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Scratching the surface: Speech and Hearing Science students and their approaches to learning

Jim Elliott
University Counselling Services
Curtin University of Technology

Neville Hennessey
School of Speech and Hearing Science
Curtin University of Technology


This study grew out of concerns expressed by faculty members in Speech and Hearing Science staff meetings. It was observed anecdotally that first year students appeared to have a different learning style from students in later years of the course. New students seemed to adopt a more superficial approach to study, whereas students in later years appeared to take a deeper and more conceptual approach to learning. Clearly, the latter style was perceived as more desirable.

It was suggested that it would be worthwhile collecting data to see whether this surmise was supported by evidence. Originally, it was planned to conduct this as an educational exercise, with the aim of feeding back the information derived to students and staff members. It was thought that an outcome may be the modification of both teaching styles and learning styles. Subsequently, it was considered that the project also has merit as research in itself. The consequence has been to gather longitudinal data from students in first and fourth year of the course from 1996-1999. The data derived and its implications may have relevance to a wider audience than the students and staff within the School of Speech and Hearing Science.

Background and overview of the literature

There is a considerable literature which has commented on the notions that there is an apparent division in the learning orientation of students in higher education. This study is primarily based on the work of Biggs (1987 and 1993) who has developed a model which divides learning approaches into three broad styles - Surface, Deep and Achieving. Biggs is not the only researcher to put such a view. Dahlgren (in Marton et al., 1984), Meyer (1991), Meyer at al. (1994), Entwistle and Ramsden (1983), and Pintrich and De Groot (1990) have all noted the differing learning styles of students and devised models which bear a resemblance to that advanced by Biggs. Biggs (1993: 75) has noted that "it has now become accepted terminology to speak of three kinds of approach to the task: surface, deep and achieving".

There is a view that Deep learning is the only approach that is truly what higher education ought to be about, and the other approaches may be framed as pathologies (Biggs, 1993). Biggs sees a Deep approach as "task-centred and task-appropriate" (p.75). Surface learning is characterised as an attempt to minimise effort - that is, to pass with the least amount of input (although many students may see this as eminently sensible rather than as a pathology). The Achieving approach is viewed as outcome oriented rather than task oriented. That is, the goal is high grades. The tasks necessary to achieve them are simply the means of obtaining the high grades. An Achieving orientation may lead to Deep learning - but an equally valid strategy to meet the goal would be undetected and skilful cheating.

There is a paucity of literature which specifically addresses learning issues within the profession of Speech and Hearing Science. However, some views have a bearing on gaining an understanding of learning issues within specific disciplines. Ramsden and Entwistle (1981: 368) observe that "Éstudents respond to the context of learning defined by the teaching and assessment methods of academic departments". Ramsden and Moses (1992) address the supposed link between good teaching and research. There has been a commonly held view that good teaching and good research are mutually beneficial activities. Ramsden and Moses dispute this, and have found no correlation between the two. Entwistle (in Marton et al., 1984: 2) states that approaches to teaching and assessment encourage "a passive reproductive form of learning which is contrary to the aims of the teachers themselves".

Becher (1994) observes that different disciplines imply and foster different learning approaches. He sees that universities are not a single culture of scholars, but rather may be viewed as "different tribes" (p. 151). Becher's analysis is concerned with highlighting the cultural differences between what he terms "Cognitive Communities" (p. 153). Kolb (in Chickering, 1981) has also noted the interdisciplinary differences which impact upon learning styles. He observes that differing disciplines vary in the primary tasks expected of students, technologies employed, criteria for measuring academic performance, teaching and research methods, and so on (p. 233). In the context of this study, these views remind us that we are dealing with students from a single discipline that borders the line between Science based professions and the Humanities. It is important to remember this framework, in that the findings may not be generalisable to significantly different Cognitive Communities.

A related issue is that students in the first year of the course may not be in the same Cognitive Community as those in the later years. To a large extent, course content in the first year of Speech and Hearing Science consists of basic introductory science units. In the final year, much of the course requirement is structured around clinical practice units with and emphasis on competency in specific skills. Effective learning approaches which lead to sound grades may differ. Meyer et al. (1994) make reference to such differences in observing that students' characteristic learning styles may not be something that is stable but rather is "orchestrated" (their term) to meet the differing demands of different classes.

Nonetheless, many teachers in higher education would probably like to believe that they are encouraging students to adopt a Deep learning approach, in that it may be professed that Deep learning is what differentiates higher education from technical training. It is also hoped (but not assumed) that those who do not take such an approach are less likely to be successful. A key research question for the group under study is to determine what learning approaches are typically adopted by each year group, and how this may be related to academic outcomes. It is hypothesised that first year students show a more Surface learning approach than final year students.

The Study Process Questionnaire (SPQ) as developed by Biggs (1987) provides scores which shed some light on the issue. Whilst it is not the only measure of learning styles, it has acceptable psychometric properties for the purposes of this study. Some writers have questioned this kind of measure - see Meyer (1990) and Christensen (1991). One question has revolved around the stability of such measures. This is not a problematic issue, in that one would hope that learning styles would indeed not remain stable over time. An objective of a university education ought to include changes in the way students process and understand the material within their courses.

The SPQ provides a score on each of the basic dimensions of Surface, Deep and Achievement learning. For each, the score is separated into Motive and Strategy. Further scores are derived by combining Motives and Strategies together. The whole profile results in scores on the following variables (after Biggs, 1987: 11):

Variable Description
Surface Motive (SM)A high SM score would indicate that a student's main purpose is to meet requirements minimally.
Surface Strategy (SS)Essentially a reproductive strategy. High scores indicate a student who limits targets to bare essentials, and aims to reproduce material through rote learning.
Deep Motivation (DM)A high score indicates that the student's motive is to actualise interest and competence.
Deep Strategy (DS)A high scoring student likely seeks to read widely and inter-relate knowledge with previous relevant knowledge.
Achievement Motivation (AM)A view based on competition and ego enhancement, where a student seeks to obtain the highest grades whether or not the material is interesting.
Achievement Strategy (AS)Based on organising one's time and working space seeking to behave as a "model" student.
Surface Approach (SA)Combined score of SM and SS; that is, a general score on Surface learning.
Deep Approach (DA)Combined score of DM and DS; that is, a general score on Deep learning.
Achievement Approach (AA)Combined score of AM and AS; that is, a general score on Achievement learning.
Deep Achievement Approach (DAA)Combined score of AM, AS, DM and DS; that is, a general score on Deep and Achievement learning.

Additionally, students are asked to self rate their ability by responding to an item which states "Compared with most of the students in your year, how good at most of your subjects are you?" They may respond by choosing from a five point scale ranging through "Excellent", "Above average", "Average", "Below average" and "Quite poor". This rating variable is abbreviated as RTG.

A question of interest deriving from using the SPQ is the extent to which the data are predictive of academic outcomes. Biggs (1987: 49-68) discusses descriptive research on the instrument. He notes a lack of information connecting scores on the SPQ with academic results. However, another instrument developed by Biggs is the Learning Process Questionnaire (LPQ). The LPQ has been developed for use with secondary school students, and is very similar in structure to the SPQ. Research on the LPQ indicates that a high Surface approach is associated with lower academic results, and conversely a higher Deep approach is associated with better academic results. It is inferred that such a relationship is possibly also the case for the SPQ. This study is able to investigate this question for a selected sample of students.


Research questions

There are six research questions:
  1. Are there significant differences between 1st and 4th year students in their learning approach as measured by the SPQ? It is predicted that 1st years are more likely to adopt a Surface learning style, and 4th years more likely to a adopt a Deep learning style.

  2. Have there been changes in the SPQ scores in 1st and 4th year students across the year 1996-1999? There have been some course and assessment alterations over these years which may have influenced students' learning styles. No prediction is made of this effect.

  3. How do variables on the SPQ compare with actual academic results? /that is, do the different learning styles have any relationship with students' academic outcomes? We predict that a Deep learning style would be more associated with academic success in 4th year, but less so in 1st year.

  4. What is the best predictor of the academic results of 1st and 4th year students derived from the SPQ?

  5. Some SPQ profiles would lead us to suspect that a student would be at risk in a university setting. Is this born out by the academic record? We predict that a 4th year student with a Surface learning style in particular to be at risk.

  6. Do the SPQ results of the 1996 1st year students show significant change by the time the same students are in 4th year in 1999? It is predicted that these individuals have shifted away from a Surface learning style to a Deep learning style.


The participants in the study are the 1st and 4th year students in the School of Speech and Hearing Science in 1996-1999. Speech and Hearing Science is a course where there has been a high level of competition for entry. As a consequence, newcomers to the course typically enter the university with well above average entrance scores. Additionally, it is a course which has attracted far more female than male students. The number and gender of participants is show below.

1st Year
1st Year
1st Year
4th Year
4th Year
4th Year
1996 3213317219
1997 2432710111
1998 2422619019
1999 3623820020
Total 116812466369

The age of 1st year students ranged from 17.7 to 45.0 years. However, the substantial majority were under 20 years old. Fourth year students ages ranged from 20.6 to 46.7, with most being under 25 years.


Students in both year groups were asked to complete the SPQ early in second semester of 1996-1999. First year students had thus experienced one semester of university education (assuming no previous study in other courses), and 4th year students had experienced at least three and a half years. The SPQ was administered during scheduled class time. It was clearly communicated both verbally and in writing to students that their participation in this study was voluntary, and that it would have no effect on their academic progress. It was further stated that no individual student's results would be provided to any lecturer in their course. However, students were asked to place their name on the questionnaire so that:

A]  individual feedback could be provided to any student who desired it
B]  results could be linked with academic histories for the purposes of analysis.

All identifying information regarding students was handled exclusively by Elliott as a staff member unconnected with teaching and assessment processes in the School of Speech and Hearing Science. Students were informed that this would be the case. Elliott removed any identifying information from the data before the analysis was carried out.


The data consist of:

A]  a profile of student study styles derived from the SPQ
B]  the Semester Weighted Average (SWA) of each student

The meaning of the SWA for 1st year students is a little different from that for 4th year students. In the former case, the SWA is based upon the grades obtained in five units. Fourth year students take only two units. However, one of those units is a clinical practicum unit, and is assigned a pass/fail grade only. Hence, the SWA for 4th year students is based on academic performance in a single unit.


The SPQ provides both raw scores and deciles. The decile scores for the Speech and Hearing students ranged from 1 to 10 for all SPQ measures suggesting that these students as a group are not atypical in their range of responses compared to other tertiary students. The main analyses use raw scores not deciles. Preliminary inspection of the underlying distributions for each dependent measure revealed no violation of normality and homogeneity of variance. Therefore, analysis of variance was conducted to assess statistical significance.

Table 1 gives the mean of each aspect of learning style as a function of year of study. Considering learning strategies first, Table 1 shows that first year students have a higher surface learning strategy score and higher achievement strategy score than fourth year students, although the difference is greater for surface learning. Conversely, the fourth year students have a higher deep learning strategy score. Strategy scores were subject to a three-way analysis of variance with year (first vs. fourth) and cohort (years 1996 to 1999) as between subjects factors, and type of strategy (surface vs. deep vs. achievement) as a within subject variable. The main effects of year and strategy were significant - F(1,186) = 5.66, p < .05, for year, and, F(2,372) = 20.45, p < .001, for strategy - but not the main effect of cohort, F < 1. These main effects are, however, of marginal interest. The primary aim of the analysis was to test for an interaction between these factors. The tendency for final year students to take a lower surface learning strategy and a higher deep learning strategy than first years was supported by a significant interaction between year and strategy, F(2,372) = 20.45, p < .001. Moreover, a simple effect contrast showed the difference in surface strategy score between first and fourth years was significant, F(1,186) = 37.33, p < .001, as was the difference between first and fourth years in deep learning strategy scores, F(1,186) = 9.85, p < .01. The difference in achievement strategy was, however, not significant, F(1,186) = 1.84, p > .05. The interaction between year and strategy, therefore, appears to be due mainly to the cross-over difference between first and fourth year students in surface and deep learning strategy scores.

A three-way interaction between year, strategy and cohort was also observed, F(6,372) = 2.95, p < .01. No other interaction effect was significant. The nature of the three-way interaction is best viewed graphically. Figure 1 shows the strategy scores comparing first and fourth year students separately for each cohort. The trend for first year students in each cohort to take more of a surface learning strategy is apparent. Similarly, there is a trend for final year students to take more a deep learning strategy for each cohort, although the difference is small for the 1999 cohort. There is, however, an apparent difference between the cohorts in the achievement strategy scores, and this presumably underlies the significant three-way interaction. The 1996 and 1997 cohorts shows a trend for first year students to take less of an achievement strategy compared to final year students, whereas for the 1998 and 1999 cohort there is a larger trend for first year students to take more of an achievement strategy. Separate simple effect contrasts comparing first and final year students on the achievement scores for each cohort showed a non-significant difference for the years 1996 and 1997, F < 1, and F(1,186) = 1.55, p > .05, respectively. The tendency for final year students to take less of an achievement strategy than first year students in the years 1998 and 1999 is statistically reliable, however, F(1,186) = 6.84, p < .05, and F(1,186) = 6.93, p < .05, for 1998 and 1999, respectively.

Table 1: Mean of each study process questionnaire dimension for first and fourth year students

   1st Year
   4th Year



   1st Year
   4th Year



   1st Year
   4th Year




A three-way analysis of variance was used to examine type of motivation (surface, deep and achievement) as a function of year of study and cohort. The ANOVA design matched that of the analysis of learning strategy. The means of the motivation scores, averaging across cohort, are also provided in Table 1. There was a main effect of motivation, F(2,372) = 28.72, p < .001. A contrast analysis revealed that surface motivation was significantly higher than deep motivation, F(1,372) = 24.30, p < .001, which was, in turn, significantly higher than the achievement motivation, F(1,372) = 10.45, p < .001. It is apparent, therefore, that Speech and Hearing Science students overall are motivated more by meeting the minimal requirements of the course compared to actualising interest and competence. It is also apparent that there is not a strong motivation towards achieving high grades regardless of interest. The remaining two main effects were not significant, that is, first year and final year students did not differ significantly in overall motivation scores, F < 1, and there was no difference in overall motivation scores among the different cohorts, F(3,186) = 1.13, p > .05.

Of interest, however, is whether first and fourth years differed for each type of motivation. The means appear to follow a similar trend to the strategy data with first years having a higher surface motivation mean compared to final year students, while final year students had a higher deep motivation mean. The interaction between type of motivation and year of study was significant, F(2,372) = 3.21, p < .05. However, the cross-over effect in the motivation data is not as clear as in the strategy scores - simple effect contrasts showed the first years were marginally higher in surface motivation scores, F(1,372) = 2.91, p <.10, but the final year students were not significantly higher in deep motivation scores, F(1,372) = 2.11, p > .05. First and final year students did not also differ in achievement motivation scores, F(1,372) = 2.02, p > .05. Excepting the marginal difference in surface motivation scores, it is not clear that first and fourth year students differed in motivation. The interaction between year of study and type of motivation, however, still needs explaining. An alternative way of looking at the interaction is in terms of the difference between deep and surface motivation scores for first and final year students. First years are characterised by a low deep motivation score compared to their surface motivation score, which is high. Final year students, however, show a smaller difference between their deep and surface motivation scores; arising from the fact that their surface motivation scores are lower than first year students, while their deep motivation scores are slightly higher. A simple effect contrast based on separate analyses for first and final year students, showed that the difference between the surface motivation score and deep motivation score is significant for first year students, F(1,123) = 19.32, p < .001, but not significant for fourth year students, F(1,69) = 1.33, p > .05. It is apparent, therefore, that the motivation to study for first years is more strongly characterised by an intention to meet the course requirements, and not to actualise interest and competence. This asymmetry is not as great for final year students where there is a trend for being less motivated by meeting minimal course requirements than first year students, and for students to be concomitantly more motivated by actualising interests and competency.

Returning to the main analysis of the motivation scores, no other interactions were significant, suggesting that cohorts did not differ significantly in achievement motivation in the same way as in the achievement strategy scores.

Figure 1

Figure 1: Mean surface, deep and achievement strategy scores for first (1) and final (4) year students from 1996 to 1999 (error bars are + 1 standard error of the mean).

It appears, therefore, as though first and final year students differ significantly in surface strategy scores and in deep strategy scores. In particular, first years appear to take more of a reproductive approach to their study, focusing on rote learning, etc. (indicated by higher surface strategy scores). Final year students seem to be more inclined to integrate content with a range of knowledge sources and seek the bigger picture (indicated by higher deep strategy scores). While the strategies to study are different, the results also show that the motivation scores are less different. First and final year students produced similar surface motivation scores compared to surface strategy scores. An analysis that compared surface strategy and surface motivation scores for first and fourth year students confirmed the significant interaction between those two factors, F(1,186) = 19.56, p < .001. The corresponding analysis for deep strategy and deep motivation scores showed a marginally significant interaction with year of study, F(1,186) = 3.04, p < .09. The analysis was not carried out for achievement strategy and achievement motivation scores because first and fourth year students, collapsing across cohort, did not differ in these measures (see above). In short, greater differences are found between first and final year students in their strategies towards their study, as opposed to their motivation in studying.

The SPQ dimension of approach (see Table 1) is a combination of the strategy and motivation scores. Separate two-way analyses of variance with year (1st vs 4th) and cohort (1996 to 1999) as between subjects factors confirmed the main findings of the separate strategy and motivation analyses. First year students tended to take more of a surface approach than final year students, F(1,186) = 18.92, p < .001, and final year students tended to take more of a deep approach, F(1,186) = 6.45, p < .05. First and final year students did not differ significantly in their achievement approach, F(1,186) = 2.63, p > .05, although there was a trend for first year students to score more highly than final year students. This trend is probably influenced by the 1998 and 1999 cohorts for whom the data shows first year students as having significantly higher achievement strategy scores than final year students. The analyses also showed no overall differences between the cohorts in approach scores and no significant interaction between cohort and year for each type of approach (ie., surface, deep, & achievement).

A correlational analysis was used to examine the relationship between academic results and learning styles. Table 2 gives the correlations between each type of learning approach and semester weighted averages (SWAs) from the second semester of the year in which each student completed the SPQ. The correlations between SWAs and the other SPQ scores (eg., strategy and motivation) are similar and are consequently not reported. Note also that the SWAs were not available for eight students. As seen in Table 2, the correlations are small with learning style accounting for a minimal amount of variance in SWAs. One notable feature is that the largest correlation for the first years - that of achievement approach with SWA - is positive and significant. The corresponding correlation for fourth year students is smaller and non-significant. It is difficult to draw a conclusion from this, however, because the smaller number of students in the fourth year group means that the test of the null hypothesis is less sensitive.

The correlation between self ratings and SWA was low but significant (r = .29, p < .05).

To analyse the relationship between academic results and learning styles further, students in each year of study were separated into two groups based on their SWAs. The students with SWAs above the median formed one group (high SWA); students with SWAs at or below the median formed the second group (low SWA). Note that for the analysis reported below the data were collapsed across cohort and the median was calculated separately for each year of study. Differences associated with level of academic performance (SWA group) were significant for learning strategy scores and learning approach scores but not for learning motivation scores. Because of the similarity between the strategy and approach scores only the strategy scores are reported. Figure 2 presents the mean of each type of strategy score for high performing and low performing students at each year of study. A combination of a three-way analysis of variance and focused contrasts was used to test for significant differences. The independent variables for the three-way design were type of strategy (surface vs. deep vs. achievement), a within subjects factor, year of study (1st vs. 4th), and SWA group (low vs. high SWA). The latter two factors are both between subjects.

Table 2: Correlations between Semester Weighted Averages (SWA) and
Surface, Deep and Achievement approach scores for first and fourth year students

First Year



Fourth Year



Note: * is significant at the .05 alpha level. N = 118 for 1st year students. N = 68 for 4th year students.

Figure 2

Figure 2: Mean of each SPQ strategy score for 1st year and 4th year students with low (SWA 1) and high (SWA 2) semester weighted averages (error bars are +1 standard error of the mean).

The analysis of variance confirmed some of the results already reported above. A main effect of year was obtained, F(1,182) = 7.76, p < .01, as well as a main effect of strategy, F(2,364) = 16.40, p < .001, and an interaction between year and strategy, F(2,364) = 22.18, p < .001. As can be seen in Figure 2, there was a tendency for final year students to take less of a surface strategy and more of a deep strategy than first year students. Of particular interest, however, are effects associated with SWA group. There was a main effect of SWA group, F(1,182) = 5.36, p < .05, with high SWA students scoring more highly overall than low SWA students. This is difficult to interpret, however, because it involves differences in an average calculated across each type of strategy. There was no interaction between year and SWA group, F(1,182) = 1.02, p > .05. An interaction was observed between SWA group and strategy, F(2,364) = 4.33, p < .05. The surface strategy score was higher for the high SWA group (M = 20.4) compared to the low SWA group (M = 19.4). This difference was not significant according to a simple effect contrast, however, F(1,182) = 2.26, p > .05. The deep strategy scores were not significantly different for the high and low SWA students (22.4 vs. 22.7, for means of high and low SWA students, respectively), F < 1. The high SWA students, however, had a significantly higher achievement score (M = 22.6) than the low SWA students (M = 20.2), F(1,182) = 9.04, p < .01. Finally, there was a significant three-way interaction between year, strategy and SWA group, F(2,364) = 3.49, p < .05. Figure 2 shows that for first year students there was little difference between low SWA and high SWA students for both surface and deep strategy scores, both Fs < 1. There was a relatively large and significant difference, however, between low and high SWA students in achievement scores, F(1,116) = 13.78, p < .001. This pattern mimics that found with first and fourth year students combined. For fourth year students, there is a trend for high SWA students to take a higher surface strategy compared to low SWA students. This difference was however only marginally significant, F(1,66) = 3.36, p = .07. A trend in the opposite direction is observed for deep strategy scores, although the corresponding contrast was not significant, F(1,66) = 1.63, p < .05. The tendency for high SWA students to take more of an achievement strategy compared to low SWA students was not as clear for final year students as it was for first year students. The difference between high and low SWA students in achievement scores was not significant for fourth year students, F < 1, which is distinct from first year students where a large difference was found.

To summarise the results so far with respect to SWA, the following points should be highlighted. Across all first year students, from the years 1996 to 1999 in the Speech and Hearing Science course, there is no relationship between academic result and whether a surface or deep strategy is adopted. The scores for surface and deep strategy were not different for high and low SWA students. On the other hand, higher SWA first year students are characterised by adopting more of an achievement approach to study compared to low SWA students. This pattern breaks down for fourth or final year students. Firstly, there is an indication that better performing students are taking more of a surface approach, even though as fourth year students their surface strategy scores are significantly lower than first year students. High and low SWA fourth year students did not differ in either their deep strategy scores, nor in their achievement strategy scores. The only SPQ variable, therefore, most strongly related to academic result for the Speech and Hearing Science students is that of achievement strategy. Adopting a higher achievement strategy, regardless of whether a surface or deep learning strategy is followed, would appear to pay off, but mostly for first year students. For fourth year students a surface strategy is possibly more related to academic performance. The marginal nature of this latter result, however, should be taken into account.

Because the SPQ was administered to students over a period of four years, it is possible to compare first and fourth year data using the same students. Unfortunately, data from only 13 students is available for this analysis. Apart from a small number of students from the 1996 cohort withdrawing from the course, other students from that same cohort were either already in or changed to part time study and, consequently, did not reach their fourth year of study in 1999. Figures 3 and 4 give the mean of strategy and motivation scores for these students from when they were in first year and when they were in their final year of study. The change in strategy scores between first and fourth year of study is consistent with the cross sectional design. Firstly, the surface strategy scores are significantly higher in first year than in fourth year, F(1,11) = 8.98, p < .05. The mean of the deep strategy scores is lower for first year than for fourth year, but this difference is not significant, F(1,11) = 2.96, p > .05. There is a trend for achievement strategy scores to be higher in first year than in fourth year for these students, the mean difference was marginally significant, F(1,11) = 3.86, p = .075. An analysis comparing first and fourth year data with the different type of strategy scores in the same design produced a significant interaction between these two factors, F(2,24) = 7.34, p < .01. The interaction appears to be due to the fact that in first year these students tended to have a higher surface strategy and achievement strategy score than their deep strategy score. By the time they reach fourth year, however, their deep strategy score has tended to increase while both their surface strategy and achievement strategy score has decrease to below their deep strategy score. Although the raw scores for the surface, deep and achievement strategy types may not be directly comparable, because of the longitudinal design, the results do suggest that the students resort to more of a deep learning strategy in their final year in comparison to their first year of study where the surface and achievement strategies dominated their learning style. None of the motivation scores produced any significant differences. The analysis of learning approach scores, which combines the strategy and motivation scores, was consistent with these analyses in that a difference between surface approach in first and fourth year was observed, F(1,11) = 6.11, p < .05, but no difference in deep approach or achievement approach. The interaction between year of study and type of approach was significant, F(2,24) = 5.11, p < .05, however.

To investigate the association between learning style and academic performance, the 13 students in this longitudinal design were separated in to two groups based on a median split of their SWAs in first year. There were 7 students in the low SWA group and 6 students in the high SWA group. Because of the small number of students, the non-parametric Mann Whitney U test was used to compare the two groups in their learning styles. The results showed no significant differences, although one contrast approached significance. A higher surface strategy score was observed for high SWA students (M = 24, SEM = 1.47) compared to low SWA students (M = 21.7, SEM = 1.36) in their first year of study. This was marginally significant, U = 9, p < .09. In fourth year the difference between the low and high SWA students was in the same direction, that is, the surface strategy scores tended to be higher for high SWA students (M = 21.8, SEM = 1.69) than for low SWA students (M = 17.86, SEM = 1.54). Note that the SWA is based on their first year academic results. This would suggest that even though the surface strategy scores are changing from first to fourth year (ie., become lower for fourth year), students with higher surface strategy scores in first year tended to produce higher scores in their fourth year of study. This suggests that students relative standing in the group remains consistent (see correlation analysis below). The grouping of students into high and low SWA based on a median split of their SWA in fourth year, as opposed to first year, did not show a strong relationship with the grouping based on first year SWAs. Of the seven students in the low SWA group in first year, three remained in the low SWA group in fourth year. Four low SWA students therefore progressed into the higher SWA group in their fourth year. Of the six high SWA students in first year, only two remained in the high SWA group in fourth year, with four achieving grades that brought them into the low SWA group in their fourth year. Is this shift of grouping related to the type of learning style approach? Unfortunately, the small number of students that falls into each category precludes statistical analysis. In spite of this, the correlation between SWA in first year and SWA in fourth year is moderate and significant (r = .61, p < .05).

A correlation analysis was undertaken to examine the reliability of SPQ scores from first to fourth year. Table 3 presents the correlations between first year and fourth year raw scores for each SPQ measure. All correlations are moderate to high and statistically significant. Given the small number of subjects, these significant positive correlations imply a good level of reliability in the SPQ measures.

Table 3: Correlations between SPQ Scores in first year and fourth year for 13 students

Strategy .62*.66* .59*
Motivation .72**.74** .64*
Approach .72**.72** .75**
Note: * is significant at .05 level, ** is significant at .01 level.

Figure 3

Figure 3: Mean of SPQ surface, deep and achievement strategy scores for 13 students assessed in their first year and in their fourth year of the Speech and Hearing Science course (the error bars are +1 standard error of the mean).

Figure 4

Figure 4: Mean of SPQ surface, deep and achievement motivation scores for 13 students assessed in their first year and in their fourth year of the Speech and Hearing Science course (the error bars are +1 standard error of the mean).


The specific research questions may be answered clearly and briefly.
  1. Are there significant differences between 1st and 4th year students in their learning approaches as measured by the SPQ? First year students show a significantly higher Surface Strategy, and 4th years a higher Deep Strategy. There is also a tendency for 1st year students to show a higher Achievement Strategy. It is also interesting to note that Surface Motivation was higher than both Deep and Achievement Motivation for both year groups. For 1st year students, the difference between Surface Motivation and Deep Motivation scores was larger than that for 4th year students. Surface Approach scores were higher for 1st year students. No other significant differences were noted.

  2. Have there been changes in the SPQ scores for 1st and 4th year students across the years 1996-1999? The 1998 and 1999 cohorts of 1st year students were more likely to be committed to an Achievement Strategy than their counterparts in the 1996 and 1997 cohorts.

  3. How do variables on the SPQ compare with actual academic results? Learning styles as measured by the SPQ accounts for very little variance in SWA. The highest correlation obtained was for 1st year students with respect to Achievement Strategy and SWA. There is a tendency for students with high SWAs to take a higher Surface Strategy than low SWA students - although this is more apparent with 1st year students. The students own self ratings (RTG) of ability correlated higher than any other variable.

  4. What is the best predictor of the academic results of 1st and 4th year students derived from the SPQ? The best predictor was the students' own self rating.

  5. Some profiles would lead us to suspect that a student would be at risk in a university setting. Is this born out by the academic record? Low Achievement Approach scores were associated with lower SWA scores for 1st year students. However, it would be difficult to describe these students as being at risk. No particular SPQ profile appears to e associated with being at risk for either 1st or 4th year students.

  6. Do the SPQ results of the 1996 1st year students show significant change by the time the same students are in 4th year in 1999? Surface Strategy scores are higher in 1st year than 4th year. Deep Strategy scores have tended to increase by 4th year. There was no significant change in Motivation scores. Students with higher Surface Strategy scores tended to score higher SWAs in both 1st and 4th year.
The data obtained thus shed some light on the research questions posed, but also raise some further questions. With respect to the first research question, it is clear that there are significant differences between 1st and 4th year students on the variables measured by the SPQ. The data support the view expressed anecdotally by staff in the School that 1st year students do indeed show a more Surface oriented learning style, and that 4th year students show a Deeper learning style. This is consistent with previous research using the SPQ. However, the data show a substantial range. That is, there are 4th year students who scored highly on Surface learning variables, and 1st year students who scored highly on Deep learning variables.

There are several ways of interpreting this finding. One is that students respond to changing demands in the course. That is, the course increasingly sets tasks that are most usefully accomplished by a Deep learning approach, and that students adapt to this. A second possibility is that the entire course requires a Deeper approach from the beginning, but that 1st year students have not entirely worked this out. A third possibility is that taking a Deep learning approach is a developmental issue. Certainly earlier research indicates higher scores on Deep learning with increasing age. The data provide no way of differentiating between these alternative explanations. All three may be implicated.

A finding of interest in answer to this question is that 1st and 4th year students differ significantly on strategy but less so on motivation. That is, what they profess their motivations in study to be differ somewhat from what they actually do in addressing their study. For example, 1st and 4th year students do not differ significantly in their Surface Motivation scores, but 1st year students are more likely to carry this through to Surface Strategies. This may mean that 1st year students do not necessarily have the knowledge about university level study to distinguish whether the tasks they are undertaking are appropriate for the higher education context.

The second research question examined whether there were changes in SPQ scores across the years. Little clear data emerged from this. The tendency for 1st years in 1996 and 1997 to take less of an Achievement Strategy than the 1st years in 1998 and 1999 was observed. By 4th year, all students have tended to lower their Achievement Strategy. It is possible to account for this in the context of their educational commitment and subsequent employment prospects. In 4th year, much emphasis is given to the acquisition of clinical skills where only a pass/fail grade is given. There is little incentive to seek high grades in a competitive manner. Further, the employment market for these graduates is very positive. Few are likely to experience difficulty in finding a job.

The third research question sought to find any association between SPQ scores and academic results. There are some indications in the data which are highly interesting. It appears that taking a higher Achievement Strategy has some association with obtaining a higher SWA - more especially in 1st year than 4th year. Deep learning has little association with good academic results. Trigwell and Prosser (1991) argue that the failure to show a connection between Deep learning and positive outcomes is something to do with an emphasis on quantitative assessment. This is not the only way of explaining these data however. The incoming students to this course are typically those who have in the past achieved high grades. Entry to the course is highly competitive and selective - the sample consists of a group of highly able students as measured by their entrance score to the university (typically this score is at the 90th percentile or higher). The group may this consist of relatively competitive students. They may be able to apply methods that have worked effectively in the past to their new situation - perhaps with some modification or greater effort - and consequently obtain reasonably sound academic results. However, the data are not unequivocal, and it would be premature to draw any firm conclusion as a consequence. It is also impossible to estimate the significance of a sample which is so heavily dominated by female students - although Biggs (1987) has observed a tendency for female students to have higher Achievement Strategy scores.

With respect to the fourth research question, there was a small correlation between the students' own self rating and their academic results. That is, their own estimate of ability produced the best predictor - although this was not an especially good predictor. No measure obtained on the SPQ was a useful predictor of academic performance. It should be noted that the grades of the group were all somewhat high. Anecdotal evidence suggests that the very few low grades obtained by students in this course are more commonly linked to extraneous factors such as personal difficulties, financial problems and the like rather than academic issues.

The SPQ manual indicates that certain profile scores would be a concern. The third research question sought to learn whether it was possible to identify at risk students. Very few of the students in either year group obtained an at risk profile on the SPQ. There was no association of any such profile with poor academic results. It is likely that this is due to the high standard of entry. There is no case for using the SPQ in the context of this group of students to identify those who may do poorly.

The final research question looked at the results of the 1996 1st year and 1999 4th year students - the same cohort. There was only a small group with complete data from both administrations of the SPQ, and thus the results must be interpreted with caution. However, there was a trend in the direction expected. That is, learning approaches shifted from Surface towards Deep learning.

A limitation of this study is that it is a "snapshot" of the students in 1st and 4th year in 1996-1999 in a single course. It is limited in the number of subjects, singularity of course of study, selectivity with respect to entrance qualifications, and in the substantial proportion of female students in the sample. In order to clarify the findings of this pilot study, it would be worth repeating the data collection with a larger and more differentiated sample of students from other courses.


The two chief findings of this study were that 1st and 4th year students do indeed differ in their learning styles, but that this is largely unrelated to academic outcomes. The latter finding is especially worthy of further investigation. It may be a result of obtaining data from a narrowly defined sample, and that there is a more consistent relationship between learning style and academic outcomes across a wider population of university students. The implications are significant. For example, if there is indeed such a relationship between Deep learning and academic result, there may be a case to intervene to develop such approaches as early as possible. There are implications if no such relationship can be found. We would then seek to explain why university results do not appear related to learning style. It could be that the mixture of teaching and assessment approaches provides opportunities for individuals wit a range of learning styles - and it could be argued that this is not only acceptable but also desirable. Conversely, such a finding could be argument to modify the quality of teaching and assessment if Deep learning is seen to be more highly valued.

A second area worthy of further study is to seek to understand the link between Motivation and Strategy. In this stud, the Strategies adopted by different year groups varied more consistently than expressed differences in Motivation. It would be worth investigating whether this result holds with a wider population. If so, it is some indication that there is a disparity between what students think they are doing and what they are actually doing with respect to their study.

A third area of interest is in teaching and assessment. This study has made no more than a superficial examination of the nature of teaching styles and assessments in the course in question. An analysis of both and how they may or may not be linked to students' learning styles may cast further light on the lack of predictability of learning style of academic outcomes.


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Authors: Dr Jim Elliott, Counsellor, University Counselling Services Dr Neville Hennessey, Lecturer, School of Speech and Hearing Science Curtin University of Technology

Please cite as: Elliott, J. and Hennessey, N. (2001). Scratching the surface: Speech and Hearing Science students and their approaches to learning. In A. Herrmann and M. M. Kulski (Eds), Expanding Horizons in Teaching and Learning. Proceedings of the 10th Annual Teaching Learning Forum, 7-9 February 2001. Perth: Curtin University of Technology. http://lsn.curtin.edu.au/tlf/tlf2001/elliott.html

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