Effects of data driven learning approach on EFL learners’ speaking engagement

The application of corpus to language instruction encouraged the use of data-driven learning (DDL). It is assisted by computer technology, and uses authentic language data as the basis for language instruction. Previously conducted studies have paid little attention to the use of corpus tools in speaking instruction. Thus, this study aimed to examine the effects of datadriven learning approach on EFL learners’ speaking skills development. A quasi-experimental research design that employed an interrupted time series design with single group participants was used. The participants were fourth-year EFL major undergraduate students at Mekdela Amba University, Ethiopia. In the intervention, which lasted for six weeks, the participants were taught target language features for speaking via data-driven learning approach. Tests, questionnaires, and the students’ reflective journals were used to collect the data. ANOVA and a one-sample t-test were used to analyze the quantitative data, while a qualitative analysis was employed for the students’ reflective journals. The findings indicated that the data-driven learning approach improved the speaking skills of EFL students. Additionally, participants felt that data-driven learning was beneficial to their speaking skill development and had positive attitudes on the utilization of DDL approach in speaking instruction. Finally, considering the results, it was recommended that data-driven learning approach ought to be included into EFL speaking instruction.

The purpose of this study was to explore the relationship between behavioral and emotional engagement of Iranian EFL learners and their attachment to their teachers. Both teachers’ and students’ perceptions of their relationships and students’ engagement have been taken into account. Questionnaire data provided by 311 EFL students and 70 EFL teachers was used to investigate the relation between (a) teacher-student attachment and EFL learners’ engagement, (b) teacher-student secure attachment and EFL learners’ engagement, (c) teacher-student insecure attachment and EFL learners’ engagement, (d) teacher-student secure attachment and EFL learners’ behavioral engagement, (e) teacher-student insecure attachment and EFL learners’ behavioral engagement, (f) teacher-student secure attachment and EFL learners’ emotional engagement, and (g) teacher-student insecure attachment and EFL learners’ emotional engagement. The analysis of teachers and students’ questionnaire data showed that teacher-student attachment was correlated with learners’ engagement. The student participants reported that secure attachment positively affected behavioral and emotional engagement while insecure attachment had a negative effect. The teacher participants reported that attachment with students had weak and non-significant relationship with their students’ behavioral and emotional engagement. These findings and their major pedagogical implications are discussed.

We often read or hear about how important engagement is, most likely because it has been positively linked to student “achievement, satisfaction, and persistence” (Kuh, 2009, p. 694). This is often the case not only in face-to-face learning settings, but also in online and blended learning settings. However, engagement means different things to different people in different contexts. In fact, Axelson and Flick (2011) contended, “few terms in the lexicon of higher education today are invoked more frequently, and in more varied ways, than engagement” (p. 38). Numerous types of engagement exist—behavioral engagement, civic engagement, community engagement, cognitive engagement, emotional engagement, learning engagement, school engagement, and student engagement— just to name a few. Clearly, engagement is a construct with varying definitions and typologies (Coates, 2007) depending on myriad contextual factors. Therefore, it is important for designers, researchers, and practitioners, when referring to engagement, to provide their definition of this construct. In this column, I introduce several scholarly definitions of engagement.Much of today’s literature about student engagement stems from Astin’s research and theory of student involvement (Axelson & Flick, 2011), as well as Kuh’s (2003) definition of student engagement, and Astin’s and Kuh’s leadership, along with numerous other researchers, on the development, creation, and administration of the National Survey of Student Engagement (“NSSE timeline: 1998–2009,” para. 3). Astin (1984/1999) conceptualized the theory of student involvement to consist of “five basic postulates” (Astin, 1984/1999, p. 519) which are:He developed this theory because of “the tendency of academicians to treat the student as a kind of ‘black box’” (Astin, 1984/1999, p. 519). However, it is Kuh’s definition that is most often cited in the higher education literature. According to Kuh (2003), student engagement is “the time and energy students devote to educationally sound activities inside and outside of the classroom, and the policies and practices that institutions use to induce students to take part in these activities” (p. 25).Other researchers, such as Fredricks, Blumenfeld, and Paris (2004) and O’Brien and Toms (2008), have also sought to define engagement, resulting in a more complex understanding of this concept. For example, Fredricks et al. (2004) conducted a literature review of school engagement and found that it is “multidimensional” (p. 61), consisting of behavioral, emotional, and cognitive engagement, as the following summarizes:An important aspect of the three dimensions of engagement is the frequent overlap of these constructs, as well as others that help define engagement.The multifaceted nature of engagement is also evident in a study by O’Brien and Toms (2008), whose purpose was to define engagement vis-à-vis technology through a “critical multidimensional literature review and exploratory study” (p. 938). In their study, they determined that engagementis a process that consists of a:In their model (see Figure 1), it is clear that the level of intensity of engagement varies within the period of engagement. They also discovered three “threads” of engagement based on sensual, emotional, and spatiotemporal attributes. These are depicted in Table 1.Clark and Mayer (2016) have focused their description of engagement on two particular forms that they have found to be pertinent to e-learning: behavioral and psychological engagement. They explained, “Behavioral engagement refers to overt actions taken by a learner during a lesson intended to improve learning” (Clark & Mayer, 2016, p. 219), whereas, “psychological engagement promotes learning that helps learners to achieve the instructional goal by engaging in relevant cognitive processing during learning” (Clark & Mayer, 2016, p. 219). Behavioral engagement involves what learners do (e.g., read, click, answer, type, etc.) and psychological engagement consists of the “mental activity that promotes achievementof the learning objectives” such as “mentally organizing the material into a coherent structure” (Clark & Mayer, 2016, p. 223). For Clark and Mayer (2016), two critical features of engagement are that: (1) “psychological not behavioral engagement … leads to learning” (p. 220) and (2) psychological engagement may occur with or without behavioral engagement.Designing instruction to foster student engagement, therefore, should involve at minimum, careful thought about how students are to interact with the content, with their peers, and with their instructor(s) with attention to the mental activity (psychological engagement) the interaction(s) will promote, as well as what they are expected to do (behavioral engagement). Engagement is a complex, multidimensional concept that requires some unpacking when we refer to it—and its benefits— in teaching and learning.

Investigating consumers’ cognitive, emotional, and behavioral engagement in social media brand pages: A natural language processing approach

Purpose Brands are investing heavily in content marketing within digital communication channels, yet there is limited understanding of the effectiveness of this content on consumer engagement. This paper aims to examine how consumer engagement with branded content is created through consumer-initiated online brand communities (OBCs) and brand-initiated digital content marketing (DCM) communications. Self-brand connections are examined as an important antecedent to the cognitive, affective, behavioural and social dimensions of consumer engagement and the subsequent impact of engagement on loyalty is explored across these two channels. Design/methodology/approach A survey approach was used with two consumer samples for one focal retail brand, namely, a consumer-initiated OBC (Facebook) and email subscribers of the retail brand’s DCM communications. A multi-group analysis of structural invariance procedure was used to comparatively examine the formation of engagement for consumers within the OBC and DCM channels. Findings This study demonstrates the different ways in which engagement forms across different digital communication channels. Self-brand connection (SBC) was found to strongly drive behavioural, cognitive, affective and social engagement. The cognitive, affective and behavioural engagement was found to mediate the self-brand connection and consumer loyalty relationship. Overall, this relationship was most strongly and significantly mediated by affective and cognitive engagement within the OBC channel when compared to the DCM channel. Research limitations/implications The findings of this study should be interpreted with several limitations in mind. First, the research was conducted within the confines of one OBC, within one social networking site platform characterised by self-selected membership based on a passion and immersion with the brand. This means that consumers within the OBC were highly connected to one another and the retail brand and highly socialised in-group norms and mores. This type and intensity of connection may not be the case for all forms of OBCs. Second, this study was limited to one retail brand, from one brand category. Future research should examine OBCs across a range of utilitarian and hedonic brands to comprehensively contextualise the dimensions of engagement. Third, the data for this study was cross-sectional. The use of netnographic analysis and qualitative interviews across a range of OBCs would support the triangulation of the findings of this research, especially with regard to the narrative that consumers’ express when discussing how their SBC manifests through the dimensions of engagement. Fourth, this study explored a single antecedent of engagement, namely, self-brand connections. Future research may consider how SBC operates in conjunction with other complementary factors to enhance consumers’ affective, cognitive, social and behavioural engagement such as brand awareness, satisfaction and participation/interactivity. In addition, future research could examine an expanded array of engagement outcomes such as purchase intention, the share of wallet and reputation. Finally, future research should examine the operationalisation and validation of the dimensions of engagement using multiple competing scales to assess the suitability of these engagement scales across multiple brand categories and contexts. Practical implications Given the increasing investment in branding within social media and the fragmentation of brand communications across multiple communications platforms, the management of effective brand communications remains a significant challenge. This study found that the relationship between self-brand connections, affective, social, behavioural and cognitive engagement and loyalty was context-specific and moderated by a digital communication channel (OBC vs DCM email marketing), thus providing insights as to the effectiveness of OBCs and DCMs as two tools for enhancing consumer loyalty. Originality/value This study makes a novel contribution to the engagement literature by examining the antecedent role of self-brand connections in predicting consumers’ engagement; the moderating role of digital communication platforms (OBC vs DCM) on the formation of cognitive, affective, behavioural and social engagement; and the mediating effect of these dimensions on loyalty.

While generative artificial intelligence (GenAI) chatbots show transformative potentials in English as a foreign language (EFL) education, critical gaps persist in understanding how chatbot-assisted language learning affect multidimensional learner engagement. Grounded in self-determination theory (SDT), this sequential explanatory mixed-methods study examines the predictive effect of perceived supports on learners’ engagement in chatbot-assisted language learning, and the mediating role of basic psychological needs (BPNs) in the relationship. Data from 506 Chinese EFL learners (quantitative phase: structural equation modeling of survey responses) and 18 follow-up interviewees (qualitative phase: thematic analysis) indicated that: (1) perceived supports had a significant and positive impact on cognitive and emotional engagement, as opposed to behavioral and agentic engagement; (2) perceived supports significantly and positively predicted BPNs (autonomy, competence, and relatedness); (3) BPNs significantly and positively influenced behavioral, cognitive, emotional, and agentic engagement; and (4) perceived supports indirectly affected emotional and agentic engagement through autonomy, and behavioral engagement through relatedness, while competence consistently mediated all four engagement dimensions. Furthermore, qualitative insights from the semi-structured interviews triangulated the quantitative results, offering a comprehensive understanding of EFL learners’ engagement. The findings shed light on the future research avenues and the practical deployment of GenAI chatbots in EFL education.

ABSTRACTContextualised in the AI–supported English‐speaking learning, this study examined the roles of AI affordances in influencing EFL learners' emotional, cognitive, and behavioural speaking engagement, and explored the moderating roles of gender and learner types (on‐campus vs. on‐job) in influencing AI‐supported English‐speaking engagement. Data collected from 332 Chinese EFL learners (159 on‐campus and 173 on‐job learners) were analysed by using structural equation modelling. Results indicated that Chinese EFL learners perceived AI affordances to be significant in influencing their emotional, cognitive and behavioural engagement in practicing their spoken English. The results from the PLS‐SEM model revealed that AI affordances accounted for 54.7%, 52.4% and 56.0% of the variance in emotional engagement, cognitive engagement and behavioural engagement, respectively. Learner type was not found to significantly moderate the relationships between AI affordances and speaking engagement. Gender was found to be a significant moderator for the AI affordances–behavioural engagement and AI affordance–cognitive engagement relationships. These findings enrich existing literature about AI–empowered speaking engagement and provide practical implications for English teachers to design effective speaking‐teaching models.

Secondary teachers’ beliefs about the importance of teaching strategies that support behavioural, emotional and cognitive engagement in the classroom

Recent investigators have illuminated the crucial role of positive psychology in learning English as a Foreign Language (EFL). However, little is known about academic buoyancy as a potential mediator in the relationship between foreign language anxiety and learners' academic engagement in English learning. To address this gap, we adopted a quantitative approach to explore these links. We surveyed 970 Chinese high school EFL students to measure their English learning anxiety, academic buoyancy, and learning engagement. Of the four dimensions of engagement - behavioral, emotional, cognitive, and agentic engagement - we discarded the emotional and cognitive dimensions of academic engagement in factor analyses. Structural equation modelling indicated that English learning anxiety directly predicted behavioral but not agentic engagement, and academic buoyancy partially mediated between English learning anxiety and behavioral engagement and completely mediated between English learning anxiety and agentic engagement. These results provide insights for EFL teachers seeking to enhance students' language learning experiences.

The COVID-19 pandemic had forced medical students to study at home, transitioning to an emergency remote learning mode of instruction. Its impact on students was unknown and likely to be of concern. Therefore, this study assessed cognitive, emotional and behavioural engagements of medical students during emergency remote learning, and examined its associations with regard to their age, gender, stages of study and ethnic groups. A self-administered questionnaire was distributed to undergraduate medical students at one public medical school in Malaysia. Emergency remote learning was conducted via Microsoft Teams (synchronous) and web resources (asynchronous). The questionnaire consisted of four sections: demographic background, emotional, behavioural, and cognitive engagements with emergency remote learning. Three hundred twenty-nine students (n = 329) completed the questionnaire. The three engagement dimension scores were 3.36/4.00 (behaviour – act), 3.16 (cognition – think) and 3.07 (emotion – feel), respectively. There was a significant difference between the engagement dimension scores (paired data), implying that what students feel, think and act on emergency remote learning did not seem to align. Next, engagements of these students were not significantly associated with their age, stages of study, and ethnic groups, but male students had higher dimension mean scores for cognitive and emotional engagements. Emergency remote learning had a considerable impact on student engagements. The study calls for continuing efforts in improving effectiveness and equity in learning engagements among medical students in the post-pandemic era.

Effects of teacher role on student engagement in WeChat-Based online discussion learning

The impact of psychological traits on learning engagement (LE) is a crucial area of research, particularly in the context of English as a foreign language (EFL). However, there is a notable lack of studies examining how optimism and pessimism, as two key psychological constructs, influence engagement in foreign language (FL) learning. This study aimed to investigate the relationship between optimism, pessimism and LE levels among EFL students. Specifically, it explored the association between these traits and the overall engagement of 131 Saudi EFL learners, focusing on their behavioural, cognitive, agentic and emotional engagement. A quantitative methodology was adopted for this research. Statistical techniques, including correlational analyses and linear regression, were applied to examine the relationships among these variables. The findings reveal a strong positive correlation between optimism and overall engagement, with the most significant association observed between optimism and behavioural engagement, followed closely by cognitive and emotional engagement. In contrast, pessimism demonstrated only a weak positive correlation with overall engagement. The results of the linear regression analysis confirmed that both optimism and pessimism serve as significant predictors of LE. This study offers valuable insights for educators and researchers interested in understanding the role of psychological traits in shaping LE within FL contexts. KEYWORDS Psychological traits, positive emotions, EFL learners, behavioural engagement, cognitive engagement, emotional engagement

This study aims to examine the influence of academic self-efficacy, perceived usefulness of online learning systems, and teaching presence on student engagement (behavioural, emotional, and cognitive engagement) and student satisfaction with online learning. Data were collected from undergraduate students who experienced a fully online learning process during the COVID‐19 pandemic. Based on social cognitive theory, the relationships among the personal and environmental influences on student behaviour and outcomes were examined using structural equation modelling. The results indicated that academic self-efficacy had significant direct relationships with behavioural engagement and emotional engagement, while perceived usefulness significantly influenced emotional engagement and cognitive engagement. Furthermore, teaching presence significantly influenced all engagement dimensions. Student satisfaction was significantly and directly influenced by behavioural engagement and emotional engagement, but not by cognitive engagement. Finally, the mediation role of each engagement dimension is proven in this study. This study was conducted in Egypt; thus, it contributes to add an empirical evidence regarding online student engagement and satisfaction in the context of a developing country.

Objectives This study attempted to analyze the structural relationship between self-efficacy, self-directed learning, learning engagement, and major satisfaction of college students, and studied learning engagement by dividing it into cognitive engagement, emotional engagement, and behavioral engagement. Methods The fourth year of university nationwide became the study population, and the final 245 data were used for analysis. The structural equation was evaluated for goodness of fit of the research model using Mplus, and the significance of direct and mediating effects was verified. Results First, self-efficacy was found to have a positive (+) effect on self-directed learning and major satisfaction, but the effect on cognitive engagement, emotional engagement, and behavioral engagement was found to be statistically insignificant. Second, self-directed learning was found to have a positive (+) effect on cognitive engagement, emotional engagement and behavioral engagement, but the effect on major satisfaction was not statistically significant. Third, the effect of cognitive engagement on major satisfaction was found to be statistically insignificant, but emotional engagement and behavioral engagement appeared to have a positive (+) effect on major satisfaction. Fourth, in the relationship between self-efficacy, cognitive engagement, emotional engagement, and behavioral engagement, the mediating effect of self-directed learning was found to be statistically significant. Fifth, in the relationship between self-directed learning and major satisfaction, the mediating effect of cognitive engagement was not statistically significant, but the mediating effect of emotional engagement and behavioral engagement was statistically significant. Finally, in the relationship between self-efficacy and major satisfaction, the double mediating effect of self-directed learning and cognitive engagement was not statistically significant, but the double mediating effect of self-directed learning and emotional engagement and self-directed learning and behavioral engagement was statistically significant. Conclusions Through this, it was revealed that college students' self-efficacy, self-directed learning, and learning engagement are important factors in major satisfaction. Therefore, in order to improve college students' major satisfaction, implications for finding ways to improve college students' self-efficacy, self-directed learning, and learning engagement were also provided.

The immersive and interactive nature of panoramic video empowers learners with experiences that are infinitely close to the real environment and increases the use of imagination in learners' knowledge acquisition. Studies have shown that embedding question feedback in traditional educational videos can effectively improve learning. However, little research has been conducted on embedding question feedback in panoramic videos to explore what types of question feedback effectively improve the dimensions of learners' learning engagement and yield better learning experiences and learning effects. This study embedded questions with feedback within panoramic videos by categorizing feedback into two types: simple feedback and elaborated feedback. Using eye tracking, brainwave meters, and subjective questionnaires as measurement tools, this study investigated which type of question feedback embedded in panoramic videos improved various dimensions of learner engagement and academic performance. Participants (n = 91) were randomly assigned to the experimental group (simple feedback, elaborated feedback) or the control group (no feedback). The results of the study showed that (1) the experimental group significantly improved in cognitive engagement, behavioral engagement, and emotional engagement compared to the control group. When the precision of feedback information was greater, the learner's behavioral engagement was greater; however, the precision of feedback information did not significantly affect cognitive and emotional engagement. (2) When the feedback information was more detailed, the learners' academic performance was better. The findings of this study can support strategic recommendations for the design and application of panoramic videos.