Asia-Pacific Edu Res DOI 10.1007/s40299-014-0202-5

REGULAR ARTICLE

Demographic Factors, Personality, and Ability as Predictors of Learning Approaches Qiuzhi Xie • Li-fang Zhang

Ó De La Salle University 2014

Abstract This study investigated the extent to which learning approaches can be accounted for by personal factors (i.e., demographics, ability, and personality). The participants were 443 students in a university in mainland China. The Revised Two-factor Study Process Questionnaire, the NEO Five-Factor Inventory-3, and the short form of Raven’s Advanced Progress Matrices were respectively applied to test students’ learning approaches, personality, and ability. The results of correlations and structural equation modeling indicated that male students were more likely to be deep learners than female students; relative to Year-one students, Year-three students were more likely to use the surface learning approach and less likely to use the deep learning approach. Openness to experience and conscientiousness had strong positive effects on the deep learning approach. Neuroticism had positive effect, whereas conscientiousness had negative effect on the surface learning approach. Approximately 44 % of the variance in the deep learning approach and approximately 18 % of the variance in the surface learning approach could be explained by the three personal factors. Personality was the strongest predictor of learning approaches, whereas ability was the weakest predictor. The implications of the results were discussed.

Q. Xie
L. Zhang Faculty of Education, The University of Hong Kong, Pokfulam, Hong Kong Q. Xie (&) Department of Applied Social Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong e-mail: [email protected]

Keywords Learning approaches
Demographic factors
Ability
Personality

Introduction Learning approaches refer to the preferred manners to deal with learning tasks (Biggs 1987). Biggs’ (1987) theory classifies learning approaches into three types: the surface, deep, and achieving learning approaches. Each learning approach is composed of learning motivation (the affective component) and the corresponding learning strategy (the cognitive component). Surface learners tend to make minimal effort (strategy) to pass examinations (motivation). Deep learners tend to read broadly (strategy) to have a thorough understanding of what has been learned (motivation). Achieving learners tend to have the motivation to maximize their academic achievements by applying useful strategies. Students may use any of the learning approaches to different extent according to different situations (Biggs 1987). For instance, a student is intrinsically motivated to search information widely for good comprehension of a theory; meanwhile, she/he would like to prepare for an examination by rote. Biggs (1987) stated that the surface and deep learning approaches are different from the achieving learning approach. The surface and deep learning approaches describe how students are involved in learning tasks, whereas the achieving learning approach describes how students organize learning. Scholars found that the achieving learning approach overlapped with the surface and deep learning approaches (Phan and Deo 2008; Zhang 2000); therefore, the instrument for assessing learning approaches has been updated based on the two-factor (i.e., deep and surface factors) model (Biggs et al. 2001).

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It has been demonstrated that learning approaches are important in addressing individual differences in learning outcomes (Swanberg and Martinsen 2010; Trigwell and Prosser 1991). Biggs (1987, 2001) contented that both personal and instructional factors influence learning approaches. The relationships between learning approaches and personal factors, including demographic factors, personality, and ability, were predominantly explored in the Western context (e.g., Chamorro-Premuzic and Furnham 2009; von Stumm and Furnham 2012); however, they are insufficiently explored in the Chinese learning context. Learning approaches are likely to be affected by specific learning contexts and cultural attributes (Biggs 1987; Phan 2012). For example, Watkins (1998, 2001) revealed that the classifications of learning approaches are valid among Chinese learners; however, Chinese learners tend to view memorization as pertinent to both surface and deep learning, whereas Western learners merely regard memorization as a feature of surface learning (Watkins 2001). Such difference is likely due to the fact that the education in China tends to emphasize the absorption of knowledge and facts presented in textbooks. Therefore, the relationships between learning approaches and personal factors, especially demographics, found in the Western learning context may not be generalized in the Chinese learning context. Exploring the influences of personal factors on learning approaches in the Chinese learning context is significant for educators in China to understand what personal factors may affect students’ learning approaches and, accordingly, to apply the relevant strategies to cultivate the deep learning approach for Chinese students. Moreover, comparing the findings obtained in the Chinese learning context with those found in the Western learning context can facilitate the understanding in reference to the roles of learning contexts and socio-cultural attributes in learning approaches. Furthermore, the investigation into the degree to which learning approaches can be explained simultaneously by demographic factors, personality, and ability is lacking. Building on the above background, the present study, conducted in China, investigates the extent to which learning approaches can be explained by demographics, personality, and ability altogether as well as the relative importance of the three personal factors in learning approaches. Along with basic demographics (i.e., gender, age, year of study, and academic discipline), parents’ education levels and family income that represent socioeconomic status were included in the demographic factors in this study, given that their influences on learning approaches are insufficiently explored, but they are important in parenting (Bean and Northrup 2009) and available educational resources, which in turn may affect students’ learning approaches.

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Literature Review Learning Approaches and Demographic Factors Scholars have investigated the differences in learning approaches based on demographics (e.g., Severiens and Ten Dam 1997; Zhang 2003). It was consistently found that compared with younger students, mature-aged tertiary students (aged 23 or above) were more likely to be deep learners rather than surface learners (e.g., Severiens and Ten Dam 1997; Watkins and Hattie 1986). Arts majors were more likely to apply the deep learning approach, whereas science majors were more likely to apply the surface learning approach (e.g., Severiens and Ten Dam 1997; Watkins and Hattie 1985). Although some studies reported that females were more inclined to be deep learners, more studies reported the opposite trend (Xie 2013a). The findings on the relationships between learning approaches and year of study are rather inconsistent. For example, Watkins and Hattie (1981) reported that senior university students were more inclined to be deep learners, whereas Zhang’s (2003) study indicated that the more years students spent in a university, the less likely they used the deep learning approach. Zhang (2000) also disclosed that work and travel experiences were related positively to the deep learning approach and negatively to the surface learning approach; parents’ education levels were positively related to the deep learning approach among students in the United States, but not among students in mainland China. Learning Approaches and Personality Investigating the association between personality and learning approaches is significant in both theoretical and practical perspectives (Chamorro-Premuzic and Furnham 2009). Theoretically, it addresses the conceptual similarities between the two constructs. Practically, it helps educators compare the predictive power of personality and learning approaches for educational outcomes. Moreover, knowing the relative influence of personality versus instructional contexts on learning approaches is conducive for educators to modify students’ learning approaches (Chamorro-Premuzic and Furnham 2009). In investigating into the relationships between the two constructs, researchers predominantly tested personality based on the Big Five model (Costa and McCrae 1992). It has been consistently found that the deep learning approach was related positively to extraversion, openness to experience, and conscientiousness, whereas negatively to neuroticism. The surface learning approach was related positively to neuroticism and negatively to openness to experience and conscientiousness (ChamorroPremuzic et al. 2007; Duff et al. 2004; Zhang 2003). These

Predictors of Learning Approaches

findings suggest the conceptual similarities between the two constructs. Those who are intellectually curious about the outside world (open to experience and extraverted) and dutiful (conscientious) tend to be deep learners, whereas those who tend to worry (be neurotic) about examinations are more likely to be surface learners. Chamorro-Premuzic and Furnham (2009) argued that most studies merely reported the correlations between personality and learning approaches. To our best knowledge, only three studies used structure equation modeling (SEM) to explore the overlap between the two constructs. Two studies indicated a moderate overlap between personality and learning approaches (Chamorro-Premuzic and Furnham 2009; von Stumm and Furnham 2012), whereas the other study (Duff et al. 2004) reported a substantial overlap. However, these three studies examined learning approaches based on the old-fashioned three-factor model.

Method

Learning Approaches and Ability

Measurement

Many researchers view learning approaches as individual differences mixing both personality and ability (Furnham 2011). Existing findings on the association between learning approaches and ability are inconsistent. Some studies reported negligible relationships between the two constructs (Furnham et al. 2009), whereas others reported significant relationships: ability was positively related to the deep learning approach, whereas negatively related to the surface learning approach (Diseth 2002).

The Revised Two-Factor Study Process Questionnaire (R-SPQ-2F; Biggs et al. 2001)

Research Questions and Hypotheses Three specific research questions are raised as follows: 1. 2. 3.

What are the relationships between learning approaches and demographics, personality, and ability? To what degree can learning approaches be predicted by demographics, personality, and ability simultaneously? What are the relative effects of demographics, personality, and ability on learning approaches?

We hypothesized that the deep learning approach would be positively associated with extraversion, openness to experience, and conscientiousness, whereas negatively associated with neuroticism. The surface learning approach would be positively associated with neuroticism, whereas negatively associated with openness to experience and conscientiousness. Males and Year-one students would be more likely to be deep learners. We did not posit the specific relationships between learning approaches and ability due to the incongruent previous findings. In addition, we hypothesized that personality would be the strongest predictor, whereas ability would be the weakest predictor of learning approaches.

Participants The participants were 443 students (113 males, 328 females, and 2 students did not indicate gender) aged between 16 and 23 years (M = 19.2, SD = 1.10) in a university in mainland China. This sample included 232 Year-one students, 209 Year-three students, and 2 students did not indicate their year of study. Two hundred and fifty students majored in Science, 188 students majored in Humanities, and 5 students did not indicate their majors. Additionally, students reported parents’ education levels and family income respectively on 6- and 7-point Likert scales. Larger number indicated higher education levels and more income. Ethical approval and students’ consent were obtained before data collection.

The self-report R-SPQ-2F is composed of the surface learning approach (SA) and deep learning approach (DA) scales. Each scale consists of the two corresponding subscales of learning motivation and learning strategy. Each subscale contains five items, and each scale contains 10 items. A 5-point Likert scale (‘‘1’’ indicating ‘‘never or only rarely true of me’’ and ‘‘5’’ indicating ‘‘always or almost always true of me’’) was used for scoring. The scores on the DA and SA scales are equal to the sum of the scores on their respective subscales. Biggs et al. (2001) reported that Cronbach’s alpha coefficients were .73 and .64 for the DA and SA scales respectively, and the coefficients ranged from .57 to .72 for the four subscales. In addition, this instrument has good construct validity (Biggs et al. 2001). This questionnaire has been translated and back-translated between English and Chinese. Xie (2014) validated the Chinese version of this questionnaire among university students, and found that the internal consistency reliability obtained among Chinese students was comparable to that reported in Biggs et al. (2001) original study. In addition, the Chinese version of this questionnaire has acceptable test–retest reliability, good construct validity, and good concurrent validity (Xie 2014). In this study, the Cronbach’s alpha coefficients were .84 for the DA scale and .77 for the SA scale, and they ranged between .58 and .70 for the four subscales (see Table 1). The alpha values found in this study were acceptable (Kember et al. 2004; Xie 2014).

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The NEO Five-Factor Inventory-3 (NEO-FFI-3; McCrae and Costa 2007) The NEO-FFI-3 is a self-report inventory that tests personality based on the Big Five model (Costa and McCrae 1992). It consists of five scales: Neuroticism, Extraversion, Openness to experience, Agreeableness, and Conscientiousness. Each scale contains 12 items, and each item is scored from 1 (strongly disagree) to 5 (strongly agree). It was reported that the Cronbach’s alpha coefficients typically range from .72 to .88 for the five scales (Costa and McCrae 2008; McCrae and Costa 2007), and this instrument also has good factor structure (e.g., Ludtke et al. 2004). This questionnaire has been translated in many languages and extensively used in the Chinese cultural context (e.g. Xie in press-a; Zhang and Huang 2001). The reliability and validity of the Chinese version of the NEO-FFI (Costa and McCrae 1992) are acceptable (e.g., Zhang 2003). The first author of this paper translated several revised items in the NEO-FFI-3 from English to Chinese, and another researcher in Psychology conducted back-translation (i.e., from Chinese to English). The Cronbach’s alpha coefficients ranged from .63 to .82 for the five scales in this study (see Table 1) and could be considered as acceptable. The Raven’s Advanced Progressive Matrices (APM; Raven et al. 1985) The APM examines non-verbal reasoning ability referred to as fluid intelligence. This instrument presents multiplechoice questions and requires participants to identify the missing element that completes a pattern. It consists of two sets of items. Set I includes 12 items that familiarize participants with the test. Set II includes 36 items, and the item difficulty increases gradually. This instrument has good internal consistency reliability and construct validity (e.g., Arthur and Day 1994; Rushton et al. 2004). We used a short form of the APM developed by Arthur and Day (1994) to reduce testing time. The short form contains 12 items selected from the Set II based on item-total correlation, item difficulty, and the Cronbach’s alpha coefficients after the deletion of each item. Cronbach’s alpha coefficient and split-half coefficient of the short form were around .65, which indicates that the short form has acceptable internal consistency reliability (Arthur and Day 1994; Xie in press-a). Arthur and Day (1994) also reported that the scores on the short and the original form were highly correlated (r = .66) and the structure of the short form well represented that of the original one. In this study, the Cronbach’s alpha coefficient was .66, which was comparable to that reported in previous studies.

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Data Analysis We used bivariate correlations to investigate the relationships between learning approaches and the three personal factors. We also conducted SEM using Lisrel 8.8 to examine the prediction of learning approaches by the three personal factors. Multiple imputation was used to deal with missing data, and standardized structural coefficient estimates were reported as path coefficients.

Results Table 1 shows the descriptive statistics and the bivariate correlations. As can be seen, male students scored higher on the deep learning motivation than female students (r = -.21). Year-three students scored higher on the surface learning approach (r = .23), especially the surface learning strategy (r = .29), and lower on the deep learning approach than Year-one students (r = -.14). Year of study could represent age in this study (r = .83, p \ .001 for the correlation between year of study and age). Science students scored higher on the deep learning motivation than humanity students (r = -.13). Family’s socioeconomic status and ability were not correlated with learning approaches. The deep learning approach was correlated positively with extraversion (r = .22), openness to experience (r = .45), and conscientiousness (r = .48), whereas negatively with neuroticism (r = -.20). The surface learning approach was positively correlated with neuroticism (r = .21), whereas negatively correlated with the other four personality factors (see Table 1). Because of the high correlation between year of study and age, age was excluded in the SEM. As can be seen, the model fitted our data well: v2(df = 20) = 36.25; RMSEA = .047 (90 % CI from .021 to .071); NNFI = .95; CFI = .99. Gender had a significant effect on the deep learning approach: males were more likely to be deep learners (path coefficient = -.14). Year of study had a negative effect on the deep learning approach (path coefficient = -.15) and a positive effect on the surface learning approach (path coefficient = .27). Openness to experience and conscientiousness had strongest significant positive effects on the deep learning approach (path coefficient = .33 and .39 respectively). Neuroticism had a positive effect (path coefficient = .15) and conscientiousness had a negative effect (path coefficient = -.15) on the surface learning approach. Ability had the weakest significant positive effect on the surface learning approach (path coefficient = .12). These personal factors contributed 44 % and 18 % of the variance in the deep and surface learning approach respectively (see Fig. 1).

2.62

2.70

2.53

2.44

1.89 2.25

2.39

2.57

2.34

8.02

SA

DM

DS

SM

SS

N E

O

A

C

Ability

2.42

.54

.43

.56

.55 .49

.75

.69

.73

.76

.65

.70

SD

.66

.82

.63

.63

.79 .70

.65

.58

.70

.70

.77

.84

Alpha

-.10*

.02

.21**

-.11*

-.01 .06

.03

-.08

-.16

-.21**

-.02

-.20**

Gender

-.05

.08

-.01

-.09

-.06 -.07

.19**

.10*

-.10*

-.12*

.16**

-.11*

Age

.05

.06

-.03

-.07

-.08 -.03

.29**

.12*

-.12*

-.16**

.23**

-.14**

Year

-.10

.07

.09

-.05

-.05 .01

.03

-.01

-.08

-.13**

.01

-.11*

Major

.00

.48**

.03

.45**

-.20** .22**

-.28**

-.20**

.94**

.95**

-.26**

DA

.09

-.25**

-.22**

-.20**

.21** -.18**

.91**

.90**

-.22**

-.28**

SA

.02

.43**

.06

.45**

-.22** .23**

-.28**

-.22**

.78**

DM

-.01

.46**

.00

.39**

-.15** .20**

-.23**

-.15**

DS

.07

-.19**

-.18**

-.15**

.15** -.13**

.64**

SM

.10

-.25**

-.20**

-.18**

.21** -.19**

SS

-.08

-.39**

-.22**

-.10

-.40**

N

.32**

.17**

.18**

-.01

E

.28**

.09 -.02

O

.17** -.09

A

.01

C

*p \ 0.05; **p \ 0.01

Alpha Cronbach‘s alpha coefficient, Year year of study, DA deep learning approach, SA surface learning approach, DM deep learning motivation, DS deep learning strategy, SM surface learning motivation, SS surface learning strategy, N neuroticism, E extraversion, O openness to experience, A agreeableness, C conscientiousness, Gender coded 1 male and 2 female, Major coded 1 science and 2 humanities

As variables of family’s social economic status were not significantly correlated with any learning approach, they are not presented in this table

2.66

2.49

DA

Mean

Table 1 Bivariate correlations between personal factors and learning approaches

Predictors of Learning Approaches

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Q. Xie, L. Zhang

Gender -.14 Year

DM

-.15 .27

.12

.94

Major

DA

N

.85

.15

DS

.27

44%

E .33 O A

.60

.64

SA

.39 -.15

C

SM

1.06 18%

SS

-.13

.12

Ability Fig. 1 Structural equation modeling for demographic factors, personality, and ability as predictors of learning approaches. Standardized path coefficients were used. % indicates the percent of the variance in the latent factors accounted for by the exogenous variables. The correlations among exogenous variables are not presented in this figure to sustain graphical clarity. Dashed paths

represent insignificant relationships. Year year of study, N neuroticism, E extraversion, O openness to experience, A agreeableness, C conscientiousness, DA deep learning approach, SA surface learning approach, DM deep learning motivation, DS deep learning strategy, SM surface learning motivation, SS surface learning strategy, Gender coded 1 male and 2 female, Major coded 1 science and 2 humanities

Discussion

conscientiousness seem to be the prerequisites for employing the deep learning approach. By contrast, the surface learning approach appears to be less affected by inbuilt personality and may be more situation-sensitive and strategy-like. In addition, the specific relationships between learning approaches and the five personality factors shown in this study are mainly consistent with those found in previous studies (Chamorro-Premuzic et al. 2007; Duff et al. 2004; Zhang 2003). Demographic factors exerted the second strongest influence on learning approaches. Year of study was the strongest predictor of the surface learning approach, explaining 7 % of its variance. Specifically, Year-three students were more inclined to be surface learners and less inclined to be deep learners than their Year-one counterparts. This also indicates that the surface learning approach was more likely to be influenced by learning contexts than by inner traits because the effect of year of study may be explained by different learning contexts for Year-one and Year-three students. In addition, the results show that male students were more likely to be deep learners than female students. Gender difference in learning approaches is not consistent in literature (Duff 2002; Xie 2013a). The inconsistency is potentially due to the degree of gender stereotype in different social contexts. Severiens and Ten

Using SEM, this study investigates the contributions of personal factors to learning approaches in a Chinese learning context based on the updated two-factor model of learning approaches and a large sample size. It is also the first study that explores the total predictive power of three important personal factors (namely, demographics, personality, and ability) for learning approaches. The results show that about 44 % of the variance in the deep learning approach and about 18 % of the variance in the surface learning approach could be explained by these personal factors. Furthermore, the SEM results suggest the relative importance of demographics, personality, and ability in learning approaches. The path coefficients indicate that personality was the strongest predictor of learning approaches, especially the deep learning approach. Conscientiousness and openness to experience respectively accounted for about 15 % and 11 % of the variance in the deep learning approach. Neuroticism and conscientiousness respectively explained 2 % variance in the surface learning approach. The findings also support Furnham’s (2011) statement that the deep learning approach overlaps more substantially with personality than does the surface learning approach. Openness to experience and

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Predictors of Learning Approaches

Dam (1997) found that gender identity contributed to learning approaches more than biological gender. Specifically, the surface learning approach was positively related to feminine identity and negatively related to masculine identity. Although the deep learning approach was related to both feminine and masculine identities, its relationship to masculine identity was stronger. Therefore, in the societies where gender role is emphasized, males are more likely than females to apply the deep learning approach. Our study indicates that ability exerted rather weak influence on learning approaches, and this is in line with a number of previous findings (e.g., Furnham et al. 2009; von Stumm and Furnham 2012). The weak relationship between ability and learning approaches challenges the claim (Furnham 2011) that learning styles and learning approaches are regarded as the individual differences mixing both personality and ability; rather, it seems that learning approaches are largely independent of ability. This study supplements the insufficient literature on the exploration of learning approaches in the Chinese cultural context. The findings suggest that the specific relationships between personality and learning approaches found in this study are comparable to those found in the Western cultural context. A number of scholars emphasized the importance of socio-cultural influence in an individual’s cognition and motivation (e.g., Phan 2012; Xie in press-b). Socio-cultural beliefs potentially influence the specific learning contexts which in turn may affect the relationships between learning approaches and some demographic factors. Our findings on the relationships between learning approaches and year of study are consistent with Zhang’s (2000) findings also obtained in a Chinese cultural context, and these relationships are likely to be interpreted by the influences of different learning contexts. In China, being admitted to a university is comparatively difficult because of limited tertiary education resources; however, graduating from a university is comparatively easy. Therefore, high school students in China are more motivated to study hard than university students. Year-one students may still be somewhat influenced by their previous high school learning environments. These socio-culture-related contexts may explain as to why the more time students spent in a university, the more likely they used the surface learning approach. Additionally, in the Chinese learning context, the absorption of knowledge presented in textbooks is emphasized in learning humanities (such as education and history), but personal thoughts and critiques on the socalled knowledge are hardly encouraged. However, in learning science subjects (such as mathematics and physics), applying relevant axioms and formula to answer questions is emphasized. In Xie’s (2013b) interview with Chinese university students, science majors indicated that the assessments on science disciplines tended to require

thorough comprehension, whereas humanity majors stated that broad reading did not help them get a higher score as the assessments only focused on the facts presented in textbooks. This may explain the correlations between academic discipline and learning approaches found in this study that science students scored higher on the deep learning motivation than humanity students. Furthermore, our findings suggest that learning approaches are not fully addressed by the three personal factors, and over half of the variance in learning approaches cannot be explained by personality despite their conceptual similarities. These findings support Biggs’ (2001) statement that learning approaches are affected by instructional factors along with personal factors. In the present study, the unexplained variance in learning approaches are likely to be affected by instruction-related factors, such as the clarity of teaching goals, workload, and instructional methods, as previous studies suggested (Papinczak et al. 2008; Trigwell and Prosser 1991). Practical Significance and Future Research Directions This study has practical implications. It provides Chinese educators with detailed information as to what personal factors are associated with learning approaches. The current findings on the relationships between learning approaches and some demographic factors imply that educators should deliver positive beliefs about learning as well as modify their instructional methods and assessment format (especially for humanity students and Year-three and Year-four students), so that students are more motivated to learn and the students who use the deep learning approach are more likely to achieve higher scores in examinations. Moreover, this study shows that more than half of the variance in learning approaches may not be affected by ingrained personal characteristics and, thereby, instructional factors also potentially affect learning approaches. This suggests that cultivating the deep learning approach by modifying instructional factors may be effective. This also implies that the prediction of educational outcomes by learning approaches may be partially, but not fully, explained by personality. Such implication is congruent with previous findings suggesting that learning approaches are likely to have unique contribution to educational outcomes beyond personality (Furnham 2011; Swanberg and Martinsen 2010). The present study has three major limitations. First, it only involves single-wave data, thus limiting the explanation about the causality that underlies the reported relationships. Second, the assessments on learning approaches and personality are self-reported; these self-reported data may reflect students’ preferred, not actual, learning approaches and personality. Finally, this study only tests

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fluid intelligence. Testing other dimensions of abilities may help to reach a better understanding of the effect of ability on learning approaches. In conclusion, this study suggests the predication of learning approaches by demographics, personality, and ability. Based on the current findings, future studies are needed to investigate the effects of instructional factors along with personal variables as well as the interaction effects between personal and instructional factors on learning approaches. In so doing, a more comprehensive understanding as to what factors account for learning approaches could be reached. In addition, future studies are also needed to address the limitations of the present study. Specifically, by involving longitudinal data of learning approaches and personal factors to build causality models, the causal relationships between learning approaches and some personal factors may be suggested. Also, by using other intelligence measurements such as the one assessing verbal ability, a better understanding on the relationships between learning approaches and ability may be reached.

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