APPLIED COGNITIVE PSYCHOLOGY, VOL. 4, 189-202 (1990)

Effects of College Students’ Learning Styles and Gender on Their Test Preparation Strategies

CAROL SPETH and ROBERT BROWN University of Nebraska- Lincoln

SUMMARY

This study investigated the effects of approach to studying, gender and type of examination on test preparation strategies. Educational psychology students completed the Approaches to Studying Inventory (Entwistle and Ramsden, 1983) regarding their general learning characteristics, and thus were assigned to four approach groups. Students also answered questions about how they might study for either an essay or a multiple-choice examination. Factor analysis of those items yielded several study strategy subscales. When scores on the time-effort, integration, selection and cognitive monitoring subscales were used as dependent variables in a 4 ~ 2 x 2 (cluster x gender x type of test) MANCOVA, a significant three-way interaction suggested that male and female students using different approaches react differently to multiple-choice or essay tests, and the patterns differ by strategy.

In his Foreword to Entwistle and Ramsden (1983: ix), Perry recalled that researchers 40 years ago found that most college students already knew effective learning strategies (then called ‘good study habits’), but relatively few practised them. Researchers around the world have been trying to answer the question, ‘Why?’ Some have focused on individual differences, such as academic ability, gender or learning style. Others have focused on characteristics of the learning context, such as subject-matter discipline or type of assessment.

Schmeck (1983: 235) defined learning style as a relatively consistent predisposition to use a particular strategy regardless of task demands. He defined learning strategy as a pattern of information processing activities which varies according to learning task. Schmeck (1988: 171-175) suggested that a learning strategy is made up of a group of related learning tactics or study activities. Derry and Murphy (1986: 1-39) provided a useful introduction to research on training students to use specific learning strategies, but Rohwer (1984) invited educational psychologists to focus instead on the strategies which students themselves choose to employ in routine studying or preparing for examinations. J. W. Thomas and Rohwer (1986) described a model of ‘autonomous studying’, and Strage, Christopoulos, Curley, Jensen and Thomas (1987) used an instrument based on this model to compare study activities in different subject areas in junior high, senior high, and college. Using their ‘autonomous studying’ model for thinking about learning strategies, but combining it with an individual-differences perspective, might contribute something new to our understanding of students’ learning strategies.

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Received 21 October 1988

190 C. Speth and R. Brown

INDIVIDUAL DIFFERENCES IN STUDENT LEARNING

One way of thinking about individual differences in student learning is based on research studies in Sweden, Britain, Australia and the United States, distinguishing between two approaches to a learning task: one described as deep, meaning- oriented, or internalizing; the other as surface-oriented, reproducing, or memorizing. Researchers at Lancaster University developed the Approaches to Studying Inventory to operationalize that contrast of deep and surface approach, incorporat- ing their previous research on study methods and motivation (Entwistle and Ramsden, 1983: chap. 4); along with ideas from research programmes elsewhere, including: syllabus-dependencehdependence (Hudson, 1968; Parlett, 1970: 272- 283); cue-deafnesskue-seeking (Miller and Parlett, 1974); and comprehension/ operation learning (Pask, 1976). Factor and cluster analyses of successive versions of this inventory, together with student interviews, enabled the Lancaster researchers to identify four approaches or orientations to studying. Besides meaning (deep approach plus intrinsic motivation) and reproducing (surface approach motivated by fear of failure), they identified a third orientation, which they called ‘strategic’. Students using a strategic orientation are achievement- motivated, they intend to maximize their grades, usually by organized and efficient studying and either deep or surface approach, depending on which would more likely be rewarded by the assessment system (Ramsden, 1979). Interview studies at Lancaster University identified differences in how students at the same level of academic achievement, but using reproducing, strategic or meaning-oriented approaches to studying, perceived their learning experiences-it was as if they had attended three different universities. Entwistle and Ramsden (1983: 50-51, 71-73) also described a fourth ‘non-academic’ orientation, characterized by low motivation, negative attitudes and disorganized studying.

TYPE OF ASSESSMENT

P. R. Thomas and Bain (1982: 250) observed that the nature of learning that can be assessed with different types of examinations is ‘an old and continuing controversy’. According to Adams (1964: 330-331), ‘the essay stimulates use of superior study methods in preparation’, but objective tests ‘may stimulate superficial learning of many details’, because of their emphasis on recognition and not requiring students ‘to organize significant facts and ideas and reason about them’. Terry (1933) found many students believed contrasting types of tests required different study behaviours, but he could not find evidence that this was true. Watkins (1982: 373) found memorizing or rote-learning activities were more frequently reported for multiple-choice exams than for written assignments. Hakstian (1971) claimed students do not prepare differently in terms of time spent, organization of material or techniques employed for essay as opposed to objective format, and that essay preparation does not call upon any higher cognitive processes than an objective test does. According to J. W. Thomas and Rohwer (1986), although most college students are aware that different strategies might be used when preparing for a test, few actually vary their strategies. Biggs (1973) found essay evaluation did not necessarily promote superior study methods. P. R. Thomas and Bain (1982) and P.

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R. Thomas and Bain (1984) monitored students’ use of three approaches to studying (transformational, reproductive and skimming) across assessment contexts. They found main effects for approach and type of assessment, along with disordinal interactions, but they did not report analyses including gender.

GENDER AND ACADEMIC ABILITY

Biggs (1973: 158) suggested that relationships between study behaviour and test format ‘are more difficult to find than many educators assume’, but that if they do exist, ‘they may involve the values and motivation of the student’. He also said students’ test preparation activities seem to be influenced more by the way they function as persons than by the type of examinations their instructors give. This statement seemed worthy of further investigation. It seemed reasonable to ask whether students’ gender, or their perceptions about their own academic ability, might also have an influence. Some studies of Australian college students have reported gender differences related to studying-for example Watkins and Hattie (1985: 133-134). Watkins and Hattie (1981: 392) found female students were more likely to adopt a deep approach and organized study methods than male students, who were more likely to have a pragmatic or vocational motivation, to be worried about their work, and to adopt a surface approach.

Ramsden and Entwistle (1981: 370) asked students to assess their own academic progress compared with other students, and found those self-ratings were associated with organized study methods, intrinsic motivation, positive attitude towards studying, deep approach and syllabus freedom.

This study investigated the effects of gender, learning style (defined as approach to studying), and type of examination, on expected test preparation strategies, controlling for self-rating of academic ability. This study involved group but not within-student comparisons.

METHODS

Sample

Subjects were recruited in educational psychology classes at a large university in the United States. The 383 students included 125 males and 258 females. Approximately 84 per cent were between the ages of 18 and 25 years.

Materials

The Approaches to Studying Inventory (Entwistle and Ramsden, 1983: 228-233) consists of 64 statements about how students tackle everyday learning tasks, divided into 16 subscales with four items each. For this study a few minor changes in wording were made to fit the items into an American context. A five-point Likert scale was used to conform with standard mark-sense sheets. The 16 subscales, which were conceptual rather than empirical in origin, are grouped into four scales representing orientations to studying: meaning, strategic, reproducing, and non-

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academic. Several ways of grouping subscales into orientations have been suggested at different points in time (Clarke, 1986: 313; Ramsden, 1983; Ramsden and Entwistle, 1981). Table 1 shows how the subscales were combined for this study, as suggested by Ramsden (1983), but adding comprehension learning to meaning orientation and operation learning to strategic orientation. This decision can be defended on the basis of factor analyses (for more discussion see Speth, 1987). Cronbach’s alpha internal consistency reliabilities obtained in this study for the four orientation scales were comparable to those reported by Ramsden (1983, pp. 11, 18-22): meaning, .73; strategic, 3 3 ; reproducing, .72; non-academic, .66.

After completing the Approaches to Studying Inventory with regard to their relatively consistent or cross-situational learning style characteristics, students answered questions about what kind of test preparation activities they would probably use in a specific context: either an essay or a multiple-choice examination in a college American history course. A Test Preparation Activities Survey was adapted from the Study Activity Survey Form T, developed by the Far West Laboratory for Educational Research and Development in San Francisco (Auton- omous Learning Project, n.d.). Their 157-item Study Activity Survey was based on the model of ‘autonomous studying’ proposed by J . W. Thomas and Rohwer (1986), which included two broad categories of study activities. Cognitive transformational activities include selection, comprehension, memory, integration, and cognitive monitoring. Self-management activities include time management, effort management and volitional monitoring. Factor analysis of the 55 items chosen for this study yielded six test preparation subscales, followed by the number of items and Cronbach’s alpha coefficients of reliability: time-effort, 20 items, 37; integration, 13 items, .78; selection, 5 items, .79; cognitive monitoring, 5 items, .67; peer orientation, 5 items, .69; average student behaviours, 7 items, .52. Strage et al. (1987, pp. 2-3) distinguished between time and effort, but that distinction did not seem warranted on the basis of factor analyses of data from this sample with

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Table 1. Approaches to studying scales and subscales ~ ~~ ~ ~ ~

Meaning orientation-Intend to understand, personalize information Deep approach-Active questioning and involvement in learning Intrinsic motivation-Interest in learning for its own sake Use of evidence-Carefully relating evidence to conclusions Relate ideas-Linking new information to previous knowledge Comprehension learning-Focus on theory, relationships, overview

Strategic orientation-See academic achievement as game to be won Extrinsic motivation-Study to gain employment qualifications Strategic approach-Actively seek cues on assessment requirements Achievement motivation-Confident, competitive, hope for success Operation learning-Focus on facts, components, logical analysis

Surface approach-Learning facts to meet assessment demands Syllabus boundness-Rely on instructor to define learning tasks Fear of failure-Pessimistic, anxious about academic outcomes Improvidence-Over-cautious reliance on facts, details

Non-academic orientation-Low motivation, study difficulty Disorganized study-Unable to work regularly and effectively Negative attitudes-Lack interest and application, disillusioned Globetrotting-Over-ready to generalize, jump to conclusions

Reproducing orientation-Intend to memorize, rote-learn

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this subset of items. Reliability of the last subscale was modest, but it was maintained because this factor emerged consistently from different types of factor analyses specifying different criteria. Table 2 shows a sample item from each subscale. A self-rating of academic ability and progress was obtained for each student by combining six items suggested by the Far West Laboratory.

Table 2. Sample items from test preparation subscales When studying for this (multiple choice or essay) test outside of class . . .

Time-effort I would probably be able to plan my time well for each study session.

Integration I would probably make a chart or picture of how different topics fit together.

Selection I would probably have trouble deciding what was important.

Cognitive monitoring I would probably be able to improve my understanding when I came across something that was hard to understand.

Peer orientation When I came across something important that I did not understand, I would probably try to get an explanation from another student.

Average student I would probably skim through the study materials to figure out how much work had to be done.

Procedure and design

Participating students were assigned to groups based on cluster analysis of the four Approach to Studying Inventory scale scores (cluster 1 = meaning-oriented had 71 students, 2 = strategic had 131 students, 3 = reproducing had 123 students, 4 = non-academic had 56 students), using an SPSSPC+ procedure called Quick Cluster, which can accommodate larger data sets than cluster analysis. In Quick Cluster, the algorithm used for determining cluster membership is based on nearest centroid sorting. According to Norusis (1986: chap. 4), ‘a case is assigned to the cluster for which the distance between the case and the center of the cluster (centroid) is smallest’. The centroid method calculates the distance between the two clusters as the distance between their means for all the variables. An index of measurement commonly used in cluster analysis is squared Euclidean distance-the sum of all of the squared differences over all the variables. The quality of a cluster solution can be compared to other possible solutions by listing each case’s squared Euclidean distance from the cluster centre. For this study the best and most tightly clustered solution obtained had no cases more than 8.0 from the centre, and most cases below 4.0. Entwistle and Brennan (1971: 268-269) said that cluster analysis is one of the few statistical procedures which follows a ‘typological’ approach, and that ‘in cluster analysis, people whose profiles of scores are similar are grouped into

194 clusters to describe types of individuals’. Cluster analysis allows one to take all four scale scores and the relationship among them into account, and resolves cases of ambiguity when two or more scale scores are similar; thus it offers certain advantages over simply grouping students according to their highest scores. There is, however, a disadvantage created by lack of comparability in future research, as clusters are not readily replicable.

There was a .35 correlation (significant at the .001 level) between cluster and self-rating of academic ability and progress, so that was used as a covariate rather than a separate independent variable.

C. Speth and R. Brown

RESULTS

This study investigated whether there were significant differences between the essay and the multiple-choice exam, among approach to studying clusters, or between males and females on a set of test preparation strategies, controlling for self-rating of academic ability (main effects); and whether approach to studying, type of test, or gender (in any combination) jointly affected a set of test preparation strategies, controlling for self-rating of academic ability (interactions). The six test preparation strategies were submitted to a 4 ~ 2 x 2 (cluster x gender X type of test) multiple analysis of covariance, controlling for self-rating of academic ability. None of the MANCOVA effects were significant. However, when scores on the four ‘cognitive’ dependent variables (time-effort, integration, selection and cognitive monitoring) were submitted to a 4 ~ 2 x 2 MANCOVA, there was a significant three-way interaction, Wilk’s lambda= .942, F(3,356)= 1.78, p<.046. No two-way interactions were significant. There were significant differences among the approach groups, as indicated by a main effect for cluster, Wilk’s lambda=.729, F(3,355)=9.87, pc4.01. There was no main effect for gender or type of test.

It appears that these four strategies are influenced jointly by approach to studying, type of test, and gender. The results of the three-way interaction are illustrated by Figures 1-6. Figure 1 shows that, on time-effort, males in all clusters were similar on time-effort for the multiple-choice but not the essay test, where they separated into (meaning + strategic) vs. (reproducing + non-academic). On multiple-choice, females separated into the same two levels: deep-capable vs. predominantly surface, but on the essay the non-academic females were substantially different from the reproducing cluster, making three levels of time and effort. Within the strategic orientation, males indicated they would invest considerably more time and effort in the essay test, females indicated they would invest slightly more in the multiple-choice test. There appears to be a female deep and a female surface approach to multiple-choice tests, and a male deep and a male surface approach to essay tests, at least on time-effort. Essay tests in history may influence the top two clusters of males and the non-academic females to invest more time and effort in studying, but in this experiment there was no way to determine whether greater interest in the task, or greater perceived task difficulty, might offer a better explanation.

One-way analyses of variance had suggested that integration separated the meaning-oriented from the other three clusters; Figure 2 shows that was especially true for males in the multiple-choice and females in the essay condition. Figure 2

Style and Strategy 195

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suggests little difference between non-academic males and females on integration for either test. Since integration is a deep approach strategy, one might expect a non-differentiated response from the two surface groups. Female strategics integrate about the same on either test, while male strategics integrate more on the essay exam (where integrative study is more likely to be rewarded). Female meaning-oriented students were higher on integration for the essay test, while males in that cluster were higher on integration for the multiple-choice test. This suggests that only males who are intrinsically motivated will bother with integrative test preparation activities in situations where they are not generally rewarded.

On selection, shown in Figure 3, there was no difference between females in the reproducing and non-academic clusters in the multiple-choice, but a slight difference in the essay condition. Selection separated the four groups fairly well for males, who had four levels of cue-deafness on both tests. The non-academic females were higher in cue-deafness than the strategics on the multiple-choice test, but equal on the essay test. Within the reproducing orientation, males were more cue-deaf in the essay, females in the multiple-choice, condition.

C. Speth and R. Brown

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Cognitive monitoring had lower reliability than integration and selection, so one hesitates to make too much of Figure 4, but on cognitive monitoring there were different patterns for males and females in all four clusters depending on type of examination. Except for the meaning-oriented cluster, males were about the same on the multiple-choice test, but spread out into three levels on the essay. The four clusters were similar for females on the essay test, but spread out into three levels

Style and Strategy 197

Cognit. Monitor: Multiule

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Figure 5. Mean scores on peer orientation subscale by type of test, gender and approach to studying cluster.

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on the multiple-choice. For both sexes there was little difference between the non- academic and reproducing clusters on cognitive monitoring.

Figure 5 shows that the reproducing cluster had the highest scores on peer orientation in both test conditions. Both sexes and all four clusters were similar on peer orientation in the multiple-choice condition, but in the essay condition, students in the reproducing cluster were substantially more inclined to seek advice from each other about how to study. Within the strategic cluster, males were higher on peer orientation in the essay, females in the multiple-choice condition.

Figure 6 suggests that males in the reproducing group were most likely to engage in ‘skimming’ or ‘filtering’ test preparation strategies, especially for essay tests. Perhaps male students already predisposed to use a surface approach view essay tests as not requiring attention to specific facts and details, and therefore see them as opportunities to ‘get by’ on vague generalizations.

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Figure 6. Mean scores on average student subscale by type of test, gender and approach to studying cluster.

DISCUSSION

Schmeck (1983) noted with regret the fact that so few studies include both learning styles and strategies, but designing an experiment to do both with quantitative methods and a large student sample-given the extent of overlap between styles and strategies-is difficult. The results of factor analyses of the subscales, indicating the extent of correlations among the subscales, are reported elsewhere (Speth and Brown, 1988). It is possible that operationalization of the (general)

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learning style variable using the Approaches to Studying Inventory, and operational- ization of learning strategies (in a specific assessment situation) using the test preparation activities survey-both self-report measures-were not sufficiently distinct to avoid overestimating the degree of relationship because of circularity, and that is one of this study’s limitations. No previously published study had used these theories, instruments or variables in quite the same way. After the interactions were sorted out, the MANCOVA results were not so surprising, except in their complexity-which went beyond what Watkins and Hattie (1981) led one to expect. The three-way interaction for cluster, gender, and type of test at least challenges the blanket assertion made by Hakstian (1971: 319), who paid no attention to individual characteristics, that there is no difference between students’ preparation activities for the two kinds of tests.

One must go outside the student learning literature to interpret the gender- related interactions. Studies summarized by Tavris and Wade (1984: 4243,4849) suggested that girls perform better than boys on verbal tasks, and boys (after adolescence) do somewhat better than girls on tests of mathematical reasoning (primarily word problems). Badger (1985: 229, 231) reviewed research on girls’ lower achievement in mathematics, and found that what might be called analytical deficiencies and over-reliance on inappropriate verbal (as opposed to spatial visualization) strategies had been suggested as possible explanations. One can accept or reject these explanations, but it seems likely that many people assume that females are better at verbal tasks, and males are better at tasks involving analytical reasoning (which might include multiple-choice items as well as word problems), and this assumption may help explain gender differences in how students think about the two types of examinations. Females may consider multiple-choice tests more problematical or challenging, requiring more time and effort to prepare for, than males in the same clusters. Within the reproducing orientation, females indicated they would probably have more trouble selecting what to study and how to prioritize the material in the multiple-choice condition, males indicated more trouble with selection (in other words, more cue-deafness) in the essay condition. Students in the reproducing cluster are most likely to seek advice from their peers; strategics would only do so when they are uncertain. Males in the strategic cluster sought more advice from their peers regarding essay tests; females in the same cluster sought more advice from their peers regarding multiple- choice tests. A written comment by a female student sheds some light on this result, complaining that many multiple-choice exams test students’ logic rather than their knowledge, implying that this is unfair, except in a course on logic. One might almost expect to find a parallel comment by a male student complaining that many essay exams test students’ writing skills rather than their knowledge, implying that this is unfair, except in a course on writing. Future studies might include questionnaire items or interview questions to illuminate the role of gender and examination type, and perhaps even some measure of gender-related attributes, for example instrumentality and expressiveness (Spence and Helmreich, 1980: 150).

This study underlined the importance of monitoring the effects of examination type on different types of students, and suggests one methodology for doing so, using one of the few statistical procedures which follows a typological approach- cluster analysis. Although integration is closer to deep approach, and selection to surface approach, the design of this study did not allow one to take a stand on

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whether essay tests ‘promote superior study methods’ or multiple-choice tests encourage rote learning strategies, unlike P. R. Thomas and Bain (1982) and P. R. Thomas and Bain (1984), who used: (a) factor analysis to classify study activities into deep or surface categories, (b) real rather than hyptothetical tests, and (c) a design allowing within-student comparisons. On the other hand, this study suggested that contrasting deep and surface approaches as they did, without considering type or level of motivation, might cause us to miss details of special interest to instructors (for example the unusual reaction of female non-academics to the essay test).

It is difficult to generalize about this study’s implications for instruction because instructors, academic departments and institutions vary in what they hope to accomplish, what learning strategies they want to encourage, and whether they want to (intentionally) give certain types of students a little boost. Some instructors might agree with Biggs (1973: 166) who concluded that college teachers should use more than one type of assessment in order to be fair to students with different learning styles. Other instructors or administrators, working from a developmental or compensatory education point of view, might be interested in how different types of examinations help or hinder students in lower achievement categories. They might use that information to target their efforts to change students (rather than assessment practices). Other instructors or administrators, troubled by the under-representation of male or female students in their departments, might conclude that they need to consider the impact of their assessment practices. Perhaps it is no accident that some fields of study which attract fewer women have traditionally relied more on objective examinations. It might be worthwhile to investigate whether females prefer essay tests, whether males prefer multiple- choice tests, and whether those preferences influence decisions about courses or major fields of study. Finally, one wonders about possible gender effects if instructors tend to use multiple-choice tests in large lower-level survey classes, and essay tests in smaller, more specialized or advanced classes. Could it be that some women are discouraged from advanced study in non-traditional fields because of the prevalence of multiplekhoice tests in the introductory survey courses?

Perhaps inevitably, studies such as this suggest more questions than they answer. Another new line of investigation might be the relationship of students’ approach to learning to their evaluations of instructors. Some departments and instructors are committed to encouraging students t9 take a deeper approach, but that goal may conflict with many students’ intentions-limited to getting passing grades or getting the best possible grades with the least possible effort. Given the large proportion of students classified as reproducing or strategic in this study, departments might want to reconsider the unweighted, one-person-one-vote way in which course evaluations are often tabulated, because assessment practices which keep such students comfortable or satisfied seem unikely to deepen their approach to learning.

Studies including approach to studying, gender, and type of test may prove a fertile field for research from the perspectives of instructional evaluation and improvement, developmental education, or educational equity. However, researchers should be warned that they will need vast amounts of mainframe computer memory, patience, ingenuity and intrinsic motivation.

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