Fostering positive adjustment behaviour: Social connectedness,achievement motivation and emotional-social learning among male and female university students

Abstract

Although some attention has been given to student issues at university, the literature on the relationship between social connectedness, achievement motivation and emotional-social learning with student adjustment is relatively limited. Therefore, this study investigates the impact of social connectedness, achievement motivation and emotional-social learning upon the adjustment of students in a university context. In addition, this study looks into the differences in achievement motivation and emotional-social learning levels between the genders. The sample comprised 240 university students, both male and female. According to the findings, the relationship between the study variables does not significantly differ between genders. Emotional-social learning is significant in terms of predicting the adjustment. Furthermore, gender differences were noted in terms of emotional-social learning levels, but not in terms of achievement motivation and social connectedness. The study explores implications of the significance of emotional-social learning in the university environment and makes recommendations in light of these implications.

Keywords

achievement motivation emotional-social learning maladjustment behaviour social connectedness university students

University students and what makes for success

Over the past 40 years, researchers have attempted to determine the predictors of students’ success at university. Several researchers have linked issues experienced by students to personal commitment, college commitment, social connectedness, academic and behaviour factors and issues of social adjustment (Allen et al., 2008; Henderson-King and Smith, 2006; Townsend and Wilson, 2009). Several such studies have highlighted issues related to university students, including emotional and personal and society issues, psychological pressure, lack of leisure, self-esteem levels, self-injury, lack of motivation and inclination towards academic activities, issues in reading, academic potential, academic abilities beliefs, lack of effort and personal abilities, and lack of social connectedness (Freund and Baltes, 2002; Henderson-King and Smith, 2006; Olea et al., 2012; Reed et al., 2015). Emotional intelligence also plays its part in human behaviour, and it is naturally the case that different students have different levels or types of emotional intelligence and that this, too, impacts their thinking and behaviours (Kaushik and Rani, 2005), although there is still a need for further work in this area (Sanchez-Ruiz et al., 2010). As a consequence of these issues, students may be not as inclined towards task completion, effective studying or memorisation of details as educators’ desire. Furthermore, students may be less inclined to work towards success, select effective performance settings, establish and achieve goals and exert effort over a period of time.

Studies concluded that factors predicting university students’ academic success and outcomes are quite complex in the sense that, although a single factor may not significantly affect the adolescent’s development, the relationship among different factors would do so (e.g. Allen et al., 2008; Freund and Baltes, 2002; Kennett et al., 2013; Lee and Robbins, 2000; Vinson et al., 2010). In this regard, the literature confirms that behavioural, emotional and cognitive academic factors affect a student’s learning and growth in terms of personal, social, emotional and personal development and that such factors may be considered as predictors (e.g. Elias et al., 2010; Jdaitawi, 2015; Zepke and Leach, 2010).

From a theoretical perspective, human functioning results from a dynamic interplay among personal, behavioural and environmental influences (Bandura, 1986). Bandura defined this reciprocal determinism in terms of (1) personal factors in the form of cognitions, affects and biological events; (2) behaviours and (3) environmental influences, all of which create interactions that result in a triadic reciprocality. Bandura further suggested that through self-reflection, people make sense of their experiences, explore their cognitions and beliefs, engage in self-evaluation, and alter their thinking and behaviour accordingly. With regard to the predictor factors of student success, the role of social connectedness, social engagement, achievement motivation and social–emotional learning as success predictors, many (Duru, 2008; Elias et al., 2010; Jdaitawi, 2015) have noted that students who are emotionally connected to peers and instructors and who value learning and high academic performance often adopt prosocial values (Allen et al., 2008; Henderson-King and Smith, 2006).

Despite the importance of the above studies and their contributions, there is still lack of studies dedicated to student success in terms of the effects of social connectedness, achievement motivation and emotional-social learning on the life of youth in the world (Duru, 2008; Elias et al., 2010; Jdaitawi, 2015). Studies have highlighted some of the determining factors of students’ adjustment to the college setting and urged future studies to focus on students (e.g. Twenge et al., 2002).

Social connectedness and adjustment

University adjustment and integration are core aspects of several theoretical models dedicated to student development, persistence and withdrawal (Tinto, 1993). Adjustment of students to the college setting has always been reported to be rife with challenges for the majority of students (Tinto, 1993). Researchers categorised and identified various types of adjustments, namely, academic, social and emotional-personal adjustment (Baker and Syrik, 1999; Tinto, 1993). Connectedness is referred to as the individual’s relationship with society and is significant for psychological adjustment (Zachariah, 1994). Social connectedness is also described as a type of relational scheme, signifying patterns of interpersonal relationships (Lee and Robbins, 1998), and is characterised as an enduring and ubiquitous self-sense in world relationships (Lee and Robbins, 2000). Therefore, it can be concluded that social connectedness levels are linked to subsequent positive and negative outcomes. Individuals with social connectedness are not as likely to experience psychological distress like depression and low self-esteem in comparison to their less connected counterparts (Armstrong and Oomen-Early, 2009). Social connectedness is a basic life aspect, with evidence of linkages to enhanced degrees of adolescent well-being (Allen et al., 2008). In this regard, social connectedness significantly predicts adjustment, and students displaying greater level of social connectedness have a low level of adjustment issues (Allen et al., 2008).

Achievement motivation, social–emotional learning, gender and adjustment

Achievement motivation may be defined as the inclination to achieve something challenging, to master, manipulate and to tackle difficulties in an attempt to realise superior standards, to excel, to compete and to outperform others, and to maximise self-regard through the use of one’s talent (Murray, 1938). It also stresses self-improvement and the creation of self-referenced standards. Achievement motivation plays a key role in students’ academic achievement at various educational levels (Elliot and McGregor, 2001; Turner et al., 2009). There is a significant and positive correlation between achievement motivation and scholastic achievements (Elias et al., 2010). Similarly, undergraduate students surveyed in this study displayed autonomous motivation, which in turn significantly and positively influenced their mastery orientation, in-depth processing and academic achievement. Furthermore, extrinsic motivation regulation was a predictor of work avoidance orientation, which influences academic achievement in a significant and negative manner (Elias et al., 2010; Henderson-King and Smith, 2006). Autonomous motivation significantly and positively influenced adaptive learning attitudes, successful achievement in academics and well-being, while controlled motivation significantly and positively correlated to a greater number of dropouts, negative learning attitudes, and discomfort (Turner et al., 2009).

Considering the fact that the emphasis of current education is on the preparation of students for future survival, it stands to reason that the increasing interest in the realm of educational curriculum reforms is focused on developing student competencies in addition to academic skills and subject material. Moreover, more recently, more attention has been given to workplace and social skills. Specifically, emotional and social abilities are greater in their influence compared to conventional intelligence for different types of careers, education and personal achievements (Goleman, 1995). In this context, social–emotional learning refers to a process for assisting individuals’ development of basic effectiveness skills. Such skills include self-awareness, emotional management, social awareness and relationship management (Goleman, 1995). Social–emotional learning is a predictor of the success of the individual (Goleman, 1995; Goleman et al., 2002; Mayer and Salovey, 1997).

Studies dedicated to the comparison between the level of achievement motivation among males and females reported no such differences (Ligon, 2006; Cherti, 2014; Cokley et al., 2001). Studies dedicated to examining differences in gender in terms of social–emotional learning have generated conflicting results. Gender differences generally become clear across different dimensions of social–emotional learning from school to university over time. On verbal scales, girls are early developers compared to boys, and this in turn shows their skills in relaying feelings and in using words. Also, girls are more in touch with their emotions and others’ emotions than boys (Fernández-Berrocal et al., 2012).

Regardless of the number of studies dedicated to testing these factors individually or in combination, inconsistent findings have been reported and thus causal linkages are still elusive (Duru, 2008; Eccles and Wigfield, 2002; Elbertson et al., 2010; Elias et al., 2010; Fredricks et al., 2004; Wang and Eccles, 2012). Studies concerning social connectedness, and academic factors like achievement motivation and emotional-social learning skills, along with the nature and impacts on student life, are still few and far between (Jdaitawi, 2015). This remains true for studies that examined the relationship between social connectedness and emotional and social skills. Therefore, there is a need to extend literature by examining the effect of social connectedness on academic and emotional and social learning. In sum, the main contribution of this study is to examine students’ social connectedness, achievement motivation, emotional and social learning and gender as predictors of student adjustment. This should allow us to more closely understand the relative importance of these concepts for students’ success. Furthermore, there is still much that we need to find out about the state of achievement motivation and emotional-social learning experienced by students, and whether gender is related to social connectedness and adjustment. Therefore, three main questions were formulated. First, what are the levels of achievement motivation and emotional-social learning among students? Second, are there relationships between social connectedness, achievement motivation, emotional-social learning and adjustment among university students? And last, can students’ adjustment be predicted by social connectedness, emotional-social learning and achievement motivation?

From the above premises, three hypotheses were formulated. First, it was predicted that the relationship between the independent variables and students adjustment would be strong. Second, it was also expected that social connectedness, emotional-social learning and achievement motivation are predictors of the students’ adjustment for the whole sample. Finally, gender, as a frequently used variable in education research, is commonly included in questionnaires concerning student success, and has been shown to impact student achievement motivation and emotional-social learning and other factors. Thus, because gender groups may vary in terms of achievement motivation, emotional-social learning, social connectedness and students’ adjustment, it was predicted that the relationship between the variables of this study would be impacted by gender.

Methodology

Research design

The quantitative survey method was used. The survey method looks into the case being studied and provides a description of what the researcher observes. Many believe that the method is appropriate in collecting in-depth information concerning the attitudes and beliefs of the respondents, particularly when dealing with a significant number of respondents (Sekaran, 2003; Twenge et al., 2002).

Sample and procedures

The population of this study comprised undergraduate students from the Faculty of Arts and Sciences at the Northern Border University in the North Region of Saudi Arabia. The total number of undergraduate students in the Faculty of Arts and Sciences is approximately 1000. Data were collected from this faculty and a purposive sample was drawn from the population based on willingness to participate. A total of 240 (24%) students completed the survey; 58% were male students and 42% were female students, and data collection was conducted at the end of the first semester of the academic year 2014/2015. The researcher used purposive random sampling by dividing the undergraduate student sample into homogeneous subgroups, based on their willingness to participate in this study.

Outcome measurements

Social Connectedness Scale

Social connectedness was measured via the Social Connected Scale developed by Lee and Robbins (1998) to stress the interpersonal closeness level perceived between the individual and their network of peers, and the level of challenge in maintaining such closeness. Students were requested to respond to 20 items measured on a 5-point Likert scale, ranging from 1 = strongly disagrees to 5 = strongly agree. This scale has proven high internal reliability and significant validity (Jdaitawi et al., 2013), a fact confirmed by this research finding with an alpha coefficient of 0.913.

Emotional-Social Scale

This scale survey is taken from the Bradberry and Greaves’ (2004) measurement of emotional intelligence appraisal, developed on the basis of Goleman et al.’s (2002) model. This appraisal consists of four dimensions that measure the emotional intelligence skills of students. These four dimensions are self-awareness measured by six items, self-management measured by nine items, social awareness measured by five items and relationship management measured by eight items. Overall, the instrument consists of 28 items, rated from 1 = depicting never to 6 = depicting always. In order to compare the two groups, all items were standardised and all 28 were categorised into four factors to develop a measure that assesses emotional-social learning. Cronbach’s alpha for this measurement is 0.75.

Achievement motivation

This scale comprises two dimensions – namely, mastery dimension with four questions regarding students’ preferences of challenging tasks and the attitude dimension where the attitude of students towards work was measured by four questions (Spence and Helmreich, 1983). All items were standardised for comparison and all eight were categorised into two constructs to develop a measurement to assess achievement motivation. Cronbach’s alpha for this measure was found to be 0.902.

Adjustment measure

This measure comprises two dimensions – namely, psychological adjustment and social adjustment – and was developed by Barakat (2006). The original scale consists of 30 items. However, studies conducted in the Arab community used a 20-item scale, which was proven to be appropriate (Barakat, 2006). Therefore, the 20-item scale was used, leading to a finding with an alpha coefficient of 0.823.

Analysis

Analysis of data was by the parcelling of items (Little et al., 2002) in the form of structural equation modelling via AMOS. This was primarily driven by the need to achieve an acceptable indicators ratio to the size of the sample (Bagozzi and Edward, 1998). Additionally, the parcelling method was employed to focus on the relationship between notable variables rather than the factor structure of the measurement model. Thus, the items were categorised into parcels and were deemed to be latent variables indicators. Specifically, four indices were employed for the assessment of model fit with the use of the chi-square statistics, ratio, root mean square error of approximation (RMSEA), comparative fit index (CFI) and Tucker–Lewis Index (TLI). The four values employed are the CFI and TLI values (≥0.90) and RMSEA value (≥0.08), which represent the adequacy of model fit. Also, the normed chi-square (χ² = chi ≥ 0.5) shows the same. These values were used due to their insensitivity to the size of the sample in comparison to other alternatives (Fan et al., 1999). In an attempt to investigate potential measurment reliability and validity, confirmatory factor analysis (CFA) was used. To evaluate the level of student achivement motivation and emotional-social learning, descriptive statistics were performed. Then, multivariate analysis of variance (MANOVA) was carried out to examine the variation between different genders with regard to the study variables. Pearson correlation and linear regression via SPSS was run to assess the relationshp between variables through multiple-group models, namely, combined groups, a male group and a female group.

Measurements reliability and validity of variables

CFA was run on the variables for the measurement of measurement model robustness. All four study variables were run through the analysis in terms of their convergent validity, discriminant validity and Cronbach’s alpha. Specifically, the CFA was conducted through AMOS for confirmation of the measurement’s overall acceptability with the help of chi-square statistics, ratio, RMSEA, CFI and TLI. These values were used owing to their insensitivity to the size of the sample compared to other alternatives (Fan et al., 1999). The CFI and TLI values were found to be ≥0.90 and the RMSEA value was found to be ≥0.08, showing adequate model fit. Meanwhile, normed chi-square (χ² = chi ≤ 0.5) also shows model fit adequacy. To this end, some of the items in the first and second model of both exogenous and endogenous variables were loaded weakly to the latent variables and as such, they were deleted following the parcel technique, after which the outcomes of the CFA variables measurement models produced a suitable fit statistics for all the values of indices as shown in Figures 1 and 2.

Figure 1.

Variables model fit.

Figure 2.

Confirmatory factor analysis model fit.

Added to this, the coefficient scale reliabilities were all within the acceptable range of >0.70 as presented in Tables 1 and 2.

Table 1.

Overall measurement model fit for research model.

Fit index	SEL	SC	Ach-Mot	Adjust
Cronbach’s alpha	0.75	0.913	0.902	0.823
RMSEA	0.055	0.079	0.080	0.073
CFI	0.905	0.951	0.979	0.904
TLI	0.887	0.936	0.961	0.877
χ²	1.711	2.508	2.523	2.278

CFI: comparative fit index; RMSEA: root mean square error of approximation; TLI: Tucker–Lewis Index.; SEL: Emotional Social Learning; SC: Social Connectedness.

Table 2.

Scales mean scores and standard deviation for both males and females.

	Total (n = 240)	Males (n = 125)	Females (n = 115)
Achievement motivation	3.338 (0.868)	3.386 (0.960)	3.287 (0.75)
Emotional-social learning	3.363 (0.460)	3.449 (0.485)	3.268 (0.41)

Results

The results of MANOVA for the gender differences in achievement motivation and emotional-social learning as presented in Tables 3 and 4, showed that males had higher levels of emotional-social learning at F(9.551), p = 0.002 < 0.05 in comparison to females. With regard to the students’ level of achievement motivation, no significant differences were found between male and female students with F(0.778) p = 0.379 > 0.05.

Table 3.

Results of MANOVA of the research variable.

	Value	F	Significance
Pillai’s trace	0.039	4.826	0.009
Wilk’s lambda	0.961	4.826	0.009
Hotelling’s trace	0.041	4.826	0.009
Roy’s largest root	0.041	4.826	0.009

MANOVA: multivariate analysis of variance.

Significant (p < 0.05). *Male and female scores significantly differ at p < 0.05.

Table 4.

Results of MANOVA for research variables.

Source	Dependent variables post-test	Type sum of square	df	Mean square	F	p
Gender	Achievement motivation	0.588	1	0.588	0.778	0.379
Gender	Emotional-social learning	1.957	1	1.957	9.551	0.002
Total	Achievement motivation	2855.375		209
Total	Emotional-social learning	2765.227		209

MANOVA: multivariate analysis of variance.

Significant (p < 0.05). *Male and female scores significantly differ at p < 0.05.

Variables correlations for the entire sample were calculated and are presented in Table 5. Achievement motivation, emotional-social learning and social connectedness were found to be correlated. More specifically, achievement motivation significantly correlated with emotional-social learning and social connectedness, but emotional-social learning correlated to only social adjustment, but not to achievement motivation and social connectedness.

Table 5.

Intercorrelations among related variables.

		1	2	3	4
Total	1	–	0.400**	0.853**	0.020
	2	0.400**	–	0.452**	0.199**
	3	0.853**	0.452**	–	0.009
	4	0.020	0.199**	0.009	–
Males	1	–	0.437**	0.849**	−0.125
	2	0.437**	–	0.459**	0.052
	3	0.849**	0.459**	–	−0.128
	4	−0.125	0.052	−0.128	–
Females	1	–	0.334**	0.794**	0.127
	2	0.334**	–	0.484**	0.137
	3	0.794**	0.484**	–	0.164
	4	0.127	0.137	0.164	–

1 = achievment motivation; 2 = emotional-social learning; 3 = social connectedness; 4 = adjustment.

Achievement motivation correlated significantly with emotional-social learning but not with adjustment, and social connectedness correlated significantly with emotional-social learning and achievement motivation, but it did not correlate with adjustment.

For the male samples, the variable correlations were calculated, and it is evident from Table 5 that achievement motivation, emotional-social learning and social connectedness were correlated. In addition, achievement motivation correlated significantly with emotional-social learning and social connectedness, but did not correlate with adjustment. Emotional-social learning correlated significantly with all three (student’s adjustment, achievement motivation and social connectedness).

Finally, social connectedness correlated significantly with both emotional-social learning and achievement motivation, but not adjustment. For the female sample, it is evident from Table 5 that achievement motivation correlated significantly with emotional-social learning and social connectedness, but did not do so for adjustment. Also, emotional-social learning correlated significantly with achievement motivation and social connectedness, but did not do so with adjustment. Finally, social connectedness correlated significantly with emotional-social learning and achievement motivation with the exception of adjustment.

Three multiple linear regressions were calculated to determine the interrelations among the variables. The first regression was calculated to identify whether or not social connectedness, emotional-social learning and achievement motivation are predictors of the entire adjustment for the whole sample. All predictors were entered in a simultaneous manner. The result of the overall equation was significant with R² = 0.048, F(3.982) = 14.837, p < 0.05, with emotional-social learning as the sole significant predictor with a positive effect. It is therefore necessary to include this factor in the model. The beta values for the multiple regression models are presented in Table 6.

Table 6.

Results of multilinear regression analysis for the sample (N = 240).

Model	Unstandardised coefficients		Standardised coefficients		p
Model	B	SE	Beta	t	p
(Constant)	1.262	0.085		14.837	0.000**
Social connectedness	−0.027	0.026	−0.130	−1.037	0.301
Achievement motivation	0.007	0.025	0.033	0.269	0.788
Emotional-social learning	0.095	0.028	0.245	3.433	0.001**

SE: standard error.

R = 0.219, R² = 0.048.

p < 0.05*, p < 0.01**.

In the second and third models, all predictor variables were included in a simultaneous manner to determine the impact of predictor variables on adjustment of students for both male and female samples. The p-value was calculated for all variables and it is evident from Tables 7 and 8 that all of them are not less than 0.05, indicating that the variables were not significant for both genders. It can therefore be concluded that no significant differences between male and female models were evident in light of the impact of predictor variables on the adjustment of students.

Table 7.

Results of multilinear regression analysis for males (n = 125).

Model	Unstandardised coefficients		Standardised coefficients		p
Model	B	SE	Beta	t	p
(Constant)	1.445	0.097		14.855	0.000**
Social connectedness	−0.025	0.034	−0.127	−0.734	0.464
Achievement motivation	0.044	0.031	0.145	1.436	0.154
Emotional-social learning	−0.012	0.026	−0.081	−0.473	0.637

SE: standard error.

R = 0.184, R² = 0.034.

p < 0.05*, p < 0.01**.

Table 8.

Results of multilinear regression analysis for females (n = 115).

Model	Unstandardised coefficients		Standardised coefficients		p
Model	B	SE	Beta	t	p
(Constant)	1.264	0.082		15.375	0.000**
Social connectedness	0.021	0.028	0.125	0.751	0.454
Achievement motivation	0.020	0.028	0.076	0.708	0.481
Emotional-social learning	0.000	0.022	0.003	0.018	0.986

SE: standard error.

R = 0.177, R² = 0.031.

p < 0.05*, p < 0.01**.

Discussion

The first of the questions enquired about the level of achievement motivation and emotional-social learning among students. Significant differences were found between male and female students in terms of social-emotional learning and achievement motivation. Male students outperformed their female counterparts in social-emotional learning, but not in achievement motivation. Male students outperformed female students in emotional management and interpersonal skills. Such skills may have contributed to the significance in the case of male students. The results showed no significant differences of achievement motivation levels between both genders. This may be explained by the fact that both men and women in the context used in this study have high classroom support and a highly competitive classroom environment, motivating them to greater achievement. Therefore, both male and female students are similarly motivated to complete their academic work. Research has shown partial significant correlation between social-emotional learning, achievement motivation, social connectedness and the adjustment of students. The study described here does not only show similar results for each of the variables, but the study also adds to our knowledge by indicating that some factors such as social-emotional learning significantly correlated with achievement motivation, social connectedness and adjustment, which had not been tested before.

The findings here confirm the importance of emotional and social factors in the educational environment, and show their relevance in helping students cope with the day-to-day demands and pressures. Of course, when students manage their emotions and their relations with other students, they are more likely to be confident, which may also increase their adjustment. The findings revealed that social connectedness did not correlate with student adjustment, which is contrary to other findings (e.g. Duru, 2008). This may be explained by the fact that students are not very comfortable with their educational environment and have no sense of belonging on campus. This result is supported by a study by Pintrich (2004), who revealed that a correlation exists between achievement motivation and social connectedness.

Results showed that social-emotional learning predicts students’ adjustment, achievement motivation and social connectedness, but that it only supports the prediction of students’ adjustment without the latter two. These findings extend the discussions of Allen et al. (2008) relating to college retention from an academic, emotional and social perspective. The findings indicated that students who are emotionally aware and are capable of managing their relationships with others do not have any issues with adjustment. This shows that emotional management and management of other peoples’ emotions can facilitate the interaction with others and develop relationships that may assist students in coping with their environments and adjusting at university. Results showed that social connectedness and achievement motivation were not predictors of student adjustment, which is inconsistent with results of a study (Duru, 2008), who contended that social connectedness can impact an individual’s emotions, cognitions, perceptions and eventually, their general behaviour within the social world. The finding stating that social connectedness failed to predict students’ adjustment is expected because social connectedness is a sensitive construct that needs longer time to be represented by students. In this study, social connectedness was assessed at the beginning of the study year; thus, it is reasonable to expect that this construct would be more predictive of long-term persistence if it were measured after the students had been on campus for an extended time. Furthermore, social connectedness was measured based on three dimensions; therefore, the students may score well in one out of three social connectedness dimensions.

The insignificant result pertaining to the achievement motivation’s prediction of student’s adjustment is supported by studies such as that of Devi (2011), which reported the insignificant relation between the two. The results rejected the premise that social connectedness, social-emotional learning and achievement motivation predicted student adjustment when gender differences were controlled. None of the variables predicted students’ adjustment for both genders, and this could be because of the overlapping prospect between social-emotional learning and social connectedness as the teaching-learning methods are also similar. Therefore, it can be stated that social connectedness negatively impacted the emotions, cognitions and perceptions of students towards their university environment, which lowered their motivation level, their response to tackling challenging situations, and their behaviour within the social world.

Beyond its contributions to the studies in the field of education, future research may approach this subject matter by using qualitative methods to highlight the actual perception of students of their university success, as this study relied only on data from self-report measures. Furthermore, this study gathered data from only a single university; therefore, future studies needs to look at students from other universities, and from those in different cultures/countries, as these perceptions and behaviours may vary from one culture/country to another. This study depended on data from a non-longitudinal study in which data were collected at a single point in time. Future studies need to be conducted to include longitudinal studies to provide a rich interpretation of the causal link among the study variables. The majority of the participants used in this study were undergraduates, with gender as the only variable. Future studies should thus look at students at different levels of their studies, for example, postgraduates, but also at various stages in their journey through university, that is, aspects such as, say, adjustment may well be different at the start of their university course and at the end of it, some 3 or 4 years later. There is also the need to look at any potential differences between those from different disciplines, as well as to explore in greater depth some or all of the variables under investigation. Finally, future work needs to look at the link between these variables and actual student performance in terms of marks/grades.

In this study, the independent variables are social connectedness, social–emotional learning, achievement motivation, and gender, with maladjustment behaviour as the dependent variable. By exploring these, the literature was extended in four different ways. First, by determining the differences in levels of social-emotional learning and achievement motivation between the genders among university students. Second, by identifying the causes behind maladjustment behaviour in higher learning institutions for all levels of study years. Third, by determining the relationship between the study variables (social connectedness, achievement motivation and emotional-social learning and adjustment). Fourth, by examining whether or not the impact of social connectedness, achievement motivation and emotional-social learning upon student adjustment varies between genders. The findings explain the psychosocial outcomes of students, as previous studies had only focused on diagnosing the relationship between the variables in individual settings, with some studies providing a general discussion concerning the social connectedness level and overlooking the comparative analysis of the reliability of the various models employed in differing settings. The results of this study also stressed the role of social-emotional learning training programmes for students in augmenting their adjustment level and enabling them to adjust more easily to the university environment. Moreover, counselling centres at the university need to collaborate with other faculties to mitigate issues that students commonly face. It is notable that organising informal gatherings or programmes may also assist students in their interactions and in building of close relationships. Consequently, the creation of formal and informal sessions through training programmes may facilitate the creation of the students’ social networks and the provision of individual and social support, which can encourage students to be successful in achieving their learning goals.

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was supported by Deanship of Scientific Research, Northern Border University, under award number (435-073-8).

Author biographies

Fared Jdaitawi Turki, an assistant professor of Special Education, works with undergraduate and postgraduate students at Northern Border University, Saudi Arabia. His research interests are related to learning and teaching in higher education settings. Address: Special Education Department, Faculty of Arts and Science, Northern Border University, Rafha, Zip: 91911, Saudi Arabia. [email: farid.jdeitawi@yahoo.com]

Malek Jdaitawi, an assistant professor of Psychology, works with undergraduate students and lecturers at the University of Dammam, Saudi Arabia. His research interests are divided between teaching and learning in higher education settings. Address: University of Dammam, Dammam, Zip:31146, Saudi Arabia. [email: mtmustafa@uod.edu.sa]

Hani Sheta, an assistant professor of Special Education, works with undergraduate and postgraduate students at Northern Border University, Saudi Arabia. His research interests are related to learning and teaching in higher education settings. Address: Northern Border University, Rafha, Zip: 91911, Saudi Arabia. [email: hanysheta_77@yahoo.com]

References

Allen

Robbins

Casillas

et al . (2008) Third-year college retention and transfer: Effects of academic performance, motivation, and social connectedness. Review of Higher Education 49(7): 647–64.

Armstrong

Oomen-Early

(2009) Social connectedness, self-esteem, and depression symptom-moatology among collegiate athletes versus non-athletes. Journal of American College Health 57: 521–6.

Bagozzi

Edwards

(1998) A general approach for representing constructs in organizational research. Organizational Research Methods 1(1): 45–87.

Baker

Siryk

(1999) Student Adaptation to College Questionnaire Manual. Los Angeles, CA: Western Psychological Services.

Bandura

(1986) Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall.

Barakat

(2006) Religious Commitment Effected on Psychological and Sociological Adjustment and Related with Some Variables. Amman: Al-Quds Open University.

Bradberry

Greaves

(2004) The Emotional Intelligence Appraisal Technical Manual Update. San Diego, CA: Talent Smart.

Cherti

(2014) Achievement motivation of students and its relationship with academic achievement. International Journal of Humanities and Social Science Invention 3(6): 8–15.

Cokley

Bernard

Cunningham

et al . (2001) A psychometric investigation of the academic motivation scale using a United States sample. Measurement and Evaluation in Counseling and Development 34(2): 109–19.

10.

Devi

(2011) A study of adjustment of students in relation to personality and achievement motivation. Bhartiyam International Journal of Education & Research 1(1). Available at: http://www.sciepub.com/reference/84047.

11.

Duru

(2008) The predictive analysis of adjustment difficulties from loneliness, social support and social connectedness. Educational Science: Theory & Practice 8(3): 849–56.

12.

Eccles

Wigfield

(2002) Motivational beliefs, values, and goals. Annual Review of Psychology 53(1): 109–32.

13.

Elbertson

Brackett

Weissberg

(2010) School-based social and emotional learning (SEL) programming: Current perspectives. In: Hargreaves

Lieberman

Fullan

et al . (eds) Second International Handbook of Educational Change. Berlin: Springer, pp. 1017–32.

14.

Elias

Noordin

Mahyuddin

(2010) Achievement motivation and self-efficacy in relation to adjustment among university students. Journal of Social Sciences 6(3): 333–9.

15.

Elliot

McGregor

(2001) A 2 x 2 Achievement goal framework. Journal of Personality and Social Psychology 80(3): 501–19.

16.

Fan

Thompson

Wang

(1999) Effects of sample size, estimation methods, and model specification on structural equation modeling fit indexes. Structural Equation Modeling 6(1): 56–83.

17.

Fernández-Berrocal

Cabello

Castillo

et al . (2012) Gender differences in emotional intelligence: The mediating effect of age. Behavioral Psychology 20(1): 77–89.

18.

Fredricks

Blumenfeld

Paris

(2004) School engagement: Potential of the concept, state of the evidence. Review of Educational Research 74(1): 59–109.

19.

Freund

Baltes

(2002) Life-management strategies of selection, optimization and compensation: Measurement by self-report and construct validity. Journal of Personality and Social Psychology 82(4): 642–62.

20.

Goleman

(1995) Emotional Intelligence. New York: Bantam Books, Inc.

21.

Goleman

Boyatzis

McGee

(2002) Primal Leadership: Realizing the Power of Emotional Intelligence. Boston, MA: Harvard Business School Press.

22.

Henderson-King

Smith

(2006) Meanings of education for university students: Academic motivation and personal values as predictors. Social Psychology of Education 9(2): 195–221.

23.

Jdaitawi

(2015) Social connectedness, academic, non-academic behaviors related to self-regulation among university students in Saudi Arabia. International Education Studies 8(2): 84–100.

24.

Jdaitawi

Motawa

Al-Nabrawi

(2013) Analysis of adolescent period in Saudi Arabia: Social connectedness. In: 7th international technology, education and development conference (INTED2013), Valencia, 4–6 March.

25.

Kaushik

Rani

(2005) A comparative study of achievement motivation, home environment and parent child relationship of students. Journal of Psychological Research 49(9): 189–94.

26.

Kennett

Reed

Stuart

(2013) The impact of reasons for attending university on academic resourcefulness and adjustment. Active Learning in Higher Education 14(2): 123–33.

27.

Lee

Robbins

(1998) The relationship between social connectedness and anxiety, self-esteem, and social identity. Journal of Counseling Psychology 45(3): 338–45.

28.

Lee

Robbins

(2000) Understanding social connectedness in college women and men. Journal of Counseling and Development 78(4): 484–91.

29.

Ligon

(2006) Assessing the achievement motivation across grades and gender: Dissertation abstracts international section A. Humanities and Social Sciences 67(6–A): 20–52.

30.

Little

Cunningham

Shahar

et al . (2002) To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling 9(2): 151–73.

31.

Mayer

Salovey

(1997) What is emotional intelligence? In: Salovey

Sluyter

(eds) Emotional Development and Emotional Intelligence Educational Implications. New York: Basic Books, pp. 3–34.

32.

Murray

(1938) Explorations in Personality. New York: Oxford University Press.

33.

Olea

Bernal

Hernandez

(2012) Self-esteem and its correlates among university freshmen Biotechnology Major. International Journal of Educational Research and Technology 3(3): 64–70.

34.

Pintrich

(2004) A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review 16(4): 385–407.

35.

Reed

Kennett

Emond

(2015) The influence of reasons for attending university on university experience: A comparison between students with and without disabilities. Active Learning in Higher Education 16(3): 225–36.

36.

Sanchez-Ruiz

Pérez-González

Petrides

(2010) Trait emotional intelligence profiles of students from different university faculties. Australian Journal of Psychology 62(1): 51–7.

37.

Sekaran

(2003) Research Methods for Business: A Skill Building Approach. Hoboken, NJ: John Willey & Sons.

38.

Spence

Heilmreich

(1983) Achievement related motives and behaviors. In: Spence

(ed.) Achievement and Achievement Motives. San Francisco, CA: Free Press, pp. 1–67.

39.

Tinto

(1993) Leaving College: Rethinking the Cause and Cures of Students Attrition, 2nd edn. Chicago, IL: University of Chicago Press.

40.

Townsend

Wilson

(2009) The academic and social integration of persisting community college transfer students. Journal of College Student Retention: Research, Theory & Practice 10(4): 405–23.

41.

Turner

Chandler

Heffer

(2009) The influence of parenting styles, achievement motivation, and self-efficacy on academic performance in college students. Journal of College Student Development 50(3): 337–46.

42.

Twenge

Catanese

Baumeister

(2002) Social exclusion causes self defeating behavior. Journal of Personality and Social Psychology 83(3): 606–15.

43.

Vinson

Nixon

Walsh

et al . (2010) Investigating the relationship between student engagement and transition. Active Learning in Higher Education 11(2): 131–43.

44.

Wang

Eccles

(2012) Adolescent behavioral, emotional, and cognitive engagement trajectories in school and their differential relations to educational success. Journal of Research on Adolescence 22(1): 31–9.

45.

Zachariah

(1994) Mother-daughter and husband-wife attachment as predictors of psychological well-being during pregnancy. Clinical Nursing Research: An International Journal 3(4): 371–92.

46.

Zepke

Leach

(2010) Improving student engagement: Ten proposals for action. Active Learning in Higher Education 11(3): 167–77.