Exploring the Motivation Orientation and Learning Strategies of First Year University Learners

MARK WATSON, MICHELLE MCSORLEY, CHERYL FOXCROFT ANDANDREA WATSON

EXPLORING THE MOTIVATION ORIENTATION AND LEARNINGSTRATEGIES OF FIRST YEAR UNIVERSITY LEARNERS

ABSTRACT. Both internationally and within South Africa the failure rates at universitiesare presently high and throughput and graduation rates are low. It is thus imperative thatthe cognitive and non-cognitive predictors related to identifying learners who will succeedacademically are explored. This paper focuses on two potentially important non-cognitivepredictors, namely motivational orientation and the use of learning strategies. Learnersare described according to these constructs, and the relationship of these constructs toacademic performance is also explored. In addition, the patterns of performance of thelearners are explored. Using a convenience sampling technique, learners from a first yearpsychology class were surveyed using the Motivated Strategies for Learning Questionnaire(MSLQ). Academic performance was measured using the percentage score learners earnedat the end of their first semester. The statistical analysis was descriptive and correlationalin nature. Important trends and relationships are reported. The implications of the findingsfor the admission and development of first year higher education students are discussed.

CONTEXT OF THE RESEARCH

There has been an international movement in higher education institutionsto re-examine selection and admission criteria in order to broaden access,improve graduation rates and qualify learners in accordance with labourmarket demands (Harman 1994). The consequences of such broader accesspolicies have been high failure and dropout rates and low levels of insti-tutional efficiency, with only 10% to 25% of graduates completing theircourses in the prescribed time (Harman 1994).

At South African universities, graduation rates are currently so low asto warrant an investigation by the Department of Education. Specifically,the failure rate at tertiary institutions is presently high and the throughputrate is low. The Minister of Education, Kadar Asmal, in his report ofFebruary 2001 announced that throughput rates would have to improvefrom the current 15% to at least 30% over the next five years (Ministry ofEducation 2001). It is thus imperative that those learners who will succeedacademically are identified and their developmental needs addressed.

A large body of literature suggests that many of the differencesbetween successful and unsuccessful learners can be explained in terms

Tertiary Education and Management 10: 193–207, 2004.© 2004 Kluwer Academic Publishers. Printed in the Netherlands.

194 MARK WATSON ET AL.

of the use of self-regulated learning strategies (e.g., Pintrich & DeGroot 1990; Pintrich et al. 1991). The use of learning strategies enableslearners to actively process information, thereby influencing their masteryof academic material and subsequent academic achievement. Althoughinstruction in the use of self-regulation strategies has a positive impact onthe academic achievement of learners, researchers agree that instructionalone is not sufficient to ensure self-regulated learning (Livingstone 1996;Pintrich et al. 1991).

In addition to appropriate instruction and practice in the use of cognitiveand meta-cognitive self-regulation strategies, learners also need to bemotivated. Researchers who have examined the relationship betweenlearning and the use of self-regulated strategies have concluded thatmotivational factors are critical to strategy use (Ames & Archer 1998;Pintrich et al. 1991).

In the search for non-cognitive factors related to academic success, it isbest to be grounded in a theory. While Tinto (1974) and Chickering (1987)have proposed insightful theories, the theory that predominates the field oflearning and cognition at present is the social cognitive theory.

SOCIAL COGNITIVE THEORY

Social cognitive theory (SCT; Bandura 1986) seeks to understand anddescribe human cognition, emotion, action and motivation. The theoryfocuses on the influence of self-efficacy beliefs and outcome expectationson goals and behaviour (Stitt-Gohdes 1997). Specifically, SCT proposesthat, if individuals believe in their ability to undertake an endeavour andhave an expectation of the outcome of that behaviour, they will behave ina way that will help them achieve their goal.

Bandura (1986) developed a triadic model of reciprocal causality whichindicates that, while the environment influences behaviour, the environ-ment is also a product of the person’s own making. According to De Bruin(1999), “people contribute actively to shaping their environment (whilethe environment is shaping them), and are therefore not viewed as help-less victims of their environment” (p. 93). The theory emphasises the roleof cognitive processes by suggesting that people have the unique humanability for symbolic thought and are able to symbolise possible outcomesof actions and, in so doing, anticipate or predict the consequences oftheir behaviour. Another important aspect of SCT is that people learnvicariously by observing others’ behaviour, as well as the subsequentconsequences that behaviour elicits (Bandura 1986). In this manner people

EXPLORING THE MOTIVATION ORIENTATION AND LEARNING STRATEGIES 195

acquire an efficient learning of complex skills that would not be masteredthrough trial and error.

PROBLEM FORMULATION AND MOTIVATION

From the literature overview it is clear that social cognitive mechan-isms have a role to play in aspects regarding motivation and learningstrategies. There is a need to examine non-cognitive factors that areassociated with academic performance. Prior research has suggested thatself-efficacy beliefs, goal orientation and self-regulation have a signifi-cant effect on academic performance (Bandura 1986; Pintrich & Smith1993). Consequently, it is important that research be conducted using theseconstructs with South African learners to determine whether similar corre-lations with academic performance are found. If so, these constructs mayprove to be of value in creating academic development initiatives with at-risk learners. The present article reports on a study that has attemptedto initiate preliminary research of non-cognitive factors associated withacademic performance.

Primary aims

The primary aims of the present research were:

1. To explore and describe the motivational orientation and learningstrategies of first year psychology learners;

2. To explore the relationship between motivational and learning strategyconstructs and academic performance of first year psychologylearners; and

3. To explore and describe the patterns of motivational orientation andlearning strategies of first year psychology learners, and the relation-ship of such patterns to academic performance.

The implications of the findings for the admission and development of firstyear tertiary learners will also be discussed.

RESEARCH METHODOLOGY

Research design

A quantitative methodological approach was used as numerical valueswere assigned to the data (De Vos 1998). To achieve aim one, anexploratory-descriptive design was employed. Furthermore, a correlational


design was used for aim two which investigated the relationship betweenmotivation and learning strategy constructs and academic performance.A correlational design was also employed to achieve aim three, with abetween-groups comparison method used when testing for differencesbetween clusters.

Participants and sampling procedure

Using a convenience sampling technique (De Vos 1998), learners from afirst year psychology class were surveyed using the Motivated Strategiesfor Learning Questionnaire (MSLQ; Pintrich et al. 1991). The sampleconsisted of 81 participants, of whom 79% were female and 21% weremale. In terms of culture, 48% of the learners were White (n = 39); 31%African (n = 25); 20% Coloured (n = 16); and 1% Indian (n = 1). The meanage was 21 years, with the age range varying between 18 and 38 years.

Measure

The Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich etal. 1991) is a self-report instrument designed to measure college learners’motivational beliefs and use of learning strategies. The MSLQ is based on ageneral social-cognitive perspective of motivation and learning strategies,with the learner represented as an active processor of information whosebeliefs and cognitions are important mediators of instructional input andtask characteristics (Pintrich 1988, 1989; Weinstein & Mayer 1986). TheMSLQ can provide student development educators with essential informa-tion for establishing structured training for university learners (Pintrich1995). This viewpoint is based on the learning strategy literature thatassumes that learners’ motivation and use of learning strategies can becontrolled by the learners and changed through teaching. Universities areoften in need of ways to help learners succeed once they have enrolled.The information that can be gained from assessment with the MSLQ canbe valuable in guiding high-risk students to success.

There are two sections to the MSLQ, a motivation section and a learningstrategy section. Table I illustrates these sections.

The Motivational Section proposes three general motivationalconstructs (Pintrich et al. 1991): value, expectancy, and affect. Valuecomponents focus on the reasons why learners engage in an academictask. The expectancy components refer to learners’ beliefs that they canaccomplish a task. The affect component has been operationalised in termsof responses to the test anxiety scale, which taps into learners’ concernover taking examinations. The motivation section consists of 31 items thatassess learners’ goals and value beliefs for a course, their beliefs about


TABLE I

MSLQ scales

Motivation section Learning strategies section

Scales Subscales Scales Subscales

Value Intrinsic goal orientation Cognitive Rehearsal

components Extrinsic goal orientation strategies Elaboration

Task value Organisation

Critical thinking

Expectancy Control of learning beliefs

components Self-efficacy for Meta-cognitive Meta-cognitive

learning performance control strategies self-regulation

Affective Test anxiety Resource Time and study

component management environment

strategies management

Effort regulation

Peer learning

Help-seeking

their ability to succeed in a course, and their anxiety about tests on thecourse.

The Learning Strategy Section is based on a general cognitive modelof learning and information processing (Pintrich et al. 1991). It containsthree general types of scales: cognitive strategies, meta-cognitive controlstrategies and resource management strategies. There are 31 items thatassess learners’ use of different cognitive and meta-cognitive strategies.In addition, the learning strategies section includes 19 items concerninglearners’ management of different resources.

The MSLQ has received broad acceptance and use by others (Pintrich etal. 1991). Pintrich et al. (1991), Pintrich and Smith (1993) and McClendon(1996) have demonstrated that the MSLQ is a reliable and valid measureof self-regulated learning. The total reliability of the motivation scales is0.79 and the values of Cronbach’s alpha for each motivational subscaleare acceptable, ranging between 0.57 and 0.84. The total reliability ofthe learning strategies scales is 0.89 and the values of Cronbach’s alphafor each of the learning strategies subscales are also acceptable, rangingbetween 0.62 and 0.83.


Procedure

The measure was administered to a first year psychology class. Permissionwas gained from the learners for the researcher to use the data generatedfor research purposes, under the guarantee of anonymity. Participants wereinformed that they would receive feedback on their performance as a wholeclass.

The MSLQ data was captured on to an Excel spreadsheet and analysedaccording to the procedures described in the Statistical Analysis section.Biographical information was downloaded from the specific university’sIntegrated Tertiary System (ITS) and incorporated into the Excel spread-sheet. The Statistica software statistical package (StatSoft 2001) wasused.

Statistical analysis

The data analysis followed three stages. First, descriptive statistics werecarried out, with means, standard deviations and ranges computed for eachsubscale. Second, simple correlation analysis was conducted to determinewhether the motivation and learning strategy variables were relatedto academic performance. Finally, cluster analysis was performed todetermine whether learners could be grouped according to their academicperformance on the six subscales of the MSLQ.

RESULTS AND DISCUSSION

The results of the study are reported according to the steps followed inthe statistical analysis. Whenever appropriate, relevant interpretation anddiscussion of the results are presented together with the results.

Descriptive statistics

Table II presents the means, range and standard deviations for the sixmotivation subscales and the three scales. The mean scores of the extrinsicgoal orientation, task value, control of learning beliefs, and self-efficacyfor learning and performance subscales are above the scale mid-points,indicating that the learners generally reported relatively high levels offunctioning in these respective domains. The means for intrinsic goalorientation and test anxiety fall within the average range.

The mean scores for the value and expectancy components are high.This indicates that learners are interested in the content area of their courseand feel confident that they will master the course material. The mean score


TABLE II

Descriptive statistics: motivation section

Mean Minimum Maximum SD

Value component 4.32 3.39 4.94 0.37

Intrinsic goal orientation 3.97 2.25 5 0.63

Extrinsic goal orientation 4.41 3 5 0.56

Task value 4.57 3.67 5 0.32

Expectancy component 4.23 2.75 5 0.42

Control of learning beliefs 4.28 2 5 0.57

Self-efficacy 4.17 2.5 5 0.46

Affective component 3.04 1 4.8 0.86

Test anxiety 3.04 1 4.8 0.86

for the affective component is average and suggests that learners are notoverly anxious in test situations.

Table III presents the means, range and standard deviations for thenine learning strategy subscales and the three scales. The mean scorefor the elaboration subscale is high and indicates that these learners usethis strategy often. The remaining subscales all fall in the average rangerevealing appropriate use of study skills and learning strategies. The meanscores for the cognitive, meta-cognitive and resource management scalesare all average. This indicates that the learners generally make use ofappropriate learning strategies and study skills. Furthermore, the learnerstry and plan their work and exert effort in their studies.

Correlation analysis

To examine the interrelationship between motivation, learning strategy andacademic performance variables, Pearson product-moment correlationswere computed. The results are shown in Tables IV and V.

The three subscales that form the value component of the MSLQ showsignificant correlations with academic performance. There is a significantpositive correlation between the value component and academic perfor-mance, r = 0.34 (p < 0.05). The results indicate that the greater the intrinsicand extrinsic value, interest, importance and utility the learners found inthe course, the higher their marks.

There is a moderately positive correlation between the self-efficacy forlearning and performance subscale and academic performance, r = 0.35(p < 0.01). This element of the expectancy component demonstrated that


TABLE III

Descriptive statistics: learning strategy section

Mean Minimum Maximum SD

Cognitive strategies 3.87 2.44 4.89 0.58

Rehearsal 3.9 1.75 5 0.74

Elaboration 4.07 2 5 0.61

Organisation 3.86 1.75 5 0.82

Critical thinking 3.65 1 5 0.79

Meta-cognitive strategies 3.65 2.67 4.83 0.53

Meta-cognitive self-regulation 3.65 2.67 4.83 0.53

Resource management 3.54 2.36 4.7 0.53

Time and study environment management 3.65 2.13 5 0.66

Effort regulation 3.77 1.5 5 0.78

Peer learning 3.2 1.33 5 0.94

Help-seeking 3.51 1.75 4.75 0.72

learners who believed in their own effectiveness and who expected to dowell were more likely to earn higher marks. The expectancy component issignificantly correlated with academic performance, r = 0.33 (p < 0.05).

Anxiety was inversely related to academic performance: the higherthe anxiety, the poorer the academic performance. However, the correla-tion between the affective component and academic performance was notsignificant, r = 0.04 (p > 0.05).

Of the cognitive strategies, rehearsal, elaboration and critical thinkingwere significantly correlated with academic performance. The cognitivestrategies scale was significant at r = 0.35 (p < 0.01). Thus enga-ging directly with the material to be learned is important for academicachievement.

Meta-cognitive strategies, that is consciously thinking about one’sapproach to learning (Pintrich & Smith 1993), showed a moderate, positiverelationship with academic performance. Learners who planned theirstudying, monitored ongoing results, and regulated or adjusted their beha-viour in response to changing demands of the course performed better interms of academic achievement.

The resource management scale revealed a significant correlation withacademic performance, r = 0.33 (p < 0.01). The time and study environ-ment management and effort regulation subscales were significantly corre-lated with academic performance. Learners who organised their study


TABLE IV

Correlation analyses: motivation and academic performance

Motivation scales and subscales Academic performance

Value component 0.34*

Intrinsic goal orientation 0.26*

Extrinsic goal orientation 0.32**

Task value 0.29*

Expectancy component 0.33*

Control of learning beliefs 0.2

Self-efficacy 0.35**

Affective component –0.04

Test anxiety –0.04

*p < 0.05; **p < 0.01.

TABLE V

Correlation analyses: learning strategies and academic performance

Learning strategy scales/subscales Academic performance

Cognitive strategies 0.35**

Rehearsal 0.26*

Elaboration 0.34**

Organisation 0.22

Critical thinking 0.28*

Meta-cognitive strategies 0.33**

Meta-cognitive self-regulation 0.33**

Resource management 0.33**

Time and study environment management 0.39***

Effort regulation 0.40***

Peer learning 0.03

Help-seeking 0.13

*p < 0.05; **p < 0.01; ***p < 0.001.

time, established a regular place to study, and consciously persisted in theirefforts to learn the material earned higher marks.

Cluster analysis

Both hierarchical cluster analysis (HCA) and k-means cluster analysisprocedures were used in this study. The HCAs were conducted as a


TABLE VI

Descriptive statistics for the clusters

Cluster 1 Cluster 2

Subscales Mean SD Mean SD

Value component 4.12 0.31 4.55 0.28

Expectancy component 4 0.41 4.48 0.25

Affective component 3.15 0.77 2.92 0.94

Cognitive strategies 3.46 0.41 4.33 0.34

Meta-cognitive strategies 3.28 0.32 4.06 0.39

Resource management strategies 3.17 0.38 3.94 0.33

Figure 1. Plot of cluster means.

preliminary step in determining the number of clusters to fit the data. Basedon the results, a two-cluster solution was selected in the subsequent k-means analysis. In all cluster analyses, the distances among the learners,or among learners and cluster, were computed using the squared Euclideandistance formula. Descriptive statistics for the k-means solution arepresented in Table VI.

The means for each cluster for each subscale are graphically presentedin Figure 1. The clusters were labelled as follows:

Cluster 1. Average functioning: This group generally performed in theaverage category on the six variables and can be regarded as anaverage functioning group.

Cluster 2. Above average functioning: This group generally functionedin the above average range on the six variables and was thusregarded as being an above average functioning group.


TABLE VII

Differences between the clusters

Subscales df t p S/NS*

Value component 79 –6.47 0.00 S

Expectancy component 79 –6.26 0.00 S

Affective component 79 1.2 0.24 NS

Cognitive strategies 79 –10.28 0.00 S

Meta-cognitive strategies 79 –9.89 0.00 S

Resource management strategies 79 –9.62 0.00 S

*S = significant, NS = not significant.

To further explore differences between the clusters, an independentHotelling’s T 2-test was performed. The Hotelling’s T 2-test results wereas follows: F (39,875) = 3.233, p < 0.001. The performance of the twogroups was thus found to differ significantly on the six MSLQ variables.Post hoc t-tests on the six MSLQ subscales are reported in Table VII.

Results obtained from the post hoc t-tests showed differential perfor-mance on five of the six subscales for the two clusters. The averagefunctioning cluster performed significantly lower than the above averagefunctioning cluster. The value component, expectancy component,cognitive strategies, meta-cognitive strategies and resource managementsubscales discriminated best between the two clusters.

Validating the cluster solutions

Clustering procedures cluster the data regardless of whether truly differentgroups of examinees are present (Sireci & Robin 1999). Thus, an externalcriterion is needed to provide evidence of the validity of the cluster solutionto ensure that the resulting clusters are quantitatively different from oneanother. The final grades obtained by the learners for the first semesterwere used as the external criteria to validate the cluster solutions.

An independent t-test was conducted on the k-means solution usingcluster membership as the independent variable and academic perfor-mance as the dependent variable (see Table VIII). Given the academicprofile of the present sample in which most learners passed their first yearpsychology modules, it seemed appropriate to follow the convention ofidentifying the upper 25% and lower 25% of the learners. In view of thenegatively skewed nature of the distribution of the academic performanceof the learners, the identified groups were labelled as average and aboveaverage. Results revealed a significant difference between the academic


TABLE VIII

Cross-tabulation: cluster group and academic performance category

Lower 25% Upper 25% Row

Cluster 1 13 5 18

Column percent 65.00% 23.81%

Row percent 72.22% 27.78%

Cluster 2 7 16 23

Column percent 35.00% 76.19%

Row percent 30.43% 69.57%

Totals 20 21 41

Total percent 48.78% 51.22% 100.00%

performance of the average functioning group (M = 63.98) and the aboveaverage functioning group (69.27), t(79) = –3.40, p < 0.001. The aboveaverage functioning cluster performed better than the average functioningcluster.

CONCLUSIONS

The mean scores reported for the motivation and learning strategy scalesindicate that the learners are generally functioning well. They are inter-ested in the course, feel confident that they will master course material,and are not anxious in exam situations. Furthermore, the scores indicatethat they are able to effectively use different study skills and learningstrategies.

In summary, with regards to academic performance, 9 out of 15 motiva-tion, learning strategies, and resource management scales were related toacademic performance for this first year psychology class. In general, thosewho saw personal value in the course, who believed they exerted specialeffort, who knew how to plan and direct their study efforts, who rehearsedand elaborated on elements of the course, and who managed their timeand study place well, performed better than those who did not. These find-ings are supported by international research. For instance, Moody (1993)established that 20 out of 25 motivation, learning strategies, and resourcemanagement scales of the MSLQ were related to the academic learning ofcollege learners, while Pintrich and Smith (1993) found that the motivationsubscales of the MSLQ were significantly related to the final academicgrades of tertiary learners. In addition, there are several American studies


that have established that the motivation and learning strategies scalesof the MSLQ are significantly related to the academic performance ofsecondary and tertiary learners (DeKeyrel et al. 2000; McManus 2000;Pokay & Blumenfeld-Phyllis 1990).

Cluster analysis results indicate that the profiles differ on the MSLQsubscales and on academic performance. On post hoc analyses, theabove average functioning group differed significantly from the averagefunctioning group on five of the MSLQ subscales and on academicperformance. The above average functioning group performed betterthan the average functioning group. A chi-square test of independencerevealed statistically significant differences between the two clusters andthe proportion of learners in the academic performance categories. Highachievers had significantly higher scores on the MSLQ than low achievers.

The implication of this finding is that if this information were availableat the time of admission, it could assist in identifying high risk learners,who could then be placed in appropriate developmental programmes.The findings present another important implication. The fact that lowerachieving learners reported less use of learning strategies and motivationindicates that if lecturers could encourage these learners to use learningstrategies, these learners could improve their academic performance. Theteaching of these strategies should be built into induction and staff devel-opment programmes. The fact that the measure used in the present researchis based on an established theory would help in providing a framework forthe designing of such programmes.

LIMITATIONS AND RECOMMENDATIONS

Future research should address the limitations of the present study. Suchlimitations include the small sample size, the exploratory nature of thestudy that prevented cause-effect conclusions, and the lack of qualitativemethods for confirming or disconfirming the quantitative results. Whilethe level of statistical analysis is appropriate given the preliminary natureof the present research, more sophisticated correlational and regressionanalyses should be performed with larger, more representative and inter-national samples in future research. Given the preliminary nature of thefindings, the present exploratory research needs to be extended to variousacademic institutions and different academic disciplines.


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Department of PsychologyUniversity of Port ElizabethP.O. Box 1600Port Elizabeth, 6000South AfricaE-mail: [email protected]

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Exploring the Motivation Orientation and Learning Strategies of First Year University Learners