The present article aims to:

• explore gender differences in NUS students’ learning approach and academic performance;
  
  
• discuss any differences in light of previous research; and
  
  
• suggest possible steps to improve students’ learning.

This article does not intend to stereotype any gender; rather, it is hoped that the findings reported here will shed more light on NUS students’ approaches to learning.

The Study Process Questionnaire and approaches to learning

The Study Process Questionnaire by John Biggs (1987) based on Marton & Saljo’s theory of deep and surface learners, operationalises approach to learning by measuring a student’s learning motive and strategy. Motive refers to the reason why students approach learning tasks and their studies, while strategy refers to the methods and habits they engage in to accomplish the task. Surface motives include fear of failure, or wanting just to obtain a paper qualification, and may drive students to employ such surface strategies as memorising learning material without first comprehending it. Deep motives, on the other hand, entail an intrinsic interest in the subject and a desire for understanding per se, which usually drives students to deep strategies like taking the initiative to find out more about a topic and seeing interrelationships among different concepts.

In addition to deep and surface approaches, Biggs also defined a third: the achieving approach. The achievement-motivated student is driven by competition with peers for the highest marks. As such, achieving strategies are engaged, such as: choosing modules that the student feels confident in, and studying material deeply insofar as it is pertinent to the examination. In relation to academic outcomes, the use of a surface approach is associated with inappropriate learning and poor grades, while an achieving approach is associated with high grades. The deep approach is deemed best, as it both stimulates optimum learning and produces good grades.

The model of student approaches to learning outlined above is based on Biggs’ (1987) 3P model, where learning comprises 3 inter-related components: presage (student-based factors and the learning environment), which affects the process (how students engage in the task), which determines the product—the learning outcome. The present study examines gender as a presage factor, particularly focussing on differences in dominant motives and preferred learning strategies.

CDTL’s study on NUS students’ approach to learning

Since 2001, CDTL has been working on a University-wide project to study the approaches to learning of NUS students over the course of their studies, using an adapted form of Biggs’ (1987) SPQ. The details of this project are available at: http://www.cdtl.nus.edu.sg/research/learnprofile.htm. In this article, the following areas of gender differences are addressed:

1. performance at NUS in terms of CAP;
   
   
2. study motives and strategies; and
   
   
3. the influence of motives and strategies on performance (i.e., CAP).
   

Results

For the purposes of this article, only data collected in 2002 were analysed. The sample comprised 1061 first-year students (344 males; 717 females) across all faculties except Medicine, Dentistry, and Law, as these do not operate under the CAP grading system.

Gender differences in CAP scores

On average, males scored higher than females: 3.56 (S.D.=.74) vs. 3.34 (S.D.=.67) respectively. A stepwise regression confirmed that gender is a significant predictor of CAP (adjusted-R²=.021, F=23.261, p<.001) and that gender differences in CAP are not due to differences in age (R²-change=.003, F-change=3.206, n.s.). However, it should be noted that the size of this gender effect is rather small.

Gender differences in motives and strategies

Descriptive statistics, listed separately by gender, are summarised in Table 1 below.

Across genders, one-way ANOVAs revealed that males scored slightly higher than females on achieving motives (F=7.033, p<.01), although this effect was small. Males also scored slightly higher than females on deep strategies (F=10.362, p<.001).

Within each gender, one-way ANOVAs and post-hoc tests revealed that for males, both deep and achieving motives are the most dominant, with surface motives being less endorsed, i.e. DM, AM > SM (F=39.586, p<.001). However, for females, deep motives are the most dominant, followed by achieving motives, and then surface motives, i.e. DM > AM > SM (F=112.346, p<.001). Both genders also seem to prefer deep strategies the most, and achieving strategies over surface strategies, i.e. DS > AS > SS (F=40.113, p<.001 for males; F=42.420, p<.001 for females).

Do motives and strategies influence how gender affects CAP?

To answer this question, each individual was assigned a highest-motive and a highest-strategy based on his or her highest score on deep, achieving or surface dimensions. Individuals with equally high scores on more than one dimension were grouped uniquely according to the combination of dimensions they scored highest on. All the groups had more than 20 cases each, except for the groups with all three dimensions at equally high levels. These two groups were excluded from the analysis, leaving 340 male and 710 female cases for the gender by highest-motive analysis, and 343 male and 713 female cases for the gender by highest-strategy analysis. Following this, separate two-way ANOVAs were performed against CAP, with gender and each of the new grouping variables as independent variables.

No significant interaction was found between gender and either highest-motive or highest-strategy. However, these analyses revealed that motive, at least slightly, related to CAP (F=3.485, p<.01). Students who scored the highest on only achieving motives obtained higher CAP than those who scored the highest on just deep or surface motives. No effect was observed for strategy.

Discussion

In summary, the analyses revealed several gender differences. In comparison to females, males

1. performed slightly better in terms of CAP;
   
   
2. endorse Achieving Motives slightly more; and
   
   
3. utilise marginally more Deep Strategies.

It should be noted, however, that all of these gender differences were small in magnitude, and that motives and strategies do not influence how gender affects CAP.

How then can males’ better grades be explained? A review of gender research using Kolb’s Learning Style Inventory found that males scored higher on the Abstract Conceptualisation scale, indicating a preference for logical thinking and rational evaluation, which are deep strategies; they were also found to excel in impersonal learning situations emphasising theory and systematic analysis (Severiens & Ten Dam, 1994). In contrast, female students using a deep approach (identified as ‘comprehension approach’) tend to look for personal connections and relevance (identified as ‘elaborative processing’) with learning material (Meyer et al., 1994).

These two findings could explain why female NUS students score lower on deep strategies, since they may find it harder to relate some course material to their personal experiences. It is also possible that if the more distinct deep strategies of abstract conceptualisation and elaborative processing were studied, then it would be found that males’ higher grades reflect an emphasis on learning outcomes associated more with abstract conceptualisation than with elaborative processing. However, this calls for further research.

Finally, it is surprising to note that those who scored the highest on just deep motives or strategies performed no better than those who scored the highest on just surface motives or strategies, since deep learning is supposed to be a fundamental goal of education. Although no gender differences were found with regards to this, it is an important issue that should be addressed in the future.

Teaching implications

Although the SPQ has been noted for its tenuous relationship with grades (Najar & Davies, 2001), it is still helpful for examining the quality of student learning (Sivan et al., 2000)—seeing how deeply students engage in their learning, as defined by a propensity toward deep motivation and deep strategies. The present results suggest that both male and female students here at NUS are comparably deep learners, but male students tend to be more achievement-driven, and seem to have a slight edge over their female peers in their usage of deep strategies.

Raise awareness of effective learning strategies

Taking a closer look at gender differences in specific responses to the SPQ, males were more likely than females to engage the following deep strategies: thinking of real-life applications of subject material, and drawing links between previous knowledge and new information. As such, instructors could take care to present new knowledge by building on students’ existing knowledge base, and teach them to reflect and do the same.

Assign mixed-gendered discussion and study groups

Another way to increase students’ awareness of learning strategies is to encourage them to learn from each other—mixed-gendered groups afford the opportunity for both male and female students to benefit from each others’ strengths as they collaborate on assignments, prepare tutorial questions, compare notes and prepare for examinations. In class, friendly debates among groups provide an avenue for students to find out each others’ unique perspectives. Out of class, these study groups would also provide good support, especially given that this is when most learning happens, and will moreover help students to move away from over-reliance on the instructor.

Help students connect personally with subject material

It was suggested earlier that female students may encounter difficulties with subjects that are not perceived as being personally relevant to them. To address this need for personal connection with subject material, instruction can be designed to create engaging experiences with course content. Examples include talks or seminars by renowned females in respective fields; role-play exercises [an example is described in Sivan et al., (2000)]; or even spending some time to introduce course material as a personal story. Additionally, faculty can find out about students’ study approaches, and address the appropriateness of these in the context of course content, delivery and requirements (Meyer et al., 1994). This may actually help remove perceived barriers to learning—including the challenge of finding personal relevance to subject material—and to enable students to understand learning at a higher level.

Concluding remarks

The findings reported here are based on first-year students at NUS. It has been observed elsewhere that students tend to move toward a more surface approach to learning as they continue in their studies, and that gender differences become more apparent over the years at University [e.g. Najar & Davis, (2001)]. CDTL will be continuing this project to determine how NUS students change their approaches over time. Lastly, the present study also shows that gender differences are best understood within a broad framework, of which the SPQ is just one aspect. It is hoped that this article will encourage instructors to consider alternative ways of making learning at NUS a deeper and more rewarding experience for both genders.

References

Biggs, J.B. (1987). Study Process Questionnaire. Melbourne: Australian Council for Education Research.

Marton, F. & Saljo, R. (1976). ‘On Qualitative Differences in Learning-I: Outcome and Process’. British Journal of Educational Psychology. Vol. 46, pp. 4–11.

Meyer, J.H.F.; Dunne, T.T. & Richardson, J.T.E. (1994). ‘A Gender Comparison of Contextualised Study Behaviour in Higher Education’. Higher Education. Vol. 27, pp. 469–485. Netherlands: Kluwer Academic Publishers.

Najar, R.L. & Davis, K. (December 2001). ‘Approaches to Learning and Studying in Psychology: A Revised Perspective’. Paper for AARE International Education Research Conference—Fremantle, 2001. http://www.aare.edu.au/01pap/naj01247.htm (Last Accessed: 8 Dec 2003).

Severiens, S.E. & Ten Dam, G.T.M. (1994). ‘Gender Differences in Learning Styles: A Narrative Review and Quantitative Meta-Analysis’. Higher Education. Vol. 27, pp. 487–501. Netherlands: Kluwer Academic Publishers.

Sivan, A.; Leung, R.W.; Woon, C.C. & Kember, D. (2000). ‘An Implementation of Active Learning and its Effect on the Quality of Student Learning’. Innovations in Education and Training International, Vol. 37, No. 4, pp. 381–389. UK: Taylor & Francis Ltd.

Acknowledgement

The authors wish to thank Kevin Carlson for his valuable comments on earlier drafts of this article.