LANGUAGE LEARNING STRATEGIES, E-LEARNING SELF-EFFICACY, AND ACADEMIC DISHONESTY AMONG INTERNATIONAL STUDENTS AT ASIA-PACIFIC INTERNATIONAL UNIVERSIT

LANGUAGE LEARNING STRATEGIES, E-LEARNING SELF-EFFICACY, AND ACADEMIC DISHONESTY AMONG INTERNATIONAL STUDENTS AT ASIA-PACIFIC INTERNATIONAL UNIVERSITY By ROGER OCONNOR VALENZUELA A Thesis Submitted in Partial Fulfillment of the Requirements For the Degree of Master of Education Emphasis in Teaching English to Speakers of Other Languages (TESOL) Faculty of Education Asia-Pacific International University Year 2022

i Thesis Title: Language Learning Strategies, E-Learning Self-Efficacy, And Academic Dishonesty Among International Students At Asia-Pacific International University Author: Roger Oconnor Valenzuela Thesis Principal Advisor: Darrin Thomas Thesis Co-Advisor: Josephine Katenga Program: Master of Education with Emphasis in Teaching English to Speakers of Other Languages (TESOL) Academic Year: 2022

i ABSTRACT Academic dishonesty is one of the major challenges students, teachers, and educational institutions face at the tertiary level. The present study sought to look at this challenge in the context of online learning at an international university where students not only need to cope with learning new content but also struggle to acquire English as a second language. As such, the main research objective of this study was to explore the relationship between academic dishonesty, language learning strategies, and e-learning self-efficacy among international students at Asia-Pacific International University. A conceptual framework was developed for the study. Also, a survey composed of three scales was used to collect data. A total of 225 students participated in the research. Descriptive, comparative, and relational statistics were utilized to answer the research questions. Results indicated that APIU students seldom engage in academically dishonest behavior (M = 2.13, SD = 0.84). Moreover, respondents affirmed they preferred metacognitive strategies (M = 3.89, SD = 0.81) over other L2 strategies for acquiring English as a second language. In terms of self-efficacy, participants demonstrated a neutral stance in relation to their perceptions of e-learning self-efficacy (M = 3.40, SD = 0.61). Another important finding was that there was a difference by major for academic dishonesty [F(6, 218) = 2.53, p = 0.02], with Nursing students’ mean score (M = 2.67, SD = 0.96) being significantly different from that of Education undergraduates (M = 1.88, SD = 0.62). Regression results suggested that the thirteen independent variables explained

ii 22% of the variance (R 2 =.22, F (22,202) = 3.34, p < .001, R 2 adjusted = .20). In this sense, it was found that the sub-variable affective strategies (β = .29, p <. 01) was an important predictor of academic dishonesty. Also, it was noteworthy that Sophomore students (β = .13, p <. 05) had a significant association with academic dishonesty. Recommendations for students, teachers, and administrators were given in order to address academic honesty issues at APIU. In this sense, it was acknowledged that teachers have an important role to play by making clear to students what academic dishonesty is at the tertiary level. Lastly, suggestions for further study were made.

iii ACKNOWLEDGEMENTS I would like to thank the Lord Jesus Christ for allowing me to reach this stage in my academic journey. Also, I want to thank my parents, girlfriend, and close friends who have supported me in dissimilar ways. In a more general sense, I wish to thank the APIU community for helping me grow spiritually, academically, and professionally. Still, I should mention some important people that have directed my steps in the master’s program. Recognition needs to be given to Dr. Josephine Katenga for her incessant assistance in academic matters. Additionally, I would like to thank Dr. Amanda Simon and Dr. Jimmy Kijai for motivating me to do research more efficiently. Lastly, special thanks must be given to my thesis advisor and mentor Dr. Darrin Thomas, who always was available for me and challenged me to a better student, teacher, and researcher. Roger Oconnor Valenzuela

iv TABLE OF CONTENTS ABSTRACT........................................................................................................................I ACKNOWLEDGEMENTS ........................................................................................... III LIST OF TABLES .........................................................................................................VII LIST OF FIGURES ..................................................................................................... VIII CHAPTER 1 INTRODUCTION..................................................................................... 1 Background of the Study................................................................................................. 1 Statement of the Problem ................................................................................................ 4 Purpose of the Study ....................................................................................................... 5 Research Questions ......................................................................................................... 6 Significance of the Study ................................................................................................ 6 Limitations ...................................................................................................................... 7 Delimitations................................................................................................................... 7 Definition of Terms......................................................................................................... 8 Organization of Chapters ................................................................................................ 8 CHAPTER 2 LITERATURE REVIEW...................................................................... 10 Academic Dishonesty (AD) .......................................................................................... 10 Definitions................................................................................................................. 10 Theories..................................................................................................................... 13 Relevant Studies........................................................................................................ 17 Language Learning Strategies (LLS) ............................................................................ 20 Definitions................................................................................................................. 21 Theories..................................................................................................................... 25 Relevant Studies........................................................................................................ 28 E-Learning Self-Efficacy (ELSE) ................................................................................. 32 Definitions................................................................................................................. 32 Theories..................................................................................................................... 36 Relevant Studies........................................................................................................ 38 Conclusions................................................................................................................... 42

v CHAPTER 3 METHODOLOGY................................................................................. 45 Introduction ................................................................................................................... 45 Research Design............................................................................................................ 46 Population and Sample.................................................................................................. 46 Data Collection.............................................................................................................. 47 Instrumentation.............................................................................................................. 48 Definition of Variables.................................................................................................. 49 Academic Dishonesty (AD) Scale................................................................................. 49 Language Learning Strategies (LLS) Scale................................................................... 50 E-Learning Self-Efficacy (ELSE) Scale........................................................................ 50 Data Analysis ................................................................................................................ 51 Ethical Considerations................................................................................................... 53 Conclusions................................................................................................................... 53 CHAPTER 4 RESULTS................................................................................................ 54 Introduction ................................................................................................................... 54 Overview of the Chapter ........................................................................................... 54 Demographics............................................................................................................ 55 Descriptive Statistics..................................................................................................... 56 Level of Academic Dishonesty ................................................................................. 57 Use of Language Learning Strategies........................................................................ 58 Perception of E-Learning Self-Efficacy .................................................................... 60 Comparative Statistics................................................................................................... 61 Differences in Academic Dishonesty by Demographics........................................... 61 Differences in Language Learning Strategies by Demographics.............................. 63 Differences in E-Learning Self-Efficacy by Demographics ..................................... 67 Relational Statistics....................................................................................................... 69 Correlation Results.................................................................................................... 70 Regression Results..................................................................................................... 72 CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS ............................... 76 Conclusions................................................................................................................... 76 Recommendations......................................................................................................... 80 For Students............................................................................................................... 80 For Teachers.............................................................................................................. 81

vi For Administrators..................................................................................................... 82 For Further Study ...................................................................................................... 83 REFERENCE LIST..................................... ERROR! BOOKMARK NOT DEFINED. APPENDICE ................................................ ERROR! BOOKMARK NOT DEFINED. APPENDIX A: Language Learning Strategy Scale .......Error! Bookmark not defined. APPENDIX B: E-Learning Self-Efficacy Scale ............Error! Bookmark not defined. APPENDIX C: Academic Dishonesty Scale .................Error! Bookmark not defined. APPENDIX D: Informed Consent and Survey Instrument.......... Error! Bookmark not defined. APPENDIX E: Coding for Statistical Analysis in R Studio ........ Error! Bookmark not defined.

vii LIST OF TABLES Table 1. Structure of the Survey ...................................................................................... 52 Table 2. Demographics of the Study................................................................................ 56 Table 3. Descriptive Statistics and Reliability Estimates of Scales and Subscales ......... 57 Table 4. Statistics of Academic Dishonesty Items........................................................... 58 Table 5. Descriptive Statistics of LLS Subscales ............................................................ 59 Table 6. Statistics of Metacognitive Items....................................................................... 60 Table 7. Descriptive Statistics of ELSE Subscales.......................................................... 60 Table 8. Descriptive Statistics of Selected ELSE Items.................................................. 61 Table 9. Results for Gender Differences in AD............................................................... 62 Table 10. ANOVA Results for AD as Criterion and Year as Predictor .......................... 62 Table 11. ANOVA Results for AD as Criterion and Major as Predictor ........................ 62 Table 12. ANOVA Results for AD as Criterion and Religion as Predictor .................... 63 Table 13. Results for Gender Differences in LLS ........................................................... 64 Table 14. ANOVA Results for LLS as Criterion and Year as Predictor......................... 64 Table 15. ANOVA Results for LLS as Criterion and Major as Predictor....................... 65 Table 16. ANOVA Results for LLS as Criterion and Religion as Predictor................... 66 Table 17. Results for Gender Differences in ELSE......................................................... 67 Table 18. ANOVA Results for ELSE as Criterion and Year as Predictor....................... 68 Table 19. ANOVA Results for ELSE as Criterion and Major as Predictor...................... 68 Table 20. ANOVA Results for ELSE as Criterion and Religion as Predictor................. 69 Table 21. Correlations with Confidence Intervals........................................................... 71 Table 22. Regression Models in Hierarchical Order ....................................................... 74 Table 23. Multiple Regression Analysis Results for AD as Criterion............................. 75

viii LIST OF FIGURES Figure 1. Conceptual Framework of the Study................................................................ 44 Figure 2. Scatter Plot of Academic Dishonesty and Affective Strategies ....................... 72 Figure 3. Scatter Plot of Technology Use and Social Strategies ..................................... 72

1 CHAPTER 1 INTRODUCTION Background of the Study Academic dishonesty is a topic of growing concern among higher education students, teachers, and institutions (Austin et al., 2005; Coffey & Anyinam, 2012). Cuadrado et al. (2019) not only list down issues related to academic honesty, such as “cheating of examinations, absenteeism, property damage, inappropriate use of resources, unpermitted collaboration, plagiarism, or fabrication,” but also assert that academic dishonesty “is not restricted to a concrete geographical area” since it “occurs on campuses all around the world,” thus constituting an “alarming situation” (p. 2). Thompson et al. (2017) suggest that while academic honesty is associated with the students’ respect for policies that condemn cheating and plagiarism, such policies and understanding may need to be addressed and explained in cross-cultural contexts. International universities are one of the best settings for understanding how culture affects students’ perception of academic honesty (Payan et al., 2010). Studies suggest that issues related to academic honesty, such as plagiarism and cheating, are common among international university students for whom English is their second language (Conzett et al., 2010; Ducar & Schocket, 2018). As such, many international students have a triple challenge: (1) acquire English as a second language, (2) learn new content in it, (3) and adjust to university life and policies, including those related to academic honesty. The first two parts of the challenge can be met by using language

2 learning strategies. However, the third aspect could be quite difficult to overcome, especially for students that do not understand what academic honesty policies entail. In Asian contexts, studies suggest that college students at universities where English is the medium of instruction (EMI) engage in academic dishonesty (Chapman & Lupton, 2004; Deckert, 1993; Kwong et al., 2010; Thomas, 2020). Thus, it is important to ask the question of whether language learning strategies can not only help international university students acquire the English language but also have a positive impact on their respect for academic honesty policies. Furthermore, such a question is all the more relevant in light of the fact that international students with low levels of English proficiency are among those who struggle to follow academic honesty guidelines (Click, 2012; McKay et al., 2019). It is in this context that exploring the relationship between language learning strategies and academic honesty among international university students becomes relevant. Academic dishonesty is also common among students that rely on e-learning for pursuing higher education (Black et al., 2008). Still, experts are divided when it comes to deciding whether plagiarism and cheating are more recurrent in e-learning settings. In this line of thought, Spaulding (2009) addresses the question “how much cheating is actually going on?” (p. 184) by suggesting that academic dishonesty is “the same in online and traditional classrooms” (p. 195). Nevertheless, Spaulding (2009) acknowledges that other scholars’ findings agree “with the theory of academic dishonesty being more prevalent in online courses than in traditional courses due to ease of accessibility of resources” by citing Carnevale (1999), Kennedy et al. (2000), and Wang (2008) (p. 195).

3 At the tertiary level, academic honesty issues have increased due to the ethical challenges brought about by online learning (Muhammad et al., 2020). This calls for better ways to foster academic honesty at the tertiary level (Bylieva et al., 2020; Cole et al., 2019). Asia is no stranger to this pressing matter (Costley, 2019). In countries like Thailand, academic dishonesty in online settings is not only common (Bangthamai, 2009) but seems to grow in the context of a global pandemic that has forced international universities across Thailand to go online (Pipattarasaku & Phoophuangpairoj, 2021). Understanding why students taking online classes engage in academic dishonesty is a complex issue. An important question to answer is whether learners’ e-learning selfefficacy is related to their practice of academic honesty. E-learning self-efficacy may be one of the factors affecting how well a student performs academically when taking an online course, especially in the area of academic honesty. Liu et al. (2010) conducted a study among Asian international students, exploring how they perceived academic dishonesty, online learning, and English as a second language. The results of such a study indicated that poor reading, writing, and communication skills, along with not knowing what plagiarism is, warranted the need for language learning strategies and awareness of academic honesty policies among international undergraduate students in the context of e-learning. However, Liu et al. (2010) focused their research on the cultural differences prevailing in an international educational setting and did not explore the relationship between language learning strategies, e-learning, and academic honesty. Bista (2011) highlighted how low levels of English proficiency are associated with academic dishonesty among international students but did not consider the online setting in her study. Other studies have explored

4 the association between language learning strategies and e-learning but have not considered the area of academic honesty (Mohammadi et al., 2011; Solak & Cakir, 2015; Yang & Wu, 2015). Two things can be said concerning the available literature on the topic. First, although there is a vast body of research concerned with academic honesty in online educational settings and many studies have focused on the prominence of online learning, there are few to none studies on the relationship between e-learning self-efficacy and academic honesty. Second, while some studies have looked at the impact of language learning strategies on academic dishonesty and other surveys have explored the association between language learning strategies and e-learning, there is scarce research on the correlation between all the three variables. Therefore, it can be stated that there is a need for looking at the relationship between language learning strategies, e-learning selfefficacy, and academic honesty among international university students, especially in the Southeast Asian context. Statement of the Problem It is widely acknowledged that online education has become the main modality of instruction due to the spread of coronavirus in many regions of the world (Basilaia & Kvavadze, 2020; Mishra et al., 2020; Scull et al., 2020; Williamson et al., 2020), including Southeast Asia (Moralista & Oducado, 2020; Phraratsutaporn, 2021; Wajdi et al., 2020). This has brought about several challenges for students, teachers, and educational institutions transitioning from face-to-face classes to e-learning modalities (Aboagye et al., 2021; Al-Balas et al., 2020; Almaiah et al., 2020). As a result, concerns

5 related to academic dishonesty are of significant preoccupation in online educational settings (Abdul Rahim, 2020; Eaton, 2020; Elsalem et al., 2021). Academic dishonesty issues among international students are matters that ought to be revisited. This is even more relevant for higher education students that are finding strategies for acquiring the English language while being forced by the spread of a global pandemic to continue their tertiary education via e-learning. Moreover, e-learning selfefficacy seems to be an overlooked factor that might have an impact on academic honesty attitudes among university students (Bylieva et al., 2020). Also, teachers and administrators need to look for better ways to ensure academic honesty in the context of online learning. Thailand seems to be a perfect setting for exploring the concerns previously stated due to the country’s numerous international universities (Michael, 2018), transition to online modalities in the educational arena due to the mild but continuous spread of COVID-19 (Imsa-ard, 2020), openness to welcome students from every continent, and ever-present “concerns with academic dishonesty” (Phutikettrkit & Thomas, 2019, p. 1454). Purpose of the Study The purpose of this study is to explore the relationship between language learning strategies, e-learning self-efficacy, and academic dishonesty among international students at Asia-Pacific International University (APIU). The objective of exploring the aforementioned variables is to gain a deeper understanding of how language learning strategies and e-learning self-efficacy are associated with academic dishonesty among students at international universities. Such an objective, when reached, can help instructors find more effective ways for encouraging and enhancing academic integrity.

6 Research Questions There are five research questions developed for the present study: 1. What is the level of academic dishonesty among APIU students? 2. What learning strategies do APIU students use to learn English as a second language? 3. What are the APIU students’ perceptions of their e-learning self-efficacy? 4. Are there differences in the academic dishonesty levels, language learning strategies preferences, and e-learning self-efficacy perceptions of APIU students based on demographics? 5. Is academic dishonesty related to language learning strategies, e-learning selfefficacy, gender, year of study, major, and religious affiliation among APIU students? Significance of the Study The present study will be of benefit to students, teachers, and administrators at Asia-Pacific International University for several reasons. First, it will give insights about students’ academically dishonest behaviors in the context of online learning and second language acquisition. Such insights can help teachers and administrators create policies that specifically address the issues identified in the study. Second, the research will show which subgroups of international students, determined by demographic variables, are more prone to engage in academic dishonesty. This information can be helpful to produce strategies to prevent the identified subgroups from falling into the pit of academic dishonesty. Third, the study will provide useful information about the relationship between academic honesty, language learning strategies, and e-learning self-efficacy

7 among APIU students. Exploring such a relationship can help teachers and administrators better understand how students relate to academic dishonesty while acquiring English as a second language and using online technologies for educational purposes. Limitations The sample for this study was obtained from an international university located in Saraburi, Thailand. Thus, this study has limitations in terms of its generalizability. Another limitation is the number of participants expected to participate in the study. Across the literature, sample sizes concerned with any or several of the variables considered for this study were in the range of 300-350 participants. However, the current number of international students at Asia-Pacific International University is around 500, which means that at least 50% of the targeted student population would have to participate to reach a sample of 250 respondents. Lastly, it is important to acknowledge that there may be other factors related to academic dishonesty that were not considered in the present research. Delimitations There are several delimitations set by the primary researcher and his thesis advisors to focus the study. First, participants must be undergraduate international students. As such, international students in graduate programs will not be included in the research. This specification will lead to more accurate results that can be compared with those achieved by other researchers since most of the literature focuses on international students in undergraduate programs. A second delimitation of the present research is that a survey will be employed to collected data using convenience sampling. This delimitation was made due to time, effort, and level of response from the student

8 population in relation to surveys. A final delimitation is that, although the present research may be beneficial to other international universities, the goal of this study was to benefit Asia-Pacific International University. Definition of Terms The researcher has provided definitions to the following terms to diffuse confusion for readers who may not be as familiar with this particular area of research as they are with their own fields of study. Academic Dishonesty: An attitude of disrespect toward academic policies that involves engaging in plagiarism, cheating, falsification, among others forms of dishonesty in the school setting. Language Learning Strategies: A set of thoughts, procedures, and actions, both conscious and unconscious, learners go through while acquiring a language. E-Learning Self-Efficacy: The ability to perform academic tasks that demand the use of technology, namely computers, online platforms, learning management systems, and other technology-related educational tools. Organization of Chapters Here is a brief description of the organization of this thesis. Chapter one introduces the study, including sections like background of the study, statement of the problem, purpose of the study, research questions, significance of the study, as well as its limitations and delimitations. Chapter two aims to define the variables considered in the present study, look at theories related to each variable, and discuss relevant studies that looked at language learning strategies, e-learning self-efficacy, and academic honesty. Such a goal will be achieved by a review of the existing literature on the topic at hand.

9 Chapter three is concerned with the methodology employed in this study. A such, details of the sample, scales, instrument, data collection, and data analysis techniques related to this research project are discussed. Also, ethical concerns are considered in chapter three. Chapter four focuses on the results obtained from the data analysis. In this sense, the statistical analysis will focus on descriptive (means and standard deviations), comparative (t-test and ANOVA), and relational statistics (correlation and regression) of the variables language learning strategies, e-learning self-efficacy, and academic honesty among APIU international students. Lastly, chapter five discusses the findings of the present study, providing inferences related to the topic, as well as giving recommendations for students, teachers, administrators, and further study.

10 CHAPTER 2 LITERATURE REVIEW Academic Dishonesty (AD) The first part of this literature review will aim to explore several definitions of academic honesty and dishonesty. After looking at some definitions of academic honesty and its counterpart, academic dishonesty, the literature review will focus on some of the theories that have been associated with academic dishonesty. The last part of this first section will discuss relevant studies that have looked at academic dishonesty, associating it with the use of online technologies, linking attitudes toward academic dishonesty with religious beliefs, and exploring academic dishonesty in tertiary settings where English is the medium of instruction. Definitions Szabo et al. (2018) stated that “academic honesty or integrity means putting one’s own effort into completing required assignments,” pointing out that integrity “means believing in a set of principles that cause others to trust” (p. 27). Such a definition underlines several things. First, scholars see the terms ‘academic honesty’ and ‘academic integrity’ as compatible since they point to the same thing, values. Following this line of thought, this thesis will use the terms interchangeably. Second, academic honesty is concerned with the way individuals go about their academic obligations and assignments, implying that using someone else’s effort or ideas as one’s own is unacceptable. Third, academic integrity is associated with a set of

11 principles or beliefs that uphold an honor code that binds students, teachers, and institutions. Other definitions of academic honesty are in line with the one discussed above and expand the ideas previously mentioned. For instance, East and Donnelly (2012) described academic integrity as “understanding what it means to be honest in the particular culture of the academic world, and being able to apply the scholarly conventions of acknowledgement” (p. 1). In other words, academic honesty allows the use of other people’s ideas as long as they are recognized as someone else’s intellectual property or effort. Others have suggested that academic integrity means “honesty in all matters relating to endeavours of the academic environment” (Turner & Beemsterboer, 2003, p. 1122). This means that academic honesty should the guiding principle or value when performing academic tasks, which can go from respecting academic honesty guidelines for a term paper to properly acknowledging others’ ideas when authoring a dissertation. Another aspect of academic honesty is honor codes since they are considered institutional tools for preventing issues of academic honesty (McCabe & Treviño, 1993). Tatum and Schwartz (2017) outlined the different components of honor codes, stating it includes “an honor pledge, a peer-reporting requirement, a student-run adjudication system, and a requirement that faculty turn all suspected cases over to the judiciary body” (p. 123). Also, Turner and Beemsterboer (2003) addressed the concept of honor code as closely related to academic honesty by affirming: Put in simplest terms, an honor code is a statement of the values of the institution and the established level of expected behavior from all persons who function in

12 the educational arena. Traditionally, honor codes have been developed to protect the academic integrity of the university, encourage ethical behavior, and foster a climate of fair competition. Honor codes reinforce expected faculty/student behavior by establishing a mechanism to emphasize the university’s position based on its articulated values. (p. 1124) This understanding of honor codes underlines that their major objectives are: (1) to ensure academic honesty among students and teachers and (2) to promote academic values that contribute to the good name of the institution in which the honor code is implemented. One more concept that needs to be addressed when discussing academic integrity is its counterpart, academic dishonesty, which is defined as “any unauthorized assistance in completing a task” (Krou et al., 2021, p. 427). Shoaib and Ali (2020) portrayed academic dishonesty as “a major problem,” present “in many educational systems in the world” and “increasing at a rapid rate with each passing day” (p. 62). Moreover, Shoaib and Ali (2020) cited Akbulut et al. (2008) to list down different types of academic dishonesty, among which are fabrication, falsification, finagling, plagiarism, duplication, least publishable units, neglecting support, and misusing credit. They also pointed out that “plagiarism and dual submission are the most common forms of academic dishonesty at all levels of higher education” (2020, p. 62). Although the aforementioned forms of academic dishonesty cover most of the manners in which issues related to academic integrity take place, it can be said that there are newer and more efficient ways for students to fall in the pit of academic dishonesty thanks to the Internet and online technologies (Muangprathub et al., 2021; Sayed &

13 Lento, 2015). In her study, Peterson (2019) gave specific examples of how students use online technologies to engage in academically dishonesty behaviors. She acknowledged that (1) purchasing term papers online, (2) utilizing digital media and devices to share exam information through sites like “Yik Yak,” (3) watching YouTube videos that teach students how to cheat more effectively, and (4) hiring companies that can do students’ online assignments for them are some of the most advanced ways of engaging in academic dishonesty (Peterson, 2019, p. 25). Thompson et al. (2017) not only explored the concepts discussed above but also came up with a definition that related them to students in cross-cultural contexts. In this sense, Thompson et al. (2017) indicated that academic honesty has been associated with students’ respect for policies that condemn cheating and plagiarism, although such policies may need to be explained in educational settings where students have dissimilar Theories One of the theoretical frameworks that have been related to issues of academic honesty is Ajzen’s (1991) theory of planned behavior (TPB). In his classic study on factors associated with academic dishonesty, Whitley (1998) summarizes the postulates of the aforementioned theory by stating that it “deals with the relationship between attitudes and behavior,” holding that three constructs must be acknowledged, namely “perceptions of social norms governing the behavior, perceived ability to perform the behavior, and perceptions of one’s moral obligation to perform positive behaviors and avoid negative behaviors” (p. 245). Accordingly, he asserted that “students with favorable attitudes toward cheating” are “more likely to cheat than students with unfavorable attitudes” (Whitley, 1998, p. 245).

14 Other experts have tried to explain the theory and associate its hypotheses with issues of academic honesty. For instance, Chudzicka-Czupala et al. (2016) defined theory of planned behavior as a model that considers factors like attitudes, perceived behavioral control, and moral obligation, affirming that these components can forecast students’ plans to take part in the act of cheating. Another explanation of the theory comes from Meng et al. (2014), who wrote concerning theory of planned behavior: The components of TPB directly affect [students’] intention to complete behaviour and intention in turn influences whether an individual ultimately engages in the behaviour. Individuals’ perception is in agreement with actual behavioural control. Ajzen (1991) suggested that perceived behavioral control serves a proxy for actual behavioural control, therefore having a direct influence on both intention and the actual behaviour. It can serve as a model to predict undergraduate students’ engagement in unethical behaviours, specifically academic dishonesty (cheating, plagiarism, fraud, etc.). Moreover, it also serves as a model for the decision-making process used by students when forming the academic dishonesty intention and its subsequent behaviour. (p. 128) This view of theory of planned behavior puts forth the idea that the model not only functions as a predictor of misconduct in the area of academic honesty but also pinpoints what factors influence the decision-making process students go through when engaging in dishonest behavior. Another theoretical model associated with academic dishonesty is deterrence theory (Zimring & Hawkins, 1973), which implies that students are more likely to engage in academic dishonesty if they do not suffer the consequences. Moreover, the application

15 of this theory in the school setting holds that, in order to change learners’ misbehavior regarding academic honesty, teachers and institutions should punish offenses “with consequences severe enough to discourage students, including failing the assignment, failing the course, academic probation, or even expulsion” (DiPietro, 2010, pp. 251–252). One of the important aspects of the application of deterrence theory to educational contexts is the emphasis the model puts on authority figures for preventing undesired behavior. Regarding academic honesty, teachers and institutions are not only expected but also tasked with discouraging academic dishonesty among students. As such, the question of whether the application of deterrence theory makes a difference in students’ behavior remains a valid one. Researchers have sought to evaluate the application of deterrence theory, examining the effects of more severe punishments over displays of academic dishonesty. Chirikov et al. (2020) explored the impact of the application of deterrence theory on university students and concluded that stronger punishments substantially enhanced academic honesty among pupils. Consequently, Chirikov et al. (2020) study suggested that teachers should have a firmer stance when dealing with academic dishonesty. However, not only teachers are responsible for effecting change in educational institutions. Schools can implement policies that lead students to avoid academic dishonesty. Young et al. (2018) carried out a study that looked at factors that influenced students to abstain from academic dishonesty. The study showed that learners “refrain[ed] from academic dishonesty simply because they fear[ed] punishment for their actions” (Young et al., 2018, p. 12). Hence it can be said that the application of deterrence theory produces positive results.

16 There are two more theories that have been related to academic honesty and should be mentioned in this part. The first one is Fletcher’s (1966) situational ethics, which is a theoretical framework that posits that unique situations can lead to unique decisions an individual would not make under normal circumstances. In the educational arena, this would mean that students cheat and plagiarize because extreme conditions have led them to do so. However, “the high incidence of reported cheating suggests that students don’t cheat in response to extreme–and therefore rare –circumstances, where breaches of academic integrity would possibly be understandable” (DiPietro, 2010, p. 254). Thus, although students are pressured in different ways and by different people that can ultimately push them to cheat (Rowland et al., 2018), such situations should not be given as reasons for engaging in academic dishonesty. The second one is neutralization theory (Sykes & Matza, 1957), which focuses on people’s ability to justify their misconduct in order to not damage their self-concept. It was in their effort to contextualize neutralization theory to the school environment that Storch et al. (2002) adapted Sykes and Matza’s (1957) neutralization techniques into four common practices used by university students to excuse or even explain why they engaged in academically dishonest behavior. These practices or techniques include: (1) denial of responsibility (cheaters do not acknowledge their offense), (2) denial of injury (students claim no one was hurt by their act of academic dishonesty), (3) condemnation of the condemners (offenders blame teachers for being unfair in the task of grading or examining students), and (4) appeal to higher loyalties (learners state that helping a friend pass an exam is more important than a set of rules) (DiPietro, 2010; Storch et al., 2002; Sykes & Matza, 1957).

17 Relevant Studies Studies on academic honesty issues date back to the 1930s. One example of this comes from Howells (1938), who evaluated some of the factors influencing honesty in academic settings, indicating that the intelligence of the subject, the drive for dishonesty, and the degree to which dishonesty is personal are major indicators to consider among higher education students who succumb to cheating. Another researcher from the aforementioned decade is Nelson (1939), who stated that personal ideals played a major role in students’ decision to be academically honest or dishonest. On the other hand, the 1980s was a prolific decade in relation to studies addressing academic dishonesty. Some of those studies focused on trends in cheating at the university level (Baird, 1980), coping mechanisms for plagiarism (Brownlee, 1986; Factor et al., 1989), measuring attitudes toward cheating (Gardner & Melvin, 1988), and explaining plagiarism as a major academic honesty issue among college students (St. Onge, 1988). In more recent years, plagiarism is one of the major themes in studies concerned with academic honesty issues among university students. A nationwide cross-sectional survey carried out by Birks et al. (2018) among 361 undergraduates in an Australian nursing degree program underlined that plagiarism was the most frequently reported form of academic misconduct. Moreover, majority of respondents believed that severe punishment from teachers and the school would deter academic dishonesty in the student population. This finding is consistent with the premise that fear of harsh punishment can discourage undesired behavior (i.e., deterrence theory). Lastly, a strong correlation was found between plagiarism and professional misconduct, which suggested that college students engaged in academic dishonesty are likely to also engage in malpractice.

18 Certain forms of plagiarism can be considered sophisticated due to the number of hurdles students go through to commit an academic offense. According to Walker and Townley (2012), such is the case of contract cheating. The term contract cheating refers to “a form of academic dishonesty, where students contract out their coursework to writers or workers, usually found via the internet, in order to submit the purchased assignments as their own work” (Walker & Townley, 2012, p. 27). Research indicated that millions of students engage in this form of plagiarism (Newton, 2018). One of the reasons why students pursue this manner of plagiarism is because websites concerned with this activity promise quality work, which facilitates the hassle of plagiarizing on one’s own. However, Sutherland-Smith and Dullaghan (2019) reported that many of the sites and people associated with providing this so-called ‘service’ are scams that even make students pay for fail grade work or simply do not send assignments on time. Sadly, students that practice this kind of plagiarism tend “to do so repeatedly” (Curtis & Clare, 2017, p. 123). In the Southeast Asian context, some researchers have looked at the issue of plagiarism. Bowen and Nanni (2021) conducted a study among 365 Thai undergraduates to assess beliefs about and exposure to plagiarism. One of the particulars of the study is that it took place at university where English was the medium of instruction (EMI). Results showed that students understood that committing plagiarism was morally wrong; nevertheless, findings also suggested that most of the participants had a broad understanding of plagiarism, which could be of the reasons why they continued to engage in it. Another study concerned with academic dishonesty and done at an EMI university in Thailand was carried out by Thomas (2020). Although the findings of his study

19 evinced that there was “an overall disagreeing view towards academic dishonesty,” issues of academic dishonesty were found among students, which led the researcher to acknowledge that “the beliefs that the participants stated may not be” consistent with “the behavior of the context” (Thomas, 2020, p. 133). Additionally, one of the survey’s interesting features is that it correlated religious beliefs with academic dishonesty among undergraduate students. The idea that religious beliefs are an influencing factor that affects students’ attitudes toward academic dishonesty has been explored by several researchers. Concerning this topic, Hongwei et al. (2017) looked at the association between religious variables and academic dishonesty among college students. The findings of their study indicated that undergraduates who actively participate in religious activities were less likely to be involved with academic honesty issues. On the other hand, findings from a survey conducted by Soto et al. (2018) suggested that, although religious involvement has a strong relationship with academic ethics, it does not predict learners’ academic honesty, performance, or personal stress. The notion that religiosity does not affect students’ cheating behavior is also supported by Hadjar’s (2017) study. Cultural diversity is another factor that has been associated with academic dishonesty at international universities. A relevant study on this theme was conducted by Thompson et al. (2017) among international students pursuing graduate degrees at American universities. Results from the study suggested that Asians have different views from their classmates on academic dishonesty. As such, Asian students felt that university teachers and policies were too harsh or severe in the way they dealt with academically dishonest behavior such as cheating in exams and plagiarizing someone else’s ideas.

20 Thompson et al. (2017) recommended that teachers and administrators need to educate international students concerning policies and practices related to academic honesty if they want to foster a standard academic culture in cross-cultural educational settings. Other studies have shown similar results and given the same recommendations (e.g., Altaibekova, 2020; Cavico & Mujtaba, 2009; McKay et al., 2019). Students’ level of English proficiency seems to be another factor associated with academic dishonesty at the tertiary level. In this sense, researchers have suggested that improving students’ academic writing skills can reduce plagiarism and cheating among pupils who are acquiring English as a second language while taking an international program (Perkins et al., 2020). Bista’s (2011) study indicated that, although explaining academic dishonesty among ESL students is a complex undertaking, international students’ learning style and English language proficiency are important factors to consider when explaining academic dishonesty. Based on the relationship between English proficiency levels and academic dishonesty among international students, it is relevant to look at language learning strategies as tools pupils use to acquire the target language while learning new content in it and adjusting to higher education and academic honesty policies. Language Learning Strategies (LLS) The second section of chapter two will deal with definitions of language learning strategies. Furthermore, theories related to second language learning will be discussed. Lastly, the section will look at factors associated with language learning strategies by examining relevant studies on the topic.

21 Definitions Language learning strategies are succinctly described by Oxford (2018) as “purposeful mental actions used by a learner to regulate his or her second or foreign (L2) learning” (p. 81). In a more comprehensive definition, the same author underlined core features of language learning strategies, the L2 learner’s self-regulating role, and the contexts in which LLS take place by pointing out that: LLS are purposeful, conscious (or at least partially conscious), mental actions that the learner uses to meet one or more self-chosen goals, such as (a) overcoming a learning barrier, (b) accomplishing an L2 task, (c) enhancing long-term L2 proficiency, and (d) developing greater self-regulation (ability to guide one’s own learning). Like most aspects of L2 learning, LLS occur in real contexts (specific settings), are complex (with multiple, interacting factors), and are dynamic (flexible, usable in different ways, and changeable along with learners’ changing needs). LLS can be learned with help from a teacher, a friend, a book, or the internet, although many learners creatively and effectively generate their own LLS. (p. 82) Oxford’s (2018) concept of language learning strategies is based on her in-depth literature review on the subject, which she labeled as a “content-analytic study of strategy definitions” where themes such as “purposefulness” and “consciousness” were considered prominent ones (Oxford, 2017, p. 47). Though at times Oxford (2018) uses the terms ‘language learning strategies’ and ‘L2 learning strategies’ interchangeably, she acknowledges the ongoing debate regarding “definitional issues” in the arena of L2 language teaching and prefers to use ‘L2 learning

22 strategies’ “for the sake of clarity” (Oxford, 2017, pp. 47–48). Thomas and Rose (2019) joined the debate on language learning strategies definitions by admitting that TESOL researchers have had a hard time producing a definition the majority can agree with. However, such challenge has not stopped experts like Oxford (2017) from not only coming up with definitions of L2 learning strategies but also linking them with “mediated learning, the zone of proximal development (ZPD), and self-regulation” (p. 65), which are all part Vygotsky’s (1978, 1981) theoretical framework. Plonsky (2011) viewed L2 strategies as “specific practices or techniques that can be employed autonomously to improve one’s L2 learning and/or use” (p. 994). Horwitz (2013) also defined language learning strategies as “techniques that learners can use to improve or enhance their target language ability” (p. 274). Other researchers change the term ‘learning’ for ‘learner’ when conceptualizing L2 strategies. For instance, Cohen (2011) described L2 learner strategies as “thoughts and actions, consciously chosen and operationalized by language learners, to assist them in carrying out a multiplicity of tasks from the very onset of learning to the most advanced levels of target-language (TL) performance” (p. 7). Purpura (2014) not only agreed with Cohen (2011) in terms of focusing on the ‘learner’ when defining strategies for second or foreign language (SFL) acquisition but also characterized them as “thought[s] or behavior[s] used by learners to regulate SFL learning or use” (p. 533). Some worth-noting stances concerning L2 learning strategies definitional issues come from Dörnyei and Ryan’s (2015) comprehensive literature review on language learning strategies. They suggested that, although language learning strategies “deal with issues of how proactively and in what way L2 learners engage in the learning process,”

23 such “strategies appear to constitute an aspect of the learning process rather than being learner attributes proper” (p. 140). Also, they asserted that “the various definitions of L2 learning strategies offered in the L2 literature” are “inconsistent and elusive” (p. 146), citing earlier research on the subject from O’Malley et al. (1985) about the lack of definitional consensus and from Ellis (1994) about the need for a more specific theoretical framework for L2 learning strategies. Despite such critiques, Dörnyei and Ryan (2015) acknowledged the attempts of several experts who have addressed and defined language learning strategies. For example, Griffiths’ (2013) characterization of language learning strategies is summarized by Dörnyei and Ryan (2015), stating that there are six major characteristics of language learning strategies, namely activity, consciousness, choice, goal orientation, regulation, and learning focus. They also cited and commended Cohen (2011), Oxford (2011), and others who have worked on the “task of reinterpreting language learning strategies” in recent years, “although at the basic definitional level no breakthrough has occurred” (p. 148). An important part in the task of defining language learning strategies is pinpointing the taxonomies or categories that group them. Dörnyei and Ryan (2015) deemed Oxford’s (1990) and O’Malley and Chamot’s (1990) language learning strategies’ taxonomies as “well-known” and “highly compatible” (p. 149). Following this line of thought, Dörnyei and Ryan (2015) evaluated the taxonomies in question and produced a third one that integrates both models. They stated: If we make some slight adjustments on the basis of the arguments just outlined— (a) exclude communication strategies from the scope of learning strategies, (b) combine Oxford’s memory and cognitive strategies, and (c) separate O’Malley

24 and Chamot’s social/affective strategies—the resulting typology comprises the following four main components: 1. Cognitive strategies, involving the manipulation or transformation of the learning materials/input (e.g., repetition, summarizing, using images). 2. Metacognitive strategies, involving higher-order strategies aimed at analyzing, monitoring, evaluating, planning, and organizing one’s own learning process. 3. Social strategies, involving interpersonal behaviors aimed at increasing the amount of L2 communication and practice the learner undertakes (e.g., initiating interaction with native speakers, cooperating with peers). 4. Affective strategies, involving taking control of the emotional (affective) conditions and experiences that shape one’s subjective involvement in learning. (p. 149) These categories of L2 learning strategies constitute a guide for grouping and analyzing the different thoughts, behaviors, techniques, activities, and emotions L2 learners engage in while acquiring the target language. Although other taxonomies have been produced (e.g., Oxford, 2011), they are not far from the ‘typology’ suggested by Dörnyei and Ryan (2015) based on Oxford’s (1990) and O’Malley and Chamot’s (1990) classifications of language learning strategies. Several things can be asserted after exploring quite a few definitions of language learning strategies. First, the literature reviewed thus far uses the terms ‘language learning strategies,’ ‘language learner strategies,’ and ‘L2 learning strategies’ to refer to the same processes, thoughts, and actions involved in the task of acquiring a second language. For this reason, I decided to use the terms ‘language learning strategies’ and

25 ‘L2 learning strategies’ interchangeably throughout this thesis. Second, the multiple definitions that TESOL experts have developed to conceptualize these strategies not only demonstrate how diverse L2 learning strategies are but also evince the core attributes they have in common since most researchers include the words ‘thoughts,’ ‘behaviors,’ ‘actions,’ ‘techniques,’ among other terms to describe language learning strategies. Third and last, although different taxonomies have been produced to classify L2 learning strategies, such taxonomies should be seen as complementary due to their major similarities and minor differences. Theories The task of conceptualizing L2 learning strategies is closely related to identifying theoretical models associated with them. Sociocultural theories like mediated learning, the zone of proximal development (ZPD), and self-regulation (Vygotsky, 1978, 1981) are at the core of language learning strategies, according to Oxford (2017). Consequently, it is crucial to explore the basic postulates of these theories, how they relate to each other, and how they are linked to L2 learning strategies. The sociocultural model developed by Vygotsky (1978) can be applied to second language learning because it acknowledges the “interweaving of cultural and biological inheritances” (Lantolf & Thorne, 2006, p. 59) that learners in general, and in particular L2 learners, experience. In other words, sociocultural theories acknowledge that the individual’s natural and nurtured abilities and strategies for learning are interwoven with his cultural background, interaction with instructors and peers, and present surroundings. Such ‘interweaving’ means that acquiring/learning a second language “is both a social and cognitive process” (Ellis, 2015, p. 233). Also, sociocultural perspectives are evinced

26 in online learning through the creation of online communities where software, teachers, and peers function as mediators of the learning process (Erdogan, 2016). Mediation or mediated learning is one of the cornerstones of Vygotsky’s (1978) theoretical model. According to Ellis (2015), mediation is a process or social activity in which learners receive help from others or themselves that enables them to acquire knowledge and ultimately be independent as performers. In this sense, Vygotsky sought to study “how children make use of assistance and how over time they internalize the assistance to the point where they no longer need it” (Ellis, 2015, p. 233). Some language learning strategies can be deemed as mediation in the form of, but not limited to, social interaction. For instance, teachers interacting with L2 learners in the target language, ESL students correcting themselves through the use of materials, or even listening to a song in English to assimilate idioms or common expressions are forms of mediation that take place among L2 learners in ESL contexts. Another important concept based on the sociocultural model and closely related to mediated learning is the zone of proximal development (ZPD), also considered a particular form of mediation. The main difference between mediation and the zone of proximal development is that mediation may not need the presence of a facilitator to take place; however, ZDP requires collaborative mediation, which means there must be someone guiding the learner to help him develop and acquire the skills or abilities expected of him (Mitchell et al., 2013, p. 223). Teachers and peers with a higher level of proficiency in the target language play a vital role because they can aid students who attempt to speak and/or write in the target language, which are L2 learning strategies, by

27 assisting the L2 learner and explaining in which ways the message they try to convey can be better understood. Oxford (2017) connected the ZPD with mediation when she stated: The ZPD is the difference between the individual’s current level of development and the potential level that can be reached with assistance of a more capable person. To facilitate internalization of the dialogues and help the learner traverse the ZPD, the more knowledgeable individual offers various forms of assistance. Vygotsky did not use the term scaffolding, but his concept was that learners would need less and less mediation from the more capable other as learners developed their own egocentric speech and then their inner speech. In other words, assistance to the learner is viewable as scaffolding if it is present when the learner needs it and removed when no longer necessary to the learner. (p. 67) The idea that someone with a higher level of proficiency in the target language can help L2 learners cross the zone of proximal development is clearly explained in the quotation given above. Moreover, such understanding of scaffolding is relevant for teachers with students who are learning English by using language learning strategies and require assistance in the process (e.g., trying to speak like a native, drafting an essay with new English words, asking how to pronounce a difficult word correctly). Self-regulation is the third pillar of the sociocultural theoretical model and “Lev Vygotsky described [it] as an outcome of mediation” (Oxford, 2017, p. 66). In other words, L2 learners that experience the zone of proximal development and mediated learning (i.e., receive help from teachers, materials, computer programs, or themselves while employing L2 learning strategies) reach a point where they can assert which

28 language learning strategies are most effective for them, which not only enhances their proficiency in the target language but also helps them become more autonomous, something that is “closely tied to self-regulation” (Oxford, 2017, p. 66). Likewise, “action is the very heart of self-regulation, just as it is at the core of L2 learning strategies,” affirmed Oxford (2017, p. 69), who also asserted that self-regulation is goaldriven and involves strategies by citing the research of Zimmerman and Schunk (2011). In simple terms, self-regulation is the ultimate stage of the sociocultural model, which depicts the learner as going through three stages: (1) social speech (i.e., coregulation, social interaction), (2) egocentric speech (the learner talks to himself but does not self-regulate), and (3) inner speech (self-regulation, the learner chooses/guides his own strategies/actions) (Moll, 2014, as cited in Oxford, 2017). Although language learning strategies can be present in all three stages of the sociocultural model when it is applied to ESL settings, the relationship between L2 learners and the strategies they use varies from stage to stage. Stages one and two are a combination of the zone of proximal development and mediation theories, which means that students use L2 learning strategies heavily relying on the guidance of their teachers/peers at first; then, learners move on to receive a milder support as they continue to search for strategies on their own. In stage three, learners are aware of what metacognitive processes, actions, and language learning strategies are more beneficial to them, which empowers them to take control of their own L2 learning experience. Relevant Studies Early studies explored several factors affecting learners’ use of language learning strategies. For instance, Oxford and Nyikos (1989) pointed out that motivation, sex, and

29 year of study were important variables related to the choice of L2 strategies among international university students. Other researchers focused on children’s strategies for learning a second language, stating that ‘goofing’ or making mistakes was a common strategy employed by young children when acquiring a second language (e.g., Dulay & Burt, 1972). Scholars also looked at adults and their use of L2 strategies, with most of them stating that the particular age group previously mentioned heavily relied on their native language to make sense of the new information received in the new language (e.g., Nayak et al., 1990; Taylor, 1975) In recent years, one of the variables that research has widely documented concerning the use and selection of language learning strategies among L2 learners is gender. A study among EFL students in Saudi Arabia showed that female students used more L2 learning strategies than male students (Alhaysony, 2017). In terms of strategy selection, a study conducted among Indonesian students found that women preferred cognitive, compensation, and affective strategies, while men mostly relied on memory, metacognitive, and social strategies when engaging in learning the English language (Mahmud & Nur, 2018). Such findings are similar to those of a study conducted among Myanmar university students, indicating that male students mostly used metacognitive strategies while female students preferred cognitive strategies (Oo & Oo, 2018). However, other studies suggest that there are no differences between genders when it comes to choosing and/or using language learning strategies (Melvina & Wirza, 2020; Ranjan & Philominraj, 2020). Some researchers have put aside the question of whether there are gender differences in terms of choosing language learning strategies and have focused on what

30 university students in general prefer when searching for effective ways to learn English. Zhao (2019) indicated that Mongolian undergraduate students’ first strategy is to use their mother tongue when trying to read, write, or speak in English. Bravo et al. (2017) found that college students showed interested in listening to music in English, learning the correct pronunciation of words, and acquiring a richer vocabulary to communicate with their peers. Oveisi and Nosratinia (2019) stated that L2 undergraduate students highly esteem speaking in the target language. On the other hand, Nguyen and Terry (2017) acknowledged that L2 learners at the tertiary level tend to see memorization, repetition, and grammar exercises as ineffective strategies for learning English. These findings point out that university students favor cognitive, metacognitive, and social L2 learning strategies in their pursuit to acquire the English language. Another important factor related to language learning strategies and L2 acquisition at international tertiary institutions is academic dishonesty. Conzett et al. (2010) recognized the need for proactively dealing with academic dishonesty among L2 writers. Undergraduate and graduate students who have English as a second language and pursue a degree in international programs not only use writing in English as an L2 learning strategy but also as an academic exercise that demands “strategic behaviors” associated with “paraphrasing and plagiarism avoidance” (Marzec-Stawiarska, 2019, p. 115). Click (2012) referred to academic writing as a “dauting task” (p. 48) for L2 learners in higher education. Some researchers have asserted that freshmen and sophomores commit plagiarism unintentionally, thus being unaware of it (Liu et al., 2018; Premat, 2020). Patak et al. (2020) suggested that English as a foreign language (EFL) college students in Indonesia plagiarize due to their poor writing skills.

31 Raoofi et al. (2017) had two noteworthy findings while exploring the relationship between writing strategies and L2 writing proficiency among ESL university students. First, there was a strong correlation between the use of L2 writing strategies and high writing abilities among students. Second, it was reported that learners with a high level of L2 writing proficiency used more metacognitive, cognitive, affective, and effort regulation strategies than those with a lower L2 writing proficiency level. Ideally, tertiary students who often use L2 writing strategies are more likely to develop a higher level of L2 writing proficiency, which makes it easier for them to write in the target language, follow academic writing guidelines, and avoid plagiarism. However, students might need help when it comes to finding L2 learning strategies that can enhance their academic writing skills (Basri et al., 2019; Stander, 2020). Online technologies have also been associated with the use, spread, and enhancement of L2 learning strategies and English learning (Ahmadi, 2018; Zhou & Wei, 2018). In this sense, Bin-Hady et al. (2020) not only agree with the previous statement but also point out that higher education students who learn English in virtual spaces favor cognitive, metacognitive, and socio-affective strategies over other types of language learning strategies. Solak and Cakir’s (2015) study revealed that second-language elearners relied on metacognitive and memory strategies the most while taking an English course using online technologies, highlighting that females preferred cognitive strategies while males chose metacognitive ones. On a more general note, Mohammadi et al. (2011) discussed the effects of e-learning on L2 acquisition and affirmed that students become more independent when using the Internet to learn English, giving them the chance to choose which L2 learning strategies might be more effective for them. Results from other

32 studies highlight that students acquire English faster when exposed to e-learning modalities (Lai et al., 2018; Li, 2019; Yang & Wu, 2015). Nevertheless, it is important to keep in mind that online technologies have enhanced L2 learning and the use of language learning strategies to the point that it can be difficult to choose which form of e-learning might be best for certain learners, classrooms, and (Kessler, 2018). The third and last part of this second section focused on relevant studies that have associated L2 learning strategies with other factors. Themes like gender, students’ choice of language learning strategies, academic dishonesty, and online technologies emerged from the different studies that were looked at. The third section of this literature review will address the variable e-learning self-efficacy. E-Learning Self-Efficacy (ELSE) The third section will follow the same structure used in the two previous sections. The first part will explore definitions of self-efficacy in general and e-learning selfefficacy in particular. The second part will look at theories concerned with self-efficacy. The third and last part will discuss relevant studies on e-learning self-efficacy and its relationship with other variables. Definitions According to Bong and Skaalvik (2003), self-efficacy “explain[s] and predict[s] one’s thought, emotion, and action,” being “less concerned with what skills and abilities individuals possess” and more focused on “what individuals believe they can do with whatever skills and abilities they may possess” (p. 5). Zulkosky (2009) defined selfefficacy as “a concept that influences how people think, feel, motivate themselves, and act” (p. 101). These definitions are not mutually exclusive but rather complementary

33 since they both highlight cognition, affection, and action as key components of selfefficacy, with beliefs (Bong & Skaalvik, 2003) and motivation (Zulkosky, 2009) as distinctive factors that contribute to a general understanding of the concept at hand. A broader understanding of self-efficacy comes from Bandura (1997), who wrote: Perceived self-efficacy refers to beliefs in one’s capabilities to organize and execute the courses of action required to produce given attainments.... Such beliefs influence the course of action people choose to pursue, how much effort they put forth in given endeavors, how long they will persevere in the face of obstacles and failures, their resilience to adversity, whether their thought patterns are self-hindering or self-aiding, how much stress and depression they experience in coping with taxing environmental demands, and the level of accomplishments they realize. (p. 3) In this sense, Bandura (1997) characterized self-efficacy as the individual’s beliefs about his abilities to accomplish certain tasks, pointing out that beliefs of personal efficacy have a great impact on factors like decision-making, motivation, coping mechanisms, and realization. Tus (2020) described self-efficacy as “the individual’s judgment on executing the course of action based on the situation,” adding that “self-efficacy depends on an individual’s ability to execute his coping attitudes, behaviors, and consistent efforts exerted in the face of hardship” (p. 49). Although the word ‘beliefs’ is not present in Tus’ (2020) definition of self-efficacy, the emphasis she gives to people’s judgment and ability to deal with a certain situation or task is somewhat similar to the definitions mentioned above since they also refer to cognition, action, motivation, and coping mechanisms as

34 key elements in the conceptual framework of self-efficacy. It is also important to acknowledge that Tus (2020) defines self-efficacy from an educational perspective, focusing on how it influences students in their academic journey. Although definitions of academic self-efficacy tend to overlap with general characterizations of self-efficacy, some experts have pinpointed particular aspects of the former in relation to the latter. For instance, Zhen et al. (2017) termed academic selfefficacy as the students’ perception of their skills to achieve academic goals and tasks, which is another way for referring to Bandura’s (1997) beliefs of personal efficacy and adapting his theory to the school setting. Zhen et al. (2017) also cited Pajares (1996) and Wright et al. (2012) to portray academic self-efficacy as a feeling that motivates students to use learning strategies, improves their cognitive competency, and pushes them to persevere when facing learning challenges. Also based on Bandura’s (1997) comprehensive concept, Wang et al. (2018) defined academic self-efficacy as a strong predictor of academic achievement that has a “positive” impact on students “across different cultures” (p. 236). Moreover, Marsh et al. (2018) narrowed down the notion of academic self-efficacy as a predictor by depicting it as “prospective” answers that “address what one is able to accomplish in the future in relation to a specific task” (p. 334). In this line of thought, it can be inferred that academic self-efficacy serves as a bridge between general notions of self-efficacy and more specific definitions of the term since it contextualizes Bandura’s (1997) allinclusive explanation of the concept in the school environment. E-learning self-efficacy is one of those more specific descriptions of self-efficacy that has been defined and explored in the academic setting, specifically in the area of

35 online learning. Alqurashi (2016) identifies e-learning self-efficacy with three main components, namely computer self-efficacy, Internet and information-seeking selfefficacy, and learning management system (LMS) self-efficacy. These subsidiary concepts or components of e-learning self-efficacy are defined as “learners’ confidence in their capability” to: (1) operate with computers, (2) use “the Internet to seek information,” and (3) work with LMS in online settings (Alqurashi, 2016, pp. 48–49). Another definition of e-learning self-efficacy consistent with the one previously discussed is given by Yavuzalp and Bahcivan (2019). Based on Bandura’s (1977) social learning theory and notion of self-efficacy, Yavuzalp and Bahcivan (2019) described elearning self-efficacy as the individual’s perception of how well he or she can perform tasks related to online learning, courses, and technologies. Also, they identified elearning self-efficacy as “an important psychological factor in online learning environments” (Yavuzalp & Bahcivan, 2019, p. 32). Zimmerman and Kulikowich (2016) enriched the concept of e-learning selfefficacy by developing a scale that measured several dimensions of e-learning. They recognized that other researchers have focused on technology as the only aspect related to e-learning self-efficacy. Seeking to broaden the scope of what online learning selfefficacy comprises, Zimmerman and Kulikowich (2016) included time management and online learning along with technology in their e-learning self-efficacy scale. After looking at some general and specific definitions of self-efficacy, several things can be acknowledged regarding this concept. First, self-efficacy is more concerned with people’s beliefs about their skills than with their actual capabilities to perform a certain task. Second, such beliefs (i.e., self-efficacy) are understood to have a major

36 impact on the individual’s cognition, emotion, motivation, and action when a goal is pursued. Third, academic and e-learning self-efficacy are concepts with practical implications that find their theoretical roots in Bandura’s (1977) work. As such, it is relevant to look at the theoretical framework of self-efficacy. Theories Ghazi et al. (2018) explained the theoretical framework of self-efficacy by acknowledging the origin of the concept and its relationship with social learning theory and social cognitive theory, which are the product of Bandura’s (1977, 1986, 1997) effort to explain the learning process based on a behaviorist approach that acknowledges the influence of several factors. They stated: Self-efficacy refers to a person’s confidence in their ability to successfully perform a behavior. With the addition of self-efficacy as the key construct, the Social Learning Theory evolved into the social cognitive theory (SCT) placing greater emphasis on each person being the primary agent in control of their own life (Bandura, 1977). In agreement with SCT, learning occurs in a social context with a dynamic and reciprocal interaction between a person, their environment, and the behavior that is displayed (Bandura, 1986, 1997). (Ghazi et al., 2018, p. 495) From the viewpoint of Ghazi et al. (2018), self-efficacy is the connecting link between social learning theory and social cognitive theory, being the latter an improved version of the former due to the inclusion and development of self-efficacy as a key concept. Nevertheless, it is important to look at both theories individually.

37 Social learning theory emphasizes the impact of observational learning on individuals as “a powerful effect,” which “is enhanced when the observers believe that the person demonstrating the behavior is similar to themselves” (Deeming & Johnson, 2009, p. 204). This is what Bandura (1977) called ‘modeling,’ asserting that “by observing a model of the desired behavior, an individual forms an idea of how response components must be combined and sequenced to produce the new behavior,” which is the end result of observational learning (p. 35). Also, social learning theory explains ‘perceived self-efficacy’ as an important component related to observational learning since it “determine[s] how much effort people will expend, and how long they will persist in the face of obstacles and aversive experiences” (Bandura, 1977, p. 80). As such, social learning theory stresses the social aspect of learning, considers modeling the means through which observational learning takes place, and points to self-efficacy as a source of motivation based on individual and collective experiences, implying that all these factors affect behavior. Although social cognitive theory considers the importance of the social aspect in the learning process and shaping of behavior, Wang and Lin (2021) claimed its “central premise is that there is a relationship between cognitive or personal factors, environmental factors, and behavior” (p. 16). In this sense, the relationship between social cognitive theory’s major determinants (i.e., cognition, environment, and behavior) is represented by Bandura (1986) in a triangle that places cognitive factors at the apex and environmental and behavioral components at the base of the geometric figure. Such an illustration suggests that social cognitive theory posits a marked emphasis on the cognitive aspect.

38 The evolution of thought from social learning theory to social cognitive theory is evinced in Bandura’s (1997) later development of self-efficacy as a core concept of the cognitive aspect that influences and is influenced by environmental and behavioral factors. This does not mean that social cognitive theory rejects the concepts of modeling or observational learning predicated in social learning theory but rather changes the focus of such concepts from their social approach to a more comprehensive model that recognizes the relationship between social, cognitive, and behavioral components, having self-efficacy as “a key personal variable” (Doménech-Betoret et al., 2017, p. 1) that affects the way individuals interact with their thoughts, actions, and environment. After seeing the importance of self-efficacy and contextualizing it in online educational contexts, it is crucial to look at studies concerned with e-learning self-efficacy. Relevant Studies Research has associated e-learning self-efficacy with other factors, academic achievement being one of the most prominent. For instance, Abulibdeh and Hassan’s (2011) study on the relationship between these two variables involved around 250 university students that were enrolled in online courses. Results suggested that learners with high levels of e-learning self-efficacy were more likely to experience academic achievement. Another study with similar findings was conducted by de Fátima Goulão (2014) among adult learners taking online courses. Discoveries showed that participants’ level of self-efficacy in online classes had “a statistically significant relationship” with their academic performance (de Fátima Goulão, 2014, p. 244). In other words, the higher the level of e-learning self-efficacy, the better students’ performance was in virtual lectures, assignments, and tests.

39 In recent years, other studies have continued to explore the relationship between e-learning self-efficacy and academic achievement or performance, pointing out the impact of the former on the latter. For example, Zarrin and Montazer (2019) explored the possibility of personalizing e-learning environments based on the learner’s self-efficacy level to facilitate academic achievement. Such an argument was based on a vast body of research that suggests e-learning self-efficacy has a positive impact on student achievement in online settings. Findings highlighted that students with personalized online environments for their courses, based on their level of self-efficacy, had positive academic results, thus confirming Zarrin and Montazer’s (2019) hypothesis that personalized e-learning promotes academic achievement via considering the learner’s level of . Nevertheless, it is worth mentioning that before Zarrin and Montazer’s (2019) study, Hodges (2013) advocated for the design of e-learning environments that support the development of ELSE, which leads to academic achievement. A few more things can be affirmed after reviewing some of the literature concerned with the impact of e-learning self-efficacy on academic achievement. First, tertiary education seems to be one of settings where this topic is researched the most (Chen & Su, 2019; Chyung et al., 2010; Mothibi, 2015). Second, although research has identified other types of self-efficacy affecting academic performance in e-learning environments, e-learning self-efficacy remains the most influential one, according to Tladi (2017). Third and last, the school setting is not the only one where the impact of elearning self-efficacy on performance has been researched. Joo et al. (2012) carried out a study among Korean employees who were required by their company to take online courses related to professional development. The study’s results suggested that there was

40 no strong relationship between e-learning self-efficacy and achievement among those workers who took online classes. Joo et al. (2012) acknowledged such findings were contradictory to others’ research and stated that the inclusion of other factors might have been the cause for a different outcome. Another variable associated with e-learning self-efficacy is student satisfaction. Research has shown that components of e-learning self-efficacy (i.e., computer, Internet, and LMS self-efficacy) are primary determinants of student satisfaction. In this sense, Wu et al. (2010) found that computer self-efficacy is a strong predictor of student learning satisfaction in e-learning environments. Later studies have confirmed this discovery (Alqurashi, 2019; Li et al., 2017). Concerning Internet self-efficacy, Hamdan et al. (2021) researched on its impact on student satisfaction and discovered that Internet self-efficacy was a significant predictor of how pleased students were with their online educational experience. Prifti’s (2020) survey on learning management system (LMS) self-efficacy and student satisfaction pointed out a positive relationship between both variables. As such, Prifti’s (2020) findings consistent with those of Alkhateeb and Abdalla (2021), Diep et al. (2017), Landrum (2020), and Sahni (2019). Other studies have focused on e-learning self-efficacy as an array of components that influence student satisfaction. That is the case of Yilmaz’s (2017) research among 236 university students taking a computer class via blended learning (i.e., a combination of online and traditional classroom instruction). His investigation indicated that components of e-learning readiness (such as, computer self-efficacy, Internet selfefficacy, online communication self-efficacy, self-directed learning, learner control, and