Learning and Individual Differences 39 (2015) 199–204

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Learning and Individual Differences

j ourna l homepage: www.e lsev ie r .com/ locate / l ind i f

Effects of approach to learning and self-perceived overall competence onacademic performance of university students

Elaine S.C. Liu ⁎, Carmen J. Ye, Dannii Y. Yeung ⁎City University of Hong Kong, Hong Kong

⁎ Corresponding authors at: Department of Applied SoHong Kong, Hong Kong.

E-mail addresses: elaine.au@cityu.edu.hk (E.S.C. Liu), d(D.Y. Yeung).

http://dx.doi.org/10.1016/j.lindif.2015.03.0041041-6080/© 2015 Elsevier Inc. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 11 March 2014Received in revised form 14 January 2015Accepted 15 March 2015

Keywords:Self-perceived overall competenceApproach to learningAcademic motivationAcademic performance

This study integrated self-perceived overall competence and approaches to learning in predicting academicmotivation and performance of university students. The sample comprised 462 undergraduate students inHong Kong, who were invited to complete a set of measurements. Results of the pathway analyses confirmedour hypothesizedmodel. In particular, deep and surface approaches to learning directly and indirectly influencedgrade point average (GPA), whereas the effect of self-perceived overall competence on GPA was fully mediatedby academic motivation. The findings of this study advance the literature on higher education by revealing theimportance of self-perceived overall competence on academic success.

© 2015 Elsevier Inc. All rights reserved.

1. Introduction

“Education is the most powerful weapon which you can use tochange the world.”

[Nelson Mandela]

Education is an important means to empower individuals. Amongthe various stages of educational training, higher education is regardedas the “engine of development in the new world economy” (Castells,1994, p. 14). Tertiary education is a source of tremendous potential forthe social, economic, and cultural development of the country (Barnet,1990). Accordingly, factors that influence academic success andmotiva-tion of students have often been the focus of educators and policymakers.

Educators have attempted to develop a systematic framework forunderstanding academic performance in higher education. A meta-analysis of 109 studies investigated the relationship between psychoso-cial and study skill factors (PSFs) and academic outcomes in tertiaryeducation (Robbins et al., 2004). PSFs refer to the contextual and socialfactors (e.g., perceived social support, institutional selectivity, andfinancial support), as well as the motivational factors (e.g., academicachievement motivation). The findings of this research reveal thatacademic achievement can be better predicted by combining both psy-chosocial and motivational factors in the model (Robbins et al., 2004).

cial Sciences, City University of

annii.yeung@cityu.edu.hk

Similarly, a recent meta-analytic study treated study habits, skills, andattitudes as “the third pillar supporting collegiate academic perfor-mance” because these three cognitive constructs, taken collectively,are the key and critical factors in determining one's academic success(Credé & Kuncel, 2008, p. 425). In their proposed model of academicperformance determinants, other than cognitive factors, the researchersalso included non-cognitive factors such as personality, interests, andprior experience to capture a full picture of the determinants of academ-ic performance. In sum, these two meta-analytic studies pinpointthe importance of including personality, cognitive (e.g., approach tolearning) andmotivational factors (e.g., academicmotivation) in the ex-amination of academic performance of undergraduate students. In lightof previous research, a conceptual model that integrates both cognitivefactors and personal characteristics is therefore proposed in this studyto systematically examine their effects on academic performance of uni-versity students. In particular, we hypothesize that cognitive factors andpersonal characteristics are predictive of academic success through aca-demic motivation (see Fig. 1). In the following sections, the effects ofpersonal characteristics and cognitive factors on academic motivationand academic performance will be reviewed.

1.1. Effects of personal characteristics on academicmotivation and academicperformance

Personal characteristics have been shown to be one of the crucialinfluencing factors of academic achievements of university students(Noftle & Robins, 2007). A meta-analytic study (Poropat, 2009) witha cumulative sample size over 70,000 participants reported small tomedium correlations between personality traits and academic perfor-mance in secondary and tertiary education. A study by Tomas and

Cognitive Factors

Academic Performance

Academic Motivation

Personal Characteristics

Fig. 1. The proposed conceptual model for understanding the relationships among cognitive factors, personal characteristics, academic motivation, and academic performance.

200 E.S.C. Liu et al. / Learning and Individual Differences 39 (2015) 199–204

Adrian (2003) revealed that personality traits accounted for nearly15% of the variance in examination grades. A three-year longitudinalstudy by Chamorro-Premuzic and Furnham (2003) provided furtherevidence to support these findings. In particular, neuroticism andconscientiousness predicted the overall examination scores of Britishuniversity students, accounting for more than 10% of unique variancein overall examination marks. In addition to academic performance,personal characteristics, such as persistence, self-directedness, andself-transcendence, are also predictive of academic motivation of col-lege students (Tanaka, Mizuno, Fukuda, Tajima, & Watanabe, 2009).

The above-mentioned findings illustrate the direct effect of personalcharacteristics on academic performance and motivation. Yet, someresearchers argued that academicmotivation can also directly influenceacademic performance. In the present study, academic motivation isdefined as a type of intrinsic motivation, which refers to the motivatingforce that is derived from the interests, pleasure, and satisfaction obtain-ed fromparticipating in academic activities (Vansteenkiste, Lens, &Deci,2006). Students who are academically motivated have a strong desireto perform well in universities, are more eager to learn, enjoy thelearning-related activities, and believe that education and knowledgeare important. Therefore, individuals with higher academic motivationare more likely to achieve better learning outcomes and academic suc-cess than those with lower academic motivation (Clark & Schroth,2010; Komarraju, Karau, & Schmeck, 2009; Turner, Chandler, & Heffer,2009).

The effect of academic self-efficacy on academic performance waswidely examined in past studies (e.g., Pajares, 1996; Zimmerman,2000). However, according to Pajares and Miller (1994), self-efficacyrefers to a “context-specific assessment of competence to perform aspecific task” (p. 194), implying that this construct only captures onedimension of competence (i.e., an evaluation of one's capabilities). It istherefore suspected whether the overall competence, which includesself-efficacy, self-concept, outcome expectations, and expectancy be-liefs (Schunk & Pajares, 2005), would be a better predictor of academicmotivation and performance. To advance the literature on higher edu-cation, this study assessed the effect of self-perceived overall compe-tence on academic motivation and performance.

Self-perceived overall competence is defined as a personal charac-teristic that reflects one's global expectation or belief in his/her abilityto accomplish tasks (Eccles & Gootman, 2002; Lerner et al., 2005;Schunk & Pajares, 2005). Lerner et al. (2005) demonstrated the impor-tance of self-perceived overall competence in positive youth develop-ment. Past laboratory and longitudinal studies have demonstrated thatself-perceived competence in the academic domain is predictive ofundergraduates' learning and achievement (Fazey & Fazey, 2001) andmotivation (Harter, Whitesell, & Kowalski, 1992; Vallerand & Reid,1984). In addition, some researchers stressed that other types of compe-tence beliefs may also be related to students' academic success. Forexample, social competence at school could facilitate learning outcomesthrough promoting positive interactions with teachers and peers(Wentzel, 1991a, 1991b). Accordingly, this study aimed to test the effectof self-perceived overall competence on academic motivation and

performance. To the best of our knowledge, past literature on academicperformance hasmainly focused on academic self-efficacy, and no studyhas yet examined the influence of self-perceived overall competence onacademic motivation and performance. This study will therefore ad-vance the literature by revealing the important role of self-perceivedoverall competence in academic success.

According to the cognitive evaluation theory (CET; Deci & Ryan,1985, 1990), a sub-theory within Self-Determination Theory, the levelof autonomous academic motivation is dependent on one's perceptionof academic competence and self-determination. When universitystudents do not feel competent in their study, their academic motiva-tion will decrease, whereas when they perceive themselves with ahigh level of academic competence, their academic motivation ismaintained or even enhanced (see Elliot & Dweck, 2005 for a review).In addition, Fortier, Vallerand, and Guay (1995) suggested that the ef-fects of academic competence perceptions on academic performancemight be mediated by academic motivation. In light of past findingsreviewed above, two hypotheses are generated:

H1. Self-perceived overall competence is positively correlated withacademic performance.

H2. Academic motivation mediates the effect of self-perceived overallcompetence on academic performance.

1.2. Effects of approach to learning on academic motivation and academicperformance

Approach to learning refers to the ways or methods the studentsapply to their study, and it is strongly associated with academic perfor-mance (Marton & Säaljö, 1976). Biggs and colleagues divided approachto learning into two categories, namely, deep approach to learning andsurface approach to learning (Biggs, 1987; Biggs, Kember, & Leung,2001). Students who adopt deep approach are more likely to engagein an active learning and searching for the meaning of the learningmaterials, while those who prefer surface approach tend to use a moresuperficial way of learning and mainly focus on memorizing thelearning materials for tests or examinations. Previous research demon-strated that approach to learning is predictive of academic outcome(Chamorro-Premuzic & Furnham, 2008; Diseth, 2003; Snelgrove &Slater, 2003). In particular, deep approach to learning was found to bepositively correlated with academic achievement, whereas surfaceapproach was negatively related to examination results.

Moreover, approach to learning is strongly linked to academicmotivation, which in turn affects academic success. The relationshipbetween approach to learning and academic motivation has beenwell-documented in the literature. For example, university studentswhoapplied deep approach to learning reported increased achievementmotivation,whereas thosewhodid not systematically seek themeaningof learning materials exhibited low levels of academic motivation intheir study (Busato, Prins, Elshout, & Hamaker, 2000). Research ongoal orientations also sheds light on the relationship between approach

201E.S.C. Liu et al. / Learning and Individual Differences 39 (2015) 199–204

to learning and academic motivation. This is because both goal orienta-tion and academic motivation are regarded as intercorrelated motiva-tional constructs (Colquitt & Simmering, 1998), and thus they havebeen used interchangeably in the literature (Kizilgunes, Tekkaya, &Sungur, 2009). Kizilgunes et al. (2009) demonstrated that deep ap-proach to learningwas positively correlated with mastery-goal orienta-tion but negatively associated with performance-goal orientation.Therefore, it is expected that approach to learning affects one's academ-ic motivation, such that employing deep approach to learning is associ-ated with increasedmotivation whereas surface approach to learning islinked to lower motivation.

Furthermore, Credé and Kuncel (2008) proposed that the rela-tionship between study habits or attitudes (as a form of approach tolearning) and academic performance would be mediated by academicmotivation. It is anticipated that students who prefer deep approachtend to possess an intrinsic motivation to learn (e.g., out of personalinterest or on purpose of self-actualization), and thus are more willingto invest immense efforts in relating concepts together to systematicallyacquire knowledge and obtain better academic outcome. The use ofsurface approach is related to a utilitarian motive and an inclination torote learning, whichmay lead to poorer academic performance in com-parison with those who adopt deep approach to learning and possessgreater motivation to learn. Accordingly, we hypothesize that:

H3a. Deep approach is positively associated to academic performance.

H3b. Surface approach is negatively correlated to academic performance.

H4. Academic motivation mediates the relationship between approachto learning (deep and surface approaches) and academic performance.

1.3. The relationship between self-perceived overall competence andapproach to learning

Personality traits are found to be predictive of the learning styles andstudy strategies of undergraduate students (Blickle, 1996; Duff, Boyle,Dunleavy, & Ferguson, 2004). For example, conscientiousness is associ-ated with approach to learning (Diseth, 2003). The relationship be-tween self-perceived overall competence and approach to learning isseldom the focus in the previous research. Though, some researchershave found that students adopting deep approach to learning reportedhigher level of self-efficacy while those using surface approach to lean-ing were more likely to possess lower levels of self-efficacy (Papinczak,Young, Groves, & Haynes, 2008). In light of these past findings, we fur-ther hypothesize that self-perceived overall competence is related toapproach to learning, such that competent individuals are more likelyto explore learning materials in-depth and comprehensively, whereasindividuals who feel incompetent tend to adopt a surface approachand focus on memorization of learning materials. Therefore, we predictthat:

H5. Self-perceived overall competence is positively related to deepapproach and negatively associated with surface approach.

To summarize, the present study aims to test an integratedmodel ofcombining both cognitive factors (i.e., deep and surface approaches tolearning) and personal characteristics (i.e., self-perceived overall com-petence) in predicting academic performance through academic moti-vation. Unlike previous studies which mainly focused on individualcomponents of academic competence beliefs such as self-efficacy orself-concept, this study advances the literature on learning and educa-tion by investigating the effect of self-perceived overall competence(as a global evaluation of one's capabilities in various domains) onacademic motivation and performance. The findings of this study willprovide recommendation to the educators to understand the influenc-ing factors of students' academic motivation and performance.

2. Method

2.1. Participants

A sample of 496 undergraduate students at a local university partic-ipated in this study. Thirty-four students did not report their GPAs, thusthey were excluded from further analysis. The final sample size is 462,with 32.8% of which are male. Their study majors were social sciencesubjects. The age range is between 18 and 29, with a mean of age of20.75 (SD = 1.74).

2.2. Measures

2.2.1. Self-perceived overall competenceThe competence subscale of the Positive Youth Development Inven-

tory (PYDI; Arnold, Nott, & Meinhold, 2012) was adopted to measureself-perceived overall competence of university students. PYDI was de-veloped to assess personality characteristics of adolescents and youngpeople (Eccles & Gootman, 2002; Lerner et al., 2005). The competencesubscale of the PYDI consists of 14 items. It measures one's view ofhis/her actions in social, academic, cognitive, and vocational areas. Sam-ple items include “I can handle problems that come up inmy life” and “Iam aware of other people's needs in social situations.” Participantsresponded to each item on a 4-point Likert scale, with 1 = StronglyDisagree to 4 = Strongly Agree. Higher scores indicate higher degreesof perceived competence. The Cronbach's alpha of this scale was .80.

2.2.2. Approach to learningA revised two-factor study process questionnaire (R-SPQ-2F; Biggs

et al., 2001) was applied to assess the two approaches to learning. Thequestionnaire consists of two subscales, namely, deep and surfaceapproaches, with 10 items for each subscale. Examples of items forassessing deep approach are “I find that at times studying gives me afeeling of deep personal satisfaction” and “Ifind that I have to do enoughwork on a topic so that I can form my own conclusions before I amsatisfied.” Sample items for the surface approach subscale are “My aimis to pass the course while doing as little work as possible” and “I onlystudy seriously what's given out in class or in the course outlines.” Thequestionnaire was rated on a five-point Likert scale, with 1 = Neveror only rarely true for me to 5 = Always or almost always true for me.Higher scores indicate the more frequent use of the respective approachto learning. The reliability obtained for the two subscaleswas satisfactory(deep approach: α= .82; surface approach: α= .85).

2.2.3. Academic motivationThe motivation subscale of the Student Adaptation to College

Questionnaire (Baker & Siryk, 1989) was administrated to evaluate thelevel of academicmotivation of undergraduates. The questionnaire con-sists of six items, with sample items such as “I knowwhy I'm in the uni-versity andwhat I want out of it” and “Most of the things I am interestedin are not related to any of my course work at university.” Participantsresponded to each item using a 9-point Likert scale, with 1 = Doesnot apply to me at all to 9 = Apply very closely to me. Higher scoresrepresent a stronger academic motivation. The reliability of this mea-surement was acceptable (α = .69).

2.2.4. Academic performanceGrade point average (GPA) was utilized as an indicator of academic

performance. The respondents were requested to report their GPAs inthe last semester. Their GPAs ranged from 1 to 4.3. Higher scores indi-cate better academic performance.

In addition, participants were asked to report their majors, age, andgender in the questionnaire.

Table 2The direct and indirect effects of perceived competence and approach to learning on gradepoint average (GPA).

Standardized Beta (SE)

Deepapproach

Surfaceapproach

Self-perceivedoverallcompetence

Academicmotivation

Direct effectsAcademic motivation .26⁎⁎⁎ (.04) − .36⁎⁎⁎ (.04) .23⁎⁎⁎ (.05) –

202 E.S.C. Liu et al. / Learning and Individual Differences 39 (2015) 199–204

2.3. Procedure

The ethical approval for this study was obtained from the affiliateduniversity. Participants were recruited and completed the question-naires during classes, such that most questionnaires were immediatelyreturned to the researchers. Participation in this questionnaire wasvoluntary. Participants were assured that the data collected wouldbe kept confidential and would only be utilized for the researchpurpose.

GPA .22⁎⁎⁎ (.04) − .18⁎⁎ (.05) − .08 (.05) .17⁎⁎ (.05)Indirect effects

GPA .05⁎⁎ (.02) − .06⁎⁎ (.02) .04⁎⁎ (.02) –

⁎⁎ p b .01.⁎⁎⁎ p b .001.

3. Results

First, the means and correlations of the main constructs in the pro-posed model are presented in Table 1. Consistent with the predictionin H1, a significantly positive correlation between self-perceived overallcompetence and GPAwas found (r= .14). GPAwas significantly associ-ated with deep (r = .30) and surface approaches (r = − .28), whichsupport H3a and H3b. The expected positive correlation between self-perceived overall competence and deep approach (r = .38) and nega-tive correlation between surface approach and self-perceived overallcompetence (r = − .31) were also observed, which confirm H5. Fur-thermore, in accordance with the predictions for the two approachesto learning, deep approach was found to correlate significantly withsurface approach (r = − .20). Moreover, academic motivation wasassociated with GPA (r = .32), the two approaches to learning (deepapproach: r = .42; surface approach: r = − .48), and competence(r = .44). These associations provided the statistical foundation to testthe proposed mediating model.

Path analysis was performed by using AMOS to investigate thehypothesized relationships among self-perceived overall competence,approach to learning, academic motivation, and GPA (Arbuckle,2006b). AMOS offers several model fit indices to evaluate the appropri-ateness of the proposed model, including Chi-square (χ2) with itsdegree of freedom (df), root mean square error of approximation(RMSEA), normed fit index (NFI), and comparative fit index (CFI).These fit indices directly measure the fit of the hypothesized modelwith the observed data and of the estimated model relative to alterna-tive baseline models, including a null model with no correlationsbetween the variables (Arbuckle, 2006a).

The proposed pathways evaluated the effects of self-perceivedoverall competence and two approaches to learning on academicperformance through academic motivation. First, we found thatself-perceived overall competence has no significant direct effecton GPA (see Table 2). Thus, the direct pathway from self-perceivedoverall competence to GPA was removed from the model. The finalmodel, which is shown in Fig. 2, demonstrates a good model fit (χ2

(1)= 2.61, p N .01, NFI= .994, RMSEA= .06, CFI= .996). As present-ed in Fig. 2, academic motivation fully mediated the direct effect ofself-perceived overall competence on GPA, therefore H2 is support-ed. Moreover, both deep and surface approaches to learning havedirect and indirect effects on GPA in the expected directions. Thus,H4 is also supported.

Table 1Means of and correlations among self-perceived overall competence, approach to learning,academic motivation, and academic performance.

1 2 3 4 5 Mean (SD)

1. Self-perceived overallcompetence

– 2.97 (.33)

2. Deep approach .38⁎⁎ – 3.24 (.52)3. Surface approach − .31⁎⁎ − .20⁎⁎ – 2.63 (.64)4. Academic motivation .44⁎⁎ .42⁎⁎ − .48⁎⁎ – 5.93 (1.03)5. Grade point average .14⁎⁎ .30⁎⁎ − .28⁎⁎ .32⁎⁎ – 3.29 (.37)

⁎⁎ p b .01.

4. Discussion

This study examined the interrelationships among self-perceivedoverall competence, approach to learning, academic motivation, andacademic performance. It demonstrated the significant effect of overallcompetence on academic motivation and performance. The results ofpath analyses also supported our hypothesizedmodel, which integratesthe influences of cognitive factors and personal characteristics on aca-demic performance through the mediation of academic motivation.

4.1. Theoretical implications

Unlike past studies which focused mainly on the effect of academicself-efficacy on study habits, attitudes, motivation, and academicperformance (Bandura, 1993; Brookover, Thomas, & Paterson, 1964;Lent, Brown, & Larkin, 1986; Papinczak et al., 2008; Phan, 2007;Zimmerman, 2000), this study investigated the role of overall compe-tence in academic motivation and performance. The expected associa-tion between personal characteristics and cognitive factors wereobserved, with self-perceived overall competence positively correlatedwith deep approach and negatively correlated with surface approach.Among undergraduate students who feel competent in both academicand social contexts, they tend to perceive learningmaterials as interest-ing and consider studying as a source of intrinsic satisfaction, which inturn motivate them to spend more time and effort to their academicstudies. By contrast, students with lower levels of perceived overallcompetence tend to learn things by rote and reluctantly attempt tolearn the required subject materials (Papinczak et al., 2008; Sobral,1997). These findings are similar to the past studies examining theeffects of personality traits, such as the Big Five personality factors,on learning styles and study strategies of undergraduate students(Blickle, 1996; Duff et al., 2004). In addition to personality traits andacademic self-efficacy which were widely examined in past studies,this study demonstrates that self-perceived overall competence is alsoan important predictor of approaches to learning among universitystudents.

The present study advances the literature by demonstrating thatself-perceived overall competence, as a global construct of competencebeliefs, can be a significant predictor of academic success of universitystudents. The overall competence, which includes positive views ofacademic, social, cognitive, and vocational domains, determines thelevel of aspiration and studymotivation of students, which in turn influ-ences their academic performance and whole-person development inthe long-run. Studentswhopossess a higher level of overall competencewill perform better at university than their peers with only a higherlevel of academic competence but lower competence in other domains.The findings of the present study suggest that overall competence,including not only academic competence but also competence in otherdomains, such as good interpersonal relationships, career plan anddecision-making abilities, is important in academic success, especially

.15**

-.31***

.38***

.23***

-.36***

.26*** .20***

-.17**

Deep Approach

Self-perceived Overall

Competence

Surface Approach

GPAAcademic Motivation

-.20***

Fig. 2. Pathways of self-perceived overall competence, deep and surface approaches to learning, academic motivation, and GPA. Note. ⁎⁎p b .01; ⁎⁎⁎p b .001.

203E.S.C. Liu et al. / Learning and Individual Differences 39 (2015) 199–204

in the era when teamwork and whole-person development areemphasized.

This study also investigated the effects of the two approaches tolearning of students on academic performance. Consistent with ourpredictions, academic motivation was positively predicted by deepapproach and negatively predicted by surface approach. These findingsare in alignment with other empirical studies to demonstrate the link-age between approach to learning and academic motivation (Busatoet al., 2000; Kizilgunes et al., 2009). However, some researchers mayargue that academic motivation determines one's approach to learnand study (e.g., Diseth &Martinsen, 2003). Future studies should there-fore adopt a longitudinal design to clarify the direction of relationshipbetween these two constructs.

Furthermore, the proposed mediation model was observed. Resultsof the pathway analysis have demonstrated that both cognitive factors(i.e., approaches to learning) and personal characteristics (i.e., self-perceived overall competence) were salient predictors of academicmotivation. The present study also reveals that approach to learningdirectly and indirectly affects academic performance, which further val-idated themodel proposed by Credé and Kuncel (2008). Similar to theirresearch, this study also shows that the effect of self-perceived overallcompetence on academic performance is fully mediated by academicmotivation. The full mediation effect of academic motivation revealsthe underlyingmechanismof thepathway frompersonal characteristicsto academic success (Fortier et al., 1995). This study therefore advancesthe literature by assimilating cognitive factors and personal characteris-tics to provide a comprehensive picture of higher education through theanalytical lens of an integrated model.

4.2. Limitations and future directions

When interpreting the findings reported in this paper, a few issuesshould be taken into consideration. First, only one factor from eachdimension of cognitive constructs and personal characteristics wasselected for examination. Other personal characteristics, such persis-tence or self-directedness (Lane & Lane, 2001; Tanaka et al., 2009), aswell as other cognitive factors, such as critical thinking skills and studyhabits (Gortner & Zulauf, 2000; Macan, Shahani, Dipboye, & Phillips,1990; Stupnisky, Renaud, Daniels, Haynes, & Perry, 2008), should alsobe taken into account when investigating academic performance infuture studies. Second, social influences such as the social supportfrom family, peers, or teachers can play a vital role in affecting students'coping strategies in face of academic stress and challenges (Cutrona,Cole, Colangelo, Assouline, & Russell, 1994; Malecki & Demaray, 2006).Hence, the proposed model can be further improved by encapsulatingrelevant social factors to fully understand academic achievement.Third, this study is cross-sectional and causal relationships cannotbe drawn. Educators in higher education should interpret and makeuse of these findings with cautions. Future studies should adopt an

experimental design to fully disclose the pathways examined in thepresent conceptual framework. Last but not least, the present studyonly requested the participants to report their GPA in the questionnaireas an indicator of their academic performance. Future studies shouldalso include an objective measure of academic performance by gather-ing academic records from the university.

4.3. Practical implications

Apart from the theoretical implications and limitations discussed,the results shown in this study shed light on tertiary educationpractices. Considering the significant effect of self-perceived overallcompetence on academic achievement, teachers, instructors, and policymakers can assist students to develop an accurate perception of theircapabilities. Provided that competence level is not easily increasedwithin a short period of time, a clear and realistic perception of one'sability and efficacy can at least lead to one's awareness of his/her weak-nesses. The findings from the present study also suggest that academiccompetence (i.e., academic self-efficacy) should not be the solefocus in university education. Other types of competence beliefs,such as competence in social and vocational domains, are also influ-ential on students' academic success as well. Meanwhile, this studyprovides educators with insights for designing intervention pro-grams that will help students modify their approaches to learningand study strategies. For instance, student-centered learning envi-ronments should be prompted to increase their deep learning strat-egies (Baeten, Kyndt, Struyven, & Dochy, 2010), such as emphasison personal interest or self-actualization. Moreover, educators canconsider both cognitive factors and personal characteristics in de-signing class instruction and management methods to maximizelearning outcome in tertiary education.

5. Conclusions

This study explores the interrelationships among self-perceivedoverall competence, approach to learning, academic motivation, andacademic performance. Results strongly supported the proposed path-ways, which indicated that both overall competence and approach tolearning play critical roles, either through direct or indirect pathways,in predicting the GPAof university students. Specifically, overall compe-tence was proven to exert its influence on GPA fully through academicmotivation, whereas approach to learning directly and indirectly affectsGPA. The findings of this study advance our knowledge by unveilingthe importance of self-perceived overall competence on academicachievement. They also provide insights to educators to improve ap-proaches to learning and enhance the study motivation for universitystudents.

204 E.S.C. Liu et al. / Learning and Individual Differences 39 (2015) 199–204

Acknowledgment

This study was supported by research fund from the Department ofApplied Social Sciences at City University of Hong Kong.

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