Metacognitive Knowledge and the Self as Socially Distanced Online Learner: A Virtual Reality Assisted Analysis of Academic Self-Concept

Abstract

With online learning solutions responding to the novel coronavirus pandemic, it is important for educational technologists and other practitioners to understand how learners are experiencing the demands of socially distanced online learning and how they conceive of themselves within distant spaces and digital communities. Research into the metacognitions of learners provides a non-technocratic focal point through which such information can be extracted. Framing learner self-beliefs as a form of metacognitive knowledge, the current article presents a virtual-reality-assisted thematic analysis into the self-appraisals of 210 socially distanced online learners at a Japanese university. The study focuses on the discursive rationalizations expressed in service of the academic self-concept. Four themes were identified in the data: formal assessment, affect and emotion, self-regulation, and transformative awareness. Such research provides educators with a platform for pedagogical intervention and course design considerations relative to the challenges of the online learning experience.

Keywords

academic self-concept distance learning learner beliefs metacognitive knowledge thematic analysis

Introduction

Metacognition refers to “knowledge concerning one's own cognitive processes and products or anything related to them” in addition to “the active monitoring and consequent regulation and orchestration of these processes in relation to the cognitive objects or data on which they bear, usually in the service of some concrete goal or objective” (Flavell, 1976, p. 232). Metacognition within the context of learning concerns “thinking about thinking” as a conscious activity related to learner development and conscious awareness rising. During metalearning activities, student attention is often directed toward components of self-regulated learning such as task identification, strategic planning, comprehension monitoring, and post-task reflection (Schunk, 2005; Zimmerman, 2002). Dabbagh and Kitsantas (2005) asserted that within online learning environments “the physical absence of the instructor coupled with the increased responsibility demanded of learners to effectively engage in learning tasks may present difficulties for learners, particularly those with low self-regulatory skills” (p. 517) (see also Fisher & Baird, 2005; Hartley & Bendixen, 2001; Hill & Hannafin, 1997). The process of effective self-regulated learning is therefore a result of the degree to which an individual can attain greater self-awareness and be consciously purposeful in the application of structured learning behaviors. Awareness of the self has frequently been reflected through the term metacognitive knowledge, which is one of the three components of metacognition alongside metacognitive regulation and metacognitive experience (Flavell, 1979).

Metacognitive knowledge references the awareness individuals have about themselves and others in their role as cognitive processors. Metacognitive knowledge is often described in relation to declarative knowledge, procedural knowledge, and conditional knowledge (Pintrich, 2002). Wenden (1998) defined the metacognitive knowledge as “the relatively stable information human thinkers have about their own cognitive processes and those of others” (p. 516). Flavell (1987) framed beliefs as a component of self-awareness, inclusive of all that individuals understand about themselves as learners and thinkers. Conceptualized as learner beliefs, this self-awareness reflects thoughts, which structure realities drawn from interaction and experience. Within the context of learning, the metacognitive knowledge (i.e., self-awareness) which an individual possesses is used to guide metacognitive experience which in turn serves to provide cyclical feedback to the metacognitive knowledge underpinning the initial experience (Efklides, 2001; Garner, 1994). This captures what it means to learn from experience.

Metacognitive knowledge has been shown to inform and predict motivated learning behaviors and positive achievement outcomes (Covington & Roberts, 1994; Jia et al., 2019; Schraw & Dennison, 1994; Teng, 2020). Learner behaviors and outcomes are therefore partly a consequence of the metacognitive knowledge an individual learner possesses relative to the self, operating within a specific realm of experience (e.g., as a socially distanced online learner). Several authors have pointed to a convincing body of research demonstrating that “self-perceived academic competence influences student motivation, learning, and academic performance” which has been used to rationalize “why judgments of self-perceived competence are a key component of motivation and learning theories” (Ferla et al., 2010, p. 520). Framing the self-beliefs of learners as a valuable form of metacognitive knowledge, and positioning these self-beliefs as a “multilayered collection of viewpoints and voices rather than a well-organized and coherent schema” (Dufva, 2003, p. 146), the current article presents a virtual-reality-assisted analysis into the self-beliefs of Japanese university students relative to their role as socially distanced online learners during the novel coronavirus pandemic. The discursive rationalizations documented by learners in service of their academic self-concept (e.g., self-beliefs relative to perceptions as either effective or ineffective online learners) serve as the focal point in this study. Such research is timely in that uncovering the self-beliefs of learners within such emergent learning situations provides educators with a platform for positive pedagogical intervention and future course design improvements.

Epistemological Beliefs and Online Environments

Research has been undertaken to investigate learner beliefs relative to the nature of knowledge and learning (Schommer, 1994). Winne (1995) theorized that such “epistemological beliefs serve an important role of setting standards for students’ self-regulated learning” (cited in Hofer & Pintrich, 2002, p. 106). Hofer and Pintrich (1997) speculated that epistemological beliefs function as implicit theories which give rise to personal goals for learning, with these goals then mediating the relationship between epistemological beliefs, academic cognition, and performance. Epistemological beliefs have been commonly articulated through reference to five specific dimensions, three of which concern the nature of knowledge and knowing (e.g., the structure of knowledge, the certainty of knowledge, and the source of knowledge) and two of which concern the nature of learning and intelligence (e.g., the nature of ability and the speed of learning) (Schommer, 1990; Schraw et al., 2002).

Research has demonstrated the interactive relationships between epistemic and learning beliefs, motivation and learning (DeBacker & Crowson, 2006). Epistemological beliefs have been studied in relation to online learning processes and scaffolded interventions (Demetriadis et al., 2008), learning environment preferences (Tsai & Chuang, 2005), online cognitive performance and task interruption (Levy et al., 2016), and prior domain-specific knowledge and self-regulation in learning (Pieschl et al., 2008). Jacobson et al. (1996) demonstrated that learners who conceptualize knowledge as complex in structure, thus requiring effort and personal involvement, are better equipped to learn in demanding learning situations. Highlighting the significance of a constructivist approach to online education, Tsai (2000) demonstrated that learners who are constructivist orientated have stronger preferences toward learning environments in which they can conduct group work and open-ended tasks relative to prior knowledge. In a study of web-based learning activities, Braten and Stromso (2006) documented that learners who held the “naive epistemological belief that knowledge is given and stable were less likely to engage in discussion and communication about subject content” (p. 1027). Similarly, Lee and Choi (2017) looked at the learner factors influencing higher-order thinking in technology-enhanced learning environments. The authors found that a deep approach to learning was the most significant and direct predictor of higher-order thinking skills. A deep approach to learning was also significant in that it mediated the effect of epistemological beliefs and attitude toward technology on higher-order thinking skills.

Learner Beliefs and Conceptualizations of the Self

The knowledge systems referred to through epistemological beliefs have created some confusion between actual knowledge systems and belief systems. In teasing out important differences, Pajares (1992) stated that “belief systems, unlike knowledge systems, do not require general or group consensus regarding the validity and appropriateness of their beliefs. Individual beliefs do not even require internal consistency within the belief system” (p. 311). In other words, individual belief systems can be far removed from a consensual understanding of reality, whereas knowledge systems require the validation of others through consensus. Research into learner beliefs has been most prevalent within the domain of foreign language education due to the highly personalized demands involved in learning the language, culture, customs, and habits of others (Cohen & Norst, 1989). One of the most longstanding approaches to motivation within foreign language education concerns a “willingness to be like valued members of the language community” (Gardner & Lambert, 1959, p. 271). Such an approach is premised on the development of positive attitudes and beliefs in relation to the target language community. Several studies have attempted to document the role of learner beliefs within foreign language education across multiple contexts and in relation to various criteria (Kartchava & Ammar, 2014; Lai, 2019; Sakui & Gaies, 1999; Zhou, 2018).

The limited focus upon learner beliefs in other research domains can be attributed in part to the conflated use of terms such as self-efficacy, self-esteem, and self-concept (Shavelson et al., 1976). In a meta-analysis of such language, Hansford and Hattie (1982) identified 15 related terms which were used inconsistently. Valentine and DuBois (2005) explained that “theoretically, self-concept, self-esteem, and self-efficacy beliefs share a common emphasis on an individual’s beliefs about his or her attributes and abilities as a person” (p. 55). Moreover, self-esteem relates to a holistic evaluation of worth and value as an individual, whereas, in contrast, self-efficacy is a more context-specific future-orientated assessment of competence in relation to a specific activity (Bandura, 1977; Bong & Skaalvik, 2003). The notion of self-concept is often less specific than self-efficacy (i.e., a unidimensional approach) but is more commonly inclusive of self-perceptions within a certain conceptual domain (i.e., a multidimensional approach). Reflecting the unidimensional approach, Burns (1982) defined self-concept in broad terms as referring to perception of oneself informed by environmental experience. The centrality of experience within a specific environment has brought about modifications to self-concept terminology such as in the case of the academic self-concept (Bong & Skaalvik, 2003; Chapman et al., 2000; Griffore & Bianchi, 1984; Marsh, 1990; Marsh & Shavelson, 1985).

Academic self-concept refers to the knowledge and perceptions that individuals hold about themselves within an achievement situation (Byrne, 1986; Marsh, 1993; Shavelson & Bolus, 1982; Wigfield & Karpathian, 1991). The academic self-concept is developed through experience and feedback related to frames of reference (e.g., comparison among classmates and normative achievement standards), causal attributions (e.g., the reasons given for relative success or failure), appraisals from others (e.g., the values given to the self through feedback from others such as teachers), mastery experiences (e.g., self-schemas mapped by past achievement experience), and psychological centrality (e.g., perceptions of qualities that underpin self-esteem) (Skaalvik, 1997). The relationship between the academic self-concept and academic achievement has been well established through correlational studies (Hansford & Hattie, 1982; Marsh et al., 2006; Marsh & Martin, 2011) although its structure is often debated (Brunner et al., 2010). In addition, a positive self-concept within an achievement environment has been linked to increased motivation and effort (Hailikari et al., 2008) and better academic choices and the selection of coursework (Marsh & Yeung, 1997). Simonsmeier et al. (2020) have most recently showed how the structured online peer feedback improves the academic self-concept in higher education. Moreover, as social comparison plays a significant role in underpinning the academic self-concept, negative achievement-referenced feedback and comparison among peers is likely to undermine other elements of the self as learner including self-efficacy and self-esteem.

Significance of Research

Within learner belief research, three main points of contention remain concerning the stability of beliefs and their susceptibility to change, the value of beliefs in terms of reflecting a lack of knowledge or an erroneous reality and the degree to which beliefs are socially or cognitively constructed. In terms of belief stability, Horwitz (1987) regarded learner beliefs as preconceived notions, which are essentially static, stable, and unchanging, whereas others view beliefs as a “socially constituted, interactively sustained, time-bound phenomenon” (Goodwin & Duranti, 1992, p. 5). In terms of belief validity, Riley (1997) argued that although individual beliefs may not be parallel with what is known as scientific truth or objective fact, the individual beliefs of the learner are most likely to influence cognitions and behaviors. Pajares (1992) similarly suggested that the noncensensuality of beliefs “implies that belief systems are by their very nature disputable” (p. 311). In contrast, belief assessments taken beyond the context of specific activities have been labeled as random products of deficient self-knowledge systems. In terms of belief construction, although some researchers view beliefs as cognitive entities (Abraham & Vann, 1987; Wenden, 1998), others frame learner beliefs in relation to social experience and interactions with the environment (Sakui & Gaies, 1999) which therefore frames them as “relational and responsive to context” (Benson & Lor, 1999, p. 464).

Cotterall (1999) argued that before instructors promote autonomy-based solutions, it is essential to investigate learner beliefs and gauge the readiness of learners for greater autonomy and responsibility. This is particularly applicable among students operating under increased self-regulatory demands such as those within socially distanced online learning environments. It is also crucial for students who lack prior experience in managing their own metacognitive and self-regulatory thoughts and behaviors such as those within prescriptive education systems. Such contexts also demand attention to be given to students who have naive epistemological beliefs derived from educational experiences which prioritize rote-memorization rather than constructivist negotiations of meaning. Informed by the literature presented, the current study presents a virtual-reality-assisted analysis into the self-beliefs of Japanese university students relative to their role as socially distanced online learners during the novel coronavirus pandemic. The discursive rationalizations documented by learners in service of their academic self-concept (e.g., self-beliefs relative to perceptions as either effective or ineffective online learners) serve as the focal point in this study.

Methods

Research Context and Participants

The current study was undertaken at an Systems Information Science university in northern Japan impacted by regulations surrounding the novel coronavirus pandemic. Prior to the commencement of the first semester in AY2020, teaching activities were switched to an online delivery format. Students were prohibited from attending the university and were thus required to study from various locations around the country. Synchronous and asynchronous online programs were offered to students through two primary online learning management systems (Moodle and manaba). Participants were drawn from a sophomore population of 250 students midway through the second semester of socially distanced online learning. A final sample of 210 consenting students provided data.

Measurement

Within metacognitive knowledge and epistemological belief research, concerns with measurement validity and reliability have persisted (see Clarebout et al., 2001). However, beliefs about the self in relation to learning experience lend themselves to phenomenological narrative expressions which “describe the schemata or themes that constitute experience” (Polkinghorne, 1983, p. 231) and provide a voice to the “subjective and retrospective interpretations of how one develops” (Rossiter, 1999, p. 67). Within online learning management systems such as Moodle, asynchronous discussion boards are a central component of many courses (Cho & Tobias, 2016; Pena-Shaff & Altman, 2015). Gao et al. (2013) identified four ways that online instructors organize asynchronous online discussions, the most common of which was termed as a constrained environment wherein instructors provide students with frames of reference or sentence starters to orient students toward certain responses (Corfman & Beck, 2019). In identifying ways that teachers can cooperatively facilitate the communication of learner beliefs beyond self-reported survey responses, Zhong (2012) suggested structuring opportunities into the curriculum. This approach allows the teacher to embed the narrative activity within a learning environment foregrounded by appropriate levels of support in terms of materials and language. Galikyan and Admiraal (2019) concluded that the Moodle “online asynchronous discussion board can serve as an effective instrument to reflect individual learners’ level of cognitive engagement in the learning process” (p. 7). The current study therefore collected semi-structured discursive data through the forum discussion boards on the Moodle learning management system.

Procedure

As a means of orientating students toward greater self-awareness and empowering them with the language to express beliefs in relation to learning and experience, the teacher–researchers began structuring the data collection approach during the first 15-week semester of AY2020. Within a compulsory bilingual communications course, students spent 4 weeks learning about learning through a thematic metalearning unit. The objective was for students to be able to collaboratively define what learning is, to summarize the cognitive process dimensions of learning in relation to specific activities, to understand what makes a university student successful and to reflect on past learning experiences. During this period students also read Japanese-language research on approaches to learning (i.e., deep, strategic, and surface), and how different approaches relate to achievement outcomes. In support of these core activities, students were required to participate in several online forum discussions. The forums provided question prompts to students in addition to cues for structuring a coherent response. It has been noted that “implementing appropriate questioning strategies that trigger students to activate their context generating cognitive processes” (Demetriadis et al., 2008, p. 939) is an effective method for promoting improved learning outcomes. The second 4-week thematic unit focused on information processing and distinguishing between facts and opinions. Using examples from a variety of domains (i.e., research, politics, etc.), students were primed with the notion that claims must be supported by evidence and that facts are often used either as standalone evidence or in support of opinions relating to claims made. This information was then referenced in relation to the forum discussion answers provided in the previous unit (i.e., students were made aware that validating evidence was needed to strengthen their opinions and explanations concerning learning).

During the second 15-week semester, the same cohort of students read Japanese-language research related to the influence of personality traits on academic achievement outcomes. Students were required to document their own behaviors within an achievement context based on descriptors associated with each particular trait (e.g., conscientious students manage their study time effectively). The students also read articles on the acceptance of technology in education and were asked to consider how socially distanced online learning had impacted their own experience during AY2020. Toward the end of the second 15-week semester, the same students were presented with a Moodle forum discussion activity, which serves as the source of data in the current study. The students were asked whether they considered themselves as effective or ineffective online learners and to provide credible evidence to support their self-appraisals. After 1 week the discursive data was downloaded for analysis.

Analytical Framework

Thematic analysis has been described as a flexible “method for capturing patterns (themes) across qualitative data sets” (Clarke et al., 2019, p. 843) and was selected as the analytical framework in the current study (see Boyatzis, 1998; Guest et al., 2012; Ryan & Bernard, 2003). Rather than being a singular technique, many nuanced approaches to thematic analysis have been identified. An inductive approach to analysis was selected in which the process of coding and the development of themes are directed by the content of the data (Braun & Clarke, 2020). In terms of the analytical process, the six steps outlined by Braun and Clarke (2006) including familiarization, initial code generation, theme identification, theme review, defining and naming emergent themes, and the reporting of findings were followed (Table 1).

Table 1.

Phases of Thematic Analysis (Based on Braun & Clarke, 2006, p. 87).

Procedure	Description
1. Familiarization	Transcribing data, reading and re-reading the data, noting down initial ideas
2. Initial code generation	Coding interesting features of the data in a systematic fashion across the entire data set, collating data relevant to each code
3. Theme identification	Collating codes into potential themes, gathering all data relevant to each potential theme
4. Theme review	Checking if the themes work in relation to the coded extracts (Level 1) and the entire data set (Level 2), generating a thematic “map” of the analysis
5. Defining and naming of themes	Ongoing analysis to refine the specifics of each theme, and the overall story the analysis tells, generating clear definitions and names for each theme
6. Reporting findings	The final opportunity for analysis. Selection of vivid, compelling extract examples, the final analysis of selected extracts, relating back of the analysis to the research question and literature, producing a scholarly report of the analysis

Participant data was first organized according to self-classification as either an effective or ineffective socially distanced online learner. From the 210 complete responses, 50 (23.8%) students framed themselves as effective learners, whereas 160 (76.2%) framed themselves as ineffective learners. In terms of the thematic analysis, for steps 1–3 we created three print transcripts of the data and worked independently toward familiarization, initial code generation, and theme identification. As the process of thematic analysis requires “circling around the data set, getting to know it well, and at times parking close to some data for close scrutiny” (Harding & Whitehead, 2013, p. 8) several weeks were spent independently going through the process of read-review-read-review. At the end of this process, three face-to-face research meetings were held to share our findings and work toward consensus.

At this point in the process, the novel coronavirus restrictions within the university were increased meaning that further face-to-face meetings were compromised. Therefore, for steps 4–6 we collaborated through virtual reality. The Engage (https://engagevr.io/) virtual reality application was used with three Oculus Quest head-mounted devices. With the increased ubiquity of devices enabled for virtual reality and developments in peripherals such as displays, haptics, sensors, and trackers, Bailenson (2018) predicted that virtual reality can be expected to “utterly change how we interact with the (real) world around us, and with other people” (p. 11). Through virtual reality, students, teachers, and researchers can replicate real-world communication, supported by nonverbal cues and creative interchanges. Moreover, three-dimensional objects can be projected into the virtual environment and enlarged, dissected, and re-arranged resulting in a sense of hands-on collaboration. Communicating with colleagues through human avatars and working on shared tasks within a virtual environment promotes social presence (i.e., a sense of being with someone) and immersion (i.e., an illusion of reality) (Slater, 2009).

We located ourselves within a virtual meeting room and projected the files and notes used in steps 1–3 on to a virtual presentation screen. We were also able to share our desktops and interact with the documents through our avatars and real-time spoken communication (see Figure 1). Although the use of virtual reality in social science research methods has rarely been documented, Pan and Hamilton (2018) described how researchers may incorporate virtual reality to “create situations that cannot safely and feasibly exist in the laboratory” (p. 397). Although this observation is drawn relative to psychological research, it is also applicable to the process of collaborative analysis when confronted with face-to-face contact dangers and restrictions such as those surrounding the novel coronavirus.

Figure 1.

The virtual reality interaction during part of the thematic analysis.

Across three virtual reality sessions, the coding of the data was refined until consensual agreement was reached between the researchers. The themes agreed upon are shown in Table 2.

Table 2.

Emergent Themes and Definitions.

Label	Definition	Academic self-concept
Label	Definition	Effective	Ineffective
1. Formal assessment	Explicit reference to formalized achievement metrics such as course grades, GPA scores, and credit	27/50 (54%)	56/160 (35%)
2. Affect and emotion	Explicit reference to affective and emotional attributions such as interests, motivations, and considerations relative to learning and the online experience	3/50 (6%)	27/160 (17%)
3. Self-regulation	Explicit reference to the use of self-regulatory processes and management strategies such as planning, goal setting, reviewing/previewing, time management, self-control, and resource use	15/50 (30%)	55/160 (35%)
4. Transformative awareness	Explicit reference to awareness of engagement/agency in higher levels of cognitive activities and/or understanding the need to change approach based on experience	4/50 (8%)	22/160 (14%)

Discussion

From the initial broad quantification of the responses in which 76.2% of the students classified themselves as ineffective online learners, it is apparent that online learning presents significant challenges to students. As outlined in prior sections, learner behaviors and achievement-related outcomes are partly a consequence of the metacognitive knowledge an individual learner possesses relative to the self, operating within a specific realm of experience (e.g., as a socially distanced online learner). Several patterns were emergent within and between the effective and ineffective learners which clustered into four distinct themes. The data shows that effective and ineffective learners had a dominant trend toward belief rationalization references relating to formal assessment, this being the successful or unsuccessful attainment of the individual or overall course grades, Grade Point Average (GPA) scores, and credit. To a lesser extent, the two groups also referenced beliefs in relation to self-regulation and factors surrounding the actual process of learning. Greater divergence between the groups was observed in relation to the second (i.e., affect and emotion) and fourth themes (i.e., transformative awareness). In comparison to effective learners, students who classified themselves as ineffective online learners based their belief rationalizations on a wider range of factors in the case of affect and emotion and in relation to a failure to learn from experience in the case of transformative awareness. These observations are expanded upon in the following sections with reference to each theme extracted from the data.

Theme 1: Formal Assessment

For those students who classified themselves as effective online learners, reference to formal assessment outcomes was the dominant belief rationalization given (54%), whereas it was the joint dominant belief rationalization of students who classified themselves as ineffective online learners (35%). The dominance of this theme is to be expected given the characteristics of the educational context in which the research was undertaken. Japanese education is known for its prescriptive focus on rote-memorization and formal testing as evidence of supposed learning (see Marginson, 2011). Within such a regimented system, students have been trained to base beliefs about themselves in relation to learning and their own academic self-concept on formal achievement indicators. Although this traditional position on Japanese education remains prevalent it is perhaps not as reflective of reality as it once was due to demographic shifts and increased attention directed toward the negative impact of such a competitive system. Rappleye and Komatsu (2018) have explained how “the stereotypical image of East Asian Exam Hell does not match current realities” (p. 732) within the Japanese education system. Nonetheless, within the context of socially distanced online learning, the value attributed to formal achievement remains apparent, as do beliefs concerning formal achievement as evidence of learning.

I think that I am an effective online learner from an objective point of view because about 86% of my grades in the first semester are rated as S and A [highest formal grades]. The remaining 14% are grades such as B and C, and I haven’t dropped any units yet.

I think I am an effective online learner. My GPA is up from last year. Getting a good score on a test will raise the GPA. A higher GPA means a better understanding of each subject.

I am an ineffective online learner. The reason is that the evaluations A and S are extremely few. Effective learners get good grades and output the knowledge they have gained for research and learning. I haven’t achieved good grades.

The use of formal assessment indicators to validate the self as an ineffective learner is also suggestive of naive epistemological. Braten and Stromso (2006) cautioned how such students are less likely to engage in discussions about subject content. We suggest that low-grade outcomes should not be considered synonymous with an inability to learn or used for the purpose of affirming academic self-concept beliefs as an ineffective learner. Educators should therefore focus on communicating to students who receive lower grades that formal achievement indicators are not necessarily a reliable indication of anything more than investment and strategies. Drawing student attention toward the actual cognitive and behavioral attributes and routines that promote successful formal achievement outcomes is a more reliable and equitable basis for self-belief rationalization across contexts and subjects.

Theme 2: Affect and Emotion

It was noticeable that for students who classified themselves as effective, affect and emotion was infrequently used as a referent for belief rationalization (6%), in comparison to other referents and to those students who classified themselves as ineffective (17%). The responses by the effective learners referenced an alignment between the content of the study material and their own personal interests. Interest was used to motivate through the cyclical process of investment and reward.

I think I am an effective online learner. There are two reasons. The first is that the knowledge I learned in university class is directly or indirectly on the basis of my interest. The second is that I have an interest in other fields. I have expanded my interest by learning what other learners are interested in.

For students who self-classified as ineffective learners, a broader range of factors was evident including a lack of curiosity, a negative attitude toward studies in general, a fear of failure, learning for the benefit of others such as parents, a lack of motivation or motivation dependent on personal circumstances, in addition to only having an interest in certain subjects.

Looking back on the first semester, I cannot say that my attitude towards online learning was good. In online classes, I needed to collect class information more independently than offline. Switching my feelings to take lectures without going to school was also difficult for me. I needed to have a trigger to begin learning seriously in my house.

I do not think of myself as an effective online learner. I have not been able to show a positive attitude toward learning except in my favorite academic field that interests me. I think that learning what you are not good at is like attacking your weaknesses intensively. That’s something no one wants to do and I am no exception.

I do not think I am an effective online learner because I always leave it to my mood. I always study when I want to study. I do not study when I am not motivated. I do not want to be stressed by lectures and assignments and dislike learning, so I leave it to myself as much as possible.

I am an ineffective online learner because I find it a hassle to put in the effort and time to learn. I can learn only if I am interested in the subject and I run away from things that do not interest me or that are too much of a hassle. I get bogged down in the tasks and studies in front of me before I can do anything to improve myself.

I think I am an ineffective online learner because I am afraid of failing whatever I do, so my study is always something which doesn’t last long. In the online class in the first semester, my grades were lower than when I was offline. I had curiosity but I didn’t make the effort to match that curiosity and didn’t reflect it in my actions.

Such comments represent a considerable challenge for educators as they concern fundamental motives relating to the decision to enroll in a particular course or university program. Students who are influenced by external factors are perhaps at greater risk from failure within the context of socially distanced online learning. The comments also raise questions concerning the responsibilities of students in relation to teacher expectations and the creation of conditions conducive to learning. Educators might promote to students the interconnected nature of different subjects with activities intended to demonstrate how skills and knowledge learned within one course can then be applied to aspects of another course. Students who adopt a narrow-segmented view of education and knowledge, which is then allowed to impact attitudes, motivations, and behaviors are unlikely to be successful. For these reasons, ineffective learner belief rationalizations drawn in relation to affect and emotion are the most problematic in terms of developing a positive academic self-concept as a socially distanced online learner.

Theme 3: Self-Regulation

Self-regulation was a theme that equally served effective and ineffective students in validating their academic self-concept through belief rationalizations. Some noticeable differences existed between the two groups. Self-regulation was the second most dominant belief rationalization for effective learners, whereas it was one of the two equally dominant indicators for ineffective learners. When describing effectiveness in learning, effective learners within the current context may prefer to use a formal assessment indicator due to an awareness that good cumulative academic records such as high GPA scores are contextually believed to reflect overall excellence in a variety of factors including those represented by the other themes extracted. In other words, students who rationalized their effectiveness in relation to self-regulation may be limited to those who have been making conscious effort to practice self-regulation daily. Indeed, the responses from the effective learners point toward a variety of specific self-regulatory strategies such as planning, carrying out the plans, managing schedule and resource usage, thus highlighting the importance of attending to learning as a process of interrelated actions or behavioral sequence.

I am an effective online learner because I am prepping and reviewing. Prepping and reviewing is a necessary part of consolidating what I’ve learned. By studying on my own, I can acquire new knowledge that cannot be discovered in a lecture.

I think I am an effective online learner because I was able to properly plan how I would study online. I planned and executed a process to achieve my learning goals. In addition, I managed tasks so I can submit reports and other assignments with plenty of time without forgetting them.

In contrast, the responses of the ineffective learners are characterized by referents to a lack of overall self-regulatory abilities such as passive learning, a lack of study habits, and an inability to manage time and focus. It is therefore reasonable to assume that ineffective learners may have not yet developed self-regulatory abilities appropriate to the context. This may also explain the equal split of their responses between the two dominant themes (formal assessment and self-regulation); that is, if the student is less aware of a factor, he/she is less likely to use the factor as a reason for rationalizing one’s own ineffectiveness as a socially distanced online learner. Furthermore, although effective learners rarely mentioned the online specific nature of these belief rationalizations, suggesting that their engagement in self-regulatory strategies does not depend on a specific learning environment, many ineffective learners associated a lack of self-regulatory skills with the demands of the online learning environment. This observation is consistent with the findings in previous studies which referenced the vulnerability of ineffective self-regulatory learners within the online learning context (see Broadbent & Poon, 2015; Dabbagh & Kitsantas, 2005).

I think I am an ineffective online learner. Learning requires a deep approach, but I am a surface approach person because I was studying to take credits on the test. I do not understand the contents of my classes. I do not do the assignments until just before submitting them. Also, I rarely study anything other than assignments. Since it is an online class, I fall asleep during class and miss important points. I do not take classes seriously.

I do not think I am an effective online learner. This is because I enjoyed online lessons so much that I cut corners and reduced my grades. Online lessons have saved me time from school and helped me sleep longer. As a result, I became spoiled and overslept more often.

I think I am an ineffective online learner. There are two reasons. First, I didn’t have a deep approach to learning. Because all the lessons in the first semester were conducted online, I just listened to the lessons and didn’t check what I didn’t understand, and I only studied before the test. Second, I couldn’t take a strategic approach to learning. I couldn’t use my time effectively because I had a lot of time at home due to online classes. There are also asynchronous lessons, so I wasted my time thinking about when to study.

Informed by the finding that high academic achievers often have a larger repertoire of self-regulatory strategies than low achievers (Pintrich & De Groot, 1990; Zimmerman & Pons, 1986), we believe that this theme is potentially the most productive in terms of educational intervention and in providing students with the greatest opportunities for educational success and social readiness online. Self-regulation also provides a suitable basis for the promotion of multiple skills and competencies which can be used across all courses regardless of personal interest and appeal. Educators should not only be explicit about the value and importance of self-regulation but also incorporate a variety of self-regulatory strategies such as planning, goal setting, time management, and reflective activities into the daily learning routines of students.

Theme 4: Transformative Awareness

This theme is closely related to the notion of transformative learning defined as “the process of using a prior interpretation to construe a new or revised interpretation of the meaning of one’s experience in order to guide future action” (Mezirow, 1996, p. 162). Comments in this theme included those in which students made explicit reference to their own agency potential based on past learning experience. Ineffective learners cited reasons relating to transformative awareness (14%) more frequently than students who classified themselves as effective learners (8%). By teaching the metalanguage of cognitive processes, approaches to learning and learning types, the students were discursively equipped to make informed judgments about themselves and their experiences. Effective learners within this theme associated learning with the construct of understanding and requisite knowledge transfer across contexts. Within such comments, knowledge building through experience was often prioritized over formal assessment criteria.

I think I am an effective online learner because I don’t cut corners in my learning. When I’m learning, I try to understand as much as I can. I am trying to understand not only what is on the outside, but also what is inside. And once I have learned until I understand it, I think about how I can apply it to something else.

I have become an effective online learner in the last six months for three reasons. First, I came to think deeply. Second, I became able to manage myself. Third, I started to study actively. I can now feel the joy of studying.

Citing the taught information from the previous semester, students were able to critically describe their approaches to learning and their position as learners in-flux. Transformative learning as a “disorientating dilemma” (Mezirow, 1995, p. 50) is a process concerned with fundamental self-relevant change (Merriam & Caffarella, 1999). Reflecting upon pre-tertiary learning experiences in which the emphasis may have been more test-driven highlights the necessity to reposition the self and reframe learning priorities. Ineffective learners within this theme were able to demonstrate an awareness of the need to change behaviors and approaches to learning, although this knowledge was often not enough to utilize agency through actual action (i.e., awareness was present but motivated behavior was lacking).

I am not an effective online learner. The reason is that I did not do well last semester. I did not complete the communication course credits in the first semester. Also, in terms of the taxonomy of learning, my learning was only up to comprehension and memory. Effective learning includes creation, evaluation and analysis. I think I’ll be closer to being an effective online learner if I recapitulate what I learned last semester and explain it to others.

I do not think I am an effective online learner because I think I cannot take a deep approach to learning. I think I am less conscious of trying to connect learning to other experiences and ideas. However, I feel that a strategic approach to learning that manages study time on my own has been acquired recently. In my future learning, I think it will be important not just to do what is in front of me, but to apply what I have learned to my daily life.

While preparing students with the metalanguage to communicate the self moves them toward greater personal responsibility, agency and autonomy, an inability to ask critical questions of the self in relation to the assumptions of others has been cited as a factor inhibiting transformations in learning (Ziegler et al., 2006). Taylor (2017) suggested that teachers should make efforts to know students as individuals and understand their learning preferences and histories as a means of fostering transformative awareness and new experience. Within the context of socially distanced online learning, the creation of personal in-depth relationships is arguably more challenging to establish and maintain than during face-to-face interaction.

Limitations

Although the four extracted themes have provided structure to the analysis of belief rationalizations, it is known that beliefs are “a complex and multilayered collection of viewpoints and voices rather than a well-organized and coherent schema” (Dufva, 2003, p. 146). To this end, several student responses exhibited a greater depth of awareness and complexity than indicated. For example, certain students were able to question the use of formal assessment as an indicator for effective learner status in a way that provided a more nuanced reflection and evidence of learning through reference to taxonomies of learning. Other students contextualized their beliefs through reference to the impact of the novel coronavirus in their personal life. Comments that referenced the novel coronavirus were more commonplace among students who classified themselves as ineffective learners. The attribution of a failure to succeed to external influences can certainly be validated under the extraordinary social conditions although it is also indicative of a failure to adjust accordingly and to take responsibility for one’s own learning achievement outcomes. Future applications of such a thematic analysis would be advised to develop a more comprehensive coding system inclusive of thematic subsets which capture such multilayered responses and beliefs.

Conclusion

The academic self-concept refers to the knowledge and perceptions an individual holds about themselves within an achievement situation. Research into online learning has traditionally come second to the attention given to face-to-face learning situations. With the novel coronavirus pandemic impacting education in such dramatic ways, it is likely that we are entering a boom period for online learning research. If online learning and students working in distant isolation is to become the new norm then it is important that educators and students quickly adapt to the respective expectations and demands placed on each stakeholder. While it can be expected that many studies focus on the technological solutions being utilized in the short term, it remains imperative that technological solutions be enacted within pedagogically-sound educational frameworks. The tendency of learning technologists to somewhat sideline theoretical and methodological considerations means that educators must remain vigilant and resist the urge to implement untested technological solutions outside of recognized good practice (see Bodnar et al., 2016).

As demonstrated, drawing attention to the metacognitions of learners in relation to metacognitive knowledge and self-appraisal can provide a foundation for learning enrichment within a supportive distant community. As a basis for fostering the greater autonomy required in online distance learning situations, teachers might wish to draw attention to the explicit ways in which “thinking about thinking” impacts the processing of an activity or learning task (i.e., self-efficacy beliefs). The goal of such reflective conscious raising would be to develop learners who can assume “conscious control of learning, planning and selecting strategies, monitoring the progress of learning, correcting errors, analyzing the effectiveness of learning strategies, and changing learning behaviors and strategies when necessary” (Ridley et al., 1992, p. 295). Due to the cyclical nature of metacognitions, this heightened awareness and reflective competence can be expected to enrich the metacognitive experience of learning thereby promoting more complex and rich knowledge feedback cycles as the student develops as a learner.

The kind of data gathered in the current study also permits teachers to identify metacognitive gaps or maladaptive routines which might hinder the progress and cognitive development of learners. It is also important that teachers guide students toward avoiding potentially destructive social comparisons that underpin the salience of the academic self-concept (i.e., through a peer comparison of formal achievement outcomes). Learners should be taught to develop self-appraisals referenced against task expectations and the process of learning rather than peer-referenced or formal achievement outcomes. Perhaps of greater significance is the role of the teacher in maintaining a conceptual distance between the academic self-concept and the general sense of value a learner retains as a human being away from the achievement environment (i.e., their self-esteem). This is crucial for encouraging and supporting students who are not accustomed to experiencing academic success, have naive epistemological beliefs, have turbulent or unstable emotional lives away from school, or who have fared poorly when peer-to-peer academic comparisons have been undertaken in relation to achievement. The contextual demands of socially distanced online learning can be expected to elevate the importance of the above as learners are studying in relative isolation, and in vastly different personal environments, often far removed from what was once familiar. Learning how to reposition and reappraise the self as a socially distanced online learner within a technology-mediated digital community is an important part of the educational change currently being witnessed around the world.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Damian J. Rivers

Author Biographies

Damian J. Rivers, PhD, is a Professor in the Center for Meta-Learning at Future University Hakodate in northern Japan. He is primarily interested in quantitative educational research that aims to account for variance in academic achievement outcomes. He also researches discourse and communication in society and the application of wearable technologies within educational research.

Michael Vallance, EdD, is a Professor in the Department of Media Architecture at Future University Hakodate in northern Japan. His research interests include virtual reality, robotics, physiological data and extrasomatic communication, concurrently emphasizing the essentiality of an inter-disciplinary education.

Michiko Nakamura, PhD, is an Associate Professor in the Center for Meta-Learning at Future University Hakodate in northern Japan. Her primary research interest is in the field of cognitive science with special focus on sentence comprehension, language acquisition and learning. Her recent research focus includes the factors that affect learning in the classroom and in peer tutoring contexts.

References

Abraham

R. G.

Vann

R. J.

(1987). Strategies of two language learners: A case study. In Wenden

Rubin

(Eds.), Learner strategies in language learning (pp. 85–102). Prentice Hall International.

Bailenson

(2018). Experience on demand: What virtual reality is, how it works, and what it can do. Norton & Company.

Bandura

(1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191

Benson

Lor

(1999). Conceptions of language and language learning. System, 27(4), 459–472. https://doi.org/10.1016/S0346-251X(99)00045-7

Bodnar

Cucchiarini

Strik

Van Hout

(2016). Evaluating the motivational impact of CALL systems: Current practices and future directions, Computer Assisted Language Learning, 29(1), 186–212. https://doi.org/10.1080/09588221.2014.927365

Bong

Skaalvik

E. M.

(2003). Academic self-concept and self-efficacy: How different are they really? Educational Psychology Review, 15(1), 1–40. https://doi.org/10.1023/A:1021302408382

Boyatzis

R. E.

(1998). Transforming qualitative information: Thematic analysis and code development. Sage.

Braten

Stromso

H. I.

(2006). Epistemological beliefs, interest, and gender as predictors of internet-based learning activities. Computers in Human Behavior, 22(6), 1027–1042. https://doi.org/10.1016/j.chb.2004.03.026

Braun

Clarke

(2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

10.

Braun

Clarke

(2020). Thematic analysis: A practical guide. Sage.

11.

Broadbent

Poon

(2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. Internet and Higher Education, 27, 1–13. https://dx-doi-org.web.bisu.edu.cn/10.1016/j.iheduc.2015.04.007

12.

Brunner

Keller

Dierendonck

Reichert

Ugen

Fischbach

Martin

(2010). The structure of academic self-concepts revisited: The nested Marsh/Shavelson model. Journal of Educational Psychology, 102(4), 964–981. https://doi.org/10.1037/a0019644

13.

Burns

(1982). Self concept development and education. Holt Saunders.

14.

Byrne

B. M.

(1986). Self-concept/academic achievement relations: Ann investigation of dimensionality, stability, and causality. Canadian Journal of Behavioural Science, 18(2), 173–186. https://doi.org/10.1037/h0079982

15.

Chapman

J. W.

Tunmer

W. E.

Prochnow

J. E.

(2000). Early reading-related skills and performance, reading self-concept, and the development of academic self-concept: A longitudinal study. Journal of Educational Psychology, 92(4), 703–708. https://doi.org/10.1037/0022-0663.92.4.703

16.

Cho

M. H.

Tobias

(2016). Should instructors require discussion in online courses? Effects of online discussion on community of inquiry, learner time, satisfaction, and achievement. International Review of Research in Open and Distributed Learning, 17(2), 123–140. https://doi.org/10.19173/irrodl.v17i2.2342

17.

Clarebout

Elen

Luyten

Bamps

(2001). Assessing epistemological beliefs: Schommer's questionnaire revisited. Educational Research and Evaluation, 7(1), 53–77. https://doi.org/10.1076/edre.7.1.53.6927

18.

Clarke

Braun

Terry

Hayfield

(2019). Thematic analysis. In Liamputtong

(Ed.), Handbook of research methods in health and social sciences (pp. 843–860). Springer.

19.

Cohen

Norst

M. J.

(1989). Fear, dependence and loss of self-esteem: Affective barriers in second language learning among adults. RELC Journal, 20(2), 61–77. https://doi.org/10.1177/003368828902000206

20.

Corfman

Beck

(2019). Case study of creativity in asynchronous online discussions. International Journal of Educational Technology in Higher Education, 16(22), 1–20. https://doi.org/10.1186/s41239-019-0150-5

21.

Cotterall

(1999). Key variables in language learning: What do learners believe about them? System, 27(3), 493–513. https://doi.org/10.1016/S0346-251X(99)00047-0

22.

Covington

M. V.

Roberts

B. W.

(1994). Self-worth and college achievement: Motivational and personality correlates. In Pintrich

P. R.

Brown

D. R.

Weinstein

C. E.

(Eds.), Student motivation, cognition, and learning (pp. 157–188). Lawrence Erlbaum Associates.

23.

Dabbagh

Kitsantas

(2005). Using web-based pedagogical tools as scaffolds for self-regulated learning. Instructional Science, 33, 513–540. https://doi.org/10.1007/s11251-005-1278-3

24.

DeBacker

T. K.

Crowson

H. M.

(2006). Influence on cognitive engagement and achievement: Personal epistemology and achievement motives. British Journal of Educational Psychology, 76(3), 353–551. https://doi.org/10.1348/000709905X53138

25.

Demetriadis

S. N.

Papadopoulos

P. M.

Stamelos

I. G.

Fischer

(2008). The effect of scaffolding students’ context-generating cognitive activity in technology-enhanced case-based learning. Computers & Education, 51(2), 939–954. https://doi.org/10.1016/j.compedu.2007.09.012

26.

Dufva

(2003). Beliefs in dialogue: A Bakhtinian view. In Kalaja

Barcelos

A. M. F.

(Eds.), Beliefs about SLA. Educational linguistics (Vol. 2). Springer. https://doi.org/10.1007/978-1-4020-4751-0_6

27.

Efklides

(2001). Metacognitive experiences in problem solving: Metacognition, motivation, and self-regulation. In Efklides

Kuhl

Sorrentino

R. M.

(Eds.), Trends and prospects in motivation research (pp. 297–323). Kluwer Academic Publishers.

28.

Ferla

Valcke

Schuyten

(2010). Judgments of self-perceived academic competence and their differential impact on students’ achievement motivation, learning approach, and academic performance. European Journal of Psychology of Education, 25, 519–536. https://doi.org/10.1007/s10212-010-0030-9

29.

Fisher

Baird

D. E.

(2005). Online learning design that fosters student support, self-regulation, and retention. Campus-Wide Information Systems, 22(2), 88–107. https://doi.org/10.1108/10650740510587100

30.

Flavell

J. H.

(1976). Metacognitive aspects of problem-solving. In Resnick

L. B.

(Ed.), The nature of intelligence (pp. 231–235). Lawrence Erlbaum Associates.

31.

Flavell

J. H.

(1979). Metacognition and cognitive monitoring: A new area of cognitive- developmental inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003-066X.34.10.906

32.

Flavell

J. H.

(1987). Speculation about the nature and development of metacognition. In Wernert

F. E.

Kluwe

R. H.

(Eds.), Metacognition, motivation and understanding (pp. 21–29). Lawrence Erlbaum Associates.

33.

Galikyan

Admiraal

(2019). Students’ engagement in asynchronous online discussion: The relationship between cognitive presence, learner prominence, and academic performance. Internet and Higher Education, 43. https://doi.org/10.1016/j.iheduc.2019.100692

34.

Gao

Zhang

Franklin

(2013). Designing asynchronous online discussion environments: Recent progress and possible future directions. British Journal of Educational Technology, 44(3), 469–483. https://doi.org/10.1111/j.1467-8535.2012.01330.x

35.

Gardner

R. C.

Lambert

W. E.

(1959). Motivational variables in second-language acquisition. Canadian Journal of Psychology, 13(4), 266–272. https://doi.org/10.1037/h0083787

36.

Garner

(1994). Metacognition and executive control. In Ruddell

Ruddell

Singer

(Eds.), Theoretical models and processes of reading (pp. 715–732). International Reading Association.

37.

Goodwin

Duranti

(1992). Rethinking context: Language as an interactive phenomenon. Cambridge University Press.

38.

Griffore

R. J.

Bianchi

(1984). Effects of ordinal position on academic self-concept. Psychological Reports, 55(1), 263–268. https://doi.org/10.2466/pr0.1984.55.1.263

39.

Guest

MacQueen

K. M.

Namey

E. E.

(2012). Applied thematic analysis. Sage.

40.

Hailikari

Nevgi

Komulainen

(2008). Academic self-beliefs and prior knowledge as predictors of student achievement in mathematics: A structural model. Educational Psychology, 28(1), 59–71. https://doi.org/10.1080/01443410701413753

41.

Hansford

B. C.

Hattie

J. A.

(1982). The relationship between self and achievement/performance measures. Review of Educational Research, 52(1), 123–142. https://doi.org/10.2307/1170275

42.

Harding

Whitehead

(2013). Analysing data in qualitative research. In Schneider

Whitehead

LoBiondo-Wood

Haber

(Eds.), Nursing & midwifery research: Methods and appraisal for evidence-based practice (pp. 141–160). Elsevier.

43.

Hartley

Bendixen

L. D.

(2001). Educational research in the internet age: Examining the role of individual characteristics. Educational Researcher, 30(9), 22–26. https://doi.org/10.3102/0013189X030009022

44.

Hill

J. R.

Hannafin

M. J.

(1997). Cognitive strategies and learning from the world wide web. Educational Technology Research and Development, 45, 37–64. https://doi.org/10.1007/BF02299682

45.

Hofer

B. K.

Pintrich

P. R.

(1997). The development of epistemological theories: Beliefs about knowledge and knowing and their relation to learning. Review of Educational Research, 67(1), 88–140. https://doi.org/10.2307/1170620

46.

Hofer

B. K.

Pintrich

P. R.

(2002). Personal epistemology: The psychology of beliefs about knowledge and knowing. Lawrence Erlbaum Associates.

47.

Horwitz

E. K.

(1987). Surveying student beliefs about language learning. In Wenden

Rubin

(Eds.), Learner strategies in language learning (pp. 119–129). Prentice Hall.

48.

Jacobson

M. J.

Sugimoto

& Archodidou

(1996). Evolution, hypermedia learning environments, and conceptual change: A preliminary report. In Proceedings of the 1996 International Conference on Learning Sciences (pp. 151–158), Northwestern University, Evanston, Illinois. https://dl.acm.org/doi/abs/10.5555/1161135.1161156

49.

Jia

Cao

(2019). The role of metacognitive components in creative thinking. Frontiers in Psychology, 10, 2404. https://doi.org/10.3389/fpsyg.2019.02404

50.

Kartchava

Ammar

(2014). Learners’ beliefs as mediators of what is noticed and learned in the language classroom. TESOL Quarterly, 48(1), 86–109. https://doi.org/10.1002/tesq.101

51.

Lai

(2019). Learning beliefs and autonomous language learning with technology beyond the classroom. Language Awareness, 28(4), 291–309. https://doi.org/10.1080/09658416.2019.1675679

52.

Lee

Choi

(2017). What affects learner’s higher-order thinking in technology-enhanced learning environments? The effects of learner factors. Computers & Education, 115, 143–152. https://doi.org/10.1016/j.compedu.2017.06.015

53.

Levy

E. C.

Rafaeli

Ariel

(2016). The effect of online interruptions on the quality of cognitive performance. Telematics and Infomatics, 33(4), 1014–1021. https://doi.org/10.1016/j.tele.2016.03.003

54.

Marginson

(2011). Higher education in east Asia and Singapore: Rise of the confucian model. Higher Education, 61, 587–611. https://doi.org/10.1007/s10734-010-9384-9

55.

Marsh

H. W.

(1990). The structure of academic self-concept: The Marsh/Shavelson model. Journal of Educational Psychology, 82(4), 623–636. https://doi.org/10.1037/0022-0663.82.4.623

56.

Marsh

H. W.

(1993). Academic self-concept: Theory, measurement, and research. In Suls

J. M.

(Ed.), Psychological perspectives on the self: The self in social perspective (pp. 59–98). Lawrence Erlbaum Associates.

57.

Marsh

H. W.

Craven

R. G.

Martin

A. J.

(2006). What is the nature of self-esteem? Unidimensional and multidimensional perspectives. In Kernis

M. H.

(Ed.), Self-esteem issues and answers: A sourcebook of current perspectives (pp. 16–24). Psychology Press.

58.

Marsh

H. W.

Martin

A. J.

(2011). Academic self-concept and academic achievement: Relations and causal ordering. British Journal of Educational Psychology, 81(1), 59–77. https://doi.org/10.1348/000709910X503501

59.

Marsh

H. W.

Shavelson

(1985). Self-concept: Its multifaceted, hierarchical structure. Educational Psychologist, 20(3), 107–123. https://doi.org/10.1207/s15326985ep2003_1

60.

Marsh

H. W.

Yeung

A. S.

(1997). Causal effects of academic self-concept on academic achievement: Structural equation models of longitudinal data. Journal of Educational Psychology, 89(1), 41–54. https://doi.org/10.1037/0022-0663.89.1.41

61.

Merriam

Caffarella

(1999). Learning in adulthood. Jossey Bass.

62.

Mezirow

(1995). Transformation theory of adult learning. In Welton

M. R.

(Ed.), In defense of the lifeworld critical perspectives on adult learning (pp. 37–70). State University of New York Press.

63.

Mezirow

(1996). Contemporary paradigms of learning. Adult Education Quarterly, 46(3), 158–172. https://doi.org/10.1177/074171369604600303

64.

Pajares

M. F.

(1992). Teachers’ beliefs and educational: Cleaning up a messy construct. Review of Educational Research, 62(3), 307–332. https://doi.org/10.3102/00346543062003307

65.

Pan

Hamilton

A. F. D.

(2018). Why and how to use virtual reality to study human social interaction: The challenges of exploring a new research landscape. British Journal of Psychology, 109(3), 395–417. https://doi.org/10.1111/bjop.12290

66.

Pena-Shaff

Altman

(2015). Student interaction and knowledge construction in case-based learning in educational psychology using online discussions: The role of structure. Journal of Interactive Learning Research, 26(3), 307–329. https://www.learntechlib.org/p/114358/

67.

Pieschl

Stahl

Bromme

(2008). Epistemological beliefs and self-regulated learning with hypertext. Metacognition and Learning, 3(1), 17–37. https://doi.org/10.1007/s11409-007-9008-7

68.

Pintrich

P. R.

(2002). The role of metacognitive knowledge in learning, teaching, and assessing. Theory into Practice, 41(4), 219–225. https://doi.org/10.1207/s15430421tip4104_3

69.

Pintrich

P. R.

De Groot

E. V.

(1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33–40. https://doi.org/10.1037/0022-0663.82.1.33

70.

Polkinghorne

D. E.

(1983). Methodology for the human sciences: Systems of inquiry. State University of New York Press.

71.

Rappleye

Komatsu

(2018). Stereotypes as Anglo-American exam ritual? Comparisons of students' exam anxiety in East Asia, America, Australia, and the United Kingdom. Oxford Review of Education, 44(6), 730–754. https://doi.org/10.1080/03054985.2018.1444598

72.

Ridley

D. S.

Schutz

P. A.

Glanz

R. S.

Weinstein

C. E.

(1992). Self-regulated learning: The interactive influence of metacognitive awareness and goal-setting. Journal of Experimental Education, 60(4), 293–306. https://doi.org/10.1080/00220973.1992.9943867

73.

Riley

(1997). The guru and the conjurer: Aspects of counseling for self-access. In Benson

Voller

P.E.

(Eds.), Autonomy and independence in language learning (pp. 114–131). Longman.

74.

Rossiter

(1999). A narrative approach to development: Implications for adult education. Adult Education Quarterly, 50(1), 56–71. https://doi.org/10.1177/07417139922086911

75.

Ryan

G. W.

Bernard

H. R.

(2003). Techniques to identify themes. Field Methods, 15(1), 85–109. https://doi.org/10.1177/1525822X02239569

76.

Sakui

Gaies

S. J.

(1999). Investigating Japanese learners’ beliefs about language learning. System, 27(4), 473–492. https://doi.org/10.1016/S0346-251X(99)00046-9

77.

Schommer

(1990). Effects of beliefs about the nature of knowledge on comprehension. Journal of Educational Psychology, 82(3), 498–504. https://doi.org/10.1037/0022-0663.82.3.498

78.

Schommer

(1994). An emerging conceptualization of epistemological beliefs and their role in learning. In Garner

Alexander

P. A.

(Eds.), Beliefs about text and instruction with text (pp. 25–40). Lawrence Erlbaum Associates.

79.

Schraw

Bendixen

L. D.

Dunkle

M. E.

(2002). Development and validation of the Epistemic Belief Inventory (EBI). In Hofer

B. K.

Pintrich

P. R.

(Eds.), Personal epistemology: The psychology of beliefs about knowledge and knowing (pp. 261–275). Lawrence Erlbaum Associates.

80.

Schraw

Dennison

R. S.

(1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460–475. https://doi.org/10.1006/ceps.1994.1033

81.

Schunk

D. H.

(2005). Self-regulated learning: The educational legacy of paul R. Pintrich. Educational Psychologist, 40(2), 85–94. https://doi.org/10.1207/s15326985ep4002_3

82.

Shavelson

R. J.

Bolus

(1982). Self concept: The interplay of theory and methods. Journal of Educational Psychology, 74(1), 3–17. https://doi.org/10.1037/0022-0663.74.1.3

83.

Shavelson

R. J.

Hubner

J. J.

Stanton

G. C.

(1976). Self-concept: Validation of construct interpretations. Review of Educational Research, 46(3), 407–441. https://doi.org/10.2307/1170010

84.

Simonsmeier

B. A.

Peiffer

Flaig

Schneider

(2020). Peer feedback improves students’ academic self-concept in higher education. Research in Higher Education, 61, 706–724. https://doi.org/10.1007/s11162-020-09591-y

85.

Skaalvik

E. M.

(1997). Self-enhancing and self-defeating ego orientation: Relations with task and avoidance orientation, achievement, self-perceptions, and anxiety. Journal of Educational Psychology, 89(1), 71–81. https://doi.org/10.1037/0022-0663.89.1.71

86.

Slater

(2009). Place illusion and plausibility can lead to realistic behaviour in immersive virtual environments. Philosophical Transactions of the Royal Society of London, 364(1535), 3549–3557. https://doi.org/10.1098/rstb.2009.0138

87.

Taylor

E. W.

(2017). Transformative learning theory. In Laros

Fuhr

Taylor

E. W.

(Eds.), Transformative learning meets bildung. International issues in adult education (pp. 17–29). Sense Publishers.

88.

Teng

M. F.

(2020). Effects of cooperative–metacognitive instruction on EFL learners’ writing and metacognitive awareness. Asia Pacific Journal of Education. https://doi.org/10.1080/02188791.2020.1835606

89.

Tsai

C. C.

(2000). Relationships between student scientific epistemological beliefs and perceptions of constructivist learning environments. Educational Research, 42(2), 193–205. https://doi.org/10.1080/001318800363836

90.

Tsai

C. C.

Chuang

S. C.

(2005). The correlation between epistemological beliefs and preferences toward Internet-based learning environments. British Journal of Educational Technology, 36(1), 97–100. https://doi.org/10.1111/j.1467-8535.2004.00442.x

91.

Valentine

J. C.

DuBois

D. L.

(2005). Effects of self-beliefs on academic achievement and vice-versa: Separating the chicken from the egg. In Marsh

H. W.

Craven

R. G.

McInerney

D. M.

(Eds.), International advances in self research (pp. 53–78). Information Age.

92.

Wenden

A. L.

(1998). Metacognitive knowledge and language learning. Applied Linguistics, 19(4), 515–537. https://doi.org/10.1093/applin/19.4.515.

93.

Wigfield

Karpathian

(1991). Who am I and what can I do? Children's self-concepts and motivation in achievement situations. Educational Psychologist, 26(3), 233–261. https://doi.org/10.1207/s15326985ep2603&4_3

94.

Winne

P. H.

(1995). Inherent details in self-regulated learning. Educational Psychologist, 30(4), 173–187. https://doi.org/10.1207/s15326985ep3004_2

95.

Zhong

(2012). The beliefs and learner strategy use of low-proficiency Chinese learners and their impact on learning English in a New Zealand context [Doctoral dissertation]. The University of Auckland. https://hdl-handle-net.web.bisu.edu.cn/2292/11472

96.

Zhou

(2018). On the relationship of students’ English learning beliefs and learning strategy in the university. Journal of Language Teaching and Research, 9(1), 175–180. https://dx-doi-org.web.bisu.edu.cn/10.17507/jltr.0901.22

97.

Ziegler

M. F.

Paulus

T. M.

Woodside

(2006). This course is helpful us all arrive at new viewpoints, isn’t it?” Journal of Transformative Education, 4(4), 302–319. https://doi.org/10.1177/1541344606294819

98.

Zimmerman

B. J.

(2002). Becoming a self-regulated learner: Ann overview. Theory into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

99.

Zimmerman

B. J.

Pons

M. M.

(1986). Development of a structured interview for assessing student use of self-regulated learning strategies. American Educational Research Journal, 23(4), 614–628. https://dx-doi-org.web.bisu.edu.cn/10.2307/1163093