Learning strategies in the corporate setting.
Gary J. Conti, Rita C. Kolody, Bobby Schneider
Abstract: The learning strategies of financial planners with American Express were assessed with SKILLS. Results with this group of professionals confirm the distinct learning strategy groups uncovered in previous learning strategy research.
Introduction
American Express is one of the largest financial corporations in the world and one that prides itself on the positive relationships between its workers and the corporation. One of its divisions is American Express Financial Advisors (AEFA) which focuses on assisting clients with financial planning through advisement on balanced investment portfolios. One key element in secure financial plans is an adequate and appropriate life insurance component. Unfortunately, however, the financial planners who deal mostly in stocks and bonds have little or no training in the complicated ideas and concepts related to life insurance and life insurance's relationship to the tax code. To address this training need, American Express created the Risk Management Division six years ago; the 15 insurance specialists in this division provide training on life insurance concepts to all of the 6,000 financial planners in AEFA in the United States.
Like so many educators of adults, the trainers in the Risk Management Division evolved to their positions as a result of extensive experience and expertise in their business. Through natural charisma and sales techniques, they have been fairly successful in their endeavors to train others. However, despite record profits which can be attributed to their efforts and despite their division making it to the finals for the prestigious and highly coveted Malcolm Baldridge Award, this division is constantly seeking to improve its performance. Recently, through contacts with adult educators from Montana State University, these trainers became formally introduced to the principles in the adult education literature. Immediately, they seized upon the ideas which they saw useful for improving their performance and began to modify portions of their training program and to assess their individual teaching styles. One pilot project has led to increased sales commissions of approximately $4,000 per financial planner involved in the training and is being presented to the corporate managers as a model for future training sessions.
In this quest to improve their training, the Risk Management Division has undertaken a research study to better understand the learning strategies of the financial planners with whom they work. Utilizing Schon's (1987) ideas, they are seeking to create their own field-based knowledge upon which to improve practice and to become more reflective practitioners. Regardless of the type of setting, learners use various strategies to accomplish their learning needs. Learning strategies are those techniques or specialized skills that the learner has developed to use in both formal and informal learning situations (McKeachie, 1988). They are "the techniques and skills that an individual elects to use in order to accomplish a specific learning task....Such strategies vary by individual and by learning objective" (Fellenz & Conti, 1989, pp. 7-8).
Recent research in the area of learning strategies offers exciting findings for improving the teaching-learning transaction. For the past 20 years, research in the field of adult education has been following the twin thrusts of participation and self-directed learning (Fellenz & Conti, 1989). The participation research focuses on factors related to participation in and operation of the formal educational programs and has been conceptually fueled by Houle's learning orientations. The self-directed learning research has concentrated on the individual and upon contextual factors influencing that learning. The research by Kolody, which has uncovered five uniform groups of learners with distinct patterns of learning strategies (Kolody & Conti, 1996; Kolody, 1997), has the potential to begin to provide conceptual clarity to this area of research. In order to test the generalizability of Kolody's findings, learning strategies need to be tested with various groups in the very diverse field of adult education.
Therefore, the purpose of this study was to investigate the learning strategies of the 6,000 financial planners in AEFA in the United States. While the immediate goal of the Risk Management trainers was to gain information for improving practice, this study is another in the growing body of research on learning strategies and expands this line of research to an audience that has not been included in previous learning strategy research. Much of the past learning strategy research has been with populations that are linked to post-secondary education or which are involved in areas which are commonly recognized as education. This population is from the type of organization that Knowles has labeled as "non-educational" agency.
Methodology
The target population for this study was the 6,000 financial planners in the 15 regions of AEFA in the United States. This paper reports on the initial analysis of the data collected for this nationwide study. Each of the Risk Management specialist in seven of the regions assisted in the collection of data in their region. Each administered instruments at training sessions which contained participants that were demographically representative of the financial planners in their region. Through this process, demographic and learning strategy data were collected on 422 financial planner from around the country. Slightly over three-fourths (77.6%) were males. The ages of the participants ranged from 20 to 74 with an average age of 40.3. Approximately one-third had been a financial planner for only one year while another third had 2-4 years experience and another third had over 5 years experience in the field. The group was well educated with 39.7% having a bachelor's degree, with 43.5% having credits or degrees past the bachelor's level, and most of the remaining 16.7% having some college credits. The income level for the group ranged from $2,000 to $1,000,000 with an average of $72,604. Thus, this group was representative of the financial planners in the country and was professionally very different from past groups in learning strategies studies.
Learning strategies were measured with the Self-Knowledge Inventory of Lifelong Learning Strategies (SKILLS). This valid and reliable instrument consists of real-life learning scenarios with responses drawn from the areas of metacognition, metamotivation, memory, critical thinking, and resource management (Conti & Fellenz, 1991). Each of the five areas consists of three specific learning strategies: Metacognition--Planning, Monitoring, and Adjusting; Metamotivation--Attention, Reward/Enjoyment, and Confidence; Memory--Organization, External Aids, and Memory Application; Critical Thinking--Testing Assumptions, Generating Alternatives, and Conditional Acceptance; and Resource Management--Identification of Resources, Critical Use of Resources, and Use of Human Resources. Using the twelve scenarios in the existing forms of SKILLS, the Risk Management trainers were involved as suggested in previous studies (Lockwood, 1997; McKenna, Conti, & Fellenz, 1994) in tailoring the scenarios to fit the real-life situations of the financial planners.
Findings
Past research studies with SKILLS have utilized multivariant analysis to investigate patterns in the data (Kolody, 1997; Lockwood, 1997; Strakal, 1995; Yabui, 1993). Discriminant analysis has been used to deductively impose sense upon the data while cluster analysis has been used to tease sense out of the data. In this study, several discriminant analyses were conducted to investigate the relationship between demographic variables and learning strategies. Cluster analysis was used to investigate the stability of the clusters found in learning strategies research with SKILLS (Conti & Kolody, 1996; Kolody, 1997).
Discriminant analysis is a statistical technique which allows the investigation of the differences between two or more groups in relationship to several variables simultaneously. In discriminant analysis as with other multivariate techniques, the emphasis is upon analyzing the variables together rather than singly. In this way, the interaction of the 15 learning strategy variables upon the groupings of a demographic variable were considered. Therefore, discriminant analysis was used to describe the combination of learning strategy variables that could be used to distinguish the groups for the demographic variables of gender, age, years experience as a financial advisor, educational level, and income.
Five separate discriminant analysis were conducted with the 15 learning strategies as the discriminating variables. Each of the five demographic variables considered in this study are common socio-economic variables that are believed to often effect human behavior. The results for all five analyses were similar; all failed to account for a significant amount of variance in the groups, and each only improved correct classification in the groups by approximately 10% over chance placement in the groups. The discriminant function for gender provided for the highest discrimination of the five demographic variables; however, this function was only a 16.58% improvement over chance placement. For the analysis of age, the sample was divided into quartiles, and the discriminant function provided a 10.23% improvement over chance placement. To analyze experience in the field as a financial advisor, the advisors were grouped into those with experience of 1 year, 2-4 years, and 5 or more years; the discriminant function for this analysis was an 11.49% improvement over chance placement. For educational level, the sample was categorized into those with a bachelor's degree, those with credits or degrees beyond the bachelor's level, and those with less than a bachelor's degree; the resultant discriminant function was a 10.94% improvement over chance placement. Finally, for income the groups which were one standard deviation above and below the mean were utilized because they formed distinct groups. The low income group consisted of the 15% of the sample with incomes of $30,000 and less while the high income group consisted of the 15% with incomes of $100,000 and more. The discriminant function for income was only a 7.96% improvement over chance placement. Thus, because all of the discriminant functions accounted for only a low amount of variance as indicated by their inability to greatly improve group classification over chance, all of the discriminant functions were judged as not useful in describing the learning strategy differences among groups identified by demographic characteristics.
Other research with SKILLS has used cluster analysis to identify distinct groups of learners. Kolody (1997) uncovered five clusters of learners in Canadian two-year schools. Combining this quantitative analysis with qualitative interviews of members of each cluster, she described the groups as Navigators, Monitors, Critical Thinkers, Engagers, and Networkers (Conti & Kolody, 1996, 1997; Kolody, 1997). Investigating the learning strategies of nursing students and using a similar research design, Lockwood (1997) found four groups which are subsets of these groups. In order to test the stability and therefore the generalizability of the groups in Kolody's study, the data from this study was combined with the data from Kolody's study, and the cluster analysis for a five cluster solution was recalculated.
The new data set contained 1,565 cases which consisted of the 1,143 learners in Kolody's study and the 422 financial planners. The addition of the financial planners represented a 37% increase in the amount of variance in the original sample used by Kolody. If Kolody's grouping are accurate, then the introduction of this new variance into the sample should not allow for much change in the cluster membership of her original group. However, if her groupings are not representative of general learning groups and are only specific to her sample, then much movement should have resulted in cluster membership from the original analysis when this new variance was interjected into the analysis.
Quick cluster analysis with the combined data set produced five clusters that were remarkably similar to the original clusters from Kolody's study. Despite the existence of the new variance in the analysis, most of those from Kolody's study were retained in their original clusters. The percentage of original members of Kolody's sample who made up each cluster in the new analysis were as follows: Navigators--89.4%, Networkers--89.4%, Critical Thinkers--88.2%, Engagers--82.6%, and Monitors--79.1%. The percentage of change in the size of the original clusters was small for three clusters, moderate for one cluster, and nearly equal to the amount of variance introduced for one cluster: Monitors--4.4%, Engagers--7.6%, Critical Thinkers--9.9%, Navigators--18.3%, and Networkers--36.2%.
Although Kolody's original groups were nearly equal in size, the financial planners were not equally distributed across the five clusters. They were concentrated most heavily in the Navigator (31.5%) and Network (31.3%) groupings. Slightly fewer (17.1%) than one-fifth of the group were Engagers. The smallest groups were the Critical Thinkers (12.3%) and the Monitors (7.8%). Thus, the financial planners constitute a subset of Kolody's original groups much like the nurses in Lockwood's study. Both of these subsets were composed of samples from a professional setting which were smaller than Kolody's sample and which were tested with SKILLS scenarios with a heavy emphasis on professional situations. Hence, these more specific professional situations may be attracting learners with certain learning strategies and dissuading others from pursuing the profession.
Discussion
As a result of research studies utilizing SKILLS, a body of knowledge is accumulating related to the concept of learning strategies. Since these studies are using a common instrument and similar designs, patterns are beginning to emerge concerning learning strategies. These studies validate the situational nature of learning strategies and support that individuals do elect to use different techniques or skills in order to accomplish divergent learning tasks. Indeed, different strategies are employed in learning for personal use than for professional situations (Lockwood, 1997; McKenna, 1991; Strakal, 1995).
Distinct groups of learners who use specific learning strategies exist. Every study using cluster analysis with SKILLS has found clear groupings of learners (Hays, 1994; Kolody, 1997; Lockwood, 1997; Strakal, 1995; Yabui, 1991). While most of these studies had very specific populations, Kolody's had a large and general population. The five groups from her study appear to be the most universal of any of the studies, and the groups from the other studies can be viewed as subsets of these five groups. Results from this study support the stability of Kolody's five groups.
Learning groups are broadly dispersed throughout a population. Just as with the financial planners, demographic variables have consistently failed to be associated with discriminating between groups of learners when learning strategies are used as the discriminating variables (Kolody, 1997; Lockwood, 1997; Strakal, 1995). When such categorizations fail to produce differences, it is because the variance is equally distributed across the groups. Thus, learning strategies are not associated with common demographic variables. They cut across all visible groupings. While some specific professional situations may contain subsets of the five learning strategy groups, instructors can expect to find all groups in the learning situations which they enter. Demographics will not disclose the learning strategies of the learner to the instructors. Instead, the keys to the beginning diagnosis of the learning strategies of the learner must be sought in the general behavior displayed by the five groups.
Thus, learning strategies can provide a better understanding of the needs of adult learners. A knowledge of group and individual learning strategy preferences can furnish valuable information for planning learning activities for specific groups such as financial planners. Likewise, familiarity with the five groups of learners can be a powerful tool for initially assessing the needs of the adult learner; however, the labels from this grouping can be detrimental if they are used to avoid critical thinking about the learners (Conti & Kolody, 1997). Such a knowledge of the learner can allow adult education activities to either teach people the learning strategies they need to be successful in their learning, or it can provide them an opportunity to practice the strategies they already have (Smith, 1982). The research on learning strategies is developing rapidly. While it will undoubtedly change more in the future, it is currently providing new insights into the adult learning process which can be applied immediately by practitioners.
References
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