Abstract

The primary purpose of this study was to identify the preferred productivity and learning style preferences of beginning and experienced career and technical education teachers in West Virginia. Non-probability samples (n = 59) were used to provide data for this study. The data collection instrument was the Productivity Environmental Preference Survey (PEPS). The target population consisted of beginning and experienced health occupations, and trade and industrial education teachers. The respondents were 54.2% female and 45.8% male. Three of the independent variables accounted for significant and positive relationships with the tactile subscale. Beginning teachers (with a standard score of 60 or more) were more likely to maximize their learning and productivity through emotional, sociological, and physical elements. Productivity and learning style preferences of experienced teachers (with a standard score of 60 or more), were more likely to be influenced by physical elements in the learning environment. Overall, findings of this study suggest that career and technical education teachers (with a standard score of 60 or more) had strong preferences for mobility, structure, tactile, and authority figures present. Career and technical education teachers (with a standard score of 40 or less) were less likely to have a preference to learn in several ways, preferred cool temperatures, and had a low preference for visual learning. Individuals responsible for designing learning and working environments for learners need to design a paradigm that is flexible to meet individual preferences for optimum learning and productivity.

Introduction

Recognizing that adults learn differently is not a new idea (Fizzell, 1984). In the early 20th century, researchers examined how to increase learning retention. It was realized that learners were using different informational processing strategies, which were cognitive strategies, to assimilate knowledge and different modes of perceiving and remembering information (Reiff, 1992). Researchers recognized that learners were not only assimilating information through cognitive styles, learners were also using affective and physiological approaches to learn. Since this recognition, researchers have been trying to pinpoint unique processes of learning, making the field of learning styles intensely rich (Cambiano, De Vore, & Denny, 2000).

Thies (1979) defined learning style as a biological and developmentally imposed set of personal characteristics that make a teaching method effective for some and ineffective for others. An instructional research model by Keefe and Monk (1988) viewed learning style as an umbrella term, which encompasses cognitive, affective, and physiological/environmental dimensions.

Many environmental factors in the classroom can affect a person’s ability to concentrate and to absorb and retain information. The preferred mode of imparting knowledge is often reflective of an educator’s own cognitive preference. There is a commonly ascribed belief that, “Teachers teach the way they learned” (Dunn & Dunn, 1979).

While it is recognized that a dominant cognitive style preference for receiving and processing information is evident, the learner’s use of a particular learning mode may be situational, this is a characteristic of the most adept learner. The ability to utilize a combination of styles is essential as the learner attempts to adapt to the variety of teaching styles encountered during the course of educational attainment (Sutton, 1999).

Purpose and Objectives

The primary purpose of this study was to identify the preferred productivity and learning style preferences of beginning and experienced career and technical education teachers. The specific research objectives were as follows:

  1. To identify productivity and learning style preferences of beginning and experienced career and technical education teachers.
  2. To identify productivity and learning style preferences of experienced career and technical education teachers.
  3. To describe the productivity and learning style preferences by gender.
  4. To determine the relationship between productivity/learning style preferences and selected variables (program area, gender, education status, career and technical education teachers).

Limitations of the Study

The findings of this study may not be representative of all beginning and experienced health occupations, and trade and industrial education teachers. Caution should be exercised when generalizing the findings of this study to career and technical education teachers in general.

Theoretical Base and Related Literature

Numerous theories exist to explain how persons differ in the ways they characteristically learn. Some theories deal with cognitive processes; others focus on personality variables or surface preferences about the circumstances of learning. There seems to be little cohesiveness among theories, and questions have been raised about psychometric properties of instruments. Nevertheless, there seems to be potential benefit from understanding adult learners – and having them understand themselves – in terms of differences and not deficiencies (Price, 1996).

Literature related to four cognitive style and eight learning style theories was analyzed (Bonham, 1987) to find differences and similarities among theories and to search for issues that exist in relation to individual theories, groups of theories, and the field as a whole. Each style was reviewed by using a 16-point outline dealing with theory, instruments and special issues. Overviews are provided for the field of cognitive styles and the field of learning styles, and include discussion of the terms “cognitive style” and “learning style” (Price, 1996).

Productivity style theorizes that each individual has a biological and developmental set of learning characteristics that are unique. Productivity will improve when corporate organization training and instruction are provided in a manner that capitalizes on each individual’s learning preferences. This theory is based on the generally accepted concept that individual students at every age level differs in how they learn new and difficult information. The concept of individual differences is well established in the psychological and educational literature (Good and Brophy, 1986) and has been corroborated by the extensive research conducted with this model at more than 60 institutions of higher education in the United States (Price, 1996).

Gender Differences

The extant literature is not comprehensive relative to examining gender differences and cognitive style. Philbin, Meir, Huffman, and Boverie (1995) investigated whether the differences between males and females extend to cognitive styles. Using Kolb’s Learning Style Inventory based on active experimentation (Accommodators-doing, Divergers-watching, Convergers-thinking and, Assimilators-organizing), they concluded that significant differences exist in the learning styles between genders.

A one-way analysis of variance procedure was used to determine if there were significant differences between the productivity styles of males and females at the undergraduate level. A total 98 men and 124 women comprised the population. There were significant differences on several of the variables. Women preferred more light, a warmer environment, structured environment, and kinesthetic learning (Price, 1996).

Learning Styles and the Adult Learner

In much of learning style research, researchers have focused on learning styles in children. Yet, learning styles are just as critical to adults. According to Cross (1981), adult learning approaches are not one – dimensional. She states that one of the foundations for adult learning is life experience. Knowles (1973) notes these characteristics of adult learners: self-directed, centered on solving the problem at hand, focused on the application of the material being presented, and involved in their life experiences. Some adults, based on past learning experiences, have insight into their own learning preferences (Aronson, Hansen, & Nerney, 1996). It is important for adult learners to understand how they can use learning styles to their advantage. Knowing the style of one’s learning can provide connections between teaching strategies and learning process (Hewitt, 1995).

Learning Styles of Selected Vocational Education Teachers

Although research has not produced conclusive evidence about learning styles, there is information about learning conditions and cognitive learning styles that can provide some insight into learning styles (Gordon, 1998). Most of the learning style research (Cano, Garton, & Raven, 1992a, 1992b; Garton, 1993; Torres, 1993) done by members of the vocational education profession involved assessment of the field-dependence/independence psychological dimension. This dimension relates to global vs. analytical perceiving and the ability to perceive items without being influenced by the surrounding field (Chinien & Boutin, 1993).

A study reported by Pithers (2000) used the standardized Group Embedded Figures Test to assess field dependence-field independence among groups of vocational education teachers of varied ages and teaching backgrounds. The sample consisted of 170 volunteers who were vocational education teachers and trainers in the process of completing the requirements for either a diploma or a bachelor’s degree at a university in Australia. Overall, it was found that the sample was “moderately” field independent. The study determined that a degree of field independence is an important consideration in vocational learning because people who are more analytic appear to be able to more effectively use their differentiation and analytical skills in problem solving. The study posits that while in the short term there is a positive benefit of a match between teacher and learner field dependence-field independence, learning styles may be able to be modified. Cumulative research evidence on field dependence-field independence suggests that matching teacher and learner cognitive styles has limits, but can be used to identify varied teaching methods. Both learners and teachers should develop a flexible approach to cognitive style attitudes and behavior (Pithers, 2002).

Fifty preservice vocational teachers completed Gregorc Style Delineator and received voice-input and dictation training. No differences appeared in performance or attitude for people with different learning styles. Students with concrete sequential learning styles and high attitudes toward technology had higher scores, those with low attitudes had the poorest scores (Fournier & Schmidt, 1995).

Tennessee secondary business teachers who served as student organization advisors (n=60) had the following learning style preferences: organization, detail, people, and direct experience; and they had the following teaching style preferences: organization, authority, people, and direct experience. There was significant disparity between learning style and teaching style; 61% do not teach the way they prefer to learn. No relationships were found among teaching style, learning style, experience, and education (Ladd, 1995).

Georgia secondary business teachers (n=25) and business teacher educators (n=22) completed Canfield’s Instructional Styles Inventory and Learning Styles Inventory. Both groups preferred well-organized and logical learning situations, working with people, and hands-on activities. Both preferred teaching in an organized, logical manner, using strategies in which students work together. (Stitt-Gohdes, Crews, & McCannon, 1999).

One of the four domains of learning style theories that encompasses all aspects of the learning environment is the physiological style. It takes into account if a person is tactile, kinesthetic, visual, or auditory. Dunn (1984) found that learning styles are not affected by just one aspect of the learning environment. He contends that learning style depends on a person’s environmental, psychological, physical, emotional, and sociological characteristics; therefore, for the purpose of this study, Dunn’s learning model was used as the theoretical framework. The results of this study could be useful to the body of knowledge regarding career and technical education teachers’ productivity and learning style preferences.

Methodology

Population and Subjects

The target population consisted of beginning and experienced health occupations, and trade and industrial education teachers employed by West Virginia Department of Education during the 2002-2003 school year. Convenience sampling was used to select thirty-nine (n=39) beginning health occupations and trade industrial education teachers. Judgment sampling was used to select twenty (n=20) experienced health occupations and trade and industrial education teachers from eight vocational technical centers. Judgment sampling is a procedure in which a researcher makes a judgment that a convenience sample might be similar enough to a random sample that it could make sense to use statistical procedures designed for use on random samples. Selecting a sample according to the researcher’s judgment of its representativeness is recommended only when a probability sample is impossible or highly impractical (Vogt, 1999).

Instrumentation

The PEPS was developed by identifying the variables that describe the way individuals prefer to learn or work. Good or bad productivity styles do not exist. Items were designed to assess individual preferences in each of the areas. The responses to those items were analyzed using a factor analysis procedure. The instrument then was revised and administered to a non-random sample of 589 adults from several states and from various academic and industrial settings. The results were factor analyzed using principle components with unrelated factors as the basis for the analysis (Price, 1996). The PEPS (100 items) yield scores in 20 areas.

The standard score ranges from 20 to 80 with a mean of 50 and a standard deviation of 10. The standard score is based on a random sample of 1000 subjects from the national date base who have taken PEPS. Individuals having a standard score of 40 or less, or 60 or more find that variable important when they study or work. Individuals having scores that fall between 40 and 60 are varied with respect to how much that variable is important to them. (Price, 1996)

Ninety percent of the reliabilities (Table 1) are equal to or greater than .60. The areas with the highest include: sound, light, temperature, design, motivation, persistence, responsible (conforming), structure, learning alone/peer oriented, several ways, auditory, visual, kinesthetic, intake, learning/working in evening/morning, late morning, afternoon, and mobility. The areas with low reliabilities include authority figures present and tactile preferences.

For this study, a panel of experts assessed content validity. The panel consisted of three regional teacher educators and two professors of career and technical education. The validation panel agreed that the PEPS was a suitable instrument for the researchers to use in measuring the productivity and learning style preferences of career and technical education teachers.

Data Collection

The PEPS was administered to beginning health occupations and trade and industrial education teachers during the 2002 Summer Workshop for New Teachers. Experienced health occupations and trade and industrial education teachers completed the PEPS during fall of 2002. Three regional teacher educators were responsible for administration of the PEPS. The questions (100) were answered on a

Likert scale with responses ranging from strongly agree to strongly disagree. The estimated time to complete the PEPS was 20 to 30 minutes.

Data Analysis

Data were analyzed using the Statistical Package for the Social Sciences (SPSS Version 11.5 for Windows). Descriptive statistics were used to describe the distribution of the data. Correlation coefficients were interpreted utilizing Davis’s (1971) descriptors (negligible = .00 to .09; low = .10 to .29; moderate = .30 to .49; substantial = .50 to .69; very strong = .70 to 1.00).

Results

The respondents were 54.2% female and 45.8% male. Trade and industrial education teachers accounted for 50.8% of the sample (30.5% beginning and 20.3% experienced). The health occupations education teachers constituted 35.6% of beginning teachers and 13.6% of experienced teachers. Over 50% of the respondents had “some college education and no degree”. Experienced teachers reported an average of over 10 years (M = 11.85 years) of teaching experience.

Productivity and Learning Style Preferences of Beginning Career and Technical Education Teachers

The data shown in Table 2 indicated that beginning teachers with a standard score of 60 (or more), preferred structure, presence of authority figures, peer oriented mode of learning, required more mobility, and preferred to learning in the afternoon.

Beginning teachers with a standard score of 40 (or less), reported less than ideal preferences for learning in several ways, preferred to work and learn in cool temperatures, and were less likely to have optimum productivity and learning through visual preferences (see Table 2).

Productivity and Learning Style Preferences of Experienced Career and Technical Education Teachers

Table 3 shows the productivity and learning style preferences of experienced career and technical education teachers. Experienced teachers with a standard score of 60 or more, reported preferences for the following subscales: tactile, structure, requires intake, auditory, motivation, and afternoon. Seventy-five (75) percent of the respondents indicated a preference for tactile modality.

Career and technical education teachers with a standard score of 40 or less (see Table 3), were not influenced by learning in several ways, preferred cool temperatures, and preferred an informal design for work and learning.

Both male and female respondents with a standard score of 60 or more, had a preference for “authority figures present” and “structure” as illustrated in Table 4.

Commonalities existed between male and female respondents with a standard score of 40 or less on the following subscales: learn in several ways and visual modality (see Table 4).


Relationships Between Independent Variables and Selected Productivity/Learning Style Subscales

The relationships between independent variables and selected productivity/learning style preferences are illustrated in Table 5. Program area had a moderate and significant correlation (r = .30, r² = .09) with motivation and tactile (r = .32, r² = .1024), respectively. The impact of gender was a moderate and significant correlation with tactile (r = -.35, r² = .1225) and kinesthetic (r =.30, r² =.09). Career and technical education teachers accounted for the strongest correlation coefficient on the tactile subscale (r =.35, r² =.2704). This correlation was considered substantial and significant. Findings from the tactile subscale contradict the earned reliability rating of .33. However, research suggests that career and technical education participants are more likely to participate in hands-on activities and practicality (tactile) (Gordon, 2000; Stitt-Ghodes, 1999; Myers & McCaulley, 1985). Again, participants’ individual learning styles should be examined and, rather than basing their instructional prescriptions on the group’s overall profile, they should be designated based on each participant’s unique Learning Style Inventory (Dunn, Dunn, & Price, 1997) profile.

Discussion

Over 50% of the respondents had some college education and no degree. The fact that trade and industrial education teachers, in particular, have less formal education and more occupational experience than other career and technical education teachers, has been an issue for some time. This finding is consistent with a study reported by Gordon and Yocke (1999). In their study, slightly less than one third (31.8%) of trade and industrial education teachers had completed a bachelor’s degree. There is a controversy about whether trade and industrial teachers, or any teachers should be able to teacher in public schools without a college degree (NAVE, 1994).

As a group, beginning teachers with a standard score of 60 more on the PEPS were more likely to have a preference for: structure, authority figures, peer oriented learning, mobility. These findings suggest that beginning teachers (with a standard score of 60 or more) were more likely to maximize their learning and productivity through emotional, sociological, and physical elements. Beginning teachers with a standard score of 40 or less, accounted for a majority of the responses on the subscale “learn in several ways”. It appears that traditional lecture as a basic pattern, or any other instructional routine is unlikely to be as beneficial for these beginning teachers’ achievement as would a variety of learning experiences.

Productivity and learning style preferences of experienced teachers with a standard score of 60 (or more) were likely to be influenced by selected physical elements in the learning environment (tactile, auditory, and kinesthetic). A study in the Journal of Health Occupations Education (Gordon, 2000) revealed that both health occupations education and trade and industrial education teachers had a strong preference for sensing/perceptual strengths. The instrument used in that study was the Myers-Briggs Type Indicator. Persons oriented toward sensing perception tend to focus on the immediate experience and often develop characteristics associated with this awareness such as enjoying the present moment, realism, acute powers of observation, memory for details, and practicality (Myers & McCaulley, 1985).

Experienced teachers with a standard score of 40 or less, reported non-preferences (45%) for the following variables: sound, motivation, persistent, authority figures, tactile, kinesthetic, intake, afternoon, and mobility. These findings imply that selected experienced teachers (< 40) in this study varied with respect to how much certain variables were important to them. Other learning style preferences are likely to be more important.

Both male and female respondents with a standard score of 60 (or more), had a high preference for “structure”. It appears that beginning career and technical education teachers should be assigned close to appropriate instructors or supervisors and schedule numerous meetings among them; plan to visit and check work often; provide frequent feedback through the individual’s perceptual strengths. In addition, establish specific working and reporting patterns and criteria as each task is completed.

Learning several ways and visual preferences were common between both male and female respondents with a standard score of 40 or less. These results indicate that the individual teacher should be permitted to work in the sociological pattern(s) most preferred. These findings also support the use of resources under the perceptual preferences that are strong.

Three of the independent variables (program area, gender, and career and technical education teachers) accounted for significant and positive relationships with the tactile subscale. The data indicate that 27% of the variance on the tactile subscale was associated with career and technical education teachers (experienced) who had obtained a standard score of 60 or more. Price (1996) reported that for tactile preferences (60 or more), use manipulative and three dimensional materials; resources should be touchable and movable as well as readable; allow these individuals to plan, demonstrate, report, and evaluate with models and other real objects; and encourage them to keep written records .

The overall findings of this study suggest that career and technical education teachers with a standard score of 60 or more had strong preferences for the following variables: mobility, structure, tactile, and authority. Career and technical education teachers with a standard score of 40 or less were more likely to have less than ideal preferences for learning in several ways, preferred cool temperatures, and had less preference for visual learning.

Individuals responsible for designing learning and working environments for career and technical education learners need to design a paradigm that is flexible to meet individual preferences for optimum learning and productivity.

Pre-service and in-service education for beginning career and technical education teachers should include instruction on the following elements of productivity and learning style preferences: environment (temperature); sociological (learn in several ways); and physical (perceptual preferences).

Implications

To capitalize on these findings, this study should serve as the basis for expanding current beginning and experienced CTE teachers’ knowledge of their learning styles. Career and technical education teachers need to be aware of their own preferences and the effect this is having on the way they teach.

In preparing future CTE educators, attention should be given to learning styles in their programs of study. Further research should be conducted in the area of learning styles and technology in the classroom to better understand the effects of distance learning environments and computer centered classrooms of CTE teachers.

It is recommended that future studies use a proportional stratified-random sampling procedure to select beginning and experienced CTE teachers. Strata could include gender, age, race, program area, and predominant geographic area (urban, suburban, and rural).

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