Educ Res Policy PracDOI 10.1007/s10671-014-9165-9

ORIGINAL PAPER

Time perspectives and boredom coping strategies ofundergraduate students from Turkey

Altay Eren · Hamit Coskun

Received: 6 September 2013 / Accepted: 22 March 2014© Springer Science+Business Media Dordrecht 2014

Abstract Using person-centered and variable-centered analyses, this study examined therelationships between undergraduate students’ time perspectives and boredom coping strate-gies. A total of 719 undergraduate students voluntarily participated in the study. Resultsof the study showed that undergraduate students’ time perspectives can be reliably definedthrough four meaningful and distinctly different clusters: high past-positive and future timeperspective cluster, high present-hedonistic time perspective cluster, high past-negative andpresent-fatalistic time perspective cluster, and low present-fatalistic time perspective cluster.Results also showed that, regardless of the effects of social desirability, students in high past-positive and future time perspective and low present-fatalistic time perspective clusters usedcognitive-approach and behavioral-approach boredom coping strategies; whereas studentsin high past-negative and present fatalistic time perspective cluster used both cognitive-avoidance and behavioral-avoidance strategies. Results of the present study suggest that itmakes sense to consider students’ time perspectives together with their boredom copingstrategies in educational settings. Implications for education and directions for future studieswere also discussed in the present study.

Keywords Boredom · Time perspective · Coping with boredom · Students · University

1 Introduction

With a particular focus on the concept of future, time perspective (TP) has long been examinedin different lines of research such as psychology (e.g., McInerney 2004; Nuttin and Lens 1985;

A. Eren (B)Department of Educational Sciences, Faculty of Education, Abant Izzet Baysal University,14280 Bolu, Turkeye-mail: eren_a@ibu.edu.tr

H. CoskunDepartment of Psychology, Faculty of Arts and Science, Abant Izzet Baysal University,14280 Bolu, Turkey

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Peetz and Wilson 2014; Simons et al. 2004, 2000), forestry (Hoogstra and Schanz 2008), andeducation (e.g., de Bilde et al. 2011; de Volder and Lens 1982; Eren 2012; Peetsma and vander Veen 2011) along with different conceptualizations such as possible selves (Markus andNurius 1986), future time orientation (Gjesme 1983), and temporally extended-self (Mooreand Lemmon 2001). Given that subjective sense of time is one of the crucial aspects of thehuman cognitive system that enables humans to sense their “self” within a particular pointin time such as past, present, and future (Tulving 2002, 2005), it is not surprising to witnessthat considerable research has focused on individuals’ TPs. Likewise, a good deal of researchhas shown that students’ TPs significantly relate to important educational variables such asmotivation, academic achievement, school investment, and academic engagement (e.g., deBilde et al. 2011; Horstmanshof and Zimitat 2007; Peetsma 2000; Simons et al. 2004; Teahan1958; Zimbardo and Boyd 1999).

On the other hand, very few studies have examined students’ boredom coping strategies(e.g., Hamilton et al. 1984; Mann and Robinson 2009; Nett et al. 2010, 2011). Given thefact that a good deal of research provides strong evidence regarding the detrimental effectsof boredom on student learning, motivation, and academic achievement (e.g., Belton andPriyadharshini 2007; Mann and Robinson 2009; Maroldo 1986; Pekrun et al. 2010), it issurprising to observe that the issue of how students cope with this negative emotion duringlessons has received little empirical attention from educational researchers. This may be due tothe lack of a relevant theoretical framework regarding students’ boredom coping strategies.In fact, students’ boredom coping strategies have only recently been examined within acomprehensive and appropriate theoretical framework (Nett et al. 2010, 2011). Intriguingly,studies on boredom coping strategies were mostly conducted based on samples of high schoolstudents (Nett et al. 2010, 2011), and to a lesser extent, undergraduate students (e.g., Mannand Robinson 2009), meaning that boredom coping strategies have largely been neglected ineducational settings such as universities. Therefore, the present study attempts to examineundergraduate students’ boredom coping strategies together with another crucially relevantconcept in educational settings: TP.

2 Theoretical background

2.1 Time perspective

TP is a broad term that can be defined as “a process whereby individuals and cultures assign theflow of personal and social experiences into the temporal categories of past, present, or future,that help to give order, coherence and meaning to those events” (Zimbardo and Boyd 1999,p. 1271). In contrast to the earlier frameworks of TP (e.g., Cottle 1977; Nuttin and Lens 1985),Zimbardo and Boyd (1999) developed a multidimensional and comprehensive framework inwhich TP was not only discernible in terms of its temporal aspects (i.e., past, present, andfuture) but also evident in terms of its affective aspects (i.e., positive, negative, fatalistic, andhedonistic). More specifically, Zimbardo and Boyd (1999) examined individuals’ TPs on thebasis of five-factors: past-positive, past-negative, present-hedonistic, present-fatalistic, andfuture. Past-positive factor refers to “a warm, sentimental attitude toward the past,” whereasthe past-negative factor describes “a generally negative, aversive view of the past” (Zim-bardo and Boyd 1999, p. 1274). Present-hedonistic factor, however, “reflects a hedonistic,risk-taking devil may care attitude toward time and life,” whereas present-fatalistic factordescribes “a fatalistic, helpless, and hopeless attitude toward the future and life” (Zimbardo

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and Boyd 1999, p. 1275). Finally, future-factor reflects an individual’s general future orien-tation (Zimbardo and Boyd 2008).

Zimbardo and Boyd (1999) developed a measure (i.e., Zimbardo Time PerspectiveInventory-ZTPI) in order to assess individuals’ TPs simultaneously and reliably. The trans-lated versions of ZTPI in different countries have demonstrated a structure similar to theoriginal five-factor model (e.g., Apostolidis and Fieulaine 2004; Diaz-Morales 2006; Lini-auskaite and Kairys 2009; Milfont et al. 2008). There are also others suggesting a differentnumber of factors and items (e.g., Ittersum 2012; Ryack 2012; Worrell and Mello 2007).This means that both the factor structure and the number of items of ZTPI are more or lesssample specific. Nevertheless, studies in which the students’ TPs were assessed through thesubscales of ZTPI revealed consistent results in terms of its associations with educationalvariables such as academic engagement, motivation, and academic achievement (e.g., Bar-ber et al. 2009; de Bilde et al. 2011; Eren and Tezel 2010; Horstmanshof and Zimitat 2007;Peetsma and van der Veen 2011; Zimbardo and Boyd 1999).

For example, Zimbardo and Boyd (1999) demonstrated that undergraduate students’ GradePoint Average (GPA) was positively and significantly related to future TP, whereas it wasnegatively and significantly related to present-fatalistic TP (see also Barber et al. 2009, forsimilar results). Based on a sample of high school students and undergraduate students,de Bilde et al. (2011) demonstrated that the relationships between time orientation (i.e., thefuture and present) and academic-self regulation variables (i.e., intrinsic motivation, external,introjected, and identified regulation) were all significant and positive, indicating that the moreextensive the students’ future TP, the higher the students’ tendency to regulate their learningand academic behaviors on the basis of internal motives.

Notably, Zimbardo and Boyd (2008) claimed that a balanced and flexible TP may allowindividuals to choose the most appropriate TP depending on the situation (see also Boniwelland Zimbardo 2004; Boyd and Zimbardo 2005). According to Zimbardo and Boyd (2008),a balanced TP profile consists of high past-positive, moderately high future, moderatelyhigh present-hedonistic, low present-fatalistic, and low past-negative orientations. Likewise,recent research on TP has demonstrated that the undergraduate students from different cul-tures such as Russia, the UK, and Taiwan were indeed tended to hold balanced TP (Boniwellet al. 2010; Gao 2011). For example, based on a sample of British and Russian undergrad-uate students and using person-centered hierarchical cluster analysis, Boniwell et al. (2010)found that British students’ TPs can be explained through four distinctly different clusters:hedonistic-present-oriented TP, future-oriented TP, balanced TP, and negative TP.

The hedonistic-present-oriented TP cluster was characterized by high present-hedonisticand low future TPs, whereas the future-oriented TP cluster was characterized by high futureand low present-hedonistic TPs. The balanced TP cluster was characterized by “above-average scores on future and past-positive scales, below average scores on present-hedonisticand low scores on past-negative and present-fatalistic” (Boniwell et al. 2010, p. 30); whereasthe negative TP cluster was characterized by “high scores on the past-negative and present-fatalistic scales in association with average scores on present-hedonistic and low scores onthe future and past-positive scales” (Boniwell et al. 2010, p. 30). The Russian students’ TPs,however, was explained through five cluster patterns which also included a balanced TP con-sisting of those who were high in future and past-positive, average in present-hedonistic, andlow in past-negative and present-fatalistic scales. Of particular importance, Boniwell et al.(2010) also found that a distinct risk-taking pattern characterized by high fatalistic TP onlyappeared in the Russian sample. They explained this result on the basis of the fact that highfatalism is a specific feature of the Russian culture.

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More recently, based on a sample of Taiwanese undergraduate students, and using bothhierarchical and non-hierarchical cluster analyses, Gao (2011) examined the relationshipsbetween balanced TP and life satisfaction. Gao (2011) found that the students’ TPs canbe explained through two meaningful clusters: balanced TP and non-balanced TP whichconsisted of those who were low in past-positive, low in future, low in present-hedonisticorientation, high in past-negative, and moderately high in present-fatalistic orientations. Gao(2011) explained the slight deviation of the present-fatalistic TP from the suggested pictureof balanced TP (Zimbardo and Boyd 2008) on the basis of the mainstream belief systemsin Taiwan such as Taoism, Buddhism, and Confucianism, which may cause individuals toattribute more active and/or different meanings to fatalism.

The abovementioned studies suggest that (a) ZTPI provides a comprehensive and reliableframework to examine students’ TPs although its factor structure and number of items maysomewhat differ from one sample to another; (b) students may hold different TPs simulta-neously; and (c) profiles of students’ TPs may change depending on the distinctive charac-teristics of a culture. Thus, ZTPI was adopted as a highly relevant framework in the presentstudy. Additionally, both a person-level analysis (i.e., cluster analysis) and a variable-levelanalysis (i.e., latent variable correlation analysis) were conducted in order to examine theflexible nature of TP more accurately.

2.2 Coping with boredom

Boredom has been variously conceptualized as academic boredom (Acee et al. 2010), work-place boredom (Fisher 1993), free time boredom (Ragheb and Merydith 2001), and sexualboredom (Watt and Ewing 1996), and examined in terms of both environmental/situationalfactors that give rise to its emergence and an individual’s personal tendency to experienceboredom (e.g., Kass et al. 2001). In the former, boredom is defined as a state variable (i.e.,state boredom) (e.g., Fisher 1993), whereas, in latter, it is defined as a trait variable (i.e., bore-dom proneness) (e.g., Farmer and Sundberg 1986). As Vogel-Walcutt et al. (2012) stated,“the development of methods to target and alleviate state boredom has the potential to be ofconsiderable value to educators” (p. 90). Thus, boredom is considered as a state variable inthe present study. Accordingly, it can be defined as “an unpleasant, transient affective statein which the individual feels a pervasive lack of interest in and difficulty concentrating onthe current activity” (Fisher 1993, p. 396).

Despite the fact that boredom has long been a topic of research in psychology (e.g.,Barmack 1939; Geiwitz 1966; Vodanovich 2003), sociology (e.g., Barbalet 1999) and phi-losophy (e.g., Bigelow 1983), it is only recently that researchers have begun to focus onboredom in educational settings (e.g., Mann and Robinson 2009; Pekrun 1992; Pekrun etal. 2011). This can be due to the fact that boredom is “inconspicuous and non-disruptive”(Nett et al. 2010, p. 627). However, to neglect boredom in educational settings because it isnon-disruptive may underestimate its “self-disruptive” nature which has significant potentialto affect student learning, motivation, and achievement negatively.

Indeed, considerable research shows that boredom significantly and negatively relates toacademic achievement, whereas it significantly and positively relates to school dissatisfac-tion, truancy, psychosocial development, and high dropout rates (e.g., Gjesne 1977; Pekrunet al. 2002; Maroldo 1986). Moreover, when compared to other emotions such as anger,anxiety, and fear, boredom is frequently experienced in educational settings (Belton andPriyadharshini 2007; Mann and Robinson 2009; Vogel-Walcutt et al. 2012). If this is thecase, then the question arises: how do students cope with this negative emotion during learn-ing activities? A possible answer has come from a recent research on students’ boredom

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coping strategies. Mann and Robinson (2009) examined the contributors, moderators, andoutcomes of undergraduate students’ boredom during lectures. They found that daydreaming,doodling, switching off, coloring in letters on the handout, talking to the person next to them,and writing some notes to friends were among the preferred boredom coping strategies bythe undergraduate students during boring lectures.

Based on a sample of high school students, Nett et al. (2010) identified four boredomcoping strategies: cognitive-approach, behavioral-approach, cognitive-avoidance, and behav-ioral avoidance. Cognitive-approach strategies involve changing the perception regarding theboring situation, while behavioral-approach strategies include trying to change the boringsituation itself (Nett et al. 2010). For example, students who tend to use cognitive approachstrategies remind themselves the importance of a lesson when they experience boredom.Thus, they may experience less boredom during lessons, whereas those who use behavioralapproach strategies try to actively change the flow of lessons by asking the teacher for moreinteresting tasks, and demanding more modifications during learning activities when theyexperience boredom (Nett et al. 2010).

Conversely, cognitive-avoidance and behavioral-avoidance strategies consist of mentalor behavioral activities which are not associated with the current situation. For instance,students who use cognitive-avoidance strategies escape from the lessons mentally by think-ing about something irrelevant, whereas students who use behavioral-avoidance strategiesdistract themselves by doing something else such as chatting with a classmate (Nett et al.2010). Notably, Nett et al. (2010) showed that cognitive-approach orientation was relatedto lower levels of boredom and higher levels of perceived instrumentality of math classes.Recently, Nett et al. (2011) also demonstrated that the four-factor model of boredom copingstrategies were confirmed at both state and trait levels. Similar to the previous research (Nettet al. 2010), the results also revealed that cognitive-approach orientation was related to lowerlevels of boredom and higher levels of perceived instrumentality of math classes.

As far as the researchers are aware, there are only two studies in which boredom cop-ing strategies of undergraduate students from Turkey have been examined (Eren 2013a,b).Specifically, Eren (2013a) examined the prospective teachers’ boredom coping strategiesin terms of 11 different lessons (e.g., educational psychology, educational sociology), andfound that the prospective teachers’ boredom coping strategies could be reliably examinedthrough the four-factor model of boredom coping strategies (Nett et al. 2010). Eren (2013a)also found that the prospective teachers’ perceived instrumentality of lessons was moder-ately and positively related to cognitive-approach orientation, whereas it was negatively andsubstantially related to behavioral-avoidance orientation.

Using the four-factor model of boredom coping strategies (Nett et al. 2010), Eren (2013b)recently examined the profiles of prospective teachers’ boredom coping strategies, and foundthat the prospective teachers’ boredom coping strategies could be described through threemeaningful profiles: perception switchers (i.e., those who used cognitive-approach strategiesmore than behavioral-approach, cognitive-avoidance, and behavioral-avoidance strategies),behaviorally maladaptive ones (i.e., those who used cognitive-avoidance and behavioral-avoidance strategies more than approach-oriented strategies), and disappointed ones (i.e.,those who used behavioral-approach and cognitive-avoidance strategies more than cognitive-approach and behavioral-avoidance strategies).

Indeed, there are both similarities and differences between the combinations of Turkishundergraduate students’ boredom coping strategies and the combinations of boredom copingstrategies of students from Germany (e.g., Nett et al. 2010), Canada, and China (Tze et al.2013). Likewise, Nett et al. (2010) found that the profiles of students’ boredom coping strate-gies could be described through three clusters. They labeled these clusters as reappraisers

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(i.e., those who used cognitive-approach strategies more than other boredom coping strate-gies), criticizers (i.e., those who used behavioral-approach strategies together with the cog-nitive and behavioral-avoidance strategies), and evaders (i.e., those who used behavioral andcognitive-avoidance strategies more than approach-oriented strategies). Similarly, Tze et al.(2013) demonstrated that Canadian students’ boredom coping strategies could be identifiedthrough two clusters (i.e., reappraisers and criticizers) whereas Chinese students’ boredomcoping strategies could be grouped into three clusters (i.e., infrequent copers, reformers,and evaders). Of particular importance, Tze et al. (2013) also demonstrated that the reform-ers (i.e., those who used cognitive-approach, behavioral-approach, and cognitive-avoidancestrategies more than behavioral-avoidance strategies) and infrequent copers (i.e., those whoshowed below average preferences for all boredom coping strategies) were the least boredgroups when compared to other groups.

Although there is no study in which boredom levels of students from Turkey have beeninvestigated, it is reasonable to say that boredom is also experienced frequently by the under-graduate students from Turkey during lessons. Indeed, the results of previous studies onTurkish undergraduate students’ boredom coping strategies demonstrated that the combina-tions of Turkish undergraduate students’ boredom coping strategies were more similar to thecombinations of those groups who experienced higher levels of boredom (e.g., criticizers,evaders) than that of those groups who experienced lower levels of boredom (Nett et al. 2010;Tze et al. 2013).

To summarize, it can be said that students use particular boredom coping strategies in orderto combat this negative emotion during lessons. This signifies that to examine undergradu-ate students’ boredom coping strategies is important in educational settings such as class-rooms, as the detrimental effects of boredom may change as a function of whether adaptive(i.e., cognitive-approach and behavioral-approach strategies) or maladaptive (i.e., cognitive-avoidance and behavioral-avoidance strategies) boredom coping strategies are adopted by thestudents. It can also be said that students’ boredom coping strategies can be reliably examinedon the basis of the four-factor model of boredom coping strategies (Nett et al. 2010). Thus,the four-factor model of boredom coping strategies was adopted in the present study.

2.3 The present study

Although the relationships between undergraduate students’ TPs and boredom coping strate-gies have not been examined to date, there is evidence showing that boredom pronenessand perceptions of time are significantly related to one another. For example, Vodanovichand Kass (1990) demonstrated that students’ boredom proneness can be identified throughfive factors: external stimulation, internal stimulation, affective responses, constraint, andperception of slow time passage, indicating that boredom was not only related to the stim-ulation types, but also related to the students’ perceptions of time. Similarly, Ragheb andMerydith (2001) examined the elements of free time boredom. The results showed that freetime boredom can be defined through four factors: lack of mental involvement, meaning-ful involvement, physical involvement, and slowness of time. The results also showed thatthe higher the students experienced boredom, the higher they perceived the time as passingslowly, and the higher they experienced lack of mental, meaningful, and physical involve-ment. This means that students’ cognitive and behavioral involvement and their perceptionsof time may change as a function of the degree to which they experience boredom.

Vodanovich and Watt (1999) examined the relationship between undergraduate students’time structure and boredom proneness within two cultures (i.e., US and Ireland). They foundthat the present orientation and sense of purpose, as two of the subscales of time structure

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questionnaire (Bond and Feather 1988), were significantly related to boredom proneness.Specifically, the present orientation subscale was positively related to boredom proneness inthe US sample, whereas it was negatively related to boredom proneness in Irish sample. Thismeans that the relationship between time orientation and boredom may change as a functionof the cultural context.

It should be noted that students do not only experience boredom but also use particularstrategies in order to cope with boredom in educational settings such as high school and uni-versity (e.g., Mann and Robinson 2009; Nett et al. 2011; Tze et al. 2013). Thus, it is reasonableto claim that students’ TPs may associate with their boredom coping strategies because bore-dom coping strategies may significantly and selectively relate to the degree to which studentsexperience boredom in educational settings (Nett et al. 2010, 2011). Also, the meaning ofboredom and students’ sense of control on boredom may change as a function of the charac-teristics of TPs (Zimbardo and Boyd 2008). For example, for students who believe that theyhave less control over their goal-related behaviors, and see no particular reason to focus onthe personal future, which are the typical characteristics of those who are high in present-fatalistic TP, there is little reason to adopt cognitive-approach and/or behavioral-approachstrategies. Likewise, these strategies demand more cognitive effort, behavioral effort, andpersistence to reduce boredom during lessons when compared to cognitive-avoidance andbehavioral-avoidance strategies.

Following the same line of reasoning, it is also logical to assume that a student who tendsto connect learning activities to her future-related personal goals, and has a high sense ofcontrol over goal-related activities may adopt cognitive-approach and/or behavioral-approachstrategies instead of cognitive-avoidance and behavioral-avoidance strategies. At this point,it should also be noted that students may adopt diverse TPs simultaneously (Zimbardo andBoyd 2008), indicating that the relationships between students’ TPs and boredom copingstrategies may be more complicated than it is assumed. Thus, in the present study, both person-centered cluster analysis and variable-level analysis were conducted in order to examine theundergraduate students’ TPs and boredom coping strategies more precisely (see, for broaderexplanations regarding cluster analyses, Gan et al. 2007; Rencher 2002).

To consider students’ TPs together with their boredom coping strategies may broaden ourcurrent understanding of their beneficial and/or detrimental effects on important educationalvariables such as achievement, learning, and motivation by providing a comprehensive frame-work in which the way students’ profiles of TPs relate to their boredom coping strategies areevident. To illustrate, the strength of the relationships between students’ TPs and learning-related behaviors becomes less evident in secondary, and particularly, in tertiary levels ofeducation, while the relationships between TPs and leisure time activities and social rela-tions become more evident (Peetsma and van der Veen 2011). If students’ TPs significantlyrelate to their boredom coping strategies, then it is reasonable to claim that the relationshipsbetween TPs and learning-related behaviors may differ as a function of whether studentsuse adaptive or maladaptive boredom coping strategies. Adaptive strategies are effective inreducing boredom. Therefore, adaptive strategy use may strength the relationships betweenTP and learning-related behaviors.

It is also important to consider students’ TPs together with their boredom coping strategiesin order to interpret the relationships between crucial educational variables such as studentengagement and achievement more appropriately. In fact, both of these variables are not freefrom the effects of students’ boredom coping strategies and TPs (e.g., de Bilde et al. 2011;Nett et al. 2010; Zimbardo and Boyd 1999). These explanations advocate that to examine therelationships between undergraduate students’ TPs and boredom coping strategies are notonly reasonable, but also important.

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Therefore, the present study aimed to examine the relationships between undergraduatestudents’ TPs and boredom coping strategies. Accordingly, two research questions were for-mulated as follows: (a) what are the profiles of undergraduate students’ TPs? (b) Do theprofiles of undergraduate students’ TPs significantly relate to their boredom coping strate-gies? With regard to the first research question, it is reasonable to expect meaningful profilesconsisting of distinctly different patterns of undergraduate students’ TPs (see, for example,Gao 2011; Boniwell et al. 2010). No specific hypotheses are suggested in relation to thesecond research question due to a lack of evidence. Nevertheless, based on the abovemen-tioned explanations (e.g., Vodanovich and Watt 1999), it is reasonable to expect significantrelationships between the profiles of students’ TPs and boredom coping strategies for at leastone reason. Students do not only experience boredom, but also use particular strategies inorder to cope with boredom effectively. Understanding more about such relationships has thepotential to inform teaching and learning interventions.

3 Method

3.1 Context and participants

Undergraduate students from Turkey constituted the context of the current study. Precisely,a total of 719 undergraduate students (533 female), majoring in primary school teaching(n = 92), computer education and instructional technology teaching (n = 32), mathematicsteaching (n = 45), science teaching (n = 82), art teaching (n = 69), Turkish languageteaching (n = 124), social studies teaching (n = 66), psychology (n = 89), and sociology(n = 120) were randomly sampled from the faculty of education (approximately 4,000students) and faculty of arts and science (approximately 2,000 students) of a large university(approximately 23,000 students) located in the North-West of the Black Sea region in Turkey.The sample contained 238 first-year, 217 second-year, 165 third-year, and 99 final-yearstudents. The mean age of the participants was 20.38 (SD=1.86; Range=17–37 years). Theparticipation in the study was totally voluntary.

In Turkey, formal education consists of pre-primary education, primary education, sec-ondary education, and higher education. There are two types of universities in Turkey: publicuniversities and private universities. Regardless of the type, these universities may includediverse units such as faculties and post-secondary vocational schools. Post-secondary voca-tional schools offer two-year degree programs aimed at training manpower in various occu-pations such as cookery and accountancy (The Council of Higher Education-CoHE 2010).On the other hand, faculties (e.g., faculties of education, faculties of arts and science) offervarious four-year degree programs such as psychology, sociology, physics, primary schoolteaching, science teaching, and preschool teaching.

Indeed, the administration of higher education in Turkey was comprehensively restruc-tured in 1981, and all higher education institutions were strictly tied to the CoHE (2010).As a consequence of this reform, higher education system in general, and application tohigher education institutions in particular became highly centralized (CoHE 2010). With theexception of those students who would like to enroll in post-secondary vocational schools,all students have to pass the University Entrance Examination (UEE) given by the TurkishCouncil of Higher Education (Kilimci 2009). The UEE was revised in 2010 by the CoHE, andcurrently the UEE consists of two phases: the higher education transition examination and theundergraduate placement examination. Despite this alteration, the highly centralized natureof the UEE and placement procedure in the higher education programs did not significantly

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change. Thus, it can be said that the university and the majors of the mentioned faculties wherethe present study was carried out are highly representative due to the centralized nature ofthe Turkish higher education system.

3.2 Research materials

3.2.1 Zimbardo time perspective inventory

In the present study, the ZTPI (Zimbardo and Boyd 1999) was used to assess undergraduatestudents’ TPs. The ZTPI is widely used to assess undergraduate students’ TPs in differentcultures (e.g., Boniwell et al. 2010; D’Alessio et al. 2003; Zhang and Howell 2011). The scalecontains five subscales: past negative (10 items: e.g., it’s hard for me to forget unpleasantimages of my youth), past positive (9 items: e.g., it gives me to pleasure to think about mypast), present-hedonistic (15 items: e.g., I do things impulsively), present-fatalistic (9 items:e.g., my life path is controlled by forces I cannot influence), and future (13 items: e.g., Icomplete projects on time by making steady progress). As in the original scale, participantsresponded to the items of the ZTPI on a five-point Likert-type scale ranging from 1 (veryuncharacteristic) to 5 (very characteristic). All items in the scale were translated into Turkishby the authors of this article with the assistance of two lecturers in the foreign languagesdepartment of the university where the present study was carried out.

Due to the fact that both the number of items and factor structure of the ZTPI may changeacross diverse samples and cultures (e.g., Ryack 2012; Worrell and Mello 2007), usingprincipal component analysis with varimax rotation module from SPSS, an ExploratoryFactor Analysis (EFA) was conducted (cut-off.45). As a result, the scree plot demonstratedthat the five-factor solution was the best option when compared to other number of factorsolutions. The results showed that the first (15.10 %), second, (10.71 %), third (6.99 %), fourth(3.89 %), and fifth (3.46 %) factors explained 40.15 % of the total variance, with Eigen valuesranging from 1.94 to 8.46. No cross loadings among the items of the scale were observed.The results also revealed that each factor consisted of relevant items, indicating that thesefactors can be well labeled as in the original version of the scale (Zimbardo and Boyd 1999).However, in contrast to the original version of the scale, past-negative, past-positive, present-hedonistic, present-fatalistic and future subscales consisted of 8, 6, 10, 5, and 11 items,respectively.

Using the Maximum Likelihood method of estimation from AMOS 16 (Arbuckle 2007),a confirmatory Factor Analysis (CFA) was also conducted in order to check whether thefive-factor model with 40 items fit to the data well in the present sample. The Tucker-LewisIndex (TLI ≥ .90), Comparative Fit Index (CFI ≥ .90), Standardized Root Mean SquareResidual (SRMR ≤ .08), and Root Mean Square Error of Approximation (RMSEA ≤ .08)were used to assess the data fit, because they are relatively less sensitive to sample size andviolations of distributional assumption (Kline 2011; Iacobucci 2010; Ullman 2007). In theCFA, a full consideration was given to the modification indices (Byrne 2010). The resultsshowed that the five-factor model with 40 items had acceptable fit to the data (χ2(694) =1, 540.98, p < .001; CFI= .911; TLI= .900; RMSEA= .041—Low= .038, High= .044;SRMR =.067). However, it was observed that one item in the present fatalistic subscale (i.e.,fate determines much in my life) had low parameter estimation (.24 < .30). Therefore, thisitem was omitted from the model, and CFA was rerun.

Consequently, it was observed that the five-factor model with 39 items (χ2(658) =1, 452.66, p < .001; CFI= .915; TLI= .904; RMSEA= .041—Low= .038, High= .044;SRMR= .067) also had acceptable fit to the data. With parameter estimations ranging from

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.30 to .74, CFA results also showed that the items were significantly predicted by theirrespective factors. Thus, the five-factor model with 39 items was adopted in the presentstudy. Finally, Cronbach’s coefficient alpha was computed as .83, .72, .80, .71, and .87for past-negative, past-positive, present-hedonistic, present-fatalistic, and future subscales,respectively, signifying that the internal reliabilities of the ZTPI subscales were satisfactoryin the present sample.

3.2.2 Coping with boredom scale

The Coping with Boredom Scale (CBS), originally developed by Nett et al. (2010), was usedto assess students’ boredom coping strategies in the present study. The items in the scalewere already translated into Turkish in a previous study (Eren 2013a). The CBS containsfour factors: cognitive-approach (5 items: e.g., when I am bored in mathematics class, I tryto pay attention to the lesson more), behavioral-approach (5 items: e.g., when I am boredin mathematics class, I ask my instructor for more interesting tasks), cognitive-avoidance(5 items: e.g., when I am bored in mathematics class, I do my homework), and behavioral-avoidance (5 items: e.g., when I am bored in mathematics class, I distract myself by interactingwith my classmate). The term “mathematics class” was replaced with the term “in thisclass” because the CBS was utilized during various lessons such as general physics, materialdevelopment, introduction to social psychology, and teaching principles and methods. As inthe original scale, participants responded to the items of the CBS on a five-point Likert-typescale ranging from 1 (strongly disagree) to 5 (strongly agree).

The results of CFA showed that the four-factor model with 20 items had acceptable fitto the current data (χ2(161) = 701.65, p < .001; CFI= .936; TLI= .925; RMSEA= .068,Low= .063 High= .074; SRMR= .085). With parameter estimations ranging from.45 to.93,the results also demonstrated that the items were significantly predicted by their relevant latentfactors. Cronbach’s coefficient alpha was computed as .85, .75, .87, and .94 for cognitive-approach, behavioral-approach, cognitive-avoidance, and behavioral-avoidance subscales,signifying that the internal reliabilities of the CBS subscales were also adequate in the presentsample.

3.2.3 Social desirability scale

In the present study, the Social Desirability Scale (SDS), originally developed by Stöber(2001), was used in order to control for the possible effects of social desirability. The itemsin the scale were translated into Turkish in a previous study (Coskun and Durak unpublishedmanuscript). It is important to control social desirability because the data regarding thestudents’ TPs and boredom coping strategies were obtained from the same source (i.e.,undergraduate students) through self-report measures. Indeed, this may inflate Type I errorrates (Podsakoff et al. 2003).

The SDS contains 10 positive items (e.g., I always admit my mistakes openly and face thepotential negative consequences) and 7 negative items (e.g., I sometimes litter). Participantsresponded to the items of the SDS on a five-point Likert-type scale ranging from 1 (stronglydisagree) to 5 (strongly agree). A preliminary analysis of the SDS through the CFA revealedthat 9 items, particularly the negative items, had low parameter estimations (<.30). Thiscan be due to the students’ careless responses to negatively phrased items (Roszkowski andSoven 2010; Woods 2006). Thus, by giving full consideration to the modification indicesand omitting the mentioned 9 items, the CFA was rerun. Consequently, it was found that the

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one-factor model with 8 items had good fit to data (χ2(19) = 59.33, p < .001; CFI= .950;TLI= .927; RMSEA= .054—Low= .039, High= .070; SRMR= .038). With parameter esti-mations ranging from .32 to .67, the results also revealed that the items were significantlypredicted by the latent factor. Finally, Cronbach’s coefficient alpha was computed as .72 anddeemed acceptable.

3.3 Procedure

The data were collected during the 2011/2012 academic year by the researchers. The ZTPI,CBS, and social desirability scale were applied, respectively, during one of the class hoursof 11 different classes (e.g., general physics, special education, writing techniques, mater-ial development, teaching principles and methods, research methods, human anatomy, andpersonality psychology). The scales were presented to the students with instructions con-cerning the aim of the study. These instructions were also read aloud at the beginning ofthe process, and any questions from the participants were answered. Demographic variableswere assessed by a self-report on the ZTPI. The administration process lasted approximately17 min for the ZTPI, 9 min for the CBS and 6 min for the SDS.

3.4 Data analyses

Although the demographic variables were not of interest in the present study, it is importantto check their possible intervening effects on the ZTPI, SDS, and CBS subscales in orderto obtain a clear picture of the relationships among the variables at hand. Additionally, theeffects of lessons, during which the ZTPI, SDS, and CBS subscales were applied, were alsoexamined in order to check their possible intervening effects on the mentioned subscales. Itwas important to check the intervening effects of lessons on the CBS subscales because thestudents’ boredom levels may differ due to the contents of lessons. In turn, this may influencetheir strategy use. Thus, before addressing the research questions, two separate MultivariateAnalyses of Covariance (MANCOVAs) and one Analysis of Covariance (ANCOVA) wereconducted in order to check the possible effects of demographic variables (i.e., gender, age- as a covariate, year of study, and fields of study) and lessons on the SDS, ZTPI, and CBSsubscales. Following the MANCOVAs, a series of Analyses of Variance (ANOVAs) was alsoconducted in order to examine the univariate effects of demographic variables and lessonson ZTPI and CBS subscales (Field 2009).

The results showed that the effects of demographic variables on ZTPI, CBS, and SDSwere negligible in the present sample. Importantly, the results also showed that the univari-ate effects of classes on CBS subscales were non-significant (all ps > .16), with partialeta-square coefficients (η2

p) ranged in magnitude from .00 to .01. This indicates that thecharacteristics of the classes did not significantly relate to students’ boredom coping strate-gies. This result was in line with the results of a recent research in which the relationshipsbetween teaching characteristics (e.g., understandability, enthusiasm) and students’ academicemotions (e.g., boredom, enjoyment) were examined across four lessons (i.e., mathematics,English, German, physics) (Goetz et al. 2013). Specifically, Goetz et al. (2013) found that therelationships between the mentioned variables were highly similar across the four lessons.Hence, demographic variables and lessons were not included in further analysis.

For the first research question, using Ward’s method with squared Euclidean distancemeasure (Aldenderfer and Blashfield 1993; Rencher 2002), a person-centered HierarchicalCluster Analysis (HCA) was conducted in order to explore the profiles of undergraduatestudents’ TPs. All variables were standardized as z scores (M = 0; SD=1) before being

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subjected to HCA in order to improve the interpretability of the cluster compositions (Rencher2002). Because outliers may significantly damage the agglomeration process in the HCA,an outlier detection procedure was also applied to data (±3.00 SD). The outlier detectionprocedure resulted with 24 outliers. Given that the HCA and latent factor correlation analysesare highly sensitive to outliers (Meyers et al. 2006; Rencher 2002), these analyses wereconducted based on the outlier-free sample (n = 695).

Additionally, an ANOVA was conducted to examine the predictive validity of the clusters.In the ANOVA, the cluster membership was determined as an independent variable, whereasGPA, with possible scores ranging from 0 to 4, was determined as a dependent variable.Considerable research provides evidence that the GPA is positively related to future and past-positive TPs and negatively related to past-negative, present-hedonistic, and present-fatalisticTPs (e.g., Barber et al. 2009; Zimbardo and Boyd 1999). Therefore, it can be expected thatthe students who are high in past-positive and future TPs have significantly higher GPAs thanthat of those students who are high in past-negative, present-fatalistic, and present-hedonisticTPs.

For the second research question, a MANCOVA was conducted in which the clusterswere determined as independent variables, whereas the boredom coping strategies weredetermined as dependent variables. The sum of the students’ responses on the SDS wasentered as a covariate in the analysis in order to control its possible effect on the relationshipsbetween the profiles of TP and boredom coping strategies.

Using Maximum Likelihood method of estimation from AMOS 16 (Arbuckle 2007), acorrelation analysis was also conducted in order to examine the variable-level relationshipsbetween the undergraduate students’ TPs and boredom coping strategies. Given that thestudents’ TPs and boredom coping strategies were not actually observed variables, but latentstructures derived from the self-report measures (i.e., CBS and ZTPI), it was both appropriateand important to examine the relationships between the research variables based on the latentfactors. By allowing SDS to associate with each latent variable in the model, the possibleeffects of social desirability were also controlled for.

4 Results

4.1 Cluster analysis of time perspectives

Both dendrogram and agglomeration schedule showed that the four-cluster solution was moreappropriate than other number cluster solutions. Thus, a four-cluster solution was applied tothe data. As seen in Table 1, the cluster centroids were ranged from −.789 to .481 for thefirst cluster, while they were ranged from −1.144 to .177 for the second cluster. For the thirdcluster, the cluster centroids were ranged from −.722 to. 721 while, for the fourth cluster,they were ranged from −.453 to .881.

In addition, results of the mean-level analyses demonstrated that the cluster composi-tions were discernible in terms of past-positive (F(3, 691) = 122.29, p < .001, η2

p = .35),

past-negative (F(3, 691) = 191.90, p < .001, η2p = .45), present-hedonistic (F(3, 691) =

70.87, p < .001, η2p = .24), present-fatalistic (F(3, 691) = 77.20, p < .001, η2

p = .25),

and future TPs (F(3, 691) = 65.47, p < .001, η2p = .22). This means that the profiles of

undergraduate students’ TPs can be reliably defined in terms of the cluster centroids. Thus,based on the cluster centroids, first, second, third, and fourth clusters were labeled as “highpast-positive and future TP” (HIPAPFUT), “high present-hedonistic TP” (HIPHEDOT),

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Table 1 Cluster centroids

Cluster Variable

n Past-pos. Past-neg. Present-fat. Present-hed. Future

1 170 .313 .033 −.315 −.789 .481

2 164 −.073 −1.144 −.280 .177 −.345

3 224 −.722 .380 .721 .100 −.467

4 137 .881 .707 −.453 .604 .580

Variables were reported as standardized scores

Past-P Past_N Present-F Present-H Future

HIPAPFUT HIPHEDOT HIPANFAT LOPREFAT-1,2

-1,0

-0,8

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

1,0

Fig. 1 Time perspective clusters by the cluster centroids. Note HIPAPFUT high past-positive and futuretime perspective cluster, HIPHEDOT high present-hedonistic time perspective cluster, HIPANFAT high past-negative and present fatalistic time perspective cluster, LOPREFAT low present-fatalistic time perspectivecluster

“high past-negative and present-fatalistic TP” (HIPANFAT), and “low present-fatalistic TP”(LOPREFAT), respectively (see Fig. 1).

As seen in Fig. 1, the HIPAPFUT cluster (n = 170) contains the students who are highin future and past-positive TPs, moderately high in past-negative TP, and low in present-fatalistic and present-hedonistic TPs. This indicates that to focus on the positive aspects ofpast and the possible future was the main characteristic of the students in this cluster. However,the students in the HIPHEDOT cluster (n = 164) are high in present-hedonistic TP only.Furthermore, they are low in past-negative, future, present-fatalistic TPs, and moderatelylow in past-positive TP. This means that the students in the HIPHEDOT cluster focus on thehedonistic aspect of the present time dominantly.

The HIPANFAT cluster (n = 224) consists of the students who are high in past-negativeand present-fatalistic TPs, moderately high in present-hedonistic, and low in future and past-positive TPs. This signifies that to focus on the negative aspects of the past and present was themain characteristic of the students in this cluster. Finally, the LOPREFAT cluster (n = 137)includes the students who are high in past-positive, past-negative, present-hedonistic, futureTPs, and low in present-fatalistic TP. This suggests that to focus on diverse aspects of timeflexibly was the main characteristic of the students in the LOPREFAT cluster. In fact, withthe exception of moderate past-negative orientation, the HIPAPFUT cluster draws a patternsimilar to balanced TP (Boniwell et al. 2010).

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Table 2 Summary of the univariate analysis

Independent variable Dependent variable F d f η2p Pairwise comparisons

Clusters Cognitive-approach 15.24*** 3,690 .06 1 > 2∗∗∗; 1 > 3∗∗∗4 > 2∗∗∗; 4 > 3∗∗∗

Behavioral-approach 5.06** 3,690 .02 4 > 1∗; 4 > 2∗∗Cognitive-avoidance 4.17** 3,690 .02 3 > 1∗Behavioral-avoidance 4.65** 3,690 .02 3 > 1∗∗

Social desirability Cognitive-approach 19.39*** 1,690 .03

Behavioral-approach 2.69 1,690 .00

Cognitive-avoidance 1.58 1,690 .00

Behavioral-avoidance 55.63*** 1,690 .08

1 = HIPAPFUT cluster; 2 = HIPHEDOT cluster; 3 = HIPANFAT cluster; 4 = LOPREFAT cluster∗∗∗ p < .001,∗∗ p < .01,∗ p < .05

Finally, the results of ANOVA showed that the students in the HIPAPFUT cluster (M =2.80; SD= .44) had higher GPA than that of those students in the HIPHEDOT (M = 2.63;SD= .43), HIPANFAT (M = 2.61; SD= .45), and LOPREFAT (M = 2.58; SD= .45)clusters (F(3, 585) = 6.85, p < .001, η2

p = .03). These results were in line with theexpectation that the students who were high in past-positive and future TPs have significantlyhigher GPA than that of those students who were high in past-negative, present-fatalistic, andpresent-hedonistic TPs. Intriguingly, the students in the LOPREFAT cluster had the lowestGPA although they were high in future and past-positive TPs. This can be due to the possibilitythat these students were also high in past-negative and present-hedonistic TPs, which, in turn,may cancel, or at least decrease, the positive effects of past-positive and future TPs on GPA.Consequently, it can be said that the ANOVA results provide evidence regarding the predictivevalidity of the TP clusters.

4.2 Relational analyses of cluster patterns and boredom coping strategies

The results of the MANCOVA revealed that regardless of the effect of social desirabil-ity, multivariate effect of clusters on CBS subscales was significant (Roy’s Largest Root(4,689)= .073, p < .001, η2

p = .07). Subsequent ANOVAs revealed that the effects of clus-

ter on cognitive-approach (F(3, 690) = 15.24, p < .001, η2p = .06), behavioral-approach

(F(3, 690) = 5.06, p < .01, η2p = .02), cognitive-avoidance (F(3, 690) = 4.17, p <

.01, η2p = .02), and behavioral-avoidance strategies (F(3, 690) = 4.65, p < .01, η2

p = .02)were also significant (see Table 2).

As seen in Table 2, the results of the pairwise comparisons showed that the students inthe HIPAPFUT cluster tended to use cognitive-approach strategies more than those whowere in the HIPHEDOT and HIPANFAT clusters. Pairwise comparisons also demonstratedthat the students in the LOPREFAT cluster tended to use cognitive-approach strategies morethan those who were in the HIPHEDOT and HIPANFAT clusters. Similarly, the studentsin the LOPREFAT cluster used behavioral-approach strategies more than those who werein the HIPAPFUT and HIPHEDOT clusters. Finally, it was also observed that the studentsin the HIPANFAT cluster tended to use both cognitive-avoidance and behavioral-avoidancestrategies more than those who were in the HIPAPFUT cluster.

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Table 3 Descriptive statistics and correlation coefficients

Variable M (SD) 1 2 3 4 5 6 7 8 9

1. Past-positive

22.61 (3.76) –

2. Past-negative

28.36 (5.91) .35*** –

3. Present-hedonistic

34.46 (6.37) .17** .13* –

4. Present-fatalistic

9.33 (2.93) −.23*** .10* .04 –

5. Future 41.61 (6.40) .43*** .23*** .02 −.47*** –

6. Cognitive-approach

19.23 (3.56) .36*** .22*** −.04 −.28*** .50*** –

7. Behavioral-approach

13.18 (4.19) .18** .07 .11* .01 .07 .12* –

8. Cognitive-avoidance

12.93 (4.76) −.06 .06 .06 .20*** −.06 −.07 .25*** –

9. Behavioral-avoidance

12.99 (5.28) −.14* .03 .13* .32*** −.30*** −.38*** −.04 .26*** –

Correlation coefficients were calculated based on the latent variables. The effects of social desirability on theresearch variables were controlled for in the analysis∗∗∗ p < .001,∗∗ p < .01,∗ p < .05

Furthermore, a correlational model in which the students’ TPs and boredom coping strate-gies were allowed to associate with each other was created in order to examine the relation-ships between students’ TPs and boredom coping strategies in the variable-level. Notably, theeffects of social desirability on cognitive-approach and behavioral-avoidance strategies weresignificant (see Table 2). This means that the cognitive-approach strategy use and behavioral-avoidance strategy use were reported by the students in a socially desirable way. Thus, theeffects of social desirability were also controlled for in the correlation analysis.

In the model, all latent variables were predicted by the SDS. The results showed thatthe correlational model had acceptable fit to the data (χ2(2054) = 3, 596.14, p < .001;CFI= .907; TLI= .900; RMSEA= .033—Low= .031, High= .035; SRMR= .057), signi-fying that the ZTPI and CBS subscales did not overlap with each other. Additionally,an alternative model, in which the effects of SDS on CBS and ZTPI subscales wereset to zero, was created in order to examine whether or not the alternative model fittedto the data better than the initial model. The results showed that the alternative model(χ2(1, 628) = 2, 901.58, p < .001; CFI= .921; TLI= .914; RMSEA= .033,—Low= .031,High= .035; SRMR= .057) slightly, but significantly fitted to the data better than the initialmodel (�TLI = .014 > .01;�CFI = .014 > .01). This means that to include SDS inthe model made sense in the current sample. The latent variable correlation coefficients arepresented in Table 3.

As seen in Table 3, students’ TPs were significantly and selectively related to their boredomcoping strategies. Specifically, past-positive TP was positively and moderately related tocognitive-approach orientation (r = .36), whereas it was positively, but weakly, relatedto behavioral-approach orientation (r = .18). The relationship between past-positive TPand behavioral-avoidance orientation was weak and negative (r = −.14). Past-negativeTP was uniquely, but weakly related to cognitive-approach orientation (r = .22). Present-hedonistic TP was weakly related to behavioral-approach (r = .11) and behavioral-avoidancestrategies (r = .13). However, present-fatalistic TP was moderately and negatively related to

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cognitive-approach orientation (r = −.28), whereas it was moderately and positively relatedto behavioral-avoidance orientation (r = .32). The relationship between present-fatalisticTP and cognitive-avoidance orientation was positive and weak (r = .20). Finally, future TPwas positively and strongly related to cognitive-approach orientation (r = .50), whereas itwas moderately and negatively related to behavioral-avoidance orientation (r = −.30).

5 Discussion

Results of the present study demonstrated that the factor structures of ZTPI and CBS wereconfirmed in the present sample. Moreover, the results of the current study were also freefrom the effects of demographic variables and social desirability. This means that the presentresults can be consistently discussed in relation to the ZTPI and CBS subscales regardless ofthe effects of demographic variables and social desirability.

5.1 Profiles of undergraduate students’ time perspectives

The results of the cluster analysis revealed that the undergraduate students’ TPs can beclassified into four meaningful and distinctly different clusters. Given that individuals mayhold different TPs simultaneously, these results were not surprising. Likewise, results of theprevious studies in which students’ TPs were described based on the cluster analyses (Gao2011; Boniwell et al. 2010) demonstrated that students held different TPs simultaneously.However, moving one step further away from the previous research (Gao 2011), the presentresearch showed that the undergraduate students’ TPs can be significantly described beyondthe balanced or non-balanced TP dichotomy, because a similar composition of balanced TPwith the exception of moderate past-negative TP was also obtained in the present study. Theexception mentioned can be due to the fact that the content of balanced TP more or lessdepends on the cultural context in which students’ TPs are examined (Gao 2011; Boniwellet al. 2010).

Turkish people, like Asian people (see Nisbett 2003), generally tend to perceive cir-cular, rather than linear relationships between their positive and negative life experiences(Kagıtçıbası 2000; see also Suh et al. 1998, for a comprehensive research on life satisfactionjudgments across cultures). Accordingly, the negative life experiences may probably leadto possible positive future experiences, and/or positive life experiences may probably leadto possible negative future experiences, which, in turn, make both negative and positive lifeexperiences highly valuable and informative for the future. The positive relationships amongthe students’ past-positive, past-negative, and future TPs provide a further support for thisexplanation (see Table 3). However, it should be noted that the students’ perceptions aboutnegative and positive life experiences were not examined in the present study. Therefore, thisissue requires further investigation.

The results also showed that the students in the HIPAPFUT cluster had significantlyhigher GPAs than that of those students in the HIPHEDOT, HIPANFAT, and LOPREFATclusters. This result echoed the results of previous studies which showed that GPA waspositively related to past-positive and future TPs, whereas it was negatively related to past-negative, present-hedonistic, and present-fatalistic TPs (e.g., Barber et al. 2009; Zimbardoand Boyd 1999). However, the results of previous studies were mainly based on variable-levelanalyses. Furthermore, previous studies focused on mostly the balanced TP as an optimalcombination of TPs. Thus, the current results broadened our understanding regarding thesignificant relationships between TPs and GPA by suggesting that other combinations of TPs

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such as high past positive and future TPs, moderate past-negative TP, and low present-fatalisticand present-hedonistic TPs can also be informative in terms of undergraduate students’academic achievement.

Based on the abovementioned results of the study, it can be suggested that educators shouldbe aware of the fact that their students hold diverse TPs simultaneously. The current resultsshowed that some combinations of this diversity such as high past-positive TP, high futureTP, low present-fatalistic TP, low present-hedonistic TP, and moderate high past-negativeTP made sense in students’ academic achievement. Nevertheless, educators should also beaware of the fact that this may not be the unique adaptive TP pattern because students’ TPsare more or less context specific (Boniwell and Zimbardo 2004) and susceptible to change(Zimbardo and Boyd 2008).

In addition, it can also be suggested that the profiles of students’ TPs should be consid-ered together with other important variables such as teacher/lecturer expectations in order tounderstand the relationships between TPs, boredom coping strategies, and academic achieve-ment more comprehensively in educational settings. Likewise, a recent longitudinal studyon undergraduate students’ future TP, perceived instrumentality, and academic achievementrevealed that the effects of changes in both future TP and perceived instrumentality on stu-dents’ graded performance regarding educational psychology class were significantly medi-ated by the lecturer (Eren 2009). This suggests that the lecturer is an important factor to reflectthe beneficial effects of students’ TPs on academic achievement in educational settings.

5.2 The relationships between time perspectives and boredom coping strategies

The results of both person-level and variable-level analyses showed that the students’ TPswere significantly and selectively related to their boredom coping strategies regardless of theeffects of social desirability. This indicates that the relationships between TPs and boredomcoping strategies were not statistical artifacts. At this point, one may argue that the students’TPs refer to students’ general tendencies regarding the particular time categories, whereasboredom coping strategies refer to domain-specific strategies that students prefer to useduring lessons. However, it should be noted that the students’ boredom coping strategieswere not assessed with a particular focus on a class such as math or history in the presentstudy. Moreover, results of the preliminary analyses revealed that the effects of class typeon boredom coping strategies were non-significant, indicating that the students’ boredomcoping strategies reflected students’ general tendencies regarding boredom coping strategiesin the present sample. Therefore, it is reasonable to discuss the relationships between TPsand boredom coping strategies.

Briefly, the results revealed that the students in the HIPAPFUT and LOPREFAT clusterstended to use cognitive-approach and behavioral-approach strategies more than that of thosestudents in the HIPHEDOT and HIPANFAT clusters. The results also showed that the studentsin the HIPANFAT cluster tended to use both cognitive-avoidance and behavioral-avoidancestrategies more than that of those students in the HIPAPFUT cluster. Given that the studentswho use cognitive-approach strategies experience less boredom during lessons (e.g., Tze etal. 2013), and also given that the future-oriented students tend to perceive schoolwork asinstrumental to attain their future goals (e.g., Miller and Brickman 2004), the mentionedrelationships between the TP clusters and boredom coping strategies can be understood.

Apparently, the students in the HIPAPFUT and LOPREFAT clusters tended to take theadvantage of cognitive-approach and behavioral-approach strategies in order to engage intheir lessons more effectively. Although the present study did not examine student engage-ment, there is evidence that the relationships between undergraduate students’ perceived

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instrumentality of their lessons and diverse aspects of engagement (e.g., agentic engagementand emotional engagement) were positive and significant (Eren 2013a). This means that thecurrent relationships between the future TP and adaptive boredom coping strategies were notaccidental.

Furthermore, the results also revealed that the students who used adaptive coping strate-gies (i.e., cognitive-approach and behavioral-approach strategies) were more past-positiveoriented. This can be explained based on the possibility that the positive past experiencesmay provide a solid basis for students to perceive their future-related goals more attainable,which, in turn, may motivate students to use adaptive boredom coping strategies in orderto cope with boredom more effectively. If this is the case, it is also highly possible that thestudents in the HIPAPFUT and LOPREFAT clusters perceive boredom as a more seriousproblem than do those students in the HIPHEDOT and HIPANFAT clusters although thispossibility was not examined in the present study.

On the other hand, the students in the HIPANFAT cluster used maladaptive boredomcoping strategies (i.e., cognitive-avoidance and behavioral-avoidance strategies) during thelessons. This can be due to the fact that the composition of HIPANFAT cluster was mainlycreated by the students who were high in present-fatalistic and past-negative TPs, moderatelyhigh in present-hedonistic TP, and low in both future and past-positive TPs. This means thatthe students in this cluster did not only lack a clear picture of the personal future but alsolacked the positive evaluations of their personal past and present which are important tosupport the sense of their control over a personal future (Zimbardo and Boyd 2008). Thus,it is highly possible that the students in the HIPANFAT cluster may adopt easy to use, albeitmaladaptive, boredom coping strategies in order to cope with boredom during lessons as theybelieve that they have little or no control over their present and future-related behaviors. Incontrast to the cognitive-avoidance and behavioral-avoidance strategies, cognitive-approachand behavioral-approach strategies by their nature require students to exert greater cognitiveand behavioral effort in order to engage in lessons effectively (Nett et al. 2010, 2011).

At this point, it is important to note that boredom is a widely experienced emotion ineducational settings when compared to other emotions such as anger, anxiety, and fear.Additionally, boredom has detrimental effects on learning and academic achievement (Beltonand Priyadharshini 2007; Mann and Robinson 2009; Vogel-Walcutt et al. 2012). Thus, it istimely to suggest that educators should find ways to increase the adaptive strategy use duringthe lessons. One way to do this is to explain the benefits of using, for example, cognitive-approach strategies during lessons.

Another suitable way is to enable undergraduate students to reflect on their boredom expe-riences, because the reflection process may significantly increase their awareness regardinghow effectively they can cope with boredom during lessons. Based on the current results, itcan also be said that educators should take their students’ TPs into account in order to increasetheir awareness regarding effective strategy use (e.g., cognitive-approach strategies). Overallresults of the present study suggest that to focus on either TPs or boredom coping strategiesmay prevent educators from seeing the larger picture of how boredom coping strategies andTPs affect educational outcomes such as learning, motivation, and achievement. Thus, educa-tors should focus on both of these variables in order to influence their students’ engagement,motivation, and achievement in university settings.

5.3 Limitations and directions for future studies

This study has a number of limitations each of which provides a basis for future studies. First,although the sample size was enough large to examine the relationships between the students’

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TPs and boredom coping strategies, future studies in which the mentioned relationshipsare examined on the basis of the larger samples may provide more robust results. Second,the sample of the present study consisted of undergraduate students only, which limits thegeneralizability of the current results to other school settings such as high schools. Futurestudies which include both university students and high school students may provide a morecomprehensive picture of how students’ TPs relate to their boredom coping strategies.

Third, in the present study, profiles of students’ TPs were examined based on the observedvariables and using Ward’s method with squared Euclidian distances. Although Ward’smethod is one of the robust methods (Rencher 2002), TPs are latent variables as the students’TPs are assessed through a self-report measure (i.e., ZTPI). Thus, the profiles of students’TPs should be examined by conducting a latent profile analysis (Nylund et al. 2007) in orderto capture the exact nature of the students’ TPs.

Fourth, the cross-sectional design of the present study significantly limits the causativeinferences regarding the relationships between TPs and boredom coping strategies. How-ever, longitudinal studies may enable researchers to interpret the relationships between stu-dents’ TPs and boredom coping strategies in a causal manner. These studies should alsoinclude important educational variables such as school investment, perceived instrumental-ity of schoolwork, and academic engagement as they have significant potential to relate withboth TPs and boredom coping strategies (e.g., de Bilde et al. 2011; Horstmanshof and Zimitat2007; Peetsma 2000; Nett et al. 2011). Finally, social desirability should also be made a partof the future studies as the results of this study showed that the effects of social desirabilityon CBS and ZTPI subscales were considerable.

6 Conclusion

The results of the current study lead to three major conclusions. First, undergraduate stu-dents’ TPs were reliably defined through four meaningful and distinctly different clusterpatterns: high past-positive and future TP cluster, high present-hedonistic TP cluster, highpast-negative and present-fatalistic TP cluster, and low present-fatalistic TP cluster. Second,these clusters significantly differed in terms of their associations with undergraduate students’academic achievement. Third, and more importantly, students’ profiles of TPs were signif-icantly and selectively related to their boredom coping strategies regardless of the effectsof social desirability. Overall results of the present study suggest that the students’ TPsshould be considered together with their boredom coping strategies in educational settings.By doing so, both educators and researchers can examine the effects of TPs and boredomcoping strategies on important educational variables such as motivation, engagement, andacademic achievement more comprehensively and precisely.

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