Identifying the motivational and demotivational factors influencing students’ academic achievements in language education

Learning and Motivation 68 (2019) 101598

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Identifying the motivational and demotivational factors influencing students’ academic achievements in language education

T

Shiva Zeynali, Reza Pishghadam⁎, Azar Hosseini Fatemi Ferdowsi University of Mashhad, Mashhad, Iran

ARTICLE INFO

ABSTRACT

Keywords: Motivation Demotivation Motivational model EFL Higher education GPA Academic performance

This study aims first to design a motivational/ demotivational pattern scale (MDPAS) for English as a foreign language (EFL) students, and then proposes a model of motivation/ demotivation in EFL education. Entire population participated in the study comprised 800 students majoring in EFL. MDPAS was designed and validated by applying confirmatory factor analysis (CFA). Further investigations revealed that three motivational constructs, including Demotivation/ Motivating Demotivation, Perfectionist/Non-perfectionist motivation, and Collective/Individual motivation could predict almost 56% of the variance in students’ grade point averages. Finally, the results were discussed in the context of second/foreign language education, and some suggestions were proposed to improve the quality of language education.

1. Introduction Motivation is the most frequently used term accounting for the success or failure of any complicated task (Brown, 2000). It is defined as the sum of the incentives that positively force the choice of a specific behavior or purpose (Brophy, 1983; Dörnyei, 2001; Jarvis, 2005). Demotivation, as the counterpart of motivation, is defined broadly as “various negative influences that cancel out existing motivation,” and more specifically as “external forces that reduce or diminish the motivation basis of behavior, intention, or an ongoing action” (Dörnyei, 2001, p.143). Academic motivation refers to motivation especially related to academic functioning and success (Schunk, 2008). It is defined as the tendency of a student “to find academic activities meaningful and worthwhile and to try to derive the intended academic benefits from them” (Brophy, 1998, p. 205–206). Deci and Ryan (1985), 2000 & 2008) suggested that academic motivation encourage students to engage in learning tasks more effectively and pursue their academic goal. On the importance of academic motivation, several empirical findings suggested that there is a significant association between students’ motivation and their academic performance (Busato, Prins, Elshout, & Hamaker, 2000; Pintrich, 2003; Rotgans & Schmidt, 2012; Sogunro, 2014). Research studies have identified numerous factors, such as psychological, social, and cultural have significant effects on students’ motivation. These factors are proposed by different cognitive and behavioral theories of motivation and include, but not limited to, individuals’ beliefs and styles of information processing (Bandura, 1986), the expectation of success (Vroom, 1964), learners’ goals (Atkinson, 1974; Dweck, 1986), self-acceptance and believing in one’s own ability (Covington, 1992; Covington & Roberts, 1994), cultural values and meaning systems (Maehr & Nicholls, 1980), and external sources such as teachers and instructors (McIntyre, 1998). ⁎

Corresponding author. E-mail addresses: [email protected] (S. Zeynali), [email protected] (R. Pishghadam), [email protected] (A. Hosseini Fatemi).

https://doi.org/10.1016/j.lmot.2019.101598 Received 27 August 2018; Received in revised form 20 September 2019; Accepted 23 September 2019 0023-9690/ © 2019 Elsevier Inc. All rights reserved.

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Students’ performance in academic contexts can be operationalized by employing grade point average (GPA) as the indicator of students’ academic achievements (Steinmayr & Spinath, 2009). According to comprehensive research on the best way to measure academic success, it has been found that grades and the GPAs are the most commonly used measures of academic achievement (York, Gibson & Rankin, 2015). So far, numerous theories of motivation have examined the effects of different factors such as learning environment or individual characteristics on motivation (Ryan & Deci, 2000). However, there is a lack of comprehensive study, which deeply explores the motivating and demotivating factors influencing students majoring in EFL. Since the individuals’ success or failure in their academic performance is a major concern of the educational system, identifying the significant factors that affect students’ motivation during their courses of study is a promising research area. It is assumed that the findings of the previous studies on motivation/demotivation do not apply to and are not representative of EFL education. Since language, motivation, and culture are inherently interconnected, there is a need to consider the society and culture into account in designing a motivational/demotivational framework related to language education. English education inescapably conveys culture and all its components like ideology, values, and customs (Pishghadam & Naji Meidani, 2011), and consequently, require different models of motivation. Many of the remarkable motivational models have not considered these factors into consideration. For instance, Gardner (1985), whose theories are dominant in the field of language education, overlooked the impact of the societal factors on students’ motivation (Gu, 2009). We also found that some significant cultural concepts to be lacking in previous motivational models mainly proposed by western researchers. In reality, models of motivation are primarily in line with individualistic, low-context western cultures; hence, there is a need to consider a collective, high-context eastern society in designing and proposing a comprehensive motivational model. Iran, like other Asian countries, has mostly a collective culture (Hofstede, 1986), which means people are fundamentally interdependent. In this regard, we proposed the dichotomy of collective/individual motivation in our model that represents the role of culture and context in shaping students’ motivation. Moreover, the potentially different motivating and demotivating factors of graduate versus undergraduate university students need to be addressed to propose a holistic motivational /demotivational model in higher education. Although several researchers have focused on students’ motivation in colleges and universities at the undergraduate level (e.g., Malacinski & Winterman, 2012), fewer studies targeted the graduate level (e.g., Earl-Novell, 2006). To our best knowledge, no research has been done to comprehensively explores both levels. Moreover, MA and Ph.D. programs require discrete attention and consideration due to their different structures and perspectives. The present study proposes several motivational influences that are fundamental in graduate and undergraduate EFL courses. In the following sections, the motivation-related theoretical framework of the study is presented to establish a good understating of significant notions and theories of motivation in the context of EFL. Demotivation has several possible sources that have been examined by some researchers. These sources can be the lack of selfconfidence, teachers’ personalities or methods, style conflicts between teacher and students, and course materials (Dörnyei, 2001; Oxford, 1998). Motivating demotivation, introduced in the current study, refers to the factors that are generally considered demotivating but can cause potential challenges that increase certain students’ motivations. Although they are not necessarily challengeseeking people or they may not have an interest in complicated tasks, those individuals, who are strong in motivating demotivation, remain motivated facing with demotivating situations. Those people can make the most of their means and believe in their own agency in coping with undesired conditions. Furthermore, they do not permit dilemmas to make them demotivated and try to find the best solution for their problems. McClelland (1966), in a pioneering study, proposed that there was a psychological dichotomy in the world, including a small group of people who confront difficulties with persistence and positive attitudes, along with a large group of people who do not persist in their goals and give up effortlessly. He spoke of the minority as people who think how to get improved and do the thing better in difficult situations rather than leaving things the way they are or quitting. Pintrich and Schunk (2002) suggested that students with a higher level of motivation tend to engage in more difficult tasks. They do not give up effortlessly when encountering difficulties and persist in undesired situations; as a result, they increase their academic achievement. Motivating demotivation construct is different from McClelland’s dichotomy since it is not just a psychological or personal trait. In fact, this construct not only explains different attitudes and behaviors of the individuals in struggling with demanding, undesired, potentially demotivating situations but also calls for the ability to manipulate effective strategies and regulations skillfully in various circumstances. Motivating demotivation may be potentially related to attributing success and failure to unstable factors, such as effort rather than ability and believing in an internal locus of control instead of an external one, according to attribution theory (Weiner, 1992). It also could be associated with self-efficacy as it is believed that efficacy is the major determinant of effort, persistence, and goal setting (Bandura, 1982). It could be linked to goal-setting theory in some measure (Dörnyei, 1988) as it implies difficult, specific goals may result in a high level of individual’ commitment. The dichotomy of the collective/individual is a well-known concept in the field of sociology and several others. It is made known to the domain of motivation a number of times (Ishii, Mojaverian, Masuno, & Kim, 2017; Morling & Kitayama, 2008); yet, motivation theorists believe that social factors like this have often been underestimated in motivation studies (Murphy & Alexander, 2000; Schunk, 2000). Previous studies indicated that in many cultures “adult approval” and “peer approval” are important determinants of students’ academic achievement (Elliott & Bempechat, 2002; Trumbull, Rothstein-Fisch, Greenfield, & Quiroz, 2001; Urdan & Maehr, 1995). In this regard, Fuligni (2001) stated that in certain cultures, "sense of obligation to the family" has a major role in determining students' motivation (p. 61). Yu (1974) found that Chinese students perceived achievement as their responsibility and obligation to the family and network and had collective motivation rather than an individual one. In other studies (Wilson & Pusey, 1982; Blumenthal, 1977), 2

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it was revealed that Chinese culture stressed sharing the benefits of individual achievements with the community and setting collective goals rather than individual goals. In fact, the Chinese achievement motivation reflected in their desire to fulfill the expectations of family and society. In the present study, collective/ individual motivation, as a construct, measures the significant influences of family, peers, and society on students' motivation. The concept of "self" is substantial in collective/ individual dichotomy. In collective perception, all values are based on being self-less (Kramer & Brewer, 1984). In Dörnyei (2005, 2009) the concept of self has three main dimensions. They are ideal L2 self, ought-to L2 self, and L2 learning experience. The concept of ought-to self can be related to collectivism since family influences, and social expectations decide it. According to the findings of the previous studies, the fulfillment of ideal L2 self correlated with higher achievement in language education (Ghapanchi, Khajavy, & Asadpour, 2011; Kim & Kim, 2014; Rajab, Far, & Etemadzadeh, 2012). Moreover, previous studies reported that there is a negative relationship between Ought-2 self and achievement (Islam, Lamb, & Chambers, 2013; Subekti, 2018). Studies also indicated that the ought-to L2 self was particularly significant in Asian countries such as Iran and China, where collective culture was dominant, and families influenced individuals’ motivation to a great extent (Lockwood, Marshall, & Sadler, 2005; Markus & Kitayama, 1998). This finding is significant in the connection between Dörnyei’s ought-to self and collective motivation, presented by the current study. Another theory of motivation that could be related to collective/individual motivation is self-determination (Deci & Ryan, 1985). Independence or autonomy, the core of self-determination theory and one of the three basic human need, is regarded as the characteristic of individual-oriented people. Autonomy potentially leads to positive outcomes in education (Vansteenkiste, Simons, Lens, Sheldon, & Deci, 2004). Given these arguments, it is possible to conclude that individual motivation is more desirable, compared to collective motivation concerning the educational context. Perfectionism, as a major personality construct, has been studied in psychology since the mid-nineteenth century (Adler, 1956; Mills & Blankstein, 2000). In the realm of education, perfectionism is among the most important variables influencing academic achievement (Stoeber & Rambow, 2007; Witcher, Alexander, Onwuegbuzie, Collins, & Witcher, 2007). It has been suggested that selforiented perfectionism that refers to an individual’s attempt to fulfill their capacities is closely related to academic engagement (Slade & Owns, 1998). The associations of perfectionism with motivation and achievement have been already mentioned by some researchers (Gardner, 2007; Lovitts, 2005). Blankstein and Winkworth (2000) suggested that a perfectionist has a higher level of motivation in whatsoever he/she does. In a study, Blasberg (2006) reported that a learner’s motivation is positively related to perfectionism. Accordingly, perfectionism is a better personality trait compared to non-perfectionism concerning students’ motivation. In the same vein, Chen, Kuo, and Kao (2016) found that both intrinsic and extrinsic motivations were meaningfully associated with perfectionism in the EFL context. However, there is no scale designed for measuring perfectionism as an indicator of the student's motivation. According to Hewitt and Flett (1991), perfectionist students assume they need to complete their homework perfectly or have to perform in their examinations glowingly. More significantly, they believe that they have to achieve the top level of education. Based on the results of previous studies, perfectionism in educational contexts result in higher motivation and is a preferable personality trait compared to non-perfectionism. Despite a few studies that have been mentioned, the relationship between academic motivation and perfectionism is not adequately addressed in motivational research, and the present study tries to provide an answer to this gap Motivation is categorized as either intrinsic or extrinsic following the reasons or goals which reinforce an action or an accomplishment (Ryan & Deci, 2000). Intrinsic motivation is the feeling and enthusiasm to be involved in some certain activities because an individual believes that those activities are interesting and enjoyable. In other words, intrinsic motivation arises from within a person as a result of coping with intrinsically rewarding situations (Pintrich & Schunk, 1996). Extrinsic motivation, on the other hand, is the inclination to take part in an activity for other reasons that are not linked to the activity itself. It is directed by reinforcement contingencies and can be manipulated by the provision of rewards, which can be either tangible (e.g., money, grades, or advantages) or intangible (e.g., praise) (Harter, 1978; Noels, 2003; Pintrich & Schunk, 1996). Conventionally, intrinsic motivation has been more desirable to educators and contributes to better learning outcomes than extrinsic motivation. Intrinsic motivation correlates positively with better test performance and more in-depth processing of concepts (Vansteenkiste et al., 2004). It has also been linked to higher grades and achievement at school (Grolnick & Ryan, 1987; Miserandino, 1996), and better academic performance (Black & Deci, 2000). On the contrary, extrinsic motivation has been associated with adverse learning outcomes (Grolnick & Ryan, 1987; Vansteenkiste, Lens, & Deci, 2006). In the context of education, cooperation refers to working with peers and colleagues to accomplish shared educational goals. Within cooperative situations, individuals seek outcomes that are beneficial to themselves as well as all other group members. Cooperative learning is an instructional use of small groups so that students work together to maximize their own and each other's learning (Johnson & Johnson, 1996). There have been several studies carried out to examine the relationship between cooperation and motivation in academic environments (Courtney, Courtney, & Nicholson, 1994; Slavin, 1983). These studies denoted that studying in a cooperative environment increased students’ motivation significantly. One of the aims of the present study is to investigate the potential relationships among the motivational constructs introduced in this section. For instance, the perfectionist/non-perfectionist construct may be correlated with motivating demotivation since both of them appeared to have common features with the well-established theory of achievement motivation (Atkinson & Feather, 1966; Atkinson, 1974). Achievement motivation model conceptualizes that if a student has the prospect of success in an activity, he/she will be more enthusiastic about involving in such an action. On the contrary, students who do not expect to succeed do not tend to participate in the task. In educational and academic settings, some students tend to succeed in everything they do, while others tend 3

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to evaluate the task before making an effort. This fulfillment tendency is achievement motivation. Previous literature of motivation suggested that achievement motivation is positively correlated with students’ GPA. It helps students to perform better and maximize their potentials (Richardson & Abraham, 2009). Furthermore, Blasberg (2006) observed a positive relationship between perfectionism and achievement motivation. There is also a potential relationship between motivating demotivation and intrinsic motivation. Intrinsically motivated behaviors include being open to challenges and taking risks (Elliot & Harackiewicz, 1996). These features are similar to motivating demotivation behaviors which involve being inherently responsible for one’s own learning and improvement and not being easily affected and demotivated by surrounding environment and people (see also Lengnick-Hall & Sanders, 1997). We introduced five constructs that are the most significant motivational and demotivational factors in English education, grounded on the qualitative data collected in the present study and review of previous literature. Based on these factors, we designed the Motivational/Demotivational Pattern Scale (MDPAS) to answer the following research questions: 1 2 3 4 5

Does the Motivational/Demotivational Pattern Scale (MDPAS) enjoy the construct validity? Are there any significant relationships between the MDPAS constructs? Are there any significant differences among Ph.D., MA, and BA students regarding the constructs of MDPAS? Is there any significant difference between male and female students regarding the constructs of MDPAS? Can any of the five constructs of MDPAS predict students’ GPA?

2. Methodology 2.1. Setting and participants This study was conducted on 800 students (plus 10 students in the pilot study) majoring in EFL in Iran. In the piloting stage, ten graduate and undergraduate students majoring in EFL were selected to be interviewed. The participants were recruited based on their GPAs. Regarding the objectives of the present study, half of the participants had the highest GPAs in their classes, and the other half had the lowest, to represent overachievers and underachievers. The quantitative data were generated in two stages in winter and spring 2016. In the first stage, the Motivational/ Demotivational Pattern Scale (MDPAS) was administered to EFL graduates and undergraduates to measure the scale’s psychometric properties and substantiate its construct validity (see Appendix A in Supplementary material). Four hundred students (80% female and 20% male) participated in this stage. Convenience sampling was applied to recruit accessible and willing students. In the second stage, a revised and validated version of the MDPAS was administered to the participants. Convenience sampling was used to include 400 students (74% female and 26% male). The participants were all majored in EFL in BA, MA, and pH.D. levels. 2.2. Instrumentation Motivational/Demotivational Pattern Scale (MDPAS), had been developed for the purpose of this study, was used as the research instrument. This scale consisted of five constructs. These constructs were decided with respect to the themes and information emerged in the semi-structured interviews in the piloting stage and also existing literature reviews (Zeynali, Pishghadam, & Hosseini Fatemi, 2019, in press). The scale consisted of 36 items which were developed on a 6-point Likert-scale. 2.2.1. The emerged themes Based on the findings of the pilot study and covering the related literature, the following themes were presented: 1 Demotivation/Motivating Demotivation: those who score higher in motivating demotivation, are open to challenges and see the demotivational factors or situations as opportunities to work harder and make better progress (questions 1, 5, 21, 25, 30, 36). 2 Collective/ Individual Motivation: those who score higher in collective motivation tend to be affected more easily by others, and their motivation highly depends on other people’ feedback (questions 7, 9, 12, 17, 20, 23, 32, 35) 3 Perfectionist/non-perfectionist Motivation: this construct measures the extent to which students seek perfection in their studies and desire to be the best in everything they do, including their educational lives (questions 2, 10, 15, 16, 19, 22, 31). 4 Intrinsic/ Extrinsic Motivation: students who are intrinsically motivated find educational activities enjoyable and interesting, while those who are extrinsically motivated consider education as a means to an end (questions 3, 8, 11, 14, 18, 27, 28, 34). 5 Cooperative/Competitive Motivation: those who score higher in cooperativeness tend to collaborate with their classmates, value interaction, and prefer group work. Those who score higher in competitiveness, struggle to achieve competitive advantages (questions 4, 6, 13, 24, 26, 29, 33). 2.3. Data collection In the piloting stage, the semi-structured interviews were conducted to gain a deeper understanding of students’ motivating and demotivating factors in graduate and undergraduate programs with the purpose of designing the MDPAS. The questions were on motivating and demotivating factors in education. It took almost 20 minutes for each participant to be interviewed. The final constructs were decided by the researchers concerning the themes and information emerged in the literature review and semi-structured 4

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Table 1 Descriptive Statistics of the Participants. Ph.D. Degree

MA Degree

BA Degree

Female

Male

Total

3 55 45

3 73 75

4 272 280

7 320 296

3 80 104

10 400 400

Interview (Piloting) First Stage Second Stage

interviews in the piloting stage. After preparing the final version of the scale, 800 graduate and undergraduate students (400 in each stage) majoring in EFL were asked to fill out the MDPAS during the class hours by prior arrangement with the teachers. One of the researchers was present during all the data collection sessions. While handing out the scale, participants were asked to include their GPAs and some related demographic information concerning age, gender, academic major, and level of education. In order to employ the self-reported data more safely, students were assured that accuracy was the researchers’ primary interest and anonymity is guaranteed. As a result, it was expected that students' self-reported GPA scores were remarkably similar to their official records. The data has been obtained from 10% extra participants in the second stage in order to avoid missing value problem. In fact, in the first stage of the study, we had no missing data, but there was some missing data in the second stage of the research. To handle the problem, since we had access to a large population, instead of using imputation or model-based estimation of missing values we preferred to use listwise deletion and ask more participants to take the scale to come up with the same number of participants in both stages of the study (N = 400). Furthermore, since two phases of the study were independent, participants were also recruited independently. 2.4. Data analysis The collected data was first imported to SPSS, and descriptive statistics were used to display the mean scores and the standard deviations of the constructs. Independent t-tests were also run to examine the probable differences between variables in both stages of the study. To substantiate the construct validity of the designed scale, confirmatory factor analysis (CFA) was conducted by applying structural equation modeling (SEM) using AMOS. Then, to test whether the effects in this model are invariant for the three groups, multi-group confirmatory factor analyses (MGCFA) were run. Finally, to address the research questions of the study, some statistical tests, including correlational analyses, one-way analyses of variance (ANOVA), independent t-tests, and stepwise multiple regressions were employed (Table 1). 3. Results 3.1. Descriptive statistics and comparison of two samples Descriptive results, along with t-test analyses for all variables, are presented in Table 2. The table shows the mean values of the constructs for two samples. For each construct, these values are the mean of the sum of the scores of its all questions. As Table 2 shows, based on the t-tests analyses, all variables in both stages of the study do not differ significantly between both samples. In fact, p-value > 0.05 suggested that the difference between two samples is not statistically significant. Moreover, alpha levels demonstrate that the scales at both stages were reliable. 3.2. CFA of models To validate the designed scale and confirm the factor structure already determined by the researchers, CFA was utilized based on 2678.20 SEM. According to Table 3, x2/df is 1112 = 2.408, and since it is in the range of 1–3, it is acceptable. RMSEA (0.059) is less than 0.08, and SRMR (0.04) is less than 0.05, so they are also acceptable. NFI, GFI, IFI, and CFI are all above 0.9 and fair (Hopwood & Donnellan, 2010; Hu & Bentler, 1999). Therefore, the result of the CFA indicates that all the goodness-of-fit indices are above the cutoff points, and the five-factor structure of MDPAS is confirmed. Table 2 Descrptive Statistics for the Variables and Comparison Results. Variables

Mean (SD)*

Mean (SD)**

t value

P

Alpha*

Alpha **

1. 2. 3. 4. 5.

16.3 32.2 22.3 17.7 12.5

15.9 33.5 21.7 17.9 13.1

.08 .09 .07 .04 .06

.00 .00 .00 .00 .00

.89 .81 .90 .92 .88

.90 .85 .93 .90 .89

Demotivation/Motivating Collective/ Individual Perfectionist/Non-perfectionist Intrinsic/ Extrinsic Cooperative/Competitive

(5.2) (7.6) (7.3) (4.5) (3.5)

(4.5) (7.7) (6.9) (5.4) (3.8)

* Note. First stage: n (BA) = 272, n (MA) = 73, n (Ph.D.) = 55. ** Note. Second stage: n (BA) = 280, n (MA) = 75, n (Ph.D.) = 45. 5

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Table 3 Goodness-of-fit Indices for Motivational/Demotivational Pattern Scale. CFI

IFI

NFI

GFI

SRMR

RMSEA

χ2/DF

Models/Indices

0.67 0.69 0.77 0.65 0.93

0.65 0.62 0.73 .052 0.92

0.71 0.79 0.83 0.77 0.91

0.43 0.66 0.75 0.69 0.92

0.13 0.16 0.13 0.15 0.04

0.11 0.16 0.12 0.14 0.05

4.65 4.75 3.59 4.26 2.40

1-factor 2-factor 3-factor 4-factor 5-factor

The sample size was large enough to claim that all questions factor loadings were in the suitable ranges and not problematic, though some of these factor loadings were minimal according to Hair, Black, Babin, and Tatham (2006) guidelines for identifying significant factor loadings based on the sample size. Moreover, it has been assured that the rest of the items, which are more than 3, for each construct have high factor loadings (Guadagnoli & Velicer, 1988). To ensure the validity of the scale, the 5-factor solution were compared against alternative models: the 1-factor, 2-factor (Intrinsic/Extrinsic/ Demotivation/Motivativational demotivation and other sub-constructs), 3-factor (Perfectionist/Non-perfectionist, Intrinsic/Extrinsic/ Demotivation/Motivating demotivation, and Cooperative/Competitive/Collective/Individual), and 4factor (Perfectionist/Non-perfectionist,Intrinsic/Extrinsic, Demotivation/Motivating demotivation, and Cooperative/Competitive/ Collective/Individual) solutions. As Table 3 reveals, none of the fit indices can be considered as significant, hence confirming the accuracy of the five-factor model. Furthermore, the factor loadings of the proposed model were reported (see Appendix B in Supplementary material). The outcomes show that all items measure the five sub-constructs (Demotivation/Motivating motivation, Collective/Individual motivation, Perfectionist/ Non-perfectionist motivation, Intrinsic/Extrinsic motivation, Cooperative/Competitive motivation), which are positively and negatively related to each other. To examine if motivation is independent across different groups of students, we employed the MGCFA. Using a set-up approach, we estimated models for the three groups of students (BA, MA, and Ph.D.). The following models were compared: models in which the item-factor clusters (configural invariance), the factor loadings (metric invariance), the item intercepts (scalar invariance), the residual variances (strict invariance), and the correlations between the latent variables (complete invariance) were restricted to be equivalent for all three groups (Gregorich, 2007). As Table 4 shows, the –2ΔLL difference tests are not statistically significant, and ΔCFI and ΔRMSEA are under the acceptable cutoff score of ΔCFI = .02 and ΔRMSEA = .015 (Chen, 2007), implying measurement invariance across the groups (Daumiller, Dickhäuser, & Dresel, 2018). 3.3. Correlational analyses As Table 5 reveals, all variables are signifcantly correlated with each other. While demotivating/motivating and collective/ inidvidual motivation (r = .67), demotivating/motivating and perfectionist/non-perfectionist motivation (r = .49), collective/inidvidual and perfectionist/non-perfectionist motivation (p = .52), and intrinsic/extrinisc and cooperative/copetitive motivtion (r = .49) are positively correlated, demotivating/motivating and intrinsic/extrinisc motivation (r = -0.44), demotivating/motivating and cooperative/copetitive (r = -.63), intrinsic/extrinisc motivation motivtion (r = -.51), collective/inidvidual and cooperative/ copetitive motivtion (r = -.36), perfectionist/non-perfectionist and intrinsic/extrinisc motivation (r = -.67), and perfectionist/nonperfectionist and cooperative/copetitive (r = -.54) are negatively correlated. 3.4. Comparison of means In order to see whether there is any significant difference among Ph.D., MA, and BA students regarding the constructs of MDPAS, ANOVA was conducted. The results of the Levene’s test on the data show that the variances in three constructs are not equal, and for two remaining constructs are equal. Therefore, three constructs needed to be examined using the Welch Test and Games-Howell Test, and the other two constructs could be considered by applying one-way ANOVA and the Tukey HSD post hoc test. Table 4 Results of Measurement Invariance Testing. Model

X2/df

CFI

TLI

RMSEA

SRMR

ΔCFI

ΔRMSEA

TRd

Δdf

p

Configural invariance Metric invariance Scalar invariance Strict invariance Complete invariance

2.13 2.17 2.15 2.14 2.13

.92 .92 .91 .92 .93

.91 .93 .91 .92 .91

.04 .04 .04 .04 .04

.04 .05 .04 .06 .05

.00 .00 .00 .00

.00 .00 .00 .00

55.65 66.51 65.58 126.53

60 80 80 90

.08 .24 .09 .10

Note. n (BA) = 272, n (MA) = 73, n (Ph.D.) = 55. 6

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Table 5 Correlational Analyses for the Variables. Variables

1

2

3

4

5

1. 2. 3. 4. 5.

1 .67** .49** −.44** −.63**

1 .52** −.51** −.36**

1 −.67** −.54**

1 .49**

1

Demotivation/Motivating Collective/ Individual Perfectionist/Non-perfectionist Intrinsic/ Extrinsic Cooperative/Competitive

** p < .01.

3.4.1. Welch Test and Games-Howell Test The mean differences among the three constructs were examined using the Welch test. According to this test, there are significant differences among the three groups of Ph.D., MA, and BA students in terms of demotivation/motivating demotivation with F (2, 130.854) = 56.491, p < .05 and cooperative/competitive with F (2, 108.258) = 3.385, p < .05 , but there is no significant difference among the three groups concerning perfectionist /non- perfectionist with F (2, 106.743) = 0.040, p < .05. As for the demotivation/ motivating demotivation construct, the result indicates Ph.D. > MA > BA. Likewise, for the cooperative/competitive construct, the result is BA > Ph.D. / MA. 3.4.2. One-way ANOVA and Tukey HSD test According to the results of One-way ANOVA, there exists a statistically significant difference at the p < . 05 level among the three groups of students in terms of collective/individual motivation (F (2,397) = 55.203, p < . 05, 2 = .217 ). The effect size is large, which means the actual difference in the mean scores of the groups is considerable (Cohen's, 1988). Considering the extrinsic/ intrinsic motivation, there exists a statistically significant difference (F (2,397) = 6.597, p < . 05, 2 = . 03). The result of the Tukey post hoc test indicates that for the collective/individual construct, BA > Ma > Ph.D. and for the extrinsic/intrinsic construct, MA > Ph.D. > BA. 3.5. Independent-samples t-tests Independent-samples t-tests were applied to explore gender differences regarding the five constructs of the study. For demotivation/motivating demotivation, the result of the independent t-test indicates that there is a significant difference between female (M = 16, SD = 5.1) and male (M = 14, SD = 5.1) students (t = 3.0, sig = 0.002, p < 0.05). Regarding collective/individual motivation, the result of independent t-test displays that there is a significant difference between female (M = 33, SD = 7.4) and male (M = 28, SD = 7.6) students (t = 4.3, sig = 0.000, p < 0.05). The mean differences between female and male groups were not significant concerning the other constructs of the study. 3.6. Stepwise multiple regression The current study utilized stepwise multiple regression analysis to see whether any of the five constructs of MDPAS can predict students’ GPAs. Table 6 indicates that the three predictors of demotivation/motivating demotivation, perfectionist/non-perfectionist motivation, and collective/individual motivation are included in the analysis. The other variables did not pass the entry test of an F with the associated probability level of 0.05. The model summary in Table 7 shows that there are three models, Model 1 (in the first row), Model 2 (in the second row), and Model 3 (in the third row). First, a model with demotivation/ motivating demotivation as a predictor is tested, and then the predictor of perfectionist/non-perfectionist motivation is added, and model 2 is tested. Finally, the construct of collective/individual motivation is included in model 2 and model 3 is checked. In the first model, almost 50% of the variance of the GPAs can be predicted from the demotivation/ motivating demotivation so that it can be a strong predictor of the students’ GPAs. Moreover, the standard error of estimate shows the accuracy of the prediction model. Regarding the second model, the first and second variables together (i.e., demotivation/ motivating demotivation and Table 6 Entered/Removed Variables Resulting from Multiple Regression Analysis. Model

Variable Entered

Variable Removed

Method

1

Demotivation/Motivating Demotivation

.

2

Perfectionist/non-perfectionist Motivation

.

3

Collective/Individual Motivation

.

Stepwise (Criteria: Probability-of-F-to-enter < = .050, Probability-of-F-to-remove > = .100). Stepwise (Criteria: Probability-of-F-to-enter < = .050, Probability-of-F-to-remove > = .100). Stepwise (Criteria: Probability-of-F-to-enter < = .050, Probability-of-F-to-remove > = .100).

Dependent Variable: GPA. 7

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Table 7 Model Summary of Regression Analysis for GPA. Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

R Square Change

F Change

df2

Sig. F Change

1 2 3

.71a .74b .75c

.50 .55 .56

.50 .55 .56

.95 .90 .90

.50 .05 .00

410.59 44.60 4.46

396 395 394

.00 .00 .03

a b c

Predictors: (Constant), demotivation/motivating demotivation. Predictors: (Constant), demotivation/motivating demotivation perfectionist/non-perfectionist motivation. Predictors: (Constant), demotivation/motivating demotivation, perfectionist/non-perfectionist motivation, collective/individual motivation.

perfectionist/non-perfectionist motivation) can account for 55% of the variance of the GPAs. Also, the standard error of estimate for the second model is 0.904, which is an acceptable value. According to the result of R Square Change, perfectionist/ non-perfectionist motivation is a week predictor of GPA (5%). In the third model, 56% of the variance of the GPAs can be predicted from the three variables of demotivation/ motivating demotivation, perfectionist/non-perfectionist, and collective/individual; so, it is a strong predictor of the GPAs. However, collective/individual motivation is a very minimal predictor of GPA (0.5%), based on the result of R Square Change. The third model is the strongest predictor in comparison with the first and second models regarding the adjusted R2 value that has increased from 0.55 in the second model to 0.56 in the third model. The standard error of the estimate also confirms the accuracy of the prediction model. 4. Discussion and conclusion 4.1. Construct validity of MDPAS and relationships among MDPAS constructs This study was conducted to propose the most significant motivational and demotivational factors in English education. For this purpose, a motivational/ demotivational pattern scale (MDPAS) was designed and validated. The final version of the scale is a fivefactor model with thirty-five questions. The results of the goodness-of-fit indices showed a sufficient fit to the data and confirmed the five-factor structure of MDPAS. Although the cutoff values of CFI/TLI 0.95 would be more desirable (Hu & Bentler, 1999), CFI above 0.90 or close to 0.95 indicates a good fit (Hopwood & Donnellan, 2010). As a result, the current model is considered acceptable. Moreover, based on the literature review and theoretical background of the study, the 5-factor structure underlying the data was the best solution, and there were not theoretically sensibly improvements possible. On such grounds, in answering the first question of the present study, it is possible to claim that MDPAS can be considered as an efficient scale in measuring students’ motivation and demotivation. Moreover, measurement invariance test was applied to ensure that group comparisons on scale scores are valid, and the motivation measured in this study is not related to group membership. Results provided evidence that motivation was independent across three specified groups of the study, BA, MA, and Ph.D. 4.2. Differences among Ph.D., MA, and BA students regarding the constructs of MDPAS Concerning the second research question of the study, the relationships between all variables were examined. The results showed that there was a positive relationship between demotivation/motivating motivation and collective/individual motivation. This finding is justifiable because collective motivation deals with group comparisons, which can be considered to be part of motivating demotivation. The relationship between demotivation/motivating motivation and perfectionist/non-perfectionist was positively significant. As perfectionists like to succeed under all circumstances, it is quite predictable that they follow their goals in the case of facing any demotivating factor (Frost, Marten, Lahart, & Rosenblate, 1990). Moreover, the relationship between demotivation/motivating motivation and intrinsic/extrinsic motivation was negatively significant. This outcome can be justified if we accept the discouraging factors will not easily influence students who are intrinsically motivated during their course of education. They are inherently responsible for their learning and improvement and considered themselves as the agents of their academic lives. Demotivation/motivating motivation and cooperative/competitive motivation were also negatively correlated. It means that individuals who are open to challenges are more likely to compete with others. Further, collective/individual motivation and perfectionist/non-perfectionist motivation were positively associated. From a psychological point of view, perfectionism is a multifaceted construct (Shafran, Cooper, & Fairburn, 2002; Stoeber & Stoeber, 2009). A perfectionist person can be socially-prescribed and tries to live up to others’ expectations (Hewitt & Flett, 1991). The present study suggested that socially-prescribed perfectionism could be connected to collective motivation since it underlined the desire to please others. Additionally, the association between collective/individual motivation and intrinsic/extrinsic motivation was found to be negative. According to self-determination theory (Deci & Ryan, 1985), the sense of autonomy and, which is the basis of the individualistic mindset leads to intrinsic motivation. It implies that intrinsically motivated learners do not rely on group confirmation to be involved in academic tasks. In the same vein, but from another perspective, collective/individual motivation and cooperative/ 8

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competitive motivation are negatively related. This finding is also predictable because group-oriented individuals are more competitive than cooperative (Takemura & Yuki, 2007). Perfectionist/non-perfectionist motivation and intrinsic/extrinsic motivation are negatively correlated. The findings of the present study are inconsistent with Chen et al. (2016), who found that both intrinsic and extrinsic motivations were significantly and positively associated with perfectionism in the EFL context. The relationship between perfectionist/non-perfectionist motivation and cooperative/competitive motivation was negative. This finding can be justified if we accept that perfectionists like to achieve their ends at any cost, hence compete with others. Finally, a negative relationship was reported between intrinsic/extrinsic motivation and cooperative /competitive motivation. Since intrinsically motivated learners enjoy doing tasks, they are less likely to compete with others. Furthermore, it is possible to conclude that having extrinsic motivation encourages rivalry atmosphere in classes. 4.3. Significant difference between male and female students regarding the constructs of MDPAS Regarding the third research question of the study, the differences among Ph.D., MA, and BA students were investigated for the five constructs of MDPAS. The outcomes suggested that there were significant differences among the three groups of Ph.D., MA, and BA students in terms of demotivation/motivating demotivation. For most of the Ph.D. students, we found that the factors generally considered demotivating such as the poor performance of professors in the classrooms, heavy assignments, or underprivileged learning conditions, were considered more motivating rather than demotivating. These demotivating factors were mainly regarded as the obstacles to be overcome and the problems to get resolved. The second construct of MDPAS is collectivist/individualist motivation. Based on the results, students had more collective orientations in the undergraduate program. Quite the reverse, they had stronger tendencies towards individualism in the graduate level. There are some possible explanations for this difference. According to Todt et al. (1990), graduate students develop a stronger selfconcept, and consequently, they are less likely to be affected by others’ opinions and feedback. As for the intrinsic/extrinsic construct of MDPAS, results indicated that students tended to have more intrinsic motivation in BA education. This type of motivation was replaced by extrinsic one in the MA level, and finally, both intrinsic and extrinsic motivation were prevalent among Ph.D. students, and no significant difference was observed between the Ph.D. group and either MA or BA groups in this regard. One reason for the stronger intrinsic motivation in BA students was their interest in the subject matter they were studying. They might be less concerned about the job, economic issues, and other external factors compared to graduate students. The justification for the stabilization of intrinsic motivation in Ph.D. students could be related to their autonomy in choosing what they wanted to study or course contents. Although there were the arranged topics by the professors and programs which they needed to consider, they were free to choose the relevant sources which they realized more exciting and valuable. In choosing their research areas, they were also self-directed to a certain degree. These factors enhanced the congruence between personal interest and learning contents. According to the tenets of the self-determination theory (Deci & Ryan, 1985), a sense of autonomy leads to intrinsic motivation. However, Ph.D. students were also extrinsically motivated in their course of studies. Likewise, instrumental reasons were considered the primary source of MA students’ motivation towards perusing their degree. One reason explaining graduate students’ extrinsic motivation appears to be the economic issues and career orientations (Bair & Haworth, 1999; Clark, 2007). In terms of cooperative/competitive motivation, another construct of MDPAS, the BA group was the most cooperative, and the Ph.D. /MA groups were the most competitive. The result of the study indicated that peers and classmates appeared to be influential in numerous ways in the BA level. Classmates and friends’ communities were able to offer a social environment that allows students to collaborate and function to the best of their abilities. In this regard, several researchers support the claim that cooperation increases student motivation (Courtney et al., 1994). Moreover, since the MA and Ph.D. students are prone to find their future jobs, they need to be more competitive due to the limited job market. 4.4. The relationships among five constructs of MDPAS and students’ GPA Regarding the fourth research question, an independent t-test was conducted to see whether there was any significant difference between male and female students with respect to the constructs of MDPAS. For the first construct, demotivation/motivating demotivation, the result indicated that female students were more likely to get demotivated in facing with problematic and undesirable situations compared to male students. Concerning the collective/individual construct of the present study, the result indicated that female students had more collective tendencies compared to male students. The psychology literature suggests that men are less likely to be affected by other people’s ideas and opinions (Basow & Rubenfeld, 2003). Accordingly, women take negative feedback more than men do (Roberts & Nolen-Hoeksema, 1994). With respect to extrinsic/intrinsic motivation, no significant difference was observed between male and female students. The results of previous studies in the context of higher education are controversial in this respect. While some studies suggest that female students have more intrinsic tendencies (Schatt, 2011), others proposed male students are more intrinsically motivated (Shang, 1998). Regarding other constructs of the present study, no significant difference was found between male and female students. Concerning the fifth research question, the outcome of this study indicated that demotivation/ motivating demotivation, perfectionist/non-perfectionist motivation, and collective/individual motivation constructs could significantly predict students’ GPA. Three models are proposed based on the results of stepwise multiple regression analysis and explained as follows. In the first model, almost 50% of the variance of the students’ GPAs can be predicted from the demotivation/ motivating demotivation construct. This construct is a strong predictor of the students’ GPAs. The demotivation/ motivating demotivation, which is introduced by the present study, is a potentially influential innovation in motivational studies considering EFL education. In the 9

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context of education and particularly language education, the result of the present study suggests that this construct is the strongest predictor of students’ academic performances. In this regard, graduate students are co-producers in the educational system because macro-level demotivating and micro-level discouraging factors cannot influence them anymore. They are agents of their success, and inherently responsible for their improvements. Moreover, they are not easily affected and demotivated by the surrounding environment and people (Lengnick-Hall & Sanders, 1997). Motivating demotivation construct not only calls for possessing certain psychological traits but also requires the ability to employ good strategies and make the best out of the worst. In the second model, demotivation/motivating demotivation and perfectionist/non-perfectionist motivation constructs could account for 55% of the variance of the GPAs. Apparently, these two constructs are very strong predictors of academic success. The perfectionism/non-perfectionism construct has similarities with the deep-rooted theory of achievement motivation (Atkinson & Feather, 1966; Atkinson, 1974). The achievement motivation model conceptualized that if a student has a prospect of success in an activity, he/she will be more enthusiastic about involving in it. However, the perfectionism/non-perfectionism construct is not a taskbased and situation-specific variable of motivation, and it is a psychological trait that disposes individuals to their behaviors and lifestyles (Hewitt & Flett, 1991). In the third model, 56% of the variance of the GPAs could be predicted from demotivation/motivating demotivation, perfectionist/non-perfectionist motivation, and collective/individual motivation constructs. The third model was the strongest predictor of the GPAs compared to the first and second models. According to the outcomes, the individualistic tendencies were more connected to higher GPAs and good educational performances compared to collective tendencies. One justification is that in individualism, the concept of "self" is significant, while in collectivism, all values are based on being self-less (Kramer & Brewer, 1984). In Dörnyei (2005); Dörnyei, 2009, the fulfillment of ideal L2 self, which is in line with individual motivation, may lead to learning success, but the satisfaction of ought-to L2 self, which is relatively corresponding to collective motivation, does not contribute to learning achievement. 4.5. Conclusion All in all, this study has introduced and established a motivational/demotivational construct, demotivation/motivating demotivation, that is a powerful predictor of students’ achievements in language education. Other constructs presented in this study have a much weaker effect on students' GPA. It is held that the model can lead to the intellectual empowerment of students to achieve their educational goals. One of the aims of the current study is assisting learners in understanding the factors that affect their success or failure. According to the outcomes of the present study, motivating demotivation construct is the most important variable affecting students’ performances at the academic level. There is a need for students to develop perceptions about their abilities to perform in new, challenging, or even unwanted environments. It is very interesting to know that students at the graduate level not only are less likely to get demotivated by their professors` weak performances but also are more likely to be motivated to overachieve. The outcome of the study can also be helpful in the realm of teacher education. It may be applied to design motivational strategies for the purpose of classroom interventions. There is a need for a comprehensive motivational model to serve as an underlying organizational structure to generate systematic and context-related motivational strategies in the classroom. Moreover, it is required to educate teachers to act as a model to students, teach them to set higher goals, and encourage them to learn more about themselves, their tendencies, and their empowering or failing strategies. Integrating culture and motivation in this study, we identified some of the most significant motivational and demotivational factors that predict students’ achievement in language education. Since the study was conducted in a collective society, it can be replicated in other contexts to compare the results. Moreover, socioeconomic status, religiosity, and age can be considered as moderating factors which can more clearly illuminate the concept of motivation in second/foreign language education. For further research, two main predictors of students' GPA, proposed in the current study, are recommended to be considered and developed. Mainly, demotivation/motivating demotivation should be further analyzed since it can make a major contribution to academic achievement. Moreover, there is a need to establish strategies and tactics to enhance and develop motivating demotivation in learners. In a similar manner, perfectionist/non-perfectionist motivation, that is associated with academic achievement, is an under-researched concept in higher education that deserves more investigation. Although the present study reasonably defined the scope of the topic, it was restricted by time, place, and context, and there were some limitations. The results of the study were based on the associations with students’ GPA. The use of self-reported GPA has been applied in lack of any alternative way to obtain participants' academic performance. Accessing to 800 participants' official records of the current study was impossible due to the anonymity of the participants and the complicated process of external verification of the data. It has been assumed that the high level of accuracy under the current methodology makes this practice relatively reliable. According to several studies, students' self-reported academic records correlate with actual scores in the range of .60–.80 (Frucot & Cook, 1994). According to the result of the study, two constructs of intrinsic/extrinsic and cooperative/ competitive motivation could not predict students’ achievement. As a result, these two constructs can be eliminated in applying MDPAS with regard to students’ achievement in the future. Furthermore, all the participants in the current study majored in EFL. It is grounded in the fact that the researchers were oriented to find the most influential motivating and demotivating factors in language education. Nevertheless, any generalization of the findings of this study may be subjected to certain cautions

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