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Autonomous learning and effective self-regulatory strategies are increasingly important in foreign language learning; without these, students might not be able to exploit learning opportunities outside language classrooms. This study investigated the influence of motivational factors and self-regulatory strategies on autonomous learning behavior. The researchers developed a new questionnaire for Hungarian learners and administered it to secondary school students, university students, and adult language learners. Their structural equation models show that strong instrumental goals and international posture, together with positive future self-guides, are prerequisites for use of effective self-regulatory strategies, which in turn play an important role in influencing autonomous use of traditional and computer-assisted learning resources. Findings reveal no major structural differences between the groups, which suggests that the model is applicable to the most important language learner populations in the context investigated. Efficient management of time and boredom, as well as proactivity in seeking learning opportunities, were found to be necessary to promote autonomous use of traditional learning resources. In contrast, satiation control and time management were not important determiners of independent use of modern learning technology. Results indicate that in order to exploit the affordances of learning technology, a proactive approach to locating and using these learning resources is necessary.
In a large number of foreign language learning contexts, despite the growing presence of English, students are not exposed to the target language in everyday settings, and classroom instruction is often restricted to a few hours per week. As a consequence, this learning environment does not provide learners with sufficient input, output, or interaction opportunities, and in these settings the attainment of a high level of language competence is hardly possible unless the students effectively regulate their own learning behavior and take responsibility for their learning outside the classroom. The importance of autonomous learning behavior and self-regulation is also underscored by the explosion in technological development and the expansion of educational tools using computer-assisted technology (Blin, 2004; Groß & Wolff, 2001; Kaltenböck, 2001). On the one hand, autonomous learning behavior and the use of self-regulatory strategies are necessary for learners to be able to exploit these new tools for the enhancement of their second language (L2) competence. On the other hand, computer-assisted language learning (CALL) also supports independent learning and might develop self-regulation skills (Blin, 2004; Jones, 2001). Despite the fact that the amount of research on autonomous learning is on the increase (Benson, 2007), little is known about the role of self-regulatory strategies and autonomous learning behavior in language learning, and only a few studies have examined how motivational orientations and future self-guides influence these two constructs.
In our study, we developed a questionnaire to assess the relationship between motivational orientations, future self-guides, intended learning effort, self-regulatory strategies, and autonomous learning behavior among three groups of English language learners in Hungary, which represents a typical foreign language learning context in Central and Eastern Europe with regard to the availability of communicative input and output opportunities. Our research is novel in the field of second language acquisition, because our survey instrument is the first questionnaire that provides a theoretically and empirically validated assessment of self-regulatory strategies and autonomous learning behavior. Using structural equation modeling (SEM), we investigated which motivational characteristics affect the use of self-regulatory strategies and how the application of these strategies influences learners’ independent use of traditional and computer-assisted learning resources outside the foreign language classroom.
In this article we first provide a theoretical background to our study by explaining the most important concepts of our research; this is followed by a description of the data collection procedures. Next, we present our hypothetical model of motivational factors, self-regulatory strategies, and autonomous learning behavior and show how the data provide support for our conceptualization of the interaction of these constructs. Finally, we discuss the theoretical and pedagogical implications of the findings of our study.
REVIEW OF LITERATURE
Our research investigates three conceptual areas: motivation, self-regulation, and autonomous learning behavior. In our review of literature, we first give a theoretical overview of these constructs and provide definitions of these terms. This is followed by a brief description of previous studies that have investigated the relationship between motivational factors, self-regulation, and autonomous learning in the fields of educational psychology and language learning.
Motivation explains why people select a particular activity, how long they are willing to persist in it, and what effort they invest in it (Dörnyei, 2001). These three components of motivation correspond to goals and the initiation and maintenance of learning effort. As regards the first component of motivation, a number of different language learning goals have been proposed. Gardner (Gardner, 1985, 2006; Gardner & Lambert, 1959; Masgoret & Gardner, 2003) differentiates instrumental goals, which are associated with the utilitarian values of speaking another language, from integrative goals, which express students’ wish to learn the language in order to become integrated into the target language culture. English, however, has become an international language serving as a lingua franca in a globalized world (Jenkins, 2007; Seidlhofer, 2005; Widdowson, 1994), and consequently it has become separated from its native speakers and their cultures (Skutnabb-Kangas, 2000). Accordingly, a newly prominent language learning goal, international posture, has recently emerged in discussions in the literature on language learning motivation. International posture includes “interest in foreign or international affairs, willingness to go overseas to study or work, readiness to interact with intercultural partners … and a non-ethnocentric attitude toward different cultures” (Yashima, 2002, p. 57). Further language learning goals include friendship, travel, and knowledge orientation (Clément & Kruidenier, 1983).
Additional key elements of motivation are personal agency beliefs, which express one's views as to whether one is capable of performing a given learning task. Bandura (1986, 1997), in his social cognitive theory, argues that self-efficacy beliefs (i.e., what people believe about their capabilities) have a stronger influence on the motivation to perform a particular action than actual skills, knowledge, or previous accomplishment. In the field of L2 motivation, the best known parallel for personal agency beliefs is the L2 Motivational Self System Theory, proposed by Dörnyei (2005), who argues that the main driving force of language learning is the students’ future image of themselves as successful users of the language. His model of motivation contains two self-related components: ideal L2 self and ought-to L2 self. In this model, ideal L2 self is one's ideal self-image expressing the wish to become a competent L2 speaker. The ought-to L2 self contains “attributes that one believes one ought to possess (i.e. various duties, obligations, or responsibilities) in order to avoid possible negative outcomes” (Dörnyei, 2005, p. 106) associated with not being able to speak the L2 in question. A third element of the Motivational Self System Theory is the L2 learning experience, which covers “situation specific motives related to the immediate learning environment and experience” (Dörnyei, 2005, p. 106). Previous research that aimed to validate Dörnyei's L2 Motivational Self System Theory has found unequivocal support for the importance of the ideal L2 self in various learning contexts (see Dörnyei & Ushioda, 2009), but a number of studies (e.g., Csizér & Kormos, 2009; Kormos & Csizér, 2008) have not been successful in identifying the ought-to L2 self as a variable distinct from instrumental orientation.
Models of motivation also include the concepts of effort and persistence, which have been traditionally named as motivated learning behavior (e.g., Csizér & Dörnyei, 2005; Dörnyei, 2001, 2005; Gardner, 1985, 2006) in the field of second language acquisition (SLA). However, if we consider the equivalent concept of volition in educational psychology, which Corno (1993) defines as a “dynamic system of psychological control processes that protect concentration and directed effort in the face of personal and/or environmental distractions, and so aid learning and performance” (p. 16), it becomes apparent that there is a large overlap between motivational and self-regulatory factors. Zimmerman and Schunk's (2008) definition of self-regulation, which is based on English and English (1958), can potentially be helpful in delineating the seemingly inseparable constructs of motivation and self-regulation. In Zimmerman and Schunk's view, self-regulation is “the control of one's present conduct based on motives related to a subsequent goal or ideal that an individual has set for him or herself” (p. 1). Therefore, for the purposes of the present study, we operationalize motivation as consisting of goals, future self-guides, and intended learning effort, which correspond to ideals and conduct in Zimmerman and Schunk's definition. Factors relating to control are subsumed under self-regulation, which is discussed in the next section.
In many definitions, self-regulation is a process in which people organize and manage their learning, and this includes learners’ control over their thoughts (e.g., their competency beliefs), emotions (e.g., anxiety experienced while learning), behaviors (e.g., how they handle a learning task), and the learning environment (Pintrich & De Groot, 1990; Zimmerman, 1998). Additionally, the motivation to learn can also be consciously regulated and monitored (for a recent discussion of the self-regulation of motivation in educational psychology, see Sansone, 2008; Winne & Hadwin, 2008). These conceptualizations of self-regulation also show large overlaps with motivation and autonomy, which can be illustrated with self-determination theory, which claims that “[autonomous] self-regulation is associated with autonomous motivation and is characterized by a sense of volition and choice” (Reeve, Ryan, Deci, & Jang, 2008, p. 225). In order to clearly differentiate the concepts of motivation, autonomy, and self-regulation, in this research we understand self-regulation as self-regulatory control that involves the use of strategies which are largely conscious processes that students apply to control their learning.
Students apply a variety of strategies to regulate their learning processes. In his classic work, Kuhl (1985) proposes six action-control strategies, three of which (attention, encoding, and information control) can be regarded as means of controlling cognition. Kuhl's incentive-escalation strategy is a means of controlling motivation, and his further two control strategies are emotional and environmental control. For a long time, SLA research focused primarily on how learning strategies are used in the service of accomplishing language learning goals (e.g., Macaro, 2001; O'Malley & Chamot, 1990; Oxford, 1990; Wenden, 1998). Parallel to developments in the field of educational psychology, however, Dörnyei (2005) has argued for the need to conduct research on the processes of how learners exercise control over their learning. Dörnyei also proposes a new theoretical conceptualization of self-regulation in SLA, which is based on the previously described taxonomies of Kuhl (1985) and Corno and Kanfer (1993). The empirical study conducted by Tseng, Dörnyei, and Schmitt (2006) provides support for the validity of the five main types of control strategies in Dörnyei's (2001) taxonomy: commitment control, which regulates goal commitment; metacognitive control, which helps learners maintain focus and concentration; satiation control, with the help of which boredom can be managed and alleviated; emotion control, which is used to manage emotions; and environmental control, which assists learners in creating an appropriate study environment.
Learner Autonomy and Autonomous Behavior
Learner autonomy in the field of language learning is broadly defined as the learner's ability to exercise control over learning (Holec, 1981). As mentioned previously, this definition exhibits a number of similarities with that of self-regulation, and several researchers in educational psychology have equated effective self-regulation with autonomous behavior. For example, Reeve et al. (2008) state that “the regulation of behavior when people's interests and values are the reason for acting is said to be autonomous” (p. 224). It is important to note, however, that autonomy encompasses control over a wider range of phenomena than self-regulation. Self-regulation in its broad sense entails control over the cognitive, emotional, motivational, and behavioral aspects of learning, whereas autonomous learners are also capable of taking responsibility for the content and management of their learning (e.g., course materials) and the social-contextual environment in which learning takes place (Benson, 2001; Oxford, 2003).
Although the potential attributes of autonomous learners might constitute a long list (Benson, 2001; Littlewood, 1999; Oxford, 2003), it is possible to define the crucial elements of the wider concept of learner autonomy, which include learners’ control over the affective and cognitive processes of learning, classroom and curriculum decisions, autonomous use of learning skills, and the independent use of learning resources and technology (Benson, 2001). However, this concept of learner autonomy entails self-regulation as well as the self-control strategies we outlined earlier and is very difficult to operationalize in a questionnaire survey. Therefore, we decided to focus on one aspect of learner autonomy, which we termed autonomous learning behavior, and which is included in Benson's (2001) model of autonomy as the independent use of learning resources and technology.
As mentioned earlier, the concept of autonomous or independent learning behavior has gained increased importance with the advance of CALL. Benson (2001), in his book on learner autonomy, divides learning resources into two important categories: traditional learning resources (e.g., reference, coursebooks) and resources provided by modern educational technology (e.g., web-based applications, computer programs, CD-ROMs). Willingness to engage in autonomous learning behavior is highly important in assisting learners to exploit the potential of learning resources, both in a more traditional self-access environment and in a computer-assisted setting (Blin, 2004).
Links Among Motivation, Self-Regulation, and Autonomous Learning
The link joining motivational factors, self-regulatory strategies, and autonomous learning has been discussed in several theories of motivation in the field of educational psychology. In most models, the motivation to reach a particular goal is assumed to trigger self-regulated learning behavior (e.g., Heckhausen & Dweck, 1998; Lens & Vansteenkiste, 2008; Sansone & Smith, 2000; Wigfield, Hoa, & Klauda, 2008). In other words, motivational factors such as the strength, relevance, and orientation of goals and positive self-related beliefs are seen as precursors to the use of effective self-regulatory strategies. Ryan and Deci (2000), in their extension of self-determination theory, called organismic integration theory, also argue that identification with and integration of learning goals are prerequisites for self-regulated action. Conversely, learning environments which provide students with opportunities for exercising control over their learning processes and for autonomy might also be conducive to the development of intrinsic motivation (Deci & Ryan, 1985; Ushioda, 2003, 2006).
In the field of language learning, several empirical studies have investigated the link joining motivational factors, self-regulatory variables, and learner autonomy, although it has to be noted that these concepts have mostly not been clearly differentiated. In a series of questionnaire surveys, Noels, Clément, and Pelletier (1999, 2001) found a strong link between students’ perceived autonomy (which was measured by questionnaire items assessing how supportive the students’ learning environment was of autonomy), identified regulation, and intrinsic and integrative motivation in a number of language learning settings in Canada. Based on interview data, Ushioda (1996, 2003, 2006) also argues that learners who take responsibility for their own learning tend to be more intrinsically motivated and are able to regulate their learning processes more effectively. Spratt, Humphreys, and Chan (2002) conducted a study in Hong Kong, explicitly to address the question of whether autonomy is a cause or consequence of motivation. Although the correlational design of their research did not allow for a conclusive answer to this question, the complementary data from interviews led the authors to conclude that, in their context, the motivation to acquire an L2 triggered autonomous learning behavior. As the contradictory findings of the studies in the field of language learning suggest, it is possible that the effective use of self-regulatory strategies and autonomous learning behavior reinforce each other dynamically. This dynamic relationship, however, can only be modeled over time (see, e.g., a recent study by Ning & Downing, 2010, in the field educational psychology) or by means of qualitative research methodology. In our study, however, we were interested in the question of whether it is possible to devise a model which describes how motivational variables and self-regulatory strategies influence autonomous learning behavior at one particular point in time using questionnaire data and structural equation modeling.
In our study, we used SEM to gain more insight into the causal relationships between motivation, self-regulation, and autonomous learning behavior in three groups of English language learners in Hungary. SEM allows researchers to estimate both the links between latent variables and the direct effects between them. With the help of SEM, one can analyze how adequately the proposed model describes the data and can establish causality, but any cause–effect relationships need to be supported by relevant theory (Byrne, 2009). In other words, using this statistical method of data analysis, we can test the adequacy of a particular hypothetical model in which a set of variables are assumed to influence other variables. This procedure allows us to propose a model of motivational factors, self-regulation processes, and autonomous learning behavior that accurately describes the link among these three constructs in the context investigated.
One of the inherent difficulties in using surveys in quantitative research is that one needs to restrict and simplify the number of factors that can be analyzed in a single study. Therefore, in addition to language learning effort, we selected three motivational variables that proved important in influencing reported language learning effort in our previous research in the same context (e.g., Csizér & Kormos, 2009; Kormos & Csizér, 2008) and that are also clearly separable from self-regulation strategies and autonomous learning behavior (instrumental orientation, international posture, and the ideal L2 self). We conceptualized self-regulation processes as consisting of three important control strategies, two of which (satiation control and time management) were well-established strategies in previous studies in educational psychology (Corno & Kanfer, 1993; Kuhl, 1985; Lens & Vansteenkiste, 2008) and were used in a study conducted by Tseng et al. (2006) in SLA.1 We devised a new self-regulatory strategy, called opportunity control, which is a strategy specific to language learning in foreign language contexts. Individuals who exhibit opportunity control actively seek opportunities for learning and using an L2. In our study, we decided to focus on two aspects of autonomous learning behavior that are particularly relevant in foreign language contexts: students’ autonomous use of learning resources and learning technology. We included these two constructs because they can be considered important descriptors of what learners do autonomously. In this way, we could assess three important aspects of language learning: why learners decide to take an action (goals and self-guides), how they control their actions (self-regulatory strategies), and what kind of independent learning behavior they display.
In our research, we devised a theoretical model of the relationship of the previously described constructs and used SEM to test the hypothetical model. In order to assess the general applicability of the model to different learner groups, we included three samples of students who embody the three most typical intact groups of language learners in Hungary: secondary school students, university students, and adult learners. Accordingly, we also compared the models for the investigated subsamples to detect group-related variations in the structure of motivation, self-regulation, and autonomous learning behavior by means of multigroup structural equation modeling. Our study addressed the following two research questions:
How do motivational factors and self-regulatory strategies influence autonomous learning behavior in the Hungarian learner population investigated?
How does the relationship of motivational factors, self-regulatory strategies, and autonomous learning behavior differ between the three learner groups (secondary school students, university students, and adult language learners)?
In order to answer our research questions, we set up a hypothetical model describing the relationship among motivational factors, self-regulatory strategies, and autonomous learning behavior, which is presented in Figure 1. Based on previous research in the field of language learning motivation (Csizér & Dörnyei, 2005; Dörnyei, 2001; Gardner, 1985, 2006) and general educational psychology (e.g., Ford, 1992), we hypothesized that the goals students pursue serve as the fundamental driving force behind intended language learning effort. Previous research in the same Hungarian context indicated that the two most important goals for the target population are instrumental and international in nature (Kormos & Csizér, 2008). Hence, we assumed that learning goals influence students’ images of themselves as successful language learners, that is, the ideal L2 self (Dörnyei, 2005; Kormos & Csizér, 2008). Studies investigating the role of the ideal L2 self in language learning have also shown that the ideal L2 self is one of the best predictor variables of how much energy students are willing to invest in language learning (for an overview, see Dörnyei & Ushioda, 2009). Furthermore, as suggested by theories of self-regulation, intended learning effort was presumed to have an effect on the various control strategies that students apply to regulate their learning. Finally, we hypothesized that each of the control strategies would have an influence on the independent use of learning resources and technology.
In our research, we surveyed 638 language learners in Budapest, the capital of Hungary. Budapest is the largest city in the country; one fifth of the total Hungarian population resides there. In many respects, Budapest is similar to other major metropolitan cities in Europe, with the exception that, in Hungary, most of the population is monolingual: According to the 2000 census, 92.3% of the population claimed to be ethnic Hungarian, and the proportion with Hungarian as their mother tongue was even higher, at 98.2% (Demographic Yearbook,2004). In our research we focused on the three most important language learning contexts in Hungary—secondary schools, universities and colleges, and private language schools—and used criterion sampling.
As for secondary school students, we included three schools that fell into the range of institutions with an average quality of teaching and average student population based on the rank order of schools in terms of the number of students admitted to university (National Institute of Public Education, Hungary, 2004). Two of the schools were state schools, and in order to represent learners from the private education sector, we also selected a church-owned school. We chose three schools in different geographical locations of the city in order to gather data from students from various social backgrounds.2 All the students in the second and third year of their secondary education who were studying English were asked to fill in a questionnaire. Altogether, 205 learners, 80 male and 125 female, in the secondary school sample responded to our questions. The average age of students was 16 years and 8 months. English is not a compulsory language in Hungarian secondary schools, but it is the one most frequently studied (Halász & Lannert, 2007). When enrolling in secondary school, students can choose which foreign language they would like to study. The level of proficiency of the majority of the students in this sample was between the B1 and B2 levels (between intermediate to upper-intermediate) on the Common European Framework of Reference (CEFR; Council of Europe, 2001).
In selecting the university students, we paid attention to representing the various fields of study that one can pursue in Budapest and to including learners both from colleges and universities. A total of 105 college students and 164 university3 students responded to our questions. The students’ average age was 21 years and 6 months; 163 of them were female and 106 male. Studying foreign languages is voluntary at university, and in most institutions students are required to pay for foreign language instruction.4 Most students in the sample were preparing for one of the accredited intermediate-level proficiency exams.
The participating 164 adult language school learners were attending private language schools in Budapest. In choosing the language schools, five of the largest language schools in Budapest were approached for permission to allow their students to fill in our questionnaires, and three smaller schools were also invited to participate in the survey. All these schools were well-established high-quality language schools that had won accreditation from the Hungarian Chamber of Language Schools. Among the adult participants, 61 were male and 103 female, and their average age was 35 years. The participants worked in all spheres of life, including business, industry, tourism, healthcare, education, and services. The adult language learners’ proficiency ranged between the A2 and B2 levels (pre-intermediate to upper-intermediate) on the CEFR.
Because the study investigated the autonomous use of learning technology, it should be noted that the majority of the surveyed learner population in all the three groups had good access to modern tools of information technology. Most Hungarian secondary school and university students in Budapest own a mobile phone with access to the Internet and also have either a personal computer or a laptop with broadband Internet access. The situation is similar in the case of the young adult learner population we surveyed (see “Hogyan internetezik,” 2011).
Our questionnaire consisted of 55 items, of which 48 were 5-point Likert-scale items that aimed to measure the most important factors in L2 learning motivation that had been identified in previous research as well as self-regulation and specific aspects of learner autonomy. The variables seeking to describe language learning motivation included two scales on language learning goals (instrumentality and international posture) that were previously found to be important driving forces for the investigated population (Kormos & Csizér, 2008), and one scale on the self-image of language learners (ideal L2 self, based on Dörnyei, 2005). A further scale was designed to gain insights into learners’ intended learning effort (based on Gardner, 1985).
Based on the piloting of the questionnaire and previous studies on autonomous learning behavior and self-regulation in the Hungarian context (Mezei, 2008), three variables were selected to characterize the self-regulatory strategies of the learners. Satiation control, that is, the capacity to overcome boredom and make language learning tasks interesting, was adapted from Corno and Kanfer's (1993) taxonomy of action control strategies. The source of the items for this variable was Tseng et al.'s (2006) Self-regulating Capacity in Vocabulary Learning scale, from which questions were reworded to refer to language learning in general. Based on the work of Lens and Vansteenkiste (2008), an additional self-regulatory variable, called time management, was also included to describe learners’ ability to control procrastination and plan their study schedule. Finally, we devised a new self-regulatory control variable, opportunity control, which aimed to assess to what extent learners take control over and seek language learning and language use opportunities.
We also measured two aspects of autonomous learning behavior that were concerned with learners’ independent use of language learning resources (see Benson, 2001). One of the autonomous learning behavior scales aimed to gain insights into learners’ independent use of learning resources in general, the other into the independent use of learning technology in particular. The following list contains the name of each variable in the survey together with its definition and an illustrative example.
International posture (5 items): students’ attitudes to English as an international language. Example: Studying English will help me understand people from all over the world. Cronbach's alpha: .75.
Instrumentality (4 items): utilitarian benefits associated with being able to speak the L2, such as higher salary, better jobs. Example: Speaking English will be highly important in my future job. Cronbach's alpha: .74.
Ideal L2 self (6 items): students’ views of themselves as successful L2 speakers. Example: I like to think of myself as someone who will be able to speak English. Cronbach's alpha: .77.
Intended learning effort (6 items): students’ efforts in learning English. Example: I am willing to work hard at learning English. Cronbach's alpha: .80.
Opportunity control (6 items): students’ willingness to actively seek out opportunities for learning and using the L2. Example: I try to find opportunities to practice speaking in English. Cronbach's alpha: .76.
Time-management control (4 items): students’ ability to prevent procrastination and plan their study time efficiently. Example: I try not to leave it to the last minute to prepare for my next English class. Cronbach's alpha: .65.
Satiation control (4 items): students’ ability to overcome boredom and make language learning tasks interesting. Example: I am confident that I can overcome any sense of boredom when learning English. Cronbach's alpha: .74.
Independent use of technology (6 items): learners’ reported independent use of computer-assisted information and communication technology in language learning. Example: I use English language teaching computer programs outside class. Cronbach's alpha: .78.
Independent use of learning resources (7 items): learners’ general capacity to exercise control over learning resources. Example: If there is something that I do not understand in the English class, I try to find an answer to my question in the coursebook myself. Cronbach's alpha: .68.
In the last part of the questionnaire, we asked students background questions concerning the languages they would like to study in the future, when they started learning English, whether they were studying any other foreign language, what their age and gender were, where and what academic major they were studying (in the case of university students), what their perceived level of proficiency was, and what their job was (in the case of adults).
The questionnaire was first administered to 105 undergraduate students studying English language and literature at a university in Budapest. Following the factor and reliability analysis of this pilot run, we omitted or reworded unreliable items. The final version of the questionnaire was personally delivered to the secondary schools, universities, colleges, and language schools, where a person who agreed to take charge of administration of the questionnaire distributed it among teachers and collected the completed questionnaires.
All these completed questionnaires were computer-coded, and then SPSS 17.0 and AMOS 4.0 were used to test a hypothetical model.
In order to draw up a comprehensive model of motivational factors, self-regulatory strategies, and autonomous behavior in the three investigated sub-samples, multiple-group SEM was applied. We also used multigroup comparisons to analyze the differences in the links between the various latent variables in the three samples of participants involved in the study. First, measurement models were drawn up, based on the theoretical considerations outlined in the review of literature. Following this, the various latent variables were combined into a full structural model. The three models, for secondary school students, university students, and adult learners, were compared by a multigroup procedure; they were fitted simultaneously in order to assess possible differences in the structural models. To assess the overall model fit, we used the indices most often advised in the SEM literature (Byrne, 2009) and, besides the chi-square statistics and CMIN/df (chi-square divided by the degrees of freedom), we report additional indices: Comparative Fit Index (CFI), the Bentler-Bonett normed fit index (NFI), the Tucker-Lewis coefficient (TLI), the root mean square error of approximation (RMSEA), and the Parsimony-adjusted Comparative Fit Index (PCFI; Browne & Cudeck, 1993; Fan, Thomson, & Wang, 1999; Hu & Bentler, 1999). We compared the various paths within a multigroup framework with the help of critical ratios (CR; Byrne, 2009). When CR values were above the recommended 1.96 (Byrne, 2009), we concluded that there was a significant group-related difference concerning the given path.
The Structural Equation Models
As a first step, the initial model was submitted to evaluation using maximum likelihood estimation simultaneously for the three subsamples (secondary school pupils, university students, and adult language learners; Byrne, 2009). Next, the final models of the three subsamples were combined into a single multigroup model, and a subsequent multigroup procedure was carried out. Figure 2 provides an overview of the final model and the differences between the investigated learner groups. Table 1 presents various joint goodness of fit measures for the multigroup analysis.
Table 1. Joint Selected Fit Measures for the Final Models
Note. CFI = comparative fit index; NFI = normed fit index; NNFI = non-normed fit index; RMSEA = root mean square of error of approximation; PCFI = parsimony adjusted comparative fit index.
Chi Square/df ratio
As can be seen in Table 1, the chi square/df ratio was above the usually recommended value of 2 (Byrne, 2009); however, as we pointed out earlier, it is advisable to rely on more than one fit index. Therefore, we also examined alternative fit indices, which all indicate a very good fit for the joint models, and thus we can conclude that the models in Figure 2 provide an adequate representation of our data. As a next step, we compared the paths in the structural model in order to determine whether there are any significant differences between the structural models for the three investigated samples. Based on the critical ratios, we conclude that there is a significant difference between the subsamples for the ideal L2 self → intended learning effort path. The connection between these two variables is significantly stronger in the university student sample than in the adult and secondary school groups.
The structural equation models reveal a five-level system of motivational factors, self-regulatory strategies, and autonomous learning behavior. On the left-hand side of the model in Figure 2, we can find two strongly interrelated learning goals—instrumental motivation and international posture—which act as the precursors of the ideal L2 self. At the next level, learners’ self-concept is strongly associated with intended learning effort, which in turn predicts the use of the three self-regulatory strategies—opportunity, time management, and satiation control—which are placed on the fourth level of the model. Finally, resource-based approaches to learning are influenced by all three self-regulatory strategies, but technology-based approaches are related only to opportunity control. The comparison of the models suggests that, except for the lack of a link between international posture and the ideal L2 self among university students and young adult learners, the structural equation models do not differ significantly in the three investigated samples.
Our first research question aimed to discover how motivational factors and self-regulation strategies affect autonomous learning behavior among Hungarian language learners. In line with previous research in the field of general educational psychology and language learning (Heckhausen & Dweck, 1998; Lens & Vansteenkiste, 2008; Ryan & Deci, 2000; Sansone & Smith, 2000; Spratt et al., 2002), our models show that learning goals associated with the international status of English, instrumental orientation, and positive self-related beliefs are prerequisites for the use of self-regulation strategies. Our model also demonstrates that effective self-regulatory strategies play an important role in influencing how students use learning resources and information technology independently to improve their L2 competence. Therefore, we can conclude that motivational factors exert their effect on autonomous learning behavior through the mediation of self-regulation strategies.
The second research question inquired into how the relationship of motivational factors, self-regulatory strategies, and autonomous learning behavior differs between secondary school students, university students, and adult language learners. The comparison of the models across the age groups indicates that, in the Hungarian context investigated, little variation can be observed in the internal structure of motivational variables, self-regulatory strategies, and autonomous learning behavior. The only major difference in the models of the three learner groups is that for university students and adult learners, international posture influences the ideal L2 self with the mediation of instrumental motives, whereas for the youngest learner group, international posture is also directly linked to the ideal L2 self. This additional direct relationship to the ideal L2 self might be explained with reference to the growing importance of English as a lingua franca (Jenkins, 2007; Seidlhofer, 2005; Widdowson, 1994) among teenage learners, who often use English to communicate with members of the global Internet community.
The lack of major differences across models allows us to conclude that the presented models provide an accurate description of how motivational variables and self-regulatory strategies influence autonomous learning behavior in the most typical language learning situations in this particular metropolitan Central European setting. In what follows we discuss the models in detail, starting with the level of motivational factors.
With regard to the motivational variables, it is noteworthy that for all three learner groups there is a strong link between international posture and instrumental motivation, which suggests that the international status of English makes this language indispensable in the world of work. The models indicate that the endorsement of these two important language learning goals seems to exert a considerable influence on the ideal L2 self. Additional evidence for the important role of students’ image of themselves as successful future L2 users is provided by the strong link between language learning effort and ideal L2 self. A further finding of the study with regard to motivational variables is the strong association of the ideal L2 self with instrumental orientation in the university student and adult language learner samples. This suggests that the instrumental value of language learning is strongly internalized for these two groups of students, which is in line with earlier findings in a similar context by Csizér and Dörnyei (2005). The comparison of the findings with our survey conducted in Chile (Kormos, Kiddle, & Csizér, 2011) also reveals that goals, self-related views, and intended learning effort are interrelated in a similar way in these two different contexts. This lends support to the motivational model outlined in Kormos et al. (2011), in which we argued that motivational factors form three interrelated levels—goals, self-guides, and motivated behavior.
Students’ reported language learning effort was found to exert the strongest influence on the self-regulatory variable of opportunity control, which expresses learners’ capacity to actively seek opportunities for learning and using the L2. In a foreign language context such as Hungary, where learners’ spontaneous contact with the language outside the classroom is limited and where classroom instruction might only entail three 45-minute lessons a week, it is highly important that students find ways in which they can practice the L2 and acquire new L2 knowledge independently of their teachers. Because these opportunities need to be actively searched for, a willingness to invest effort into language learning in general acts as an important driving force in this quest. Reported learning effort also seems to have a strong influence on students’ capacity to control boredom in language learning, which is in contrast with the weaker link with time management control. Modern language teaching tasks and materials generally make language learning interesting; nonetheless, acquiring an L2 requires memorization and occasionally monotonous practice. Hence, it is understandable that learners who are willing to invest energy into language learning will have more efficient strategies to overcome boredom than those who are less prepared to make substantial learning effort. Effective time management, however, seems to be less dependent on intended learning effort, because other learner variables such as individual differences in attention control and contextual restraints might also affect how capable students are to plan their study schedule and avoid procrastination.
In line with our initial hypothetical model, all three self-regulatory strategies influence learners’ autonomous use of learning resources and, interestingly, the strengths of links are quite similar. This finding might lead to the conclusion that satiation control as well as the capacity to manage time effectively and actively to search for learning opportunities are equally necessary for the independent and autonomous use of traditional learning resources outside the classroom. Although our study is not directly comparable to that of Tseng et al. (2006), it is interesting to note that their structural model also shows that the various self-regulatory strategies contributed to a similar degree to the overall self-regulatory capacity in vocabulary learning.
Contrary to our initial hypothetical model, the autonomous use of technology seems to be related only to the self-regulatory strategy of opportunity control. The link between opportunity control and independent use of technology is, however, very strong, especially in the case of the younger language learner groups, which suggests that a proactive attitude to finding learning opportunities is a highly important prerequisite for the capacity to apply modern technological devices independently and autonomously. Lai and Gu (2011) report similar findings in a study conducted with university students in Hong Kong. Their results show that “a stronger belief in seeking language use opportunities beyond the classroom was positively associated with participants’ likelihood of using technology to regulate their learning” (p. 327). With regard to the other investigated self-regulatory strategies, we might hypothesize that learners’ capacity to control boredom is not related to using modern technology, because tasks and learning materials created with the help of recent technology are generally exciting and varied. It is also possible that using technology in language learning is independent of how effectively one can create the time to apply it and might be more closely linked to familiarity with and attitudes toward modern educational technology. This hypothesis is supported by Lai and Gu, who found that, although learners’ dispositions to self-regulated learning were related to learning technology use, other factors such as level of proficiency, learning history, beliefs about language learning, and digital literacy are also important in explaining variation in second language learners’ engagement with modern technological tools.
To summarize, the structural equation models based on our questionnaire survey in Hungary indicate that several individual difference factors contribute to the two important aspects of autonomous learning behavior investigated in this study: independent use of learning resources and technology. Strong international and instrumental goal orientations seem to enhance learners’ views of themselves as successful future language users and, in turn, positive self-image acts as an important prerequisite for investing effort in language learning. Our research adds new insights in the field of second language acquisition by showing that motivational variables exert their influence on autonomous learning behavior with the mediation of self-regulatory strategies. The models reveal that efficient management of time and boredom as well as proactivity in seeking out learning opportunities seem to be equally necessary to promote the autonomous use of traditional learning resources. In contrast, the self-regulatory strategies of satiation control and time management were not found to be important determiners in the independent use of modern learning technology. This suggests that, in order to exploit the affordances of learning technology, students might not need the traditional self-regulatory strategies of satiation control and time management, but rather that an efficient approach to locating and using these learning resources is necessary. Our research has also found empirical support for the importance of the self-regulatory strategy of opportunity control, which is a strategy that can be considered specific to foreign language learning contexts but that might also be relevant in second language environments. The structural equation models show that this newly established self-regulatory strategy has the strongest influence on the independent use of both traditional and computer-based learning resources.
Our model also points to an important pedagogical conclusion, namely, that strong learning goals and positive future self-guides without effective self-regulation are not sufficient to promote autonomous learning behavior. Although many students can find and apply the self-regulatory strategies that are particularly suited for their learning styles and personality, a large number of learners would need guidance on how to select and use self-regulatory strategies that assist their language learning processes. In the field of educational psychology, several methods exist for enhancing learners’ use of self-regulatory strategies (see, e.g., Cleary & Zimmerman, 2004), which could be easily adapted in the foreign language classroom. Sessions on time management and discussions of how learning opportunities can be found outside the classroom could raise learners’ awareness of the different strategies they might apply to exercise efficient control over their learning.
Our study aimed to establish an adequate and empirically supported model of self-regulatory strategies, autonomous learning behavior, motivational orientations, and future self-guides, in a Hungarian language learning context, which might be representative of metropolitan and large cities in the Central European region. Nevertheless, the similarity of the motivational structure of our model with the one established for the Chilean context (Kormos et al., 2011) and the comparison of the findings with regard to the independent use of learning technology with Lai and Gu's (2011) study in Hong Kong indicate that our findings might also be applicable in other settings where English is primarily taught in classroom settings.
Although the described model is also supported by relevant theories, it has to be noted that alternative models which include other mediating variables, such as self-efficacy, language proficiency, and familiarity with educational technology, might yield additional insights into the interplay of motivational variables, self-regulatory strategies, and autonomous learning behavior. In our study, certain self-regulatory variables such as emotion and environment control could not be adequately operationalized by the questionnaire items, and future research might be directed at developing survey instruments to measure these constructs. Furthermore, autonomous learning behavior and motivational factors show large contextual variations; hence, further research at other geographical locations and in different social and educational environments may be necessary to discover how the interaction of the constructs investigated might be influenced by social-contextual factors. Qualitative interviews and reflective diaries might also provide complementary and more in-depth information on the complex interaction of motivational variables, self-regulatory strategies, and autonomous learning. Furthermore, longitudinal studies on how autonomous learning and self-regulation processes evolve and change in the course of language learning could shed new light on the dynamic nature of these constructs.
Judit Kormos is a reader in second language acquisition at Lancaster University. Her research interests include the psycholinguistics of second language acquisition and individual differences in language learning. She is the author of two books and has published widely in academic journals on the topics of language learning motivation, aptitude, dyslexia, and second language speech production.
Kata Csizér holds a PhD in language pedagogy and works as a lecturer at Eötvös University, in Budapest. Her main field of research interest is the sociopsychological aspects of second language learning/teaching and second and foreign language motivation. She has published more than 50 academic papers and has coauthored three books.
The reason why we omitted the constructs of environment control and emotion control was that in a pilot study, the scales developed by Tseng et al. (2006) did not display sufficient levels of reliability. We did not include the scale of commitment control in this study due to its high intercorrelation with intended learning effort (r = .71).
In Budapest, geographical location often coincides with the socioeconomic status of the residents of the particular area.
In Hungary the difference between colleges and universities is that colleges mostly provide an undergraduate level of education (with a few exceptions where some colleges have accredited master's programs).
At the time when study was conducted, university and college education was free for the majority of the students.