The relationships among metacognitive strategies acculturation learning attitude perceived value and continuance intention to use youtube to learn

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The relationships among metacognitive strategies,acculturation, learning attitude, perceived value,and continuance intention to use YouTube to learnEnglish

Xiaohong Liu, Yinkun Zhu, Haining You & Jon-Chao Hong

To cite this article: Xiaohong Liu, Yinkun Zhu, Haining You & Jon-Chao Hong (23 Dec 2023): The

relationships among metacognitive strategies, acculturation, learning attitude, perceived value,and continuance intention to use YouTube to learn English, Computer Assisted LanguageLearning, DOI: 10.1080/09588221.2023.2297037

To link to this article: online: 23 Dec 2023.

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The relationships among metacognitive strategies, acculturation, learning attitude, perceived value, and continuance intention to use YouTube to learn English

Xiaohong Liua , Yinkun Zhua , Haining Youa and Jon-Chao Hongb

aschool of education science, nanjing normal university, nanjing, China; bChinese Language and technology Center, national taiwan normal university, taiwan, China

Facing cultural globalization and technological transformation, students need to learn how to continuously utilize social media for English learning. Previous studies have focused on exploring the relationship among students’ attitudes toward learning tools or platforms, but have not discussed those

cor-relations in the context of English learning via YouTube.

Moreover, limited studies have investigated the continuance intention of students in Taiwan to use YouTube to learn English from the perspective of acculturation, drawing on the expectancy-value model. To address this gap, based on situ-ated expectancy-value theory, this study examined the associ-ations among metacognitive strategies (MS), acculturation, learning attitude (LA) toward English, perceived value (PV) of using YouTube to learn English, and continuance intention (CI) to use YouTube to learn English. A total of 230 data were obtained from students in Taiwan. The results of structural equation modeling revealed that MS and acculturation were positively related to LA and PV; LA was positively related to PV; and LA and PV were positively related to CI. The results offer instructors valuable insights on how to purposefully reg-ulate their English teaching behaviours, and suggest that YouTube can play a role in helping individuals learn English.

1. Introduction

Social media allows learners to share resources and collaborate in spe-cific environments, thus bringing new approaches to self-directed lan-guage learning (Barrot, 2022; Joa et al., 2023). YouTube is a convenient

CONTACT Jon-Chao Hong Language and technology Center, national taiwan normal university, 162, section 1, Heping east road, taipei, taiwan, China.

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social media platform for language learning due to the fact that it offers convenient functions such as being able to watch videos in different lan-guages, switch one’s favorite channels, or interact with people from dif-ferent countries (Benson, 2015). It can effectively support learners in their English learning when they recognize the value and purpose of mastering the English language (Albahiri & Alhaj, 2020). Social media influences individuals’ acculturation experiences (Wang & Abosag, 2019). Cultural symbols have a tendency to stimulate linked cultural thought processes that may regulate individuals’ attribution and evaluation of their learning behaviours (Hong et al., 2000). For example, adolescents’ understanding of the culture can assist them in overcoming obstacles to acquiring the target language (Doucerain, 2019). As acculturation is important for promoting students’ English learning (Zhang et al., 2021), it is valuable to discuss the role acculturation plays in learners’ intentions to use YouTube to learn English.

Situated expectancy-value theory (SEVT) highlights that students’ choices related to achievement and their performance are driven by their subjec-tive task values in specific situations. This highlights that students’ expectancy-value beliefs are deeply rooted in their culture (Eccles & Wigfield, 2020). Acculturation to the cultures of English-speaking countries can positively predict Chinese students’ English proficiency (Jia et al.,

2016). Meanwhile, people in Taiwan always have an intense need to learn English, and some of them regularly post language-learning tutorials on YouTube and even identify themselves as English-teaching professionals, thus turning YouTube into a possible language-learning platform (Wang & Chen, 2020). Therefore, this study drew on SEVT to explore how students in Taiwan use YouTube to learn English outside of the classroom.

Metacognitive strategies can be used by learners to self-evaluate their learning progress and decide on necessary adjustments when their goals deviate from their initial plans (Zhang & Zhang, 2022). Metacognitive strategies refer to students’ control and self-development aspects of cog-nition (O’Malley & Chamot, 1990). Metacognitive strategies include set-ting goals, tracking what is being learned, represenset-ting it, and linking it to past knowledge. Self-directed learners can invoke their metacognitive strategies to monitor and guide the personalized learning process (Zhu et al., 2023). Moreover, metacognitive strategies can effectively slow down the decline in students’ intrinsic utility value and correspondingly improve their academic achievement (Cai et al., 2023). Therefore, we propose that the use of metacognitive strategies will be especially effective when stu-dents are in a high-level self-directed learning environment. Metacognitive strategies were therefore considered in this study.

According to SEVT, social cultural background can regulate students’ subjective task values (Eccles & Wigfield, 2020). Being exposed to diverse

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cultures and watching YouTube videos featuring various English accents can contribute to users maintaining a more favorable attitude towards diverse forms of the English language, which in turn will have an impact on their perceptions of using English materials (Lee et al., 2021). A pre-vious study revealed that a positive attitude toward language learning is often a prerequisite for students to continue to learn the language (Maclntyre & Blackie, 2012). Therefore, students’ attitudes toward lan-guage learning may predict their continuance intentions. Prior research primarily concentrated on exploring the relationship between students’ attitudes toward learning tools or platforms and their continuance inten-tions to use them (e.g. Harper et al., 2023; Ifinedo, 2018a). This study aimed to investigate students’ attitudes toward English learning and their

intentions to continue learning English via YouTube.

2. Theoretical background

2.1. Metacognitive strategies in the context of English learning

Metacognitive strategies (MS) are those strategies that involve ‘thinking about the learning process, planning for learning, monitoring the learn-ing task, and evaluatlearn-ing how well one has learned’ (O’Malley & Chamot,

1990, p. 137). MS are crucial elements for achieving academic success in technology-enhanced contexts (Teng & Yang, 2023) and in online English learning (Teng & Wu, 2023), due to the fact that they can assist learners in identifying their mistakes and can strengthen their sense of control over the learning process (Zhang & Zou, 2022).

Social media affords learners broader access to both mainstream cul-ture and a multitude of ethnic culcul-tures (Marlowe, 2020) and shapes their future second language self-direction through the usage of MS (Chen et al., 2014). When students make good use of MS for learning English, they can reflect on their learning process, identify learning obstacles, and explore various approaches to address these challenges, which will ulti-mately improve their English performance (Huang et al., 2009). For exam-ple, students who apply MS in English reading tend to have much more positive attitudes toward reading, and the level of their reading compre-hension is enhanced (Mijušković & Simović, 2016). The adoption of MS

was explored in the context of English learning via YouTube in this study.

2.2. Acculturation in the context of English learning

Acculturation refers to ‘those phenomena which result when groups or individuals having different cultures come into continuous first-hand contact, with subsequent changes in the heritage culture patterns of either

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group or both groups’ (Redfield et al., 1936, p. 149). Acculturation has been regarded as a bidirectional process, indicating that individuals incorporate the values, identities, and behaviors associated with each of their dual cultures (Nguyen & Benet-Martínez, 2013). That is, accultura-tion is the process of adopting the individual practices of a dominant culture while maintaining some aspects of one’s original culture (Jacob,

2016). In some cases, the degree to which learners acquire the target language depends on their acculturation with the target language group, that is, acculturation results in the adoption of the language of another country (Schumann, 1986). Acculturation of students in Taiwan to American culture, such as frequently eating at McDonald’s or watching American movies, was considered in this study.

Learners can accept the influence of Western cultures and spread their own culture to others through cultural exchange activities. When different cultural groups constantly interact with each other, acculturation occurs (Lefringhausen et al., 2022). Students’ experience of language learning can improve as a result of acculturation. However, one study found no relation between acculturation and English reading comprehension for Iranian immigrants in Canada (Jasemi, 2019). These conflicting findings highlight the importance of exploring the impact of acculturation through using YouTube on Taiwan learners’ acquisition of English as a foreign language.

2.3. Learning attitude towards English learning via technology

Users’ attitudes towards technology are closely linked with various inter-connected factors, such as confidence in using information technology, and personal access to information technology (Vandewaetere & Desmet, 2009). Research on the integration of information technology in the classroom has found that ‘human agency’ plays a crucial role in determining the acceptance and efficacy of computer-assisted language learning (Jahromi & Salimi, 2013). Students hold different attitudes toward English learning and educational technology, which may influence their language learning moti-vations and outcomes; therefore, students’ attitudes toward using techno-logical tools to learn a language should be considered as a vital issue in using technology successfully in language learning (Nguyen & Habók, 2022).

Learning attitude (LA) is viewed as a predictor affecting learners’ learning performance (Ren, 2021). Moreover, strong attitudes tend to remain rela-tively consistent over time, are resistant to persuasion, and serve as reliable predictors of behaviours (Ajzen, 2005). A deeper understanding of learners’ attitudes toward English learning may promote the positive use of YouTube in English learning, but few studies have aimed to understand English learn-ing attitudes or to explore how students’ learnlearn-ing disposition affects their interaction with YouTube; thus, the role of LA was explored in this study.

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2.4. Perceived value in the context of English learning via YouTube

Perceived value (PV) is an individual’s comprehensive evaluation of a product’s utility, formed through their perception of what they receive and what they give in return (Zeithaml, 1988). The value component encompasses individuals’ dedication to a task and their beliefs regarding the task’s significance, distinctiveness, and level of interest (Peng et al.,

2023). Indeed, YouTube can let users become content producers and interact with others freely, creating and disseminating information. Learning from YouTube videos enhances students’ understanding of dif-ferent types of content, and empowers them to monitor their learning process (Yang & Yeh, 2021). Students’ control affects their value apprais-als. In the context of foreign language learning, students’ self-control and their corresponding value assessments can be categorized into three pro-files: high control and high value, moderate control and moderate value, and moderate control but low value (Zhao & Yang, 2023). These findings demonstrate the value of using YouTube to learn English. Therefore, PV of using YouTube was explored in this study.

2.5. Continuance intention in the context of English learning via YouTube

Social media systems, such as Facebook, YouTube, and online streaming websites are the major applications used in language learning (Lai & Tai,

2021). Moreover, continuance intention (CI) is regarded as the strength of individuals’ intentions to perform specific activities (Bhattacherjee,

2001). When learners understand the advantages of online learning resources, their continuance intentions to use them can be enhanced (Tseng et al., 2023). As for YouTube, learners’ expectancy of success in learning English well can increase the possibility of continuously using YouTube to learn English according to SEVT. If students have strong ongoing intentions to use given platforms or websites, they can be moti-vated to use them for specific activities, and are more likely to stick with their learning (Kim & Song, 2021). Moreover, the user-centered nature

of YouTube can stimulate learners’ interest in learning English via

YouTube and enhance their intentions to continue using YouTube (Wang & Chen, 2020). Based on this, CI to use YouTube to learn English was explored in this study.

2.6. Research model and hypothesis development

The importance of specific situations and cultural backgrounds for the realization of students’ academic achievement is emphasized in SEVT (Eccles & Wigfield, 2020). According to SEVT, the beliefs about how well

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they think they will perform on tasks and PV of the importance of per-forming tasks well affect individuals’ completion of tasks (Eccles & Wigfield, 2020). Previous studies indicated that perceived benefits predict learners’ continuance intentions to use language learning applications or systems (e.g. Ni & Cheung, 2022; Wang et al., 2022), but they did not discuss the relationship between PV and CI in the context of English

learning via YouTube. Thus, this study aimed to discuss the relationships

among students’ attitudes toward language learning, PV, and CI in the

context of English learning via YouTube.

YouTube provides a wide variety of cultural resources, and learners can improve their English ability by using it to interact with people of other cultures. Subjective task values are a strong predictor of students’ performance and continued engagement (Eccles & Wigfield, 2002). For instance, perceived usefulness is positively linked to CI to use language learning apps (Wang et al., 2022). With exposure to multiple cultures, LA has an impact on perceptions of using English materials (Lee et al.,

2021). Observing a person’s attitude toward the target language commu-nity allows for predictions about how they will likely behave towards it (Gardner, 1982). Besides, CI to learn languages often presupposes posi-tive attitudes (Maclntyre & Blackie, 2012).

To be specific, the foreign language learning attitudes of students are affected by their MS (Mijušković & Simović, 2016), which slows down the decline of students’ intrinsic utility values (Cai et al., 2023). According to SEVT, PV can be influenced by the learner’s social-cultural background (Eccles & Wigfield, 2020). In the acculturation process, students who inter-nalize Western culture tend to show more positive attitudes toward English learning (Lou & Noels, 2018). Moreover, frequent video sharing and infor-mation searching motivations may promote general YouTube self-directed learning, whereas initiating online video seeking can lead to specific goal-oriented self-directed learning such as the process of problem solving (Joa et al., 2023). Consequently, the research model was developed (see

Figure 1). Seven hypotheses are displayed in Table 1.

3. Method

3.1. Data collection and participants

This study examined tertiary students’ spontaneous English learning on YouTube in their spare time. The snowballing sampling method was used to distribute questionnaires to students at a university. The snowball method is a comprehensive way of collecting more target data due to participants being requested to provide other suitable research samples (Noy, 2008). Accordingly, an invitation message was posted on social

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groups (e.g. Line) that we had joined, with a link to the online question-naire (Google Docs). Moreover, we asked the participants to pass on the link to other appropriate online groups to which they belonged. In the online questionnaire, we set up a single choice for participants to check whether they had experiences of using YouTube to learn English in their spare time. If not, they had to quit the questionnaire, but they were first asked to send the link to any friends who might have such experience. If they did have experience using YouTube to learn English, a message popped up to ask them to complete the questionnaire items. After they finished answering the items, another message appeared to ask them to send the questionnaire to anyone they knew who possibly used YouTube to learn English. Additionally, Google Docs allows researchers to check if there are no duplicate answers by looking at the modification history. Data were obtained by using online Chinese questionnaires which were collected from October 12 to November 1, 2022, in Taiwan.

Considering ethical issues, before filling in the questionnaire, partici-pants were told that the data were for academic purposes only. After agreeing to participate, they filled in the questionnaire anonymously, and they could withdraw whenever they wanted. We expressed sincere grati-tude to the participants but did not offer material rewards to those who completed the questionnaire. Finally, 232 questionnaires were obtained. Incomplete questionnaires were eliminated, leaving 230 valid responses, yielding a 99.1% effective response rate.

Regarding the distribution of respondents to this questionnaire survey, there were 88 males (38.3%) and 142 females (61.7%) comprising 44

Figure 1. research model.Table 1. research hypotheses.

H1: ms are positively related to LAH2: ms are positively related to pVH3: Acculturation is positively related to LAH4: Acculturation is positively related to pVH5: LA is positively related to pVH6: LA is positively related to CiH7: pV is positively related to Ci

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first-year (19.1%), 47 s-year (20.4%), 57 third-year (24.8%), and 82 fourth-year (35.7%) students. Regarding their study majors, 14 (6.1%) were English majors, 46 (20.0%) were pursuing other language majors, and 170 (73.9%) were pursuing non-language majors. As for the fre-quency of students’ use of YouTube, 152 (66.1%) reported that they used YouTube every day, 39 (17.0%) reported that they used it 4–6 days a week, 33 (14.3%) reported 1–3 days per week use, and six (2.6%) reported less than 1 day per week use.

3.2. Instrument

The instruments were modified and adapted from previously scales. The items were reviewed by experts to ensure content validity. To ensure face validity, five selected students were requested to complete the question-naire, and necessary revisions were made. All constructs were measured

using several items, and a 5-point Likert scale was employed (1 = totally

disagree; 5 = totally agree). The reliability and validity of items and

con-structs were subjected to retesting after data collection. All items can be seen in Appendix A.

3.2.1. Metacognitive strategies

This scale was adapted from the Metacognitive Awareness Inventory (MAI; Schraw & Dennison, 1994). The MAI is widely used in the field of language learning (e.g. Altiok et al., 2019; Teng & Zhang, 2021). To guarantee the satisfaction of respondents and to ameliorate a validity threat to the score interpretation argument, Puryear (2015) has used shortened-version scales of MAI. Based on Puryear’s (2015) shortened-version scale, five items were adopted.

3.2.2. Acculturation

The Acculturation, Habits, and Interests Multicultural Scale for Adolescents (AHIMSA) developed by Unger et al. (2002) was used to measure the participants’ level of acculturation. Items of AHIMSA were generated by experts from the Cultural Dynamics and Outreach Core of the research center and had a certain degree of reliability. In this study, four items were adopted.

3.2.3. Learning attitude

This scale referred to the Mathematics and Technology Attitudes Scale (Pierce et al., 2007), the attitudes scale toward English learning devel-oped by Ryan (2009), and Viera’s (2018) learning attitudes toward using technology in a bilingual digital environment. Thus, we designed four

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items to examine students’ English learning attitudes in the context of using YouTube.

3.2.4. Perceived value

This study attempted to explore the participants’ perceived values of using YouTube to learn English, so the scale was adapted from the perceived usefulness scale for English mobile learning systems (Chang et al., 2013). The content was modified based on Davis (1989). Each item was reviewed by two English teachers and read by five college students, and then two information technology experts to confirm their clarity and reliability. Finally, four items were adopted for this study.

3.2.5. Continuance intention

This scale was adapted from the scale of information systems CI with the expectation-confirmation model (ECM; Bhattacherjee, 2001). ECM is used to measure users’ intentions to reuse information systems, such as weblogs or mobile learning communities (Ifinedo, 2018b). Thus, this study measured CI to use YouTube to learn English based on the frame-work of ECM. Four items were designed.

3.3. Data analysis

SPSS 23.0 (IBM, Armonk, NY, USA) was used to perform descriptive sta-tistics of all data and to assess the internal and composite reliability, and the convergent and construct validity of the questionnaire. Furthermore, confirmatory factor analysis (CFA) was used to explore the relationships among the five constructs. Additionally, AMOS 20.0 (IBM, Chicago, IL, USA) was used to analyze the structural equation model to examine the degree of model fit and the relationships of the different constructs. Finally, path analysis was performed to examine the research hypotheses.

4. Results

4.1. Reliability and validity analysis

In this study, items with factor loading (FL) values smaller than 0.50 in each variable were removed (Kline, 2015), leaving five items for MS, and four each for acculturation, LA, PV, and CI. Moreover, to examine item external

validity, t values comparing the top 27% and bottom 27% were tested. The

statistical result indicated that MS was between 63.39 and 72.50, accultura-tion was between 38.13 and 48.71, LA was between 57.04 and 77.55, PV was between 59.85 and 68.18, and CI was between 61.40 and 69.18.

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The internal and composite reliability, and the convergent and con-struct validity of the questionnaire were assessed. Cronbach’s α values and composite reliability (CR) should be greater than .70 (Hair et al.,

2019). Cronbach’s α values and CR values for the five constructs were all above .70 (see Table 2), indicating good internal and composite reliability.

Convergent and construct validity can be determined by FL and the average variable extraction (AVE) of each construct. The FL and AVE values need to be higher than 0.50 (Hair et al., 2019). The values of FL and AVE were all higher than 0.50 (see Table 2), indicating that the measurement had good convergent and construct validity.

To assess whether each construct had good discriminant validity between constructs, Green and Salkind (2010) suggested that the absolute values of coefficients between each construct need to be lower than the square roots of the AVE values of each construct. Table 3 shows that all values reached the threshold, indicating that each construct had good discriminant validity.

4.2. Model fit analysis

This study tested the goodness-of-fit of the structural model. Based on Kline’s (2015) suggestion, the overall Absolute Fit Measure was χ2/df = 1.26,

indicating a good model fit. Other fit indices need to be considered apart from cardinal values to obtain a more objective conclusion (Sarstedt et al., 2016). In this study, RMSEA = 0.03, GFI = 0.91, AGFI = 0.89, NFI = 0.91, CFI = 0.98, RFI = 0.90, PNFI = 0.79, and PGFI = 0.72, all of which reached the standard suggested by Hair et al. (2019). Thus, the hypothesized model had great fitness.

4.3. Path analysis

As shown in Figure 2, the seven hypotheses were all verified. MS and acculturation were positively related to LA and PV. LA and PV were positively related to CI. Additionally, LA was positively related to PV.

The coefficient of determination (R2) was employed to signify the comprehensive influence of the exogenous variable on the endogenous

variable. Values of R2 higher than 0.60 are regarded to have a high

Table 2. reliability and validity analysis.

Variables thresholditems -- M --SD --Cronbach’s α > .70Cr > 0.70 AVe > 0.50FL > 0.50

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The relationships among metacognitive strategies acculturation learning attitude perceived value and continuance intention to use youtube to learn

<b>The relationships among metacognitive strategies,acculturation, learning attitude, perceived value,and continuance intention to use YouTube to learnEnglish</b>

<b>The relationships among metacognitive strategies, acculturation, learning attitude, perceived value, and continuance intention to use YouTube to learn English</b>

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