneficial effects of heterogeneous groups on low achieving students.  For lower achieving students the benefits of interaction with higher achieving students, even when tasks demand higher order thinking, are much more so than if they are in a homogeneously low achieving group (Cohen, 1994). Only students of average achievement are less likely to benefit from their interaction with others of higher or lower achievement, as they are less likely to engage in the group and, rather, do better in homogeneous groups of other average achievers (Blumenfeld et al., 1996; Cohen, 1994).

There are three ways to accomplish group selection: self-selection, random assignment, and criterion-based selection. In self-selection students choose their own group members.  They may choose these members at random (e.g., sitting nearest them in class, not already in a group) or by design (e.g., know or worked with in prior classes or elsewhere). Random assignment is the instructor putting students into groups by some method of chance (e.g., counting off with like numbers comprising a group). Criterion-based selection is when the instructor administers some sort of test or data collection tool (e.g., exam, learning styles inventory, background summary) and uses the results to assign students so that groups are both heterogeneous and roughly equal in overall ability (Rau & Heyl, 1990).

In this study of junior-level construction management students the assumption was that self-selection would occur by design, meaning that students would join together for this group assignment based upon prior experience with peers on other group assignments in their academic program. Further, the assumption was that like-achieving students would group together (e.g., high achievers with high achievers, middle achievers with middle achievers, and low achievers with low achievers). Similar to Colbeck, Campbell, and Bjorklund’s (2000) study, we expected that those students self-selecting group members, especially those high and middle achievers, would benefit from prior experiences enhancing their communication, planning, and technical skills. We also expected, as did Colbeck et al., that the students’ interdependence would be more developed in self-selected groups of students with prior group experience than in groups whose members had little or no prior group experiences. Therefore, the aim of this study was to test the two most common types of group selection, self-selection and random assignment, to see if there are differences in the functioning and academic success of the group based upon their selection method.

Value Analysis and Value Engineering

The definitions of value analysis and value engineering provided common ground on which the group learning project was developed. Value analysis is a useful tool for constructors to use as they strive to meet the owner’s or user’s project requirements, thereby facilitating client satisfaction.  Components of contractor’s project success criteria have been determined to include the following: “Meet the schedule; project profit; under budget including saving for the owner or the contractor; quality met or exceeded; no claims and/or litigation; safety; client satisfaction; good subcontractor buy out; good direct communication; and minimal or no surprises during the project” (Sanvido, Grobbler, Parfitt, Guvenis, & Coyle, 1992, p. 96; italics added). As one of the major criteria of a construction contractor’s measured success, client satisfaction is of utmost importance.

Value Engineering and Value Analysis Defined

Lawrence D. Miles, the father of value analysis, defined his creation as being “created for one specific purpose – the identification of unnecessary cost – value analysis is a system, a complete set of techniques, properly arranged, for the sole purpose of efficiently identifying unnecessary cost before, during, or after the fact” (Miles, 1989, p. xvi).

In the 1950s, Miles had modified the term “value analysis” to satisfy the contract funding availability of a federal agency client. In an effort to explain the need for the two terms, Miles wrote, “Value analysis is the name applied to this disciplined, step-by-step thinking system, with its specific approaches for mind setting, problem setting, and problem solving. Value engineering is often the name correctly used by qualified engineers in engineering work” (Miles, 1989, p. xvii; italics in original).

Fallon (1980) states, “Value analysis, regardless of name, location, or sponsorship, has three universal characteristics: (a) it improves value by studying the function rather than the structure of a product; (b) it deliberately stimulates resourcefulness, ingenuity, and inventiveness; and (c) it begins with a formal information phase” (p. 2).

Public Law 104-106 refers to value engineering as an “analysis of the functions of a program, project, system, product, item of equipment, building, facility, service, or supply of an executive agency, performed by qualified agency or contractor personnel, directed at improving performance, reliability, quality, safety, and life cycle costs” (National Defense Authorization Act for Fiscal Year, 1996, §36).

The importance of owner satisfaction in the value engineering process is embedded in the following observations made by Dell’Isola (1997) from a cost estimator’s perspective. Of this process he remarks 

The VE [value engineering] process identifies opportunities to remove unnecessary costs while assuring that quality, reliability, performance, and other critical factors will meet or exceed the customer’s expectations. The improvements are the result of recommendations made by multidisciplinary teams representing all parties involved. VE is a rigorous, systematic effort to improve the value and optimize the life cycle cost of a facility. VE generates these cost improvements without sacrificing needed performance levels. (p. xvii)

With these definitions providing the foundation, the following builds the process and procedures associated with value analysis. This paper will use the terms value analysis and value engineering interchangeably, and we will consider the terms synonymous.

The Value Analysis Process

According to Adams (n.d.) “A value analysis (VA) study encompasses three major periods of activity: pre-study, the value study, and post-study. All phases and steps are performed sequentially.  As a value study progresses, new data and information may cause a “looping back” within a given phase; however, phases or steps within phases are not skipped” (¶ 1).

Planning/Pre-Study Activities

The success of a VA study is dependent upon the quality of information used by the VA team during execution of the study, similar to the concept of “garbage in, garbage out”. The primary goals of pre-study activities are to gather an abundance of credible information and obtain clear definition of the client’s goals and expected outcomes of the VA study (Adams, n.d., ¶ 2). 

The Value Study

The value study is where the primary value methodology is applied. Commonly called the VA Job Plan, the effort is composed of multiple phases: Information; Function analysis; Creativity; Evaluation; Development; Presentation; and Implementation (Adams, n.d., ¶ 14). Each phase requires a significant investment of time and effort to make an impact in the VA process.

Post-Study

Adams (n.d.) states that post-study activities include disposition (i.e., either implementation or rejection) of each VA alternative developed during the VA study. Whether or not the VA team participates in the post study activities is unique to each study and organization requirements (¶ 32).

In all cases the design professional and/or owner is responsible for the implementation. Each alternative must be independently designed and confirmed, including contractual changes if required, before its implementation into the product, project, or process (Adams, n.d., ¶ 33).

Research Methodology

The research design for this project is quasi-experimental mixed methods. A quasi-experimental approach is “a type of research approach for conducting studies in field or real-life situations where there is an active independent variable that the researcher may be able to manipulate, but the researcher cannot randomly assign subjects to comparison and experimental groups” (Gliner & Morgan, 2000, p. 420). Mixed methods research “is a type of research design in which qualitative and quantitative approaches are used in types of questions, research methods, data collection and analysis procedures, and/or inferences” (Tashakkori & Teddlie, 2003, p. 711).  

The junior-level course in which this research was done is Advanced Construction Systems, which focuses on commercial construction systems. The students were issued a set of contract documents for a $5 million addition and remodel of an existing high school which they use as the basis of the value analysis group project. The students were assigned the term project at the beginning of the semester and were told they would be working in groups of three or four. The course included two one-hour lectures and a two-hour lab per week.  There were 112 students in the four lab sections of the course. The students in two of the lab sections were asked to self-select their group members. The students in the other two labs were randomly assigned into groups by the lab instructor. The groups turned in their VA project at the end of the semester with only their fictitious company name on the project. Student names were deleted from the report so the researcher grading the projects did not know who was in each group nor was it known by the grader which groups were self-selected and which groups were randomly assigned.

After all of the group projects were graded, the scores were added to a spreadsheet which listed the groups by their selection criteria. From this spreadsheet, the average scores of the self-selected and randomly assigned groups were compared to determine if there was a difference in grade performance by type of group selection.

Once all group projects were submitted to the instructor, a researcher not involved with the course presentation or grading asked the students for volunteers to be interviewed about the project and the group selection process. Eight students who had been in self-selected groups and six students who had been in randomly assigned groups volunteered to be interviewed. The independent researcher asked the following questions:

The interviews were tape recorded and then transcribed. The transcribed interviews were coded using open, axial, and selective coding to allow themes to emerge. Patton (2002) states, “Discovery and verification mean moving back and forth between induction and deduction, between experience and reflection on experience, and between greater and lesser degrees of naturalistic inquiry” (p. 67). A constant comparative approach was used after each interview was coded to “look for key issues, recurrent events, or activities in the data that become categories of focus” (Bogdan & Biklen, 2003, p. 67). 

The promotion of validity and reliability in this research design was accomplished through multiple strategies: (a) Triangulation, which is “using multiple investigators, sources of data, or data collection methods to confirm emerging findings” (Merriam, 2002, p. 31), was included in the design. In addition to both qualitative and quantitative data being collected, multiple investigators designed the study, collected the data, and analyzed data; (b) Peer review/examination as “discussions with colleagues regarding the process of study, the congruency of emerging findings with the raw data, and tentative interpretations” (Merriam, 2002, p. 31) was accomplished with co-authors and departmental colleagues; and (c) An audit trail as “a detailed account of the methods, procedures, and decision points in carrying out the study” (Merriam, 2002, p. 31) was created to facilitate the peer review.

Results

The quantitative data collected (i.e., the scores of the group projects) indicated that there is very little difference, if any, in the results of the groups based on group selection methodology. Table 1 summarizes group and individual member grades for students on the VA project in the groups.

The results of the interviews provided greater insight into the workings of the groups, providing valuable information which will be used to improve the project in future semesters. An assumption made by the researchers that the students would be familiar with their classmates from prior classes did not materialize. As a result, it was found that those students in the self-selected groups often knew no more than one other member of their group, selecting the others out of convenience (e.g., seated next to them) or chance (e.g., only student left looking for a group). One student indicated that without knowing other people in class, the self-selected group becomes a randomly selected group. So, in essence, most of the groups in all lab sections were randomly selected. Still, themes emerged related to group workings which are helpful for instructors planning group projects in their classes.

Table 1

Project grades for self-selected and randomly assigned groups

In this study, there was virtually an even split of students who liked working in the group and students who had an unpleasant experience with their group. Those students who spoke positively about their group experience came from both self-selected and randomly assigned lab sections. Students in both groups attributed their success to “good dynamics”. Those with more positive experiences remarked that they either knew some of the members prior and knew they shared a similar work ethic (e.g., self-selected) or they found it exciting to meet and work with new people (e.g., randomly assigned). The consensus from the students interviewed, from either group, was that organization, leadership, and individual commitment to meeting deadlines within the group was a more significant factor in the success and experience of the group than was the method of team member selection.  Success seemed to be determined more, when as one student said, “everyone pulled their own weight” and took an equal share of the work.

This then lead to a major theme emerging from both groups of students--that of group members not participating evenly or the “free-rider” problem Blumenfeld et al. (1996) discuss.  This problem can occur in any type of groupwork. As one student remarked, “You get people that slack off.” As example, one student in a self-selected group stated, “I drew the short end and got two guys that didn’t want to do anything.” Another student in a self-selected group expressed frustration as the members of the group didn’t attend the lab portion of the course and also wouldn’t meet outside of lab. Similarly, a student in a randomly assigned group indicated several times that two out of the four group members did most of the work.

Several students indicated procrastination within their group.  One student was honest enough to state, “I personally could have tried a little bit harder too.” Others made comments similar to, “I feel like I did most of the work.” One student attributed this procrastination to knowing members within the group saying, “If you know each other it is easier to procrastinate.” Others said the opposite and attributed this procrastination to not knowing anyone in the group, therefore not being able to rely on them. One suggestion that several students put forth was the addition of an intermediate deliverable, such as a progress report to the instructor, as helpful for helping the group keep on task. While, in this project, students were told to visit the instructor with any problems encountered in their groups, one student remarked of the inadequacy of this saying, “But since no one was putting in much effort there was nothing to tell the instructor.”

Most of the students interviewed remarked that they didn’t feel it mattered whether their groups were self-selected or randomly assigned for this project as they knew few other students in the class. However, in general, there was a split as to their preference for member selection if they were able to self-select from classmates they knew or had worked with prior. Those preferring self-selected groups did so for a variety of reasons, including the following quotes:

A couple students made note that while they preferred to self-select their group members, this doesn’t make group projects infallible. Of this they commented, “In a self-selected group you can pick people you know will do a good job, but there are no guarantees that people will work hard” and “If groups are assigned you may know that you don’t work well with someone.  Self-selection is nice; but self-selection doesn’t always work out.” One student made note that while self-selection is preferred, “random selection is more realistic”. Another, also preferring self-selection, made a similar comment saying, “Self-selection isn’t always possible in industry.”

Other students made a case for randomly assigned groups in their courses for, in addition to its greater applicability to industry, the chance it provides to get to meet more students in the program. Remarks related to this included:

One student remarked that while knowing group members helps, randomly assigned groups may help students get to know each other. Another remarked that this might be especially useful for freshmen and sophomore students.

Some students were able to relate the challenges faced in this group assignment to the real world. One of these students stated that in college not all people participate just like on the job not all workers participate. Likewise, a majority of students remarked that the value analysis group project was a good assignment and that they learned from the experience. Comments related to the project included:

Even though most students enjoyed the project, several students indicated that more explanation about the project was needed stating, “I didn’t know how to get started” or “Other students were unclear on the project requirements”.

Since the general consensus among students is that this was a valuable assignment it will remain a course requirement. The recommendations from the students will be used to improve this group project assignment, and the method of group selection will be assessed. The way in which the assignment is introduced and explained will be modified, and an intermediate progress report will be required of all groups. An additional recommendation made by many of the students of adding a peer-evaluation which will have a greater impact on each individual’s grade will also be included in future projects.

Conclusions and Recommendations

None of the expected quantitative results were experienced in this study. It was anticipated that the scores of the self-selected groups would have a broader range since individuals with common abilities seem to group together. This would have produced very high scores and very low scores. Both the self-selected and randomly selected participants had a high score exactly the same, 102 out of a possible 100 points. It was also anticipated that the average grades of the randomly assigned groups would be higher because individuals who normally achieve at higher levels would pull the rest of the group up to the high expectations of those individuals. The findings, however, found a negligible difference in the average scores of the self-selected and randomly assigned groups. Since the primary assumption that the self-selected group members would have prior knowledge of other group members did not occur, it is difficult to attribute the findings to the original intent of the study. The independent variable, group selection methodology, was manipulated by the researchers, but since the students often did not know their group members in the self-selected groups, this quasi-experimental design failed to produce the expected results. 

The qualitative data, which was intended to help explain the quantitative data, provided meaningful results. The students’ comments during the interviews became repetitive indicating we were reaching saturation (Creswell, 1998). Reaching saturation in the qualitative data allows this aspect of the mixed method design to stand alone. Significant findings in the qualitative data analysis will facilitate modification of the assignment in an effort to improve the student learning.

Recommendations for future research would be to attempt a similar study of self-selected and randomly assigned groups, but in a class where students’ prior knowledge of their peers is guaranteed. Another future study helpful to the knowledge of group learning would be to include the other primary means of group selection, criterion-based selection, in a similar study.  Interestingly, this sort of group was referred to by some students as a viable alternative with comments such as “Having someone in the group who has experience is helpful. Survey experience levels before purposeful selection” and “Put people with experience with people with no experience”.

Additional research could be done to determine if other construction education curricula includes Value Engineering (VE) and Value Analysis (VA), and to determine if learning about VE/VA teaches or improves students’ critical thinking skills.

References

Adams, G. R. (n.d.). Value analysis services. Advantage Management Solutions. Retrieved December 30, 2005 from http://www.advantage-management.net/id2.html

Bartlett, R. L. (1995). A flip of the coin. A roll of the die: An answer to the free-rider problem in economic instruction. The Journal of Economic Education, 26 (2), 131-139.

Bogdan, R. C., & Biklen, S. K. (2003). Qualitative research for education: An introduction to theory and methods (4^th ed.). Boston, MA: Allyn and Bacon.

Cohen, E. G. (1994). Restructuring the classroom: Conditions for productive small groups. Review of Educational Research, 64 (1), 1-35.

Colbeck, C. L., Campbell, S. E., & Bjorklund, S. A. (2000). Grouping in the dark: What college students learn from group projects. The Journal of Higher Education, 71 (1), 60-83.

Creswell, J. W. (1998). Qualitative inquiry and research design: Choosing among five traditions. Thousand Oaks, CA: Sage Publications.

Fallon, C. (1980). Value analysis (2^nd ed.). Washington, DC: The Lawrence D. Miles Value Foundation.

Gliner, J. A., & Morgan, G. A. (2000). Research methods in applied settings: An integrated approach to design and analysis. Mahwah, NJ: Lawrence Erlbaum Associates.

Johnson, D. W., & Johnson, R. T. (1978). Cooperative, competitive, and individualistic learning.

Journal of Research and Development in Education, 12, 3-15.

Johnson, D. W., Johnson, R. T., & Holubec, E. J. (1986). Circles of learning. Edina, MN:

Interaction.

Johnson, D. W., Johnson, R. T., & Smith, K. A. (1998, July/August). Cooperative learning returns to college: What evidence is there that it works? Change, 27-35.

Merriam, S. B. (2002). Assessing and evaluating qualitative research. In S. B. Merriam (Ed.), Qualitative research in practice: Examples for discussion and analysis (pp. 18-33). San Francisco, CA: Jossey-Bass.

Meyer, J. (1994, November 22).  Teaching through teams in communication courses: Letting

structuration happen. Paper presented at the Annual Meeting of the Speech Communication Association (80^th, New Orleans, LA, November 19-22, 1994).

Miles, L. D. (1989). Techniques of value analysis and engineering (3^rd ed). Washington, DC: The Lawrence D. Miles Value Foundation.

National Defense Authorization Act for Fiscal Year 1996. (1996, February 10). U.S. Public Law 104-106. 104^th Congress.

Patton, M. Q. (2002). Qualitative research & evaluation methods (3^rd ed.). Thousand Oaks, CA: Sage Publications.

Rau, W., & Heyl, B. S. (1990). Humanizing the college classroom: Collaborative learning and social organization among students. Teaching Sociology, 18 (2), 141-155.

Slavin, R. (1983). When does cooperative learning increase student achievement? Psychological Bulletin, 94, 429-445.

Smith, B. L., & MacGregor, J. T. (1992). What is collaborative learning? In A. Goodsell, M. Maher, V. Tinto, B. L. Smith, & J. T. MacGregor (Eds.), Collaborative learning: A sourcebook for higher education. University Park, PA: National Center on Postsecondary Teaching, Learning & Assessment, The Pennsylvania State University.

Sanvido, V., Grobler, F., Parfitt, K., Guvenis, M., & Coyle, M. (1992). Critical success factors for construction projects. Journal of Construction Engineering and Management, 118 (1), 94-111.

Springer, L., Stanne, M. E., & Donovan, S. S. (1999). Effects of small-group learning on undergraduates in science, mathematics, engineering, and technology: A meta-analysis. Review of Educational Research, 69 (1), 21-51.

Tashakkori, A., & Teddlie, C. (Eds.). (2003). Handbook of mixed methods in social & behavioral research. Thousand Oaks, CA: Sage Publications.