Learning Preferences and Farm Computer Use

"Sure, Joe has a computer, but he hasn't really gotten the hang of it yet." How many agricultural Extension agents have heard similar benedictions pronounced over a would-be farm computer user? More and more farmers own computers. The equipment is adequate to the task for farm management. Farmers are certainly capable. Yet, the task of mastering the wonders of the microchip stimulates some, while daunting others.

How much farmers use their computers is influenced by many factors.1 The ways in which a person with unique attitudes and preferences goes about learning the computer may be a particularly powerful influence. If farmers don't learn successfully, they can't efficiently use computers. Moreover, if the learning methods used in the task are distasteful, the chances of upgrading skill beyond a rudimentary level dwindle.

We sent 483 surveys2 to Midwest farmers3 who own computers. Of those, 314 (65%) were returned complete. The instrument consisted of: a computer use index; Canfield's Learning Style Inventory, form O (LSI);4 and background questions. The use index quantified both range and extent of computer application. We reasoned we'd get a fair depiction of the limits of an individual's computer learning by determining whether and how often 19 different farm management tasks were done on a computer. Canfield's LSI was included to measure the respondent's underlying attitudes concerning learning conditions, modes, and contents. Form O, an adaptation of the original LSI, includes nonformal and formal learning. Finally, background information was collected to get a profile of the farm computer user and how he or she learned how to apply the computer to farm management.

Demographic Data

Because the sample wasn't drawn from the total population of all farm computer users, we can't generalize too freely from the demographic data. The findings are interesting nonetheless. Of those who responded to the survey, 71% were men. The majority (64%) also described themselves as "farm owners," with another 20% checking "farm wife." The remaining 16% said "partner" (8%), "farm manager" (4%), "farm secretary" (3%), and "other" (1%). The farms of 25 (8%) respondents grossed less than $50,000 a year, while 23 (7%) grossed over $900,000. The median gross farm income, however, was between $100,000 and $299,000.

For 71%, farming constituted their only occupation. The 29% who worked off the farm on average figured they spent 44% of their time farming. The average age was 43.5 years. The youngest was 22 years old, the oldest 73. The majority of the farmers (65%) listed grain/corn (including soybeans and milo) as one of their primary farming operations. Other primary farm operations included hogs (21%), beef (15%), and dairy (7%).

Thirty-eight percent of the farmers surveyed had graduated from college with seven percent having done graduate work. Only two farmers said they hadn't completed high school. High school graduates constituted 27% of the group. An additional 30% had done some work at a college or vocational school. Slightly more than five percent had an associate degree or certificate.

Computer Learning and Use

Respondents owned a variety of Apple, Radio Shack, and CP/M based machines, but 79% of the sample had IBM computers or one of the many "clones." These machines were used an average of 7 1/2 hours a week. Though one individual had worked with a computer for 24 years, the mean was 5.6 years with a standard deviation of 2.8. Over 85% of the farmers started using computers after 1982. Only 15% indicated they were the sole users of their computer. For the vast majority, family members also used it.

The farmers used their computers most frequently for accounting, figuring their net worth, and preparing bank statements. Programming, database design, and charting market prices were least frequently used. How the farmers learned to use their computers and the providers of learning opportunities are listed in Tables 1 and 2.

The focus of our study was the relationship between certain learning style preferences and the breadth of computer learning. Other demographic elements were also explored with respect to their impact on computer learning. We were particularly interested in whether the way an individual went about learning to use a computer interacted with his or her preferred learning style.

Multiple regression analysis indicated that three variables predicted extensive computer learning and use. Farmers who preferred independent learning conditions and liked numeric content were more likely to master the computer than their counterparts. In addition, men seem-ed to learn and use computers more than women. In fact, gender was the single largest predictor of computer use.

Whether a farmer took a computer class or not made little difference in how well he or she used the computer. However, certain learning styles, given particular learning conditions, predisposed the learners to achievement.

As might be expected, farmers who liked to learn on their own, set their own goals, and determine their own pace, excelled at learning computing outside the classroom. Likewise, those who preferred numeric and logical content were more likely to master computing without a formal class. Preference for independence and numeric content were not significant predictors when only farmers who had taken a class were considered, however. Of farmers who took classes, those who preferred learning through direct experience didn't learn as well as those who liked reading, listening, or visual methods.5

The first, and obvious, implication of this study is that classes aren't the only way to learn computing. Over half the sample never took a computer class. No significant difference in computer learning and use existed between those who took a class and those who didn't. Some people may need the structure and discipline of organized instruction, but the method isn't for everyone. A class can't usually teach all a farmer must learn to effectively use his or her computer in farm management.

Other implications flow naturally from this conclusion. For example, a wide variety of methods and resources should be made available to encourage those who dislike learning on their own and remove potential barriers for those who do want to learn independently.

The diverse nature of farm computer learners suggests a three-pronged approach to facilitating computer use. First, information should be disseminated in as many ways as budgets allow. County Extension offices might serve as a repository for brochures, magazines, manuals, and software for interested farmers to peruse. Newsletters or electronic bulletin boards might help keep farmers up to date on what's available for various tasks.

Second, collaboration among farm computer owners might be encouraged. User groups, miniconferences, expositions, and electronic bulletin boards all help. In addition, new users could be linked with more experienced ones, fostering support and encouragement.

Finally, instruction could be offered. Short courses and workshops may effectively introduce newcomers to the computer or help the veteran master more sophisticated operations. In addition, instruction and counsel on learning "how to learn" computers might be offered. Orientation sessions, workshops, individualized instruction, or even printed handbooks that help each individual learn how they might best approach the task of mastering the computer can be made available. Farmers could be helped to understand themselves as learners and to skillfully manage the learning task.6

Not every aspect of each learning format will be used by every farmer with a computer. Those who feel most comfortable with independent approaches to learning may use only the more accessible information. Others will appreciate the instructional aspects of the program. Most farm computer owners will benefit from components of each approach as they match their individual needs and preferences with available learning resources.

1. R. K. Iddings and J. W. Apps, "What Influences Farmers' Computer Use?" Journal of Extension, XXVIII (Spring 1990), 19-21.

2. Six hundred surveys were originally mailed, but 117 were sent to bad addresses or to individuals ineligible to participate. The sample was randomly selected from lists provided by three major agricultural software vendors.

3. States represented included: Ohio, Michigan, Indiana, Illinois, Wisconsin, Minnesota, North and South Dakota, Iowa, Nebraska, Missouri, and Kansas.

4. A. A. Canfield, Canfield Learning Styles Inventory (Los

5. For more detailed description of this research, see R. K. Iddings, "Farm Computer Use and Learning Style" (Ph.D. dissertation, University of Wisconsin, Madison, 1991).

6. See, Robert M. Smith and others, Learning To Learn Across the Life Span (San Francisco: Jossey-Bass, 1990) for more information on "learning to learn."