Exploring undergraduate students’ motivation and engagement in China

Hongbiao YinThe Chinese University of Hong Kong, Shatin, Hong Kong SAR, China

yinhb@cuhk.edu.hk

Viewing student engagement as a multidimensional construct, this study explored the motivation and engagement of undergraduate students in China. A sample of 1,131 students from 10 full-time universities in Beijing participated in a survey. The results showed that the Motivation and Engagement Scale for university/college students is a promising and valid instrument for assessing student engagement in Chinese universities. Chinese undergraduates simultaneously performed well in both adaptive and maladaptive motivation and engagement, indicating some influence from the cultural context of China. Four categories of learners were generated according to their motivation and engagement characteristics. Some consistent patterns of individual differences related to gender, grade, discipline and institution type were revealed. The results have implications for enhancing student engagement.

Keywords: student engagement; motivation and engagement wheel; undergraduate students; China

Introduction

In recent decades, the quality of teaching and learning in higher education has been under close scrutiny from governments and consumers alike due to the increasing accountability of the public sector (Byrne & Flood, 2003; Stensaker, 2007). Student learning is a core goal of universities, whose governance processes must place considerable emphasis on monitoring student learning performance. Therefore, the quality of student learning must be a core objective within institutional and system-level governance arrangements (Jones, 2013).

To ensure that higher education institutions demonstrate excellence in teaching and learning, an increasing number of surveys focusing on students’ perceptions of teaching quality and learning experiences have been administered in countries such as China, the US, the UK and Australia (Coates, 2010; Kuh, 2009; Richardson, Slater, & Wilson, 2007; Shi et al., 2014). For example, the Graduate Careers Council of Australia has run an annual survey of graduates’ course experiences and destinations since 1993. Based on the instrument used by the Australian annual survey, i.e., the Course Experience Questionnaire, the UK developed a similar instrument and has administered it as part of the annual National Student Survey since 2005. However, as Coates (2005) pointed out, these quality assurance mechanisms place too much emphasis on information related to institutions and teaching, and not enough on what students are actually doing in universities. It is important to factor information about student engagement into determinations of the quality of university education. In the US, the Indiana University Center for Postsecondary Research annually administers the National Survey of Student Engagement (NSSE) (Kuh, 2009). More than 1,500 4-year colleges and universities in

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the US and Canada have participated in the survey since its launch in 2000, with 586 US and 27 Canadian institutions participating in 2013 (NSSE, 2013). Following this approach, the Australasian Survey of Student Engagement (AUSSE) has been administered in Australia and New Zealand universities since 2007 to collect information related to how students are interacting with their universities and which practices are most likely to generate productive learning (Coates, 2010). In 2009, using the NSSE as a basis, the Chinese version of the NSSE (i.e., NSSE-China) was adapted and administered nationwide by a research team at Tsinghua University (Shi et al., 2011). After years of adaptation, the updated China College Student Survey (CCSS) was launched in 2013 to reveal university teaching and learning activities and illustrate the learning behaviour of Chinese university students (Shi et al., 2014).

The quality of teaching and learning delivered by higher education institutions in China is in dispute (Liu, 2014; Yin, Lu, & Wang, 2014; Zhang et al., 2011). China’s higher education system has experienced a dramatic growth since the 1990s due to its enrolment expansion. The gross student population increased from 5% of the relevant age cohort in 1993 to 15% in 2002. In 2013, it reached 34.5%, with more than 34 million students enrolled in higher education. However, the rapid expansion of higher education has strained the finite resources of institutions and led to a series of problems, such as a decline in educational expenditure per student, deteriorating teaching conditions and broad variations in the quality of education among the universities. As a result, there has been a noticeable decline in overall quality, and various stakeholders have expressed concerns about the quality of teaching in higher education in recent years (Liu, 2014). Within this context, higher education in China must shift its priority from the expansion of quantity to the enhancement of quality. Since 2003, the Chinese Ministry of Education has initiated a five-year undergraduate teaching evaluation programme as a way to monitor the quality of teaching in higher education. However, some educationists have pointed out that the information collected by this programme has mainly related to the ‘macro factors’ influencing university teaching, such as facilities, equipment and the regulation of teaching administration, rather than the ‘micro elements’ related to the reality of teaching and learning, such as instructors’ teaching strategies and the characteristics of student learning in Chinese universities (Yin & Wang, 2014).

empirical research related to university teaching and learning in China is still in its infancy. Although the number of international studies of teaching and learning in China’s higher education institutions has recently increased (e.g., Lu et al., 2013; Liu, 2014; Shi et al., 2014; Yin, Lu, & Wang, 2014; Yin & Wang, 2014; Zhang et al., 2011), most of these studies have focused on students’ perceptions of teaching quality and instructional practices rather than their engagement or learning processes. The present study addresses this gap by exploring the measurement issues and characteristics of undergraduate students’ motivation and engagement in China.

Student engagement as a multidimensional construct‘Student engagement’ is a current buzzword applied to higher education that has been increasingly researched and debated, with growing evidence indicating its significant effects on students’ learning achievements and personal development (Kahu, 2013; Porter, 2006). Results of empirical studies have repeatedly shown that students’ engagement in educationally purposeful activities is positively related to their grades, critical thinking skills and persistence between the first and second year of college, and

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that lower-ability students and students of colour benefit more from engagement than their classmates (Carini, Kuh, & Klein, 2006; Kuh et al., 2008). A recent study of Chinese undergraduates conducted by Lu et al. (2013) found that students’ academic engagement significantly facilitated their development of intellectual skills.

The most widely adopted view of student engagement in higher education research, including NSSE, AUSSE and CCSS, mainly follows a behavioural perspective in defining and assessing student engagement (Kahu, 2013). According to this perspective, student engagement has been defined as the ‘time and effort students devote to educationally purposeful activities’ (Kuh et al., 2008; Radloff & Coates, 2010). Students must be involved in useful and productive activities determined by educators and guided by governmental policy or societal expectations (Hagel, Carr, & Delvin, 2012). These nationwide surveys have usually focused on a range of institutional practices and student behaviour related to learning and development, such as the time spent on tasks, teaching practices, student-faculty interactions and institutional requirements or services. Although these studies are helpful for explaining the relationship between student behaviour and institutional practices, the understanding of student engagement from a behavioural perspective is too narrow. Just as Kahu (2013) observed, focusing on the elements that institutions can control excludes a wide range of other explanatory variables, including students’ motivations, emotions and expectations. More importantly, some discrepancies may exist between students’ behavioural participation and their psychological states of engagement (Wefald & Downey, 2009).

According to Newmann (1992), engagement should be defined as ‘the student’s psychological investment in and effort directed toward learning, understanding, or mastering the knowledge, skills, or crafts that academic work is intended to promote’. Engagement is a complex and multifaceted construct comprising three dimensions, including behavioural, emotional and cognitive engagement (Fredricks, Blumenfeld, & Paris, 2004; Hagel, Carr, & Delvin, 2012). Behavioural engagement focuses on the extent to which students become involved in academic, social and extracurricular activities. Emotional engagement refers to students’ affective responses to their teachers, classmates, academics and institutions. Cognitive engagement relates to students’ mental investment, which incorporates thoughtfulness and a willingness to exert the effort necessary to comprehend complex ideas and master difficult skills. In this sense, engagement can be seen as an overarching meta-construct that attempts to integrate the diverse lines of research that help explain student success (Kahu, 2013).

According to the psychological perspective of student engagement, motivation and engagement are closely intertwined. The former comprises private, psychological and unobservable factors, and the latter comprises publicly observable behaviour (Reeve, 2012). Martin (2012a) argued that although ideas may differ as to which factors are deemed motivation factors as opposed to engagement factors, there appears to be broad agreement that motivation is a basis for subsequent engagement. For this reason, Martin (2007) suggested using the Motivation and Engagement Wheel as an integrative and parsimonious approach to conceptualising student engagement. The wheel aims to bridge the gap between diverse theoretical perspectives about motivation and engagement, such as expectancy-value, attribution and goal orientation theory. It also provides practitioners (e.g., educators, counsellors and psychologists) with a parsimonious framework that they can apply to their practice and clearly communicate to students.

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The wheel has been conceptualised at two levels, including four higher-order factors and eleven first-order factors. It comprises (1) adaptive motivation, reflecting students’ positive attitudes and orientations towards learning, which consist of self-efficacy, mastery orientation and valuing; (2) adaptive engagement, reflecting students’ positive behaviour and engagement in learning, including their persistence, planning and task management; (3) maladaptive motivation, reflecting the attitudes and orientations that impede students’ academic learning, which consist of anxiety, failure avoidance and uncertain control; and (4) maladaptive engagement, reflecting students’ problematic learning behaviour, including self-sabotage and disengagement (Martin, 2012a). In addition, the accompanying measurement tool, i.e., the Motivation and Engagement Scale (MES), has been implemented across the academic lifespan (e.g., elementary school, high school and university/college) and in diverse performance settings (e.g., education, sport, work, etc.), and the results have supported the generality of the motivation and engagement constructs (Martin, 2007, 2008a, 2009). After reviewing 11 self-report instruments for assessing student engagement, Fredricks and McColskey (2012) identified the MES as a comprehensive instrument with good psychometric qualities that simultaneously assessed the behavioural (e.g. task management and planning), emotional (e.g. anxiety and uncertain control) and cognitive (e.g. self-efficacy and valuing) dimensions of student engagement.

Although various versions of the MES can be applied to different student groups (e.g. elementary, secondary and university/college) and many studies have used the MES over the past decade, the vast majority of these studies have targeted high school students, and very few have considered university/college students (e.g., Martin, 2008a, 2009, 2010). Given the increasing attention paid by governments and scholars to university student engagement, it is worthwhile to examine the potential application of the MES to higher education research.

Individual differences in student motivation and engagementKrause and Coates (2008) suggested that to help shape policy and practice, student engagement research must explore how engagement varies across student group demographics and how it changes over time. In terms of gender differences, Martin (2007) found that for high school students, girls reflected a more adaptive pattern of motivation and engagement. However, Plenty and Heubeck (2011) found that although high school girls were more likely to report a stronger mastery focus than boys when learning mathematics, they were also more likely to report greater feelings of anxiety and uncertain control. As for university students, Martin (2010) found that female students tended to score lower in terms of adaptive behaviour and maladaptive motivation. However, a study of Chinese university student engagement conducted by Shi et al. (2011) found that female students were more engaged in learning than males overall.

A few studies have examined the grade and discipline differences in relation to student engagement. For example, Martin (2009) revealed that elementary school students were generally more motivated and engaged than university students, who were in turn more motivated and engaged than high school students. Shi et al. (2011) found that in Chinese universities, first-year students scored highest in terms of their interest in and expectations of learning, in addition to the significance they placed on learning. Third-year students were the most motivated to learn, and fourth-year students scored highest

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in terms of the happiness they received from learning, but lowest in terms of the significance they placed on learning. In addition, two studies have considered the differences in students’ disciplinary backgrounds. Ahlfeldt, Mehta and Sellnow (2005) suggested that students of the arts, humanities and social sciences are more engaged in learning than students of science, engineering and agriculture. However, Shi et al. (2011) considered that the disciplinary differences were inconsistent and difficult to interpret.The effects of institutional characteristics on student engagement have also been examined. For example, using NSSE data, Umbach and his colleagues found that liberal arts colleges were more likely to promote effective educational practice than other types of programmes defined in the Carnegie classification scheme, such as doctoral-research-intensive, doctoral-research-extensive, and master’s I and II programmes (Umbach & Wawrzynski, 2005). Furthermore, Umbach and Kuh (2006) found that students at liberal arts colleges were more likely than their counterparts at other types of institutions to engage in diversity-related activities. In China, Shi et al. (2011) found that students in research-oriented universities were more involved in academic or social activities, and that those in teaching-oriented universities were more likely to participate in employment-related activities.

The sociocultural perspective of student engagement plays a critical role in student experience (Kahu, 2013). Students’ motivation and engagement emanate from the goals and norms presented in their broader social contexts. Zhao, Kuh and Carini (2005) found that international students were more engaged than American students in educationally purposeful activities, especially in the first year of college. In addition, they found that Asian students tended to spend more time socialising with those from similar cultural backgrounds, and less time participating in diversity-related college activities than their counterparts from other countries. Martin (2010) found that non-native-English speakers in Australian universities exhibited more adaptive behaviour than native English speakers. Moreover, motivation and engagement may be functions of practices in different educational contexts, even for students who share an ethnicity. Martin, Yu and Hau (2014) recently revealed that some mean-level differences in motivation and engagement persisted among Chinese high school students in Australia, Hong Kong and mainland China, and that these differences were attributable to contextual characteristics. In that study, Australian Chinese students reported higher levels of motivation and engagement than Hong Kong and mainland Chinese students.

Although some studies have considered the individual differences in student motivation and engagement, little consensus has been achieved. The possible differences caused by students’ demographic and contextual backgrounds must be examined further, especially in non-Western countries such as China.

Purpose of this studyThis study considers student engagement as a multifaceted construct consisting of behavioural, emotional and cognitive dimensions. Using the well-established Motivation and Engagement Scale for University/College Students (MES-UC), this study explores the motivation and engagement of undergraduate students in the context of mainland China. It aims to answer the following two questions. First, what are the characteristics of undergraduate students’ motivation and engagement in China? Second, do students’ motivation and engagement vary across demographic groups, including by gender, grade, discipline and institution type? The answers to these two

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questions should not only contribute to the current knowledge about student engagement in higher education, but also have implications for improving the quality of teaching and learning in higher education institutions in China.MethodologyParticipants

A sample of students from 10 full-time universities in Beijing, China, participated in this study. Of these universities, five were teaching- oriented and the other five were research- oriented. All of the universities were among the Chinese Ministry of Education’s ‘Project 211’ institutions.

Two thousand copies of the questionnaire were distributed to sophomores at the ten universities in November 2013. Of these copies, 1,131 (56.6%) valid copies were returned, including 534 (47.2%) from students at the research-oriented universities and 597 (52.8%) from students at the teaching-oriented universities. In terms of gender distribution, 533 males (47.1%) and 597 females (52.8%) responded, and 1 student (0.1%) did not report his/her gender. In terms of discipline backgrounds, 626 of the students (55.3%) majored in sciences and 497 (43.9%) majored in humanities and social sciences. The remaining eight students (0.7%) did not report their disciplinary backgrounds. In addition, the sample included 36 first-year students (3.2%), 508 second-year students (44.9%), 465 third-year students (41.1%) and 100 fourth-year students (8.8%), with 22 students (1.9%) not reporting the information about their grade.

InstrumentThe MES-UC, which was developed by Martin (2012b), was implemented in this study to measure the Chinese undergraduate students’ motivation and engagement.As stated previously, the MES-UC consists of one adaptive and one maladaptive dimension, and each dimension contains two groups: motivation and engagement. It contains four higher-order factors and eleven first-orders factors, each of which comprises four items. This44-item instrument is scored on a 7-point Likert scale from 1 (strongly disagree) to 7 (strongly agree).

The original MES-US was an English-language instrument. Before it was administered in the present study, two procedures were adopted to ensure the quality of the Chinese version of the scale. First, two research assistants fluent in both Chinese and English each conducted an independent translation and back translation. Second, the two files were cross-checked by one of the authors to ensure the quality of the translations.

Results

Construct validity and reliabilityCFA was conducted instead of exploratory factor analysis to examine the construct validity because the MES-UC had a clear theoretical lineage. Table 1 shows the CFA results.

Table 1. CFA results of the three MES UC models

ModelGoodness-of-fit index

χ2 df p RMSEA NNFI CFI ECVI AICM1a 4,884.04 847 .00 .065 .93 .94 4.58 5,170.04

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M2b 5,875.84 896 .00 .070 .92 .92 5.37 6,063.84M3c 5,292.54 885 .00 .066 .93 .93 4.87 5,520.54

Note: a. The first-order 11-factor model. b. The first-order 4-factor model. c. The second-order model.

Considering the hierarchical dimensionality of the MES-UC, three models were compared when examining the construct validity: the first-order 11-factor model, the first-order 4-factor model and the hierarchical second-order model. According to the goodness-of-fit indices, all three of the models seemed to fit the data well. However, the hierarchical second-order model was deemed unacceptable after the factor loading values were checked. In the hierarchical second-order model (χ2 = 5,292.54, df = 885, p = .00, RMSEA = .066, NNFI = .93, CFI = .93), the factor loading of disengagement on the second-order factor, i.e., maladaptive engagement, was 1.02, or higher than 1. Hence, the hierarchical second-order model was abandoned.

After comparing both first-order models, it was found that the 11-factor model (χ2 = 4,884.04, df = 847, p = .00, RMSEA = .065, NNFI = .93, CFI = .94, ECVI = 4.58, AIC = 5,170.04) had a better data fit than the 4-factor model (χ2 = 5,875.84, df = 896, p = .00, RMSEA = .070, NNFI = .92, CFI = .92, ECVI = 5.37, AIC = 6,063.84), as the former had higher NNFI and CFI values and lower RMSEA, ECVI and AIC values. Therefore, the first-order 11-factor model was ultimately adopted for subsequent analyses.

The Cronbach’s a coefficient values were calculated to examine the reliability of the measures. This examination showed that apart from the values for persistence (a = .58) and planning (a = .55), the Cronbach’s a coefficient values were never less than .60, indicating that most of the subscales reached an acceptable level of internal consistency. Table 2 shows the reliability of each factor.

Table 2. Correlation matrix, reliabilities and descriptive statistics (n = 2,043)

1 2 3 4 5 6 7 8 9 10 111.

MO

(.77)

2. VA .60**

(.65)

3. SE .57**

.61**

(.68)

4. PE .38**

.45**

.40**

(.58)

5. TM .58**

.56**

.59**

.43**

(.65)

6. PL .37**

.36**

.41**

.42**

.48**

(.55)

7. UC -.18**

-.16*

*

-.20*

*

-.05 -.12**

-.09*

*

(.67)

8. AN - - - -.03 -.02 .03 . (.64

7

.10**

.12**

.15**

58**

)

9. FA -.27**

-.20*

*

-.21*

*

-.04 -.17**

-.01 .57**

.56**

(.76)

10. SS

-.27*

*

-.24*

*

-.27*

*

-.08**

-.26**

-.17*

*

.49**

.44**

.56**

(.75)

11. DE

-.46*

*

-.39*

*

-.36*

*

-.13*

*

-.32**

-.15*

*

.51**

.47**

.64**

.63**

(.69)

M 5.42 5.03 5.08 4.63 4.95

4.49 3.99 3.97 3.50 3.61 3.46

SD 0.91 0.91 0.97 0.84 0.95

0.84 1.03 1.05 1.19 1.18 1.10

Note: * p<.05; ** p<.01 (2-tailed); Cronbach’s α in parentheses along the diagonal; MO = mastery orientation; VA = valuing; SE = self-efficacy; PE = persistence; TM = task management; PL = planning; UC = uncertain control; AN = anxiety; FA = failure avoidance; SS = self-sabotage; DE = disengagement.

Descriptive statistics and correlationsTable 2 summarises the descriptive statistics and correlation matrix. In general, the students reported higher scores for the six adaptive factors (i.e., mastery orientation, valuing, self-efficacy, persistence, task management and planning) than for the five maladaptive factors (i.e., uncertain control, anxiety, failure avoidance, self-sabotage and disengagement). Among the adaptive factors, mastery orientation received the highest mean score (M = 5.42, SD = 0.91) and planning received the lowest (M = 4.49, SD = 0.84). Among the maladaptive factors, uncertain control scored the highest (M = 3.99, SD = 1.03) and disengagement scored the lowest (M = 3.46, SD = 1.10). Figure 1 presents the MES profile for the sample of Chinese undergraduate students based on the mean score of each subscale.

Figure 1. The MES profile for Chinese undergraduate students

Note: MO = mastery orientation; VA = valuing, SE = self-efficacy; PE = persistence; TM = task management; PL = planning; UC = uncertain control; AN = anxiety; FA =

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failure avoidance; SS = self-sabotage; DE = disengagement. The percentage values are shown beside the factors.

Table 2 also shows the correlation matrix for the 11 factors. As expected, moderate and positive correlations were found among the six adaptive factors and five maladaptive factors. In contrast, the six adaptive factors generally had weak and negative correlations with the five maladaptive factors, although most of the correlations reached a level of statistical significance. These results provided further information about the discriminant validity of the MES-UC.

Cluster analysisCluster analysis was conducted to differentiate the characteristics of the learners in the sample. Considering that the MES-US consists of one adaptive dimension and one maladaptive dimension, four-cluster analysis was conducted after centring the scale means of four integrative factors, including adaptive motivation, adaptive engagement, maladaptive motivation and maladaptive engagement. The four-cluster solution was parsimonious, and converged efficiently in 22 iterations. Figure 2 presents the cluster analysis results.

MM ME AM AE

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

Adaptive Motivated but less engagedMaladaptive but engaged Maladaptive

Var

iatio

n ar

ound

scal

e m

eans

Figure 2. Centred scale means for the four clusters

As Figure 2 indicates, the four categories of learners showed different characteristics in terms of their motivation and engagement. The first category, consisting of 236 students (20.9%), comprised ‘adaptive learners’ who had above-average levels of adaptive motivation and engagement and below-average levels of maladaptive motivation and engagement. The second category, containing 316 students (27.9%), comprised ‘motivated but less-engaged learners’ who had a slightly below-average level of adaptive engagement. However, their adaptive motivation was above average, and they had below-average levels of maladaptive motivation and engagement. The third category comprised 157 students (13.9%) who not only had markedly above-average levels of maladaptive motivation and engagement, but also a slightly above-average level of adaptive motivation, which was in turn lower than their level of adaptive engagement. These students were designated ‘maladaptive but engaged learners’. The fourth category, consisting of 422 students (37.3%), comprised ‘maladaptive learners’ who had above-average levels of maladaptive motivation and engagement but below-average levels of adaptive motivation and engagement.

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Table 3. Distribution of the four categories of learners in research- and teaching-oriented universities

Research oriented Teaching oriented TotalFrequency % Frequency % Frequency %

Adaptive learners 170 72.0 66 28.0 236 20.9Motivated but less-engaged learners

161 50.9 155 49.1 316 27.9

Maladaptive but engaged learners

103 65.6 54 34.4 157 13.9

Maladaptive learners 100 23.7 322 76.3 422 37.3Total 534 597 1,131

Table 3 shows the distribution of the four types of learners in the research- and teaching-oriented universities. The results indicate that most of the ‘adaptive learners’ (72.0%) and ‘maladaptive but engaged learners’ (65.6%) were students attending research-oriented universities. However, most of the ‘maladaptive learners’ (76.3%) were students attending teaching-oriented universities. As for the ‘motivated but less-engaged learners’, no salient difference in distribution was found between the students attending research-oriented (50.9%) and teaching-oriented (49.1%) universities.

DiscussionEngaging students in university learning is the core agenda of an academy (Coates, 2010). Considering student engagement as a multifaceted construct, this study explored the issue of Chinese undergraduate student engagement through the use of the MES-UC. To the best of the author’s knowledge, it is the first study to apply the MES to student engagement research in the context of Chinese higher education. The results revealed some characteristics and individual differences in students’ motivation and engagement, and should help develop an understanding of the quality of teaching of learning in Chinese higher education institutions.

As for the psychometric qualities of the MES-UC, the results of the present study showed that both first-order models (i.e., the 4- and 11-factor models) fit the data well, and that nine of the eleven first-order factors had acceptable reliabilities. These results supported the psychometric qualities of the MES-UC, indicating that it could be useful to incorporate the MES-UC into research related to student engagement in higher education. However, the following three points should be noted. First, Cronbach’s values of two MES-UC factors (i.e. persistence and planning) were lower than .60, and the range of reliabilities for the MES-UC factors (i.e. .55-.77) was lower than that reported by Fredricks and McColskey (2012) (i.e. .70-.87). Second, when checking the factor loading of each item, Item 39 (i.e. ‘I usually stick to a study timetable or study plan’) was found to have a weak factor loading value (i.e, .29) on its corresponding factor (i.e. planning), suggesting that this item may be less appropriate for measuring Chinese students’ planning behaviour. Third, similar to Plenty and Heubeck’s (2011) finding that the second-order model of the MES was not acceptable, the present study rejected the hierarchical second-order model of the MES-UC due to the abnormal factor loading of ‘disengagement’ (i.e. 1.02), although it appeared to have a good data fit according to the CFA. These findings indicate that the psychometric qualities of the

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MES-UC have room for improvement, especially when the scale is applied to a non-Western context.According to Martin’s (2007, 2012a) Motivation and Engagement Wheel, the ideal engaged student is expected to score high on the six adaptive factors and low on the five maladaptive factors. The MES profile for the Chinese undergraduate students (i.e. Figure 1) revealed in the present study was generally consistent with this expectation, seemingly echoing the finding of Lu et al. (2013) that a ‘lack of engagement’ hardly exists for Chinese undergraduates. However, different from the studies (e.g. NSSE, AUSSE, CCSS) that have adopted a behavioural perspective towards student engagement, the present study considered engagement as a multifaceted construct consisting of both psychological and behavioural elements. The details of the MES profile showed that although Chinese undergraduates achieved higher scores for the six adaptive factors, their performance on the maladaptive motivation and engagement factors was not as low as expected. The intensity of the students’ performance on the five maladaptive factors was very close to or even greater than 50%. Moreover, the cluster analysis results showed that only 236 students (20.9%) were classified as ‘adaptive learners’, 422 students (37.3%) were classified as ‘maladaptive learners’ and 161 students (27.9%) were classified as ‘motivated but less engaged learners’ with above-average levels of adaptive motivation and below-average levels of adaptive engagement. Therefore, the Chinese undergraduates simultaneously performed well on both the adaptive and maladaptive factors. These findings reflect some of the characteristics of Chinese undergraduates’ motivation and engagement, and reveal the risks inherent in student learning in Chinese higher education institutions.

In addition to the three learner categories, cluster analysis also generated the category of ‘maladaptive but engaged learners’. As shown in Figure 2, these 157 students (13.9%) were characterised as having significantly above-average levels of maladaptive motivation and engagement, a slightly above-average level of adaptive motivation and a moderately above-average level of adaptive engagement. Compared with the other three learner categories, they scored the highest on the maladaptive motivation and engagement factors. However, this does not infer that they scored badly on the adaptive factors. In contrast, they performed quite well on the adaptive factors and especially adaptive engagement, and in fact better than the ‘motivated but less-engaged’ and ‘maladaptive’ learners. This category of learners reflected some of the characteristics of Chinese students’ motivation and engagement. From a sociocultural perspective, student motivation and engagement are highly influenced by broader social and cultural contexts (Kahu, 2013; Martin, Yu, & Hau, 2014). Studies of Chinese learners have revealed that Chinese students usually consider learning to be a process of striving for perfection. According to Li (2002), ‘When experiencing failures, they feel shame and guilt, both for themselves and inferences to those who nurtured them’ (248). However, compared with students in Western cultures such as the US and Australia, Chinese students more often attribute their academic learning success to effort and diligence rather than ability or gift (Martin, Yu, & Hau, 2014). The feeling of ‘shame-guilt’ and a belief in effort may together form the category of ‘maladaptive but engaged learners’. These students’ lack of desire to learn may produce a feeling of ‘shame-guilt’, which may in turn compel them to take action and study hard regardless of the obstacles they encounter. Moreover, most of the ‘maladaptive but engaged learners’ (i.e., 65.6%) in this study attended research-oriented universities. Compared with their counterparts at teaching-oriented universities, these students might have had higher expectations of

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themselves, and might have found it easier to feel shame and guilt when considering the greater prestige of their universities.

Implications of this studyIt is difficult to claim too much about the implications of this preliminary study of university student engagement in China. However, the results do shed light on some of the issues related to the knowledge and improvement of student engagement in Chinese higher education institutions. Student engagement in Chinese universities is not as positive as it looks. The students in this study performed well in terms of both the adaptive and maladaptive motivation factors. In view of the MES profile of Chinese university students, reducing their concerns over the maladaptive factors (i.e. anxiety, failure avoidance, uncertain control, self-sabotage and disengagement) may be more relevant and meaningful than improving their performance in adaptive motivation and engagement. Martin (2008b) suggested a multidimensional intervention that proved to be effective for enhancing student motivation and engagement. University educators and counsellors may use the intervention procedure to address students’ maladaptive motivation and engagement. The procedure includes (a) providing an advance organiser for key activities, (b) enabling the students to generate and construct key learning that is relevant to their motivation, (c) requiring the students to reflect on the key messages developed through this learning and (d) revisiting important strategies and requiring students to sign off on their own work.

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