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Volume 34, Number 2
Winter 1997

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Effectiveness of Cognitive Apprenticeship Instructional Methods in College Automotive Technology Classrooms

Joseph R. Cash
Southern Illinois University at Carbondale
Michael B. Behrmann
Southern Illinois University at Carbondale
Ronald W. Stadt
Southern Illinois University at Carbondale
Harry M. Daniels
University of Florida, Gainesville

Apprenticeship, a traditional method of teaching trades through modeling, coaching, and fading, is a natural style of learning for many people (Collins, Brown, & Newman, 1989). Historically, brief references to apprenticeship appeared as early as the Babylonian Code of Hammurabi (2100 B.C.). The apprenticeship approach is still important, as evidenced by the approximately 100,000 new apprentices registered each year in the United States (U.S. Department of Labor, 1992). Renewed interest in this method of workforce preparation suggests that apprenticeship methods will be an important response to the School-to-Work Opportunities Act of 1994 (U.S. Department of Labor and Education, 1994).

According to Johnston and Parker (1987), 41% of new jobs in 2000 A.D. will require high-level reasoning skills, compared to 24% in 1987. Similarly, the Secretary's Commission on Achievement of Necessary Skills (SCANS) 1991 report identified problem-solving as one of the five competencies of effective workers (U.S. Department of Labor, 1990). Teaching high-level reasoning and diagnostic skills in college-level technical programs is critical to serving the workforce (U.S. Department of Education, 1995).

Berryman (1991a & 1991b) has identified instructional models designed to prepare workers for changing workplaces that are characterized by critical and analytical skills. In these contexts, traditional apprenticeship methodology has been transformed into a new educational paradigm. Berryman advocated the development of effective learning environments based on cognitive apprenticeship models for developing advanced-level reasoning and problem-solving skills.

The term cognitive apprenticeship was coined by Collins et al. (1989), who proposed that contemporary classroom instructional methods be combined with the concept of apprenticeship. In their landmark study, the age-old apprenticeship learning principles (modeling, coaching, fading) of on-the-job training were combined with the modern pedagogical practice of engaging students with problems in the context of real-world experiences. Their classroom methodology incorporated contextual learning, which is a natural element of apprenticeship that embeds practical application of classroom theory. Other researchers have identified cognitive apprenticeship instruction as a viable means of modernizing technical education (Brandt, Farmer, & Buckmaster, 1993; Raizen, 1989; Wilson & Cole, 1991). Differences between traditional apprenticeship and cognitive apprenticeship have been defined by Collins et al. (1989) (see Table 1).

The effects of cognitive apprenticeship under various conditions have been studied by Duncan (1996), Elliott (1994), and Fischbach (1993). Duncan reported that cognitive apprenticeship instructional methods were significantly more effective than traditional methods in the area of writing skills at the college level. Elliott's research focused on understanding teacher decision-making in a cognitive apprenticeship setting. Fischbach reported positive effects of using cognitive apprenticeship techniques to enhance problem-solving skills of community college technical mathematics students.

Studies of cognitive apprenticeship methods in automotive technology have not been reported in the literature. Given the rapid advance of automotive technologies and the associated demands that these changes are placing on technician certification, education, and training structures, an examination of the use of this

Table 1
Differences Between Traditional Apprenticeship and Cognitive Apprenticeship

Traditional Apprenticeship Cognitive Apprenticeship

Simple tasks Complex tasks
Physical skills and processes Cognitive and metacognitive processes
One-on-one learning in the workplace Learning with several students set
in the classroom and laboratory
Tasks performed by observation Tasks and processes performed by
Learning by doing physical tasks Learning by externalizing thought
processes in diagnosing problems
Learning from modeling, coaching,
and fading of performance
Learning from modeling, coaching,
fading, articulation, reflection, and
exploration of ideas
Job determined by tasks Learning determined by goals

methodology at the college level appeared useful. Revolutionizing factors such as computerization of vehicle functions, emission-control equipment, and customer satisfaction are setting new standards for technicians. It is appropriate to examine the methods and structures employed to address these changes.

Purpose of the Study

The purpose of this study was to determine whether cognitive apprenticeship instructional methods represent an improved method for helping college automotive technology students learn information and diagnose problems. More specifically, the purpose was to compare the effectiveness of cognitive apprenticeship instruction with the traditional lecture paradigm in automobile air conditioning classes. The following research questions were framed to examine the differences between the two instructional methods.

  1. Are there significant pretest, posttest, and retention differences between students receiving cognitive apprenticeship instruction and those receiving traditional lecture instruction in learning air conditioning information?
  2. Are there significant pretest, posttest, and retention differences between students receiving cognitive apprenticeship instruction and those receiving traditional lecture instruction in learning troubleshooting procedures?
  3. Are there significant pretest, posttest, and retention differences between students receiving cognitive apprenticeship instruction and those receiving traditional lecture instruction in learning the application of diagnostic skills on automobiles?

Background and Context

The term cognitive apprenticeship focuses on the development of learning and skills beyond the apprehension of subject matter content (e.g., troubleshooting procedures and applications of diagnostic skills used in workplaces) (Berryman, 1987; Cahill, 1993; Kurfiss, 1988; Lewis, 1993; McPeck, 1990; Norton, 1991; Schmidt, Finch, & Faulkne, 1992; U.S. Department of Labor, 1992). Cognitive apprenticeships include four essential features: content, methods, sequencing, and sociology (Collins et al., 1989). These four features contain descriptive elements that collectively comprise the cognitive apprenticeship methodology. In this study, the Collins et al. framework was adapted for use with teaching automotive air conditioning skills and knowledge (See Table 2).

Ideal training situations involve combining these cognitive apprenticeship features and elements into classroom instruction in various configurations (Berryman, 1992; Collins et al., 1989). Cognitive apprenticeships are adaptable to many traditional educational delivery systems including lecture situations. Various features and elements, such as contextual learning, may be used to develop cognitive apprenticeship models in accordance with facilities, students, fields of study, and teacher skills. Many educators have discovered that sociological elements of contextual learning are germane to teaching higher levels of information, troubleshooting, and diagnosis (Berryman, 1991a, 1991b; Brookfield, 1987; Flannery, 1993; Grubb, Davis, Lum, Plihal, & Morgaine, 1991; Halasz, 1988; Maynard, 1991; Orey & Nelson, 1994; Rosenbaum, Stern, Agnes, Hamilton, Berryman, & Kazis, 1992).

Table 2
Cognitive Apprenticeship Characteristics for Automotive Air Conditioning

  1. Domain Knowledge: Conceptual and factual knowledge and procedures associated with air conditioning.
  2. Tricks of the trade: Heuristic strategies, diagnostic skills experts learn from experience.
  3. Cognitive management: Thinking skills, such as goal setting and strategic planning.
  4. Learning strategies: Strategies for learning, such as associating air conditioning systems operation and service to other systems on the automobile.
  1. Modeling: Expert performs a task so that student can observe and build conceptual model.
  2. Coaching: Expert assists students by giving hints and support.
  3. Fading: Expert gradually removes support until students are on their own.
  4. Articulation: Get students to articulate air conditioning information and troubleshooting procedures.
  5. Reflection: Expert lets students compare their own diagnostic skills with expert.
  6. Exploration: Expert provides devices that push students into a mode of troubleshooting on their own.
  1. Increasing complexity: Tasks are sequenced to require more and more skills.
  2. Increasing diversity: Tasks required a wider and wider variety of strategies and skills.
  3. Global skills first: Staging learning so that students first develop an understanding for air conditioning.
  1. Contextual (Situated) learning: Students learn information and apply diagnostic skills on real air conditioning systems.
  2. Practitioners teaching: Certified technicians are part of the learning environment.
  3. Intrinsic motivation: The students' willingness to do quality performance.
  4. Cooperative learning: Students working together in troubleshooting air conditioning systems.
  5. Competitive learning: Giving students the same tasks and comparing their performances.

Troubleshooting and Applying Diagnostic Skills in Automotive Technology

Resnick, Bill, Lesgold, and Lere (1991) reported that employers want community college graduates who are adaptable and can solve problems in workplaces. In automotive technology, technicians are expected to be capable of doing more than replacing parts. They must be able to apply diagnostic skills to solve problems (Heyman & Daly, 1992).

These skills have become increasingly important as automotive technologies become increasingly more complex. The challenge for technical educators is to balance the time and emphasis given to current and emerging technologies in a context where contact and credit hours remain fixed.

Research Method and Procedures

A quasi-experimental design was used to compare cognitive apprenticeship instructional methods with the traditional lecture method when teaching students how to solve automotive problems. Specifically, the study focused on technology students' acquisition of air conditioning information, knowledge of troubleshooting procedures, and application of diagnostic skills on automobiles. The sample consisted of students in two automotive classes at the College of Technical Careers at Southern Illinois University at Carbondale who were enrolled in a unit in basic air conditioning. Because of established scheduling, intact classes were used.


The experimental treatment consisted of a series of laboratory experiences specifically designed to be consistent with the cognitive apprenticeship characteristics described in Table 2. The control group received the same content primarily through presentations of theory in a lecture setting followed by some laboratory experiences. One of the distinct contrasts between the experimental and control group methods had to do with the sequencing (Table 2) of theory and application. In the cognitive apprenticeship approach, the students received a brief overview of air conditioning systems prior to moving into intensive, laboratory-based experiences. This overview was designed to provide a broad contextual understanding within which meaningful exploration and constructivist learning could occur. By contrast, the learning sequence for the control group was conducted by concentrating on the delivery of air-conditioning theory and concepts in a lecture-discussion format followed by laboratory applications.

A second major point of contrast between the experimental and control group treatments was the teaching method employed. The control group method consisted of lecture-intensive presentations of theory with generous use of overhead transparencies. A primary focus of the learning process was on component and function identification. The instructor for the control group had over 25 years of experience and had consistently received high student evaluations. The experimental group methodology was specifically designed to employ the cognitive apprenticeship elements described in the methodology section of Table 2. Each laboratory learning activity was deliberately sequenced through modeling, coaching, and fading. This sequencing pattern was followed for all three areas of instruction, including acquisition of air conditioning information (AC), knowledge of troubleshooting procedures (TS), and application of diagnostic skills (DS) as they relate to automobiles.

Also consistent with the cognitive apprenticeship methodology approach (as defined in this study), students in the experimental group were systematically encouraged to engage in articulation and reflection during and following each laboratory exercise. For example, as students identified component parts and their locations, they were asked to visualize the functions of the parts and follow recommended troubleshooting procedures. Throughout this process, they were encouraged to verbalize their thoughts about how the components function in a system as well as to indicate what would occur if various components were to malfunction. Also, since the laboratory activities for the experimental group were conducted in a team-based, cooperative learning manner, students actively interacted with one another both within and across teams to reflect on probable causes of system failure (diagnosis). Regular reflective debriefing sessions were conducted following each exercise. Then students were asked to solve problems with systems which allowed them to apply and develop diagnostic skills. If assistance was needed, the instructor coached them through the several checks. After this practice, the instructor directed students to another car and asked them to apply diagnostic skills. During this period, the instructor faded from giving assistance, except for helping students for safety purposes and to improve skills.

The instructor of the experimental group was also experienced and had received high student ratings. He had recently been involved in training provided by a top-three American automotive company, focused on the use of strategy-based diagnostic instructional methods. This training was highly consistent with the cognitive apprenticeship methodology used in this study. The instructors of both treatment groups were certified in automotive air conditioning by Automotive Service Excellence.


The instrument format was a multiple choice test and consisted of three major areas: (a) air conditioning information (AC), (b) troubleshooting procedures (TS), and (c) application of diagnostic skills (DS). The application-of-diagnostic-skills questions directed students to diagnose various components and select alternative multiple choice responses. The instrument contained a total of 25 questions. Ten dealt with basic air conditioning information; ten dealt with troubleshooting procedures; and five related to the application of diagnostic skills on automobiles using performance assessment.

Content validity for the data collection instrument was enhanced by using the Automotive Service Excellence certification preparation tests as a guide for developing the 25 questions (Erjavec, 1992). Questions about air conditioning were selected to match the objectives of the lesson. Two college automotive air conditioning professors assisted in the construction of the instrument. This was done to ensure content validity (Allen & Yen, 1979). Additionally, internal consistency reliability coefficients (Cronbach Alpha) were computed for each subscale after Time 1 administration of the questionnaire.


A repeated measures ANOVA was used to test differences between groups (experimental vs. control), across time (Time 1 vs. Time 2 vs. Time 3), and group-by-time interaction effects. In addition to tests of the main effects (group and time) and interaction effects, post-hoc contrasts for each subtest were also calculated. A t test was used to examine pre-treatment differences (Time 1) between the 3 control group students who had previous air conditioning training and the other 11 control group students.

The Time 1 test was administered the day before the four-hour air conditioning lesson. Time 2 test measures were collected immediately after the treatment and Time 3 four weeks after the treatment. An alternate test form was used for both Time 2 and Time 3.


The sample included 28 college automotive technology majors ranging in age from 19 to 49 years (M = 23.75). Four percent were freshmen, 36% were sophomores, 14% were juniors, and 46% were seniors. Although both groups had a mixture of grade levels, the experimental group was composed mostly of sophomores and the control group was composed mostly of seniors. Three students in the control group had received air conditioning training during the four years prior to the study.

All students were enrolled in the automotive air conditioning class during the second eight-week session of the 1996 spring semester. Because one control group student dropped out and one experimental group student did not complete the Time 1 test, the total number of participants was 26 (14 in the control group and 12 in the experimental group).


The students in both the experimental and control groups completed the instrument (Automotive Basic Air Conditioning Information-AC, Troubleshooting Procedures-TS, and Basic Application of Diagnostic Skills on the Automobile-DS) for each of the three time periods. Internal consistency reliability coefficients (Cronbach Alphas) were computed for each subscale. Coefficients were AC = .82, TS = .72, and DS = .69. Nunnally (1979) reported that .70 is adequate reliability for research purposes. Subscales AC and TS exceeded the level of .70 reliability. Subscale DS was only one hundredth of a point below Nunnally's recommendation when the study was completed. T tests were calculated between the three control students who had previous AC training and the other 11 control students measured at Time 1. Results were significant for AC and not significant for TS or DS.

Air Conditioning Information Findings and Discussion

To test for AC differences between groups over three time periods, an ANOVA with repeated measures on one factor (time) was computed (see Table 3).

Table 3
ANOVA Summary Table for Air Conditioning Information

Source df SS MS F p

Between Subjects 25 119.38  
Group 1 .10 .10 .02 .8890
Residual Between 24 119.28 4.97  
Within Subjects 52 688.21  
Time 2 491.44 245.72 73.11 .0001 *
Group x Time Interaction 2 35.44 17.72 5.27 .0085*
Residual Within 48 161.33 3.36  
Total 77 807.69  
Group: Time 1 to Time 2 1 62.86 62.86 7.45 .0117 *
Group: Time 2 to Time 3 1 2.29 2.29 1.17 .2908
Group: Time 1 to Time 3 1 41.16 41.16 4.21 .0511

* Significant at > .05 alpha.

The non-significant finding for group main effects, F(1, 24) = .02, p = .8890, was anticipated since all participants were in the same academic program and were enrolled prior to placement into experimental or control groups. The elevated mean score for the control group at Time 1 may have been due to the fact that three students had received air conditioning training during the previous four years.

The significant test for time main effects, F(2, 48) = 73.11, p = .0001, is important because it demonstrates that both groups benefited from instruction and that the benefits endured after instruction had ended. The observed disordinal interaction between Time 1 and Time 2, F(2, 48) = 5.27, p = .0085, provided additional verification of the power of the experimental treatment (see Figure 1). The modest elevation of pretest control group scores (due to the presence of three students with previous training) further strengthens the experimental group treatment results.

Pre-planned contrasts of group differences across time demonstrated that students receiving the experimental treatment scored significantly higher, F(1, 24) = 7.45, p = .0117, at Time 2 than did their control group counterparts (see Figure 1 and Table 4). Thus, cognitive apprenticeship instructional methods were significantly more effective across instruction to Time 2. This is consistent with findings reported by Duncan (1996), Fischbach (1993), and Elliot (1994). Further, the magnitude of the difference in scores observed between Time 1 and Time 2 was maintained between Time 2 and Time 3, despite a slight (but non-significant) decline in the mean score for the experimental group, F(1, 24) = 1.17, p = .2908. No significant differences were detected with the other planned comparisons.

Figure 1
Air Conditioning Information

Air Conditioning Info

Table 4
Air Conditioning Information Mean Scores

  T1 T2 T3

Control Group 4.29 3.29 8.00 1.96 8.43 1.40
Experimental Group 2.33 1.97 9.17 .83 9.00 1.28
Total 3.39 2.86 8.54 1.63 8.69 1.35

The observed decline in experimental group Time 3 mean scores could be explained by a peaking of performance at Time 2. Given the small number of items on the AC information scale, it would have been difficult for experimental group participants to demonstrate additional improvement. The limited number of items created a ceiling effect for all students and subjects in the experimental group (these subjects appeared to be more susceptible to this ceiling effect than their counterparts). These students had greater opportunity to score higher at Time 3 because they did not score as well at Time 2. Finally, the confluence of AC information scores at Time 3 may be an example of regression toward the grand mean (M = 8.69). In summary, Figure 1 depicts the disordinal interaction of the distribution of air conditioning information mean scores for groups over time.

Troubleshooting Procedures Findings and Discussion

The findings for the troubleshooting procedure component of the study were similar to those of the air-conditioning information category. The main group effects result was non significant, F(1,24) = .03, p = .8609, indicating that group mean scores (when averaged over time) did not differ from one group to the other. However, there was a significant time main effect, F(2,48) = 75.01, p = .0001, which indicates that students' scores changed over time. Gains in scores were also retained over time (see Table 5).

The group-by-time interaction was significant, F(2, 48) = 12.41, p = .0001, indicating that the experimental method accelerated students' acquisition of TS procedures when compared to the control group. Preplanned contrasts to test for group differences across time demonstrated that students receiving the experimental treatment scored significantly higher at Time 2 than did their control group counterparts, F(1, 24) = 20.24, p = .0001. The disordinal interaction between Time 1 to Time 2 demonstrated the strength of the experimental instructional methods on TS procedures (see Figure 2 and Table 6).

The Time 2 to Time 3 disordinal interaction may be an artifact of a natural progression toward the grand mean over time. Also, the observed decline in experimental mean scores during this period could be attributed to a natural decline in retention over time as well as to a ceiling effect at Time 2. The increase in mean scores for the control group between Time 2 and Time 3 is more difficult to explain. It is conceivable that the posttest served as a treatment for the control group, thus enhancing their performance at Time 3.

Table 5
ANOVA Summary Table for Troubleshooting Procedures

Source df SS MS F p

Between Subjects 25 83.49  
Group 1 .12 .12 .03 .8609
Residual Between 24 93.37 3.89  
Within Subjects 52 585.60  
Time 2 394.25 197.12 75.01 .0001*
Group x Time Interaction 2 65.22 32.61 12.41 .0001*
Residual Within 48 126.13 2.63  
Total 77 679.09  
Group: Time 1 to Time 2 1 130.15 130.15 20.24 .0001*
Group: Time 2 to Time 3 1 38.11 38.11 11.52 .0024*
Group: Time 1 to Time 3 1 27.41 27.41 4.55 .0434*

* Significant at >.05 alpha.

Figure 2
Troubleshooting procedures

Troubleshooting procedures

Table 6
Troubleshooting Mean Scores

  T1 T2 T3

Control Group 4.43 2.59 6.86 1.61 8.29 1.59
Experimental Group 2.17 1.53 9.08 1.31 8.08 1.38
Total 3.38 2.42 7.88 1.84 8.19 1.47

This pattern of findings supports the effectiveness of the cognitive apprenticeship approach throughout the troubleshooting component of the instruction when compared with the traditional instructional approach. The retention results, however, do not support the long-term superiority of the cognitive apprenticeship approach over traditional methods.

Application of Diagnostic Skills on Automobiles Findings and Discussion

Significant main effects were obtained for both independent variables (group and time). The significant group effect, F(1,24) = 11.58, p = .0023, indicates the enhanced effectiveness of the experimental treatment. The scores of both groups improved significantly, up to the first posttest, after which moderate changes were observed in both groups. In contrast to the scores on the AC and TS scales, improvement in diagnostic skills was sustained over time. A significant group-by-time interaction was also obtained, F(2,48) = 6.83, p = .0024 (See Table 7).

Although cognitive apprenticeship instruction was effective, there were no significant differences across the first two planned comparisons (Time 1 to Time 2 and Time 2 to Time 3). It is important to note that the control group mean scores were significantly higher on the pretest than were the experimental group scores. This initial differential could have prevented score changes of appreciable magnitude during subsequent testing. However, it should also be noted that the t test calculated between the 3 control students who had previous AC training and the other 11 control students measured at Time 1 was not significant.

Table 7
ANOVA Summary Table for Application of Diagnostic Skills on Automobiles

Source df SS MS F p

Between Subjects 25 48.32  
Group 1 15.73 15.73 11.58 .0023*
Residual Between 24 32.59 1.36  
Within Subjects 52 135.30  
Time 2 63.12 31.56 26.97 .0001*
Residual Witin 48 56.18 1.17  
Group: Time 1 to Time 2 1 10.88 10.88 3.85 .0614
Group: Time 2 to Time 3 1 5.43 5.43 3.07 .0924
Group: Time 1 to Time 3 1 31.68 31.68 13.03 .0014*

* Significant at >.05 alpha.

Figure 3
Application of diagnostic skills on automobiles

The Time 1 to Time 3 contrast yielded a significant difference favoring the experimental group, F(1, 24) = 13.03, p = .0014. One explanation of this result is that variance of control group scores over time was limited, whereas experimental group gains were larger (Figure 3 and Table 8). Thus, cognitive apprenticeship instructional methods employed in the experimental group achieved significantly higher mean scores in the application of diagnostic skills on automobiles (solving problems) than the control group. Figure 3 depicts the disordinal interaction of the distribution of diagnostic skills mean scores of groups over time.

Table 8
Application of diagnostic skills on automobiles mean scores

  T1 T2 T3

Control Group 3.07 1.49 4.36 0.84 3.86 0.95
Experimental Group 1.00 1.04 3.58 1.31 4.00 0.85
Total 2.11 1.66 4.00 1.13 3.92 0.89

Conclusions and Recommendations

The results of this exploratory study suggest promise for the use of cognitive apprenticeship methodologies in technical areas. When compared with the traditional, lecture-based control group methodology, the cognitive apprenticeship treatment results were significantly more effective for the acquisition of air conditioning information, knowledge of troubleshooting procedures, and application of diagnostic skills. While the cognitive apprenticeship method was more effective over the duration of the instructional period, the longer term retention effects were non-conclusive.

Implications for Technical Training and Industrial-Technical Teacher Education

In many respects, the principles of cognitive apprenticeship are familiar to technical educators. Techniques such as modeling, coaching, fading, reflection, articulation, and situated learning are well understood and have been effectively used in technically-oriented, laboratory-based courses for years. Industrial and technical educators recognize these as effective instructional practice. What is somewhat new to many industrial and technical educators is the recent emphasis on constructivist instructional practices. Specifically, the sequencing of delivery has traditionally involved establishing a base factual knowledge of components, theory, etc. (typically delivered through the lecture method) prior to application in laboratory settings. The cognitive apprenticeship approach (as defined and applied in this study) reverses this sequence by beginning with the development of a broad understanding of systems as a base for exploration and learning.

The sociological aspects of cognitive apprenticeships such as situated learning, cooperative learning, and verbalization are also important. Preservice and in-service postsecondary and secondary teachers should study in laboratories which are reflective of workplace practice (i.e., construction, manufacturing, and service). Methodologies as well as content should be as realistic as possible. Cognitive apprenticeship paradigms may be used to simulate planning, production, and troubleshooting situations in technical and trade programs and should be part of the array of methods practiced in courses in pedagogy. Work simulations present excellent opportunities to engage future teachers in problem-solving much like actual employment.

The utility of cognitive apprenticeship may be even greater in technology education. Because of the emphasis on exploration in pre-specialized learning about the world of work, technology education should involve students in the kinds of tasks, projects, etc., which workers in various parts of the industrial spectrum and along the responsibility continuum deal with daily. Certainly, pre-service teachers should be well-prepared to engage students in individual and group problem-solving situations. Further, cognitive apprenticeship principles are appropriate to informing early learners about various technologies and how they are used in varieties of workplaces. Problems in robotics, materials transfer, packaging, power distribution, etc., are only a few of the myriad applications of the cognitive apprenticeship modality.

Limitations and Recommendations

Some limitations of the study should be noted. These include the presence of three previously instructed students in the control group which yielded unequal groups at the beginning of the treatment period. Also, because this was an exploratory study, the degree of formalization of the cognitive apprenticeship treatment methodology was somewhat limited. Future studies should formalize these cognitive apprenticeship instructional methods (as identified in Table 2) and then apply them within an expanded delivery system (additional teachers with larger numbers of students). It is also recommended that studies be conducted across an expanded range of technical content areas.


Cash and Behrmann are Assistant Professors, and Stadt is Professor at Southern Illinois University, Carbondale, Illinois.

Daniels is Department Chair, University of Florida, Gainesville, Florida.


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Reference Citation: Cash, J. R., Behrmann, M. B., Stadt, R. W., and McDaniels, H (1996). Effectiveness of cognitive apprenticeship instructional methods in college automotive technology classrooms. Journal of Industrial Teacher Education, 34(2), 29-49.

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