Learning Style Preferences Relating to Adult Students.

Introduction

Research pertaining to learning styles suggests that it is important to first define learning and then styles of learning. According to B. F. Skinner (1974), learning is any change in behavior. Harasyrn, Leong, Lucier, and Lorsheider (1995) define learning as "a relatively permanent change in performance by an individual" (Section s, p. 56). Sims and Sims (1995) state that learning occurs when "one adopts newly (sic) or modifies existing behavior patterns in a way that has some influence on future performance or attitudes" (p. 4).

Learning style refers to the "way in which each learner begins to concentrate on, process, and retain new and difficult information" (Dunn, Griggs, Olsen, & Beasley, 1995, p. 353). Whittington and Ravens (1995) define learning style as a "predominant and preferred manner in which individuals take in, retain, process, internalize, and recall information and can represent both inherited characteristics and environmental influences" (p. 9).

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.

Researchers have created an array of learning style inventories. Hickcox (1995) summarized the types of learning style inventories into three categories: (a) physiological styles, learning styles through environment, sociological factors, emotions, and physical stimuli (Dunn, et al., 1995; Reiff, 1992); (b) cognitive styles, learning styles through thought or mental activity (Murrell & Bishop, 1995; Reiff, 1992); and (c) affective styles, learning styles through emotions and feelings (Murrell & Bishop, 1995; Reiff, 1992).

Theoretical Framework

One of the three domains of learning style theories that encompasses all aspects of the learning environment is the physiological styles. 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 purposes of this study, Dunn's learning theory will be used as the theoretical framework.

Environmental Characteristics of an Adult's Learning Style

Sound, light, temperature, and design are all environmental characteristics that Dunn (1984) discovered could affect how well a student is able to achieve in a learning atmosphere. These characteristics may not affect some adults' learning, but it may hamper the abilities of others. Temperature in a room can affect adults, especially elderly adults' ability to concentrate on new or difficult material. Therefore, there are better learning results when the environmental surroundings are comfortable and relaxing.

Psychological Characteristics of an Adult's Learning Style

According to Dunn, Griggs, Olsen, and Beasly (1995), adults learn in one of two processing styles: (a) global and (b) analytic. Global learners have the ability to learn through short stories, illustrations, and graphics. Global learners also need to know what is expected of them and why. Analytical learners focus on fact-by-fact accounts of the learning experiences. The information should be presented in a step-by-step manner in order for analytical learners to grasp new information (Burke, 1997, Dunn, 1984; Shaughnessy, 1998).

Physical Characteristics of an Adult's Learning Style

Perceptual strengths, food intake, times of day or night, and mobility are the aspects of the physical environment that affect an adult's learning experience. Some adults may prefer to learn new material by hearing the information. Others may prefer kinesthetic involvement. Some adults may need to snack at regular intervals while studying. Others may not consume food or beverages for hours while learning. The time of the day in which learning occurs may also have an impact on the adult's ability to learn. An adult may only be able to enroll in a class in the evening, but the same adult may prefer the morning to learn information. The ability to move around while learning may play an important role in some adults' ability to learn (Dunn, 1984).

Emotional Characteristics of an Adult's Learning Style

Motivation, persistence, responsibility, and a need for some kind of structure make up the emotional characteristics of Dunn's learning theory model. Emotional characteristics are different from the other characteristics, in that these characteristics are internal. According to Dunn (1990), motivation correlates with achievement. Therefore, if the adult is not motivated, it may be difficult to keep the adult in a learning environment. According to Dunn (1990), if learning styles of the student are considered, then motivation will be increased.

Sociological Characteristics of an Adult's Learning Style

Sociological aspects of the learning environment are important factors in an adult's ability to learn. Some adults prefer to learn alone. Some may prefer to learn in groups. Some adults may need a considerable amount of structure; whereas, other adults are extremely self-directed (Dunn, 1984).

According to Sims and Sims (1995), "learning may not take place if teaching is not structured to facilitate learning" (p. 5). How an instructor: (a) sets the stage, (b) provides a chance for the student to actively participate in the learning process, (c) provides feedback, (d) teaches students to reinforce their learning, and (e) teaches students how to take responsibility for their own learning, are all factors that may facilitate the learning process. Students who can identify a specific learning style preference and learn how to use it appropriately are more capable of taking responsibility for their own learning (Sims & Sims, 1995). Shaughnessy (1998) found that when teachers changed from the traditional teaching approach to learning style teaching, test scores and grade point averages increased significantly.

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: (a) self-directed, (b) centered on solving the problem at hand, (c) focused on the application of the material being presented, and (d) 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 the learning process (Hewitt, 1995).

Learning Styles Linked with Gender

Philbin, Meir, Huffman, and Boverie (1995) tested the hypothesis that men and women have different learning styles. They studied 72 individuals, 25 males and 45 females, who were enrolled at the University of New Mexico. The study indicated that there was a significant difference in learning styles between males and females. "Females felt as though they did not fit the traditional educational learning style" (Philbin, et al., p. 491).

Mathews and Hamby (1995) examined the relationship between gender and learning styles among 501 pharmacy students. The researchers found that the sample of male students in the study preferred abstract conceptualization and active experimentation. Females preferred to generate ideas. Contradictory to Mathews and Hamby's (1995) research, Dwyer (1995) found that there was no significant correlation between learning style and gender, when he studied the differences by gender in communication and learning styles among 436 participants. Lam-Phoon (1986) conducted a study in which she compared the differences between male and female college students based on their preferred learning style. She found that the male students had a significantly higher preference for noise and were more tactile than the female students. She also indicated that females preferred to learn alone and were less persistent than males. Price (1979) administered the Productivity Environmental Preference Survey (PEPS) to 98 men and 124 women undergraduate students. He concluded that women preferred more light while learning, a warmer structured environment, and kinesthetic learning.

Learning Styles Linked with Age

According to Shaughnessy (1998), age is a variable affecting learning styles. Price (1987) administered the PEPS to 145 adult students 18 years old and older. He found that older adults are more motivated and prefer a formal design. Younger adults are kinesthetic learners and are more persistent. Price (1979) also concluded that productivity occurs in the evening for younger adults, whereas older adults are more productive in the morning. Females over the age of 55 were not as productive in the afternoon as they were in the morning. Younger males prefer to learn in the evening and did not want an authority figure present when learning new information. Price came to the conclusion that significant changes occur as an adult ages. He recommends that further research be conducted in the area of aging and learning styles.

Methodology

Graduate core courses, all courses required for all graduate level degrees at Northeastern State University in Tahlequah, Oklahoma, were randomly selected and used as the foundation for this study. Students enrolled in those classes, during the Spring 1999 semester, were surveyed using the Productivity Environmental Preference Survey (PEPS) developed by Gary Price (1979). The PEPS is designed to measures 20 adult learning style preferences (noise level, light, warmth, design, motivation, persistence, responsibility, structure, alone/other, authority, several ways, auditory, visual, tactile, kinesthetic, intake, evening, late morning, afternoon, mobility). The questions were answered on a Likert scale with responses ranging from strongly agree to strongly disagree. The participants were informed of the intent of the research, confidentiality, and that participation was on a voluntary basis.

Reliability and Validity of the PEPS Instrument

According to Price (1979), 90% of the scales had reliability ranges equal to or greater than 0.60. The average standard error was 1.34 for the 20 items. The highest reliability was found in the areas of noise level, light, temperature, informal/formal design, persistence, responsibility, structure, learning alone/peer oriented, several ways, auditory, visual, kinesthetic, intake, learning/working in the evening/morning, late morning, afternoon, and mobility. The areas of lowest reliability were authority-oriented learner and tactile preference learner. Price revised the inventory based on the findings and review of each item.

Analyses

A multiple regression analysis was conducted for each of the 20 learning style variables, with three predictors. The first independent variable was gender, coded so that female had the higher value. The second independent variable was age in years. Thirdly, the gender-by-age interaction term was computed as the product of the z-scores for gender and age.

Findings

The sample consisted of 133 graduate students. There were 52 males (39%) and 81 females (61%). Thirty-five students were 20 to 24 years old (26%); 32 were 25 to 29 (24%); 27 were in their 30's (20%); 10 were in their 40's (8%); 11 were in their 50% (8%); four were in their 60% (3%); and 14 were in their 70% (11%). By ethnicity, 111 students were Caucasian (83%); 15 were American Indian (11%); three were African-American (2%); two were international students (2%); one was Hispanic (1%); and one indicated other (1%).

Table 1

Demographics

                                     n    Percent

Gender        Female                81      60.9
              Male                  52      39.1

Ethnicity     Caucasian            111      83.3
              American Indian       15      11.3
              African American       3       2.3
              Hispanic American      1        .8
              International          2       1.5
              Other                  1        .8

Age Groups    20s                   67      50.0
              30s                   27      20.0
              40s                   10       8.0
              50s                   11       8.0
              60s                    4       3.0
              70s                   14      11.0

Note. N=133.

Based on the findings, of the 20 learning style variables, three (Structure Environment, Evening Learning Preferences, and Afternoon Learning Preferences) had a statistically significant overall regression model. Kinesthetic learning was not significant overall (p=0.053), but gender was a significant predictor in the model (see Table 2). It was found that structure, utilizing the age-and-gender regression model, had an adjusted R-square of 0.067 (F(3,129)=4.18; p=0.007). The main effects were statistically significant for gender (t=2.06; p=0.042) and for age (t=-2.73; p=0.007), and the interaction term was not significant (t=1.34; p=0.183). Raw regression coefficients indicate that female students scored an average of 2.98 points higher on the structure scale, and older students scored lower by 0.11 points a year of age.

Table 2

Significant Regression Findings

                                      B        Std Error

Structure     Constant              57.85         2.87
              Age                    2.98         1.45
              Gender by Age           .98          .73
              Adjusted R-square=      .067      F(3,129)

Kinesthetic   Constant              51.80         2.60
              Gender                 2.81         1.31
              Age                     .06          .04
              Gender by Age           .72          .66
              Adjusted R-square=      .036      F(3,129)

Evening       Constant              40.18         3.03
              Gender                 1.80         1.70
              Age                     .11          .05
              Gender by Age          1.94          .86
              Adjusted R-square=      .066      F(3,129)

Afternoon     Constant              57.09         4.25
              Gender                  .48         2.14
              Age                     .07          .06
              Gender by Age         -2.75         1.08
              Adjusted R-square=      .042      F(3,129)

                                      t        significance

Structure     Constant              20.15          .000
              Age                    2.06          .042
              Gender by Age          1.34          .183
              Adjusted R-square=     4.18;       p=.007

Kinesthetic   Constant              19.89          .000
              Gender                 1.83          .107
              Age                   -1.62          .107
              Gender by Age          1.08          .280
              Adjusted R-square=     2.63;       p=.053

Evening       Constant              11.90          .000
              Gender                 1.06          .293
              Age                    2.22          .029
              Gender by Age          2.26          .026
              Adjusted R-square=     4.10;       p=.008

Afternoon     Constant              13.43          .000
              Gender                  .23          .822
              Age                   -1.27          .206
              Gender by Age         -2.54          .012
              Adjusted R-square=     2.95;       p=.035

Note. N=133.

The overall model for evening was statistically significant (F(3,129)=4.10; p=0.008) (see Table 2) with an adjusted R-square of 0.066. The gender-by-age interaction term was statistically significant (t=-2.73; p=0.007). Separate regression equations for males and females were then computed with age as the only predictor. Age was not significantly related to evening (t=-0.40; p=0.691) for the male students. Age was a significant predictor (t=15.78; p=0.002) for females. Older females scored higher on the evening scale, with a predicted value of 0.20 points higher for each year of age.

The overall model had an adjusted R-square of 0.042 for afternoon and was statistically significant (F(3,129)=2.95; p=0.035) (see Table 2). Because the gender-by-age interaction term was statistically significant (t=-2.54; p=0.012), separate regression equations for males and females were computed with age as the predictor. Age was not significantly related to afternoon (t=1.25; p=0.217) for males. Age was a significant predictor (t=-2.61; p=0.011) for females. Older females scored lower on the afternoon scale, with a predicted value of 0.21 points lower for each year of age in years (see Table 3).

Table 3

Separate Regression Equations by Gender

                                       B       Std Error

Evening     Females    Constant      40.35       2.56
                       Age             .20        .06
            Males      Constant      47.31       3.31
                       Age             .03        .083

Afternoon   Females    Constant      62.91       3.32
                       Age           -2.1         .08
            Males      Constant      50.01       3.95
                       Age             .12        .10

                                       t      significance

Evening     Females      Constant    15.78        .000
                         Age          3.27        .002
            Males        Constant    14.27        .000
                         Age          -.40        .69

Afternoon   Females      Constant    18.97        .000
                         Age         -2.62        .011
            Males        Constant    12.66        .000
                         Age          1.25        .217

Note. N=133.

Conclusions

When examining gender and learning styles, being female was more likely to be significant in several categories. Some of the findings of this study concurred with Price (1979) in that females prefer kinesthetic learning. The findings identify females as having a preference for learning new or difficult information by way of field trips or being physically involved in the learning experience.

The data indicated that females preferred a structured learning environment. Needing a structured environment indicates that females require professors to be more thorough in their assignments. Females need more direction than male students, with clearly stated objectives. Females prefer assignments to be itemized. They prefer, in an educational setting, that nothing be left to interpretation. Older females prefer to learn new or difficult information in the evening hours; whereas, younger females prefer learning in the afternoon.

Implications

The findings of the study warrant the following implications:

* When developing curriculum for higher education, learning style preferences should be taken into consideration.

* In preparing future educators, attention should be given to learning styles in their programs of study.

* All students should be made aware of their learning style preferences in order to better comprehend the material.

* When considering the use of technology, it is imperative to recognize that learning styles must be taken into consideration in new learning environments. Some students will encounter problems in adapting to the hands-on environment of technological classrooms, computer stations, or distance learning environments.

* 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.

It is imperative for educators to have a critical understanding of individual learning styles of students in a higher educational setting. This knowledge will enable educators to better serve students' learning needs. Educators that are aware of the different learning styles will be able to narrow the existing gaps between how material is presented and how learners receive information and gain knowledge. Taking into account learning preferences in the adult population will enhance learning and retention. It is imperative that adult students realize their preference for acquiring knowledge.

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Renee L. Cambiano, Ed.D. is a visiting assistant professor in the Department of Educational Leadership, Counseling, and Foundations. Jack B. De Vore, Ph.D. is an associate professor in the Department of Vocational and Adult Education. George S. Denny, Pr. D. is an associate professor in the Department of Educational Leadership, Counseling, and Foundations.