Self-Regulation in an Advanced Language Learner: A Case Study of Language Learning Strategies

Guy Redmer, Department of English, Tamkang University, Taiwan

Redmer, G. (2022). Self-regulation in an advanced language learner: A case study of language learning strategies Studies in Self-Access Learning Journal, 13(1), 60–76. https://doi.org/10.37237/130104

Abstract

In response to questions about language learning strategies as a field of inquiry, Oxford’s (2011) Strategic Self-Regulation (S2R) Model conceptualizes strategies within the larger frame of self-regulation. While there is existing literature on language learner self-regulation, this study uniquely explores the language learning strategies of an advanced learner through the lens of Strategic Self-Regulation (S2R). A series of interviews were conducted with the participant over a period of two years. Elements of the Strategic Self-Regulation (S2R) Model were identifiable in the data. Clearly, there was dynamic and fluid interaction between personal attributes, goals, tasks, and situational contexts during the self-access learning. The participant’s strategies were expanded and adapted based on needs and perceptions. Results of the study provide a better understanding of how self-regulation actually works on the individual level.

Keywords: language learning strategies, self-regulation, Strategic Self-Regulation (S2R), self-access learning

Language learning strategies (LLS) have a rich history in second language learning inquiries (Cohen & Macaro, 2013; Griffiths, 2008; Oxford, 1990, 2011, 2017). Findings show that there is a strong relationship between LLS, proficiency, and self-efficacy (Bremmer, 1999; Cesur, 2011; Chang & Liu, 2013; Diseth, 2011; Lai, 2009; Magogwe & Oliver, 2007; Nazri et al., 2016; Rabadi, 2018; Wong & Nunan, 2011). However, some questioned the focus on LLS themselves, arguing that they merely reflect motivation (Tseng et al., 2006) and other aspects of self-regulation. The strict delineation of LLS into metacognitive, cognitive, and other categories has also been questioned. In response, Oxford (2011, 2017) presented LLS within the larger framework of self-regulation. Recognizing the need to be more lucid about the nature of LLS, she describes self-regulation as the “soul of learning strategies” (2017, 65). Her Strategic Self-Regulation (S2R) Model emphasizes LLS as being flexible, creative, context-based, and highly individualized. The model clearly expresses LLS as self-directed, dynamic, and interactive with multiple factors. It is in this context that the following study was conducted. The purpose was to understand how strategies, as acts of self-regulation, work on an individual level.

Literature Review

Language Learning Strategies (LLS)

LLS have been researched since the 1970s, when the ‘good language learner’ emerged as a topic of investigation. Rubin (1975) posited that the behaviors of proficient learners could be imparted to others. Since then, numerous studies have shown that proficient learners employ a wider variety of strategies and use them more often than lower achievers (Al-Natour, 2012; Al-Buainain, 2010; Griffiths, 2003; Lai, 2009; Liu, 2004).

Another thread of research explored the teaching and learning of strategies. One well-known result of this was Oxford’s (1990) development of the Strategy Inventory for Language Learning (SILL). It was designed for individuals to assess their frequency of strategy use. Strategies on the SILL are categorized into six groups (see Table 1).

Table 1

Categorization of the SILL

Despite an abundance of literature on LLS and the development of assessment tools, there have been criticisms. It has been argued that terminology and definitions were too ambigious and sometimes conflicting (Dörnyei, 2005; Tseng et al., 2006). For instance, some literature described strategies as actions while others emphasized thoughts and intentions.

The SILL has also been targeted (Dörnyei, 2005; Woodrow, 2005). Dörnyei asserted that it is inaccurate to measure the frequency of specific actions when the individual may be applying other equally useful strategies. Relatedly, frequency of use does not necessarily equal learning proficiency. From a broader perspective, Woodrow argued that the SILL gives a very incomplete assessment because it does not take into account specific context or cultural differences.

In sum, critics have asserted that LLS may have been overgeneralized, not taking into account the context-specific and complex nature of language learning (Rose, 2012). Dörnyei (2005) and others thus proposed putting LLS under the umbrella of self-regulation.

Self-Regulation

The social cognitive theory of self-regulation understands human behavior as resulting from the interaction of personal, environmental, and behavioral factors (Usher & Schunk, 2011). All of these interact in dynamic ways. Consequently, human agency can be exercised by intentionally and consciously guiding any one of the factors, which will in turn affect the others. Self-regulated action is developed from three cognitive subfunctions: self-observation, self-evaluation, and self-reaction (Bandura, 1986). Self-observation will provide a learner with information on their own thoughts and behavior patterns as well as the nature of a given task. This may be meditated by numerous situational factors and personal emotional states. Self-evaluation involves self-efficacy, or the belief in one’s own abilities for a particular task. But self-reaction may be the real driver of agency. The ability to react to one’s plans, feelings, and progress keeps the process of self-regulated behavior going.

The cyclical nature of self-regulation was modeled by Zimmerman (2000) with three learning task phases: forethought, performance, and reflection (see Figure 1). This model of self-regulation illustrates a flowing interaction that will necessarily be very context-specific and dependent on numerous personal and situational factors. Hence, self-regulation and agency can only be observed and understood on the individual level.

Figure 1

Cyclic Self-Regulated Learning

 

In response to Dörnyei and other critics, Oxford (2011) put forth the Strategic Self-Regulation (S2R) model. It placed LLS in the context of self-regulation. Oxford claimed that LLS have always been considered to be practices of self-regulation. But she also acknowledged that the self-regulatory nature of strategies may not have been expressed clearly. Hence, S2R could elucidate these aspects.

LLS, Self-Regulation, and Self-Access Learning

After conducting a content-analytic study of strategy definitions, Oxford (2017) offered an updated one within the framework of S2R.

L2 learning strategies are complex, dynamic thoughts and actions, selected and used by learners with some degree of consciousness in specific contexts in order to regulate multiple aspects of themselves (such as cognitive, emotional, and social) for the purpose of (a) accomplishing language tasks; (b) improving language performance or use; and/or (c) enhancing long-term proficiency. Strategies are mentally guided but may also have physical and therefore observable manifestations. Learners often use strategies flexibly and creatively; combine them in various ways, such as strategy clusters or strategy chains; and orchestrate them to meet learning needs. Strategies are. Learners in their contexts decide which strategies to use. Appropriateness of strategies depends on multiple personal and contextual factors. (p. 47)

Several parts such as ‘selected and used by learners,’ and ‘regulate’ express the notion that LLS are inherently self-regulatory. Flexibility within shifting contexts and overall individuality are also clear. Thomas and Rose (2018) noted that some learners may effectively use strategies but not be so self-regulated. These ‘other-regulated’ learners are dependent on teachers for the selection and application of strategies. But arguably, the dependency they describe is only on initial knowledge, not long-term use. In fact, explicitly taught strategies can equip learners with more choices (Hawkins, 2018).

Oxford’s definition above would also firmly place strategy use as an inherent part of self-access or self-directed learning. A few studies have explored strategies in this context (Gan, 2004; Gan, et al., 2004). These investigations found that the use of self-directed learning strategies was significantly associated with English learning success, as defined by proficiency test scores. The strategies that were described match meta-cognitive and meta-affective strategies of the S2R Model and self-regulation in general, although the author did not make this connection explicit. Rose (2012) also explored the self-directed learning strategies of kanji (Japanese written characters) within the context of self-regulation. A small group of 12 participants represented varying proficiency levels. Bi-weekly interviews questioned strategy use as well as task regulation. The researcher concluded that incorporating self-regulation into the research framework gave “insight into the struggles of the kanji learner that would have gone unnoticed had a more traditional framework been applied” (p. 143). In other words, including self-regulation in studies of LLS and self-access learning certainly provides a more complete picture.

Thus, there have been attempts to integrate LLS and self-regulation in self-access learning research. While these investigations involved small groups of learners for comparative purposes, the present study focuses on an individual for more detailed data. The participant is a highly proficient language learner engaged in self-access learning.

Elements of Strategic Self-Regulation (S2R)

Self-regulation, Agency, and Autonomy

Oxford (2017) describes the following elements of S2R: self-regulation, agency and autonomy, growth mindsets, self-efficacy, resilience, hope, and internal attributions for success. She points out that self-regulation is inherently action oriented and goal oriented. Hence, strategies are key components of self-regulation. Moreover, LLS often work together in ‘strategy chains’ (Oxford, 2011), which are simply different strategies operating together. The same strategy may be applied in varied situations and contexts, depending on the individual. However, within this framework of self-regulation, Oxford (2017) also stresses the situational and dynamic nature of strategies, by which a single strategy should not be fixed in a single category. She notes that L2 learning is complex and so concrete categories are not realistic. Oxford elaborates by considering so-called ‘categories’ to be more like ‘roles,’ which can change depending on an individual’s factors. She further notes that she had always maintained the impossibility of clear classifications.

In Oxford’s (2017) S2R Model, agency and autonomy are very closely related to each other and self-regulation strategies. Agency is all about meaning. This would include learner perceptions about themselves and the task that is before them. Does the learner believe that they can make a meaningful contribution to their learning? Likewise, if the task is not seen as meaningful, then learners will not have any reason to adopt agency. Oxford cites other definitions in the literature, which all describe agency as the launchpad for decisions about strategies and task actions. Regarding autonomy, she again cites commonalities amongst definitions. Here, the emphasis is on ability. That is, the ability to learn independently with flexibility. The following points summarize all of these elements:

  • Self-regulation is inherently action oriented and goal oriented. Consequently, self-regulation can be seen in strategy use.
  • Agency is the attribution of meaning to the learner and the task. It is the belief that a task can be affected and controlled.
  • Autonomy is the ability and desire to learn independently.

Growth Mindset, Self-efficacy, Resilience, Hope, and Internal Attribution for Success

Growth mindset, self-efficacy, resilience, hope, and internal attributions for success are all related to the learner’s belief system. Learners with a growth mindset believe that they can expand their knowledge and abilities. Relatedly, they believe that strategies can help them grow past current levels. Not surprisingly, a growth mindset is inclined to try new strategies and apply existing strategies in novel ways.

 Self-efficacy is the level of belief in one’s abilities. Self-efficacy then has a direct effect on every aspect of self-regulation. Low levels may limit goal-setting, planning, motivation, and strategy use. Needless to say, self-efficacy also affects agency and autonomy.

Within S2R, resilience is defined as the capacity to adapt after encountering difficulties or uncertainties. On resilience, Oxford (2017) notes that her observations and research have shown that self-regulated learners are extremely resilient. Her own study (Oxford, et al. 2007) found that self-regulated L2 learners used LLS for resilience in their learning processes.

Hope is characterized as desire based on a realizable chance of success. Oxford (2017) cites Snyder’s cognitive theory of hope (Snyder, 1994, 2000, 2002). In this theory, hope includes ‘pathways thinking’ and ‘agency thinking.’ Pathways thinking is the learner’s self-perceived ability to create pathways or strategies to goals. Agency thinking is the learner’s self-perceived ability to maintain progress along pathways, even in the face of obstacles.

Internal attribution for success refers to how individuals explain their success or failure. Oxford (2017) cites Weiner (1986) who ties internal attribution to optimism or pessimism, growth mindset, and agency. Learners see success or failure as coming from internal or external causes. The internal causes are ability or effort; whereas the external causes are task difficulty or luck. However, within S2R, this element is viewed as being very malleable. That is, both internal and external causes can be modified through strategy use. The concept of ‘luck’ is something that is self-made through strategies and the other aspects of S2R. All together, these are the core elements of S2R. Each one contributes to a holistic understanding of LLS. The following section elaborates on LLS as they are understood in the S2R Model.

 Research Question

There are still relatively few case studies on self-regulation in L2 learner behavior (Mezei, 2008; Rose & Harbon, 2013; Yabukoshi, 2020), and even fewer on LLS and self-regulation in self-access learning contexts. Thus, gaps remain in our knowledge of how LLS works on the individual level through self-regulation. This study aimed to explore how self-regulation works in a highly proficient, self-access language learner. LLS are viewed through the lens of self-regulation. The research questions are as follows:

  • What LLS does the learner report?
  • What traits of the S2R Model are found in the data?

Method

The Participant

The participant, David (pseudonym), was a middle-aged American male. He had acquired fluency in Mandarin, Taiwanese, Cantonese, Japanese, and Thai. His fluency was self-reported and supported by high scores on language proficiency tests. At the time of investigation, he had studied Thai for four years and was still actively learning. He had language tutors that were native Thai speakers. He met face-to-face with one on a regular basis. The others were online. David had an engineering background and had been working in sales for an air filtration company. He had done business in Asia for more than two decades, having spent considerable time in China, Japan, South Korea, Thailand, and Taiwan. During this case study, David was on his second stay in Taiwan and had already been there for 10 years. Although he had started language learning in the rather traditional way of meeting in a classroom setting, he later decided that independent study was more effective. Pointedly, he remained steadfast with his language study, continually reflecting on it and making changes. Moreover, he never actually needed any of his second languages for work or educational requirements. He continued independent study solely out of interest and conjecture that there might be some future business benefit.

Data Collection and Analysis

The data for this study was collected from a series of unstructured and semi-structured interviews. Questionnaires or other assessment tools were not considered. The objective was to get detailed self-reports so that LLS and self-regulation could emerge from the data and be accurately situated within their specific contexts. What’s more, the researcher wanted a window on the participant’s developing LLS and self-regulation over time. There were a total of 10 unstructured interviews and five semi-structured interviews conducted over a two year period in which the participant described his learning method, LLS, motivations, attitudes, and related thoughts. As Yin (2009) notes, case study data can be comprised of behavior, opinions, and attitudes. Each of the semi-structured interviews followed up on things that were said in the initial unstructured dialogues. Questions were designed to clarify points and elicit elaboration. All interviews were recorded with the participant’s informed consent. Each one lasted approximately one hour. The researcher then showed his written analysis to the participant to see if he wanted to change or revise the information. Ultimately, none of the information was changed or revised.

Results

Self-Regulation, Agency, and Autonomy in LLS

David planned his acquisition of Thai to be based on lessons learned from previous language learning endeavors. Rather than pursue strategies for reading and writing, he preferred to largely focus on vocabulary acquisition with listening and speaking as the chief modes of input and output. David expressed his reasoning as follows:

I came up with this [my strategies] due to the fact that I ran into such challenge with Thai. With Thai, you have the issue of no words being related to anything I knew. There’s nothing much in Thai related to any languages I already knew. Oftentimes, you can’t break Thai down well. You don’t know what the constituent parts of the words mean. It’s all straight memorization of syllables that don’t seem to make any sense. In Thai, the frequency of word parts having meaning is much lower. So there are unique challenges. So the memorization issue is a real issue. Also, I analyzed my particular cognitive situation and I came up with the idea that based on my experience with Chinese, what seemed to work and what seemed to be really helpful to me was talking to people, particularly listening to people, repeating, mimicking, and adapting.

We can first see self-regulation in the use of metastrategies described here. According to S2R, person knowledge, task knowledge, and whole-process knowledge are key types of metaknowledge on which metastrategies operate. As David elucidates his preferred learning style, he is certainly displaying person knowledge. Likewise, as he connects strategy formulation to his perception of Thai’s unique characteristics, he is displaying task knowledge and whole-process knowledge.

At the onset, David recorded each lesson with a Thai tutor. This triggered a process of reflection on how to best make use of the recordings. David had an immediate desire to edit the recordings so that learned items could be cut out, particular vocabulary items could be coded for repeated listening, and other items could be glossed with explanations. Consequently, he searched for audio editing software and settled on Sonocent Audio Notetaker®. This software allowed David to color code items and perform all of the aforementioned organizing. The color coding was a rather complex but meaningful system for David. Green was for portions that were comprehensible and fairly well acquired. Yellow was for vocabulary to ask the teacher. Dark blue signified a need for more review. This would typically be for receptive knowledge of vocabulary but a lack of productive knowledge. White was for explanations that David would eventually delete so he could listen to the input without explanation. Light blue was for further discussion. If there was talk about Thai manners for example, and David wanted deeper discussion about it, this would be highlighted in light blue.

Subsequent tasks included highlighting target vocabulary and listing it for use in the next lesson. At times, there would be some inferring of meaning based on the context. David would then plan to clarify with the tutor and think about ways to use these items with his tutor. This, in turn, would allow for the target items to be incorporated into the next recording for repeated listening.

David would do focused and unfocused listening to the recordings. This could be at home for the former or walking down the street for the latter. All of this represents specific cognitive strategies stemming from and interacting with the metastrategies described above. But affective roles are also evident in this dance. David is planning and applying these strategies based on beliefs about his learning style and what is most effective for him emotionally and motivationally.

Referring back to Zimmerman’s (Figure 1) and Oxford’s (Figure 2) self-regulated learning, some of David’s forethought and performance have been described. As David reflected on task performance, one of his earlier adjustments was related to location. He discovered that certain places were not conducive to recording lessons. Eventually, he was able to find cafés and other locations that had minimal background noise. David also began to search online for supplementary tutors. He registered on a language learning website and scrolled through video profiles of various native-speaking Thai tutors. David took lessons with several of these. Initially, his interest was piqued at the opportunity to hear slightly different accents and lingo. However, he reported that he had to cancel some of these lessons due to issues like voice volume, intonation, or lack of interaction. David acknowledged that these issues were most likely personality aspects of the individual tutors. But he emphasized that his approach was one that centered on listening and recording for later self-study. After this stage, he wanted to go back to the tutor with questions or requests for clarification on numerous vocabulary items. This was a sociocultural-interactive strategy of relying on input and asking questions. Both task performance and reflection resulted in vetting tutors for suitability rather than just accepting their instruction.

As previously described, David would highlight unfamiliar or questionable vocabulary in his recordings and bring them to the subsequent lesson. Not long into this process, he would ask his tutor to put this vocabulary into a short, simple, and memorable story. The impetus for this was certain words not occurring often enough in lessons. This lack of vocabulary recycling gave David difficulty in recalling them. He reports that the number of syllables in a word was another factor that made certain items hard for him to acquire. David felt that a different context would aid his memory. He would record each story and listen repeatedly. The first time would include the story with some splicing of information, namely vocabulary explanations. The second time would be with no explanations, and the third time would be listening together with all the other stories collected to date. David reported that the stories became a critical cognitive strategy for him. Simultaneously, it appeared to have been an affective strategy in that he perceived it as interesting, effective, and motivational.

Clearly, David was exhibiting autonomy throughout this learning process. Agency is also an obvious trait weaved throughout as David expressed self-awareness and proceeded to formulate corresponding strategies that would facilitate task success.

LLS and Other S2R Elements

Oxford (2017) included growth mindset into the S2R Model as a trait of self-regulated learners. Such learners believe that strategies can help them grow past current levels. Additionally, they try strategies in new ways. David exhibited this with a strategy that he termed OECI. This represented the following: original-explanation-corrected version-implementation. This was adopted after approximately one year of Thai study. It was primarily used for grammar and tone issues. In Thai, every syllable is one of five tones: low, mid, high, falling, or rising. As with any tonal language, the correct tone is necessary for the intended meaning. These were issues David recognized later as an area for growth and improvement. The ‘original’ would be the initial output which contained the error. The ‘explanation’ would be the tutor’s explicit correction with reasons. The ‘corrected version’ would be David’s immediate self-correction. Then, the ‘implementation’ would be the teacher putting it into a short passage or story with the target items being repeated more than once. David remarked that he had difficulty when trying to habitualize abstract explanations. So he formulated the OECI strategy to aid his own productive correction.

Another learner trait in S2R is self-efficacy. David reported an awareness of increased self-efficacy that he directly attributed to his strategy use. He described one very salient episode when his tutor was talking about her close friend’s personal problems. “She was talking from the heart and talking fast.” She was thinking about suspending her studies in Taiwan and going back to Thailand. David noted that he understood everything. At that moment he speculated that his tutor was just communicating in Thai because that is what she found most efficient. She was simply communicating rather than teaching.

Hope and resilience are other attributes in the S2R Model. Hope is predicated on ‘pathways thinking,’ that is the learner’s self-perceived ability to create pathways or strategies to goals. This is evident in David’s overall approach to Thai, consciously planning it to be different from failed language learning strategies of the past. Resilience can be seen most clearly in the story and OECI strategies. David adopted both of these in response to learning difficulties.

Internal attribution of success can also be seen from the interview data. Learners may perceive success and failure from either internal or external causes. But both internal and external factors are malleable and can be modified through strategy use. David reported internal attribution for his success in language learning. He expressed an acute awareness of his strategies as the primary driver for progress. At the same time, he also described certain task difficulties, or external causes for initial failure. Still, David’s hope and resilience overcame these challenges with strategies such as repeated listening and stories.

Discussion

The participant reported his LLS formulation along with goals and affective factors that inspired them. He adapted strategies and adopted new ones in order to maintain his goals in the face of challenges. Throughout this language learning experience, the core elements of self-regulated learning, namely the S2R Model, seem to be in a constant interplay with one another. Agency and autonomy are expressed in all of the interview data. The interview data describes cognitive, affective, and socio-cultural interactive strategies embedded in a single task action. Other S2R learner traits such as growth mindset and resilience are clearly identifiable in the participant’s adaption and adjustment of strategies.

Other literature has identified ome traits of highly proficient language learners, such as the conscious direction of LLS. However, the use of LLS within self-regulation and self-access contexts remains an underexplored domain. One result that the researcher did not fully expect was the participant’s lucid awareness of his own self-regulation. He instinctively responded to all interview questions with little to no need for clarification on any aspect of the S2R Model traits. Certainly, the very nature of self-regulated learning entails awareness. Still, it cannot be assumed that all self-regulated learners will have done extensive reflection on the processes themselves. Further research is needed to gather rich data on how LLS works within individual self-regulation.

Notes on the Contributor

Guy Redmer is currently an Assistant Professor of English at Tamkang University in Taiwan. His research focuses on language learning strategies, self-regulated language learning, and reading acquisition. In addition to research, his other publications include language learning textbooks, short fiction for language learning, and language assessment prep materials.

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