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Article

Motivation and Self-Regulated Learning Among Online English Learners: Profiles and Pedagogical Implications

by
Shifang Tang
1,*,
Zhuoying Wang
2,
Mei Jiang
1,
David D. Jimenez
3 and
Lei Zhang
1
1
Department of Psychology and Special Education, College of Education and Human Services, East Texas A&M University, Commerce, TX 75428, USA
2
Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78758, USA
3
Department of Curriculum, Instruction, & Learning Sciences, College of Education & Human Development, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA
*
Author to whom correspondence should be addressed.
Educ. Sci. 2025, 15(12), 1619; https://doi.org/10.3390/educsci15121619
Submission received: 31 October 2025 / Revised: 24 November 2025 / Accepted: 25 November 2025 / Published: 1 December 2025
Versions Notes

Abstract

In this study, we examined the interrelations between motivation and self-regulated learning (SRL) strategies in the context of online English language instruction among Chinese university students. Data were collected from 1100 first-year undergraduates enrolled in an online College English course. Canonical correlation analysis revealed significant multivariate associations between motivational constructs and SRL strategies. Cluster analysis further identified two distinct learner profiles, Engaged Strategic Learners and Disengaged Learners, demonstrating differences in motivation, SRL use, and online learning experiences. Thematic analysis of open-ended responses offered additional insights into students’ perceived challenges and instructional needs. Our findings contribute to a deeper understanding of how motivational and SRL characteristics influence learners’ engagement and outcomes in online English learning environments.

1. Introduction

Online English language learning has expanded significantly in both English as a Foreign Language (EFL) and English as a Second Language (ESL) contexts in higher education, largely accelerated by the COVID-19 pandemic (Masalimova et al., 2022). During the global shutdown, universities worldwide had to make rapid shifts to online course delivery, promoting greater accessibility, flexibility, and student autonomy than those offered in traditional classroom teaching (W. Han et al., 2024). Under the online learning environment, language instructors adapted pedagogical approaches to ensure effective online delivery (Bessette, 2020), while learners utilized digital platforms not only for language learning purposes but also for engaging with online language applications and online media. Collectively, these transitions underscore a global shift toward online language learning (Adedoyin & Soykan, 2023; Soyoof et al., 2023).
Online English education offers several advantages, including increased learner autonomy, flexible scheduling, and expanded access to authentic language resources (W. Han et al., 2024). Learners often value the convenience and diverse opportunities afforded by virtual environments (Masalimova et al., 2022). However, challenges remain, including limited face-to-face interaction, technological barriers such as unstable Internet connectivity, and disparities in digital literacy among both students and instructors (Adedoyin & Soykan, 2023; J. Li, 2024). These issues may reduce student engagement and raise concerns regarding equitable access and the integrity of assessments, leading some students to favor traditional classroom-based learning (W. Han et al., 2024).
In this study, we investigated the use of motivation and self-regulated learning (SRL) strategies in the context of online English language instruction among Chinese university students. Given the roles motivation and SRL play in academic performance, and their interconnection (Henry & Liu, 2024; Pintrich, 1999; Xu et al., 2023; Zhao et al., 2025), we addressed three research questions in this study:
RQ1: What is the multivariate relationship between motivational factors (Online Learning Experience, Cultural Interest, Goal Orientation) and self-regulatory strategies (Goal Setting, Environmental Structuring, Peer Learning) in students’ online English learning?
RQ2: What distinct profiles emerge from students based on their motivational and self-regulatory factors, and how do these groups differ in their online English learning experiences?
RQ3: How do students from different motivational and self-regulatory profiles describe their experiences and challenges in online English learning, and what improvements do they suggest for instructional design?

1.1. The Community of Inquiry (CoI)

Building upon Dewey’s (1933/1986) pragmatic constructivism, the Community of Inquiry (CoI) is a theoretical framework for promoting effective online teaching and learning. CoI represents the process of creating a collaborative teaching and learning environment in an online learning context through three interdependent dimensions of presence: cognitive presence, social presence, and teaching presence (Garrison et al., 2000, 2001). This three-dimensional CoI framework has been used to guide online course design and instructions in varied settings (Stenbom et al., 2012; Zydney et al., 2012). Cognitive presence indicates the extent “to which the participants in any particular configuration of a community of inquiry are able to construct meaning through sustained communication” (Garrison et al., 2000, p. 89). Cognitive presence plays a critical role in facilitating knowledge construction through discourse and reflective processes within online communication environments. In online settings, learners are expected to engage collaboratively with others to explore ideas, construct meaning, resolve problems, and confirm understandings, ultimately achieving critical thinking goals (Swan & Ice, 2010). It involves open communication, affective expression, and group cohesion to create a supportive online learning climate that encourages learners to ask questions and contribute to a meaningful learning process (Rourke et al., 1999). Teaching presence involves purposeful course design and active facilitation that cultivates social and cognitive presence in the service of achieving the desired learning outcomes.
While cognitive, social, and teaching presences collaboratively contribute to fostering a sense of community in online learning environments and enhancing students’ learning outcomes (Garrison & Arbaugh, 2007), social and emotional presence have received particular research attention. Despite its facilitating role in cognitive presence, social presence is essential in the online learning process (Cleveland-Innes & Campbell, 2012), particularly in the absence of non-verbal, direct cues. Among the three presences of CoI, social presence has been identified as the distinctive dimension that differentiates asynchronous online learning from other modalities (Garrison et al., 2000). Akyol and Garrison (2008) described social presence as “a more fluid construct”, encompassing open communication, affective expression, and group cohesion throughout the duration of an online course. More recent research has further highlighted the critical role of social connection, emotional bonding, and student–instructor relationships in facilitating meaningful learning in online environments (Cleveland-Innes & Campbell, 2012; Jiang & Koo, 2020).

1.2. Self-Regulated Learning (SRL) in Online Learning

Self-regulated learning (SRL) refers to a dynamic, recursive process involving goal-directed learning strategies (Panadero, 2017). Students who self-regulate set clear learning objectives and actively monitor and adapt their strategies to achieve their goals, thereby succeeding across various learning activities (Zimmerman, 2013; Winne & Hadwin, 1998; Winne, 1995). They also control their learning processes and objectives (Winne, 2018). Based on Winne and Hadwin’s (1998) conceptual framework, self-regulated learning (SRL) constitutes a dynamic and iterative set of cognitive and metacognitive processes, occurring across five interrelated components (i.e., conditions, operations, products, evaluations, and standards) and progressing through four sequential stages (i.e., task definition, goal setting and planning, enacting study tactics, and adapting to future learning).
Zimmerman (2000) proposed three cyclical phases that self-regulated students employ in learning: forethought (planning and goal-setting), performance (strategy use and monitoring), and self-reflection (evaluation and adjustment). In online learning environments with relaxed instructor supervision, self-regulation becomes a crucial component in online learning (Xu et al., 2023; Zimmerman, 2000). Hidayatullah and Csíkos (2025) examined the association between college students’ psychological need (i.e., perceived autonomy, competence, and relatedness) and their online self-regulated learning. It was found that students’ perceived autonomy predicted goal setting, environment structuring, time management, and self-evaluation, but not help-seeking and task strategies, whereas their perceived relatedness predicted environment structuring, help-seeking, and self-evaluation.
Individual learner experience and learner characteristics are crucial to promoting self-regulated learning in online environments (Prasse et al., 2024). In English learning, the heterogeneity of learners in online courses necessitates the implementation of adaptive and differentiated instructional methods. Students’ attitudes toward the use of the internet positively influence English language learning, which was mediated by students’ academic self-efficacy (Wang et al., 2022). Although effective SRL strategies, such as goal setting, cognitive and metacognitive processes, time management, self-reflection, help-seeking, and monitoring, are well-documented in supporting learner persistence and academic achievement (Faza & Lestari, 2025; Yu, 2023), research on emotion regulation strategies within online learning contexts remains limited (Edisherashvili et al., 2022; Xu et al., 2023). In particular, further research is needed on the preparatory and appraisal phases of the SRL cycle (Edisherashvili et al., 2022). Existing evidence highlights the importance of instructional designs that explicitly promote SRL strategies to accommodate diverse learner needs and enhance both engagement and academic performance. Moreover, learners often express a preference for interactive and emotionally supportive online environments, emphasizing the need for personalized learning experiences facilitated through adaptive learning technologies (W. Han et al., 2024; Yu, 2023).

1.3. Motivation in Online English Learning

In recent decades, online language education has been largely driven by technological advances, the expansion of Internet accessibility, and the growing market for flexible learning (Chapelle & Sauro, 2017; Çoban & Goksu, 2022; Godwin-Jones, 2016). Accelerated by the COVID-19 pandemic (Masalimova et al., 2022), the rise of synchronous and asynchronous communication technologies has facilitated online interactive learning experiences, making autonomy and collaboration possible across various subjects, such as language learning, counseling, and others (Koo & Jiang, 2024). In a meta-analysis, Klimova and de Paula Nascimento e Silva (2024) reviewed studies published between 2018 and 2022 on emerging technologies in English as a foreign language (EFL) instruction, including artificial intelligence, virtual reality, and mobile applications. They found that the effective use of technologies such as chatbots and virtual reality remained limited. More training and professional development are needed for teachers to maximize the pedagogical value of each technological tool and integrate them into teaching practice.
Meanwhile, students’ technical readiness plays an important role in online English learning. For example, students’ computer and Internet self-efficacy was found to mediate the relationship between online learning perception and students’ online discussion performance, as well as their course satisfaction (Wei & Chou, 2020). Hashmi et al. (2025) investigated the relationships between online learning interactions, technological proficiencies, and self-regulated learning. While online learning interactions played a crucial role in enhancing self-regulated learning, technology proficiency amplified their connections.
Motivation has long been recognized as a critical factor in successful second language acquisition (SLA). Gardner and MacIntyre’s (1993) socio-educational model conceptualized motivation along two dimensions: integrative (cultural integration) and instrumental (practical benefits). Both dimensions exert a significant influence on language learning outcomes (Liu & Dong, 2023). Complementarily, self-determination theory (SDT) distinguishes between intrinsic motivation, driven by personal interest and enjoyment, and extrinsic motivation, associated with external rewards. Within the online language learning context, intrinsic motivation has been identified as essential for sustaining learning engagement and promoting satisfaction (Bailey et al., 2021; Sun & Mu, 2023).
Goal orientation theory further elucidates the influence of motivation in digital learning environments. Learners with mastery-oriented goals typically demonstrate greater engagement, persistence, and effective self-regulation, whereas those with performance-oriented goals, particularly those with performance-avoidant tendencies, tend to exhibit lower self-efficacy and reduced persistence (T. Han et al., 2025; J. Li, 2024). Specific motivational factors, such as cultural interest and perceived value of online learning, are strongly associated with learner engagement and satisfaction, underscoring their critical roles in shaping learner experiences (Lee & Song, 2022; H. Li & Ni, 2024).
Empirical and theoretical research highlights the strong predictive value of self-regulated learning (SRL) strategies in academic contexts, particularly when examined in conjunction with motivational factors (Xu et al., 2023; Zhao et al., 2025). Motivation and SRL are dynamically interrelated, exerting reciprocal influences on learner engagement and achievement (Henry & Liu, 2024; Pintrich, 1999). Findings from multivariate analyses such as structural equation modeling (SEM) indicate that motivational beliefs play a significant mediating role in the relationship between SRL behaviors and academic performance (Cleary & Kitsantas, 2017). Theoretical models integrating motivation and SRL, such as Efklides’ (2011) MASRL and Henry and Liu’s (2024) integrated L2 motivation-SRL model, underscore the necessity of addressing motivational and regulatory processes concurrently to support sustained academic success. These models highlight the cyclical and reciprocal nature of motivation-SRL interactions, reinforcing the importance of examining these constructs jointly within educational research.

2. Method

We adopted a mixed-methods approach, integrating advanced quantitative analyses, specifically canonical correlation analysis (CCA) and cluster analysis, with qualitative analyses of open-ended student reflections to examine the interconnected use of motivational and self-regulated learning (SRL) strategies within the context of online English language instruction among Chinese university students. First, we employed canonical correlation analysis (CCA) and cluster analysis to examine the multivariate relationships between motivational and SRL variables, to identify integrated patterns predictive of learner outcomes (Henry & Liu, 2024). CCA allows the simultaneous analysis of multiple variables, capturing the complex and interdependent nature of motivation and self-regulation in educational contexts. Complementing this approach, we used cluster analysis to identify distinct learner profiles based on motivational and SRL characteristics, thereby facilitating the differentiation between highly engaged learners and those at risk. Such profiling supports the design of targeted interventions tailored to specific learner needs (Tsang & Yeung, 2024; Xu et al., 2023). Additionally, we conducted qualitative analyses of open-ended student reflections to provide a rich, contextual understanding of learning experiences (McCrudden et al., 2019). The integration of qualitative data offers critical insights into the underlying reasons behind the quantitative patterns, thereby deepening interpretation and ensuring that instructional strategies are more closely aligned with student experiences and needs (Ivankova & Plano Clark, 2023; Symonds & Gorard, 2008).

2.1. Participants and Context

The data were collected during the late spring 2020 semester at a top-tier comprehensive university in central China. The study focused on first-year undergraduate students enrolled in a required “College English” course, which was offered online in synchronous format twice a week. All students had access to a digital platform designed to support their self-regulated learning, including modules for reading assignments and peer interaction. Of the 3999 non-English major freshmen at the university, 1100 students voluntarily participated in an online survey administered by the School of Foreign Languages. Participants represented a range of majors, including engineering, finance, education, medicine, art, and others. The sample consisted of 60% male and 40% female students, all in their first year of university study. Survey responses were collected anonymously, and participants were informed that the purpose of the research was to understand their motivation and learning strategies in online English courses. Most students reported having some prior experience with online learning; only 29 participants indicated that this was their first time learning through an online platform.

2.2. Course Introduction

The College English I course had two parts: (a) Reading and Writing and (b) Listening and Speaking, which were delivered synchronously twice a week (90 min per session). It was tightly integrated with publisher-supported digital platforms. The Reading and Writing part adopted New Horizon College English: Reading and Writing, Book 1 (3rd ed., Foreign Language Teaching and Research Press). It was organized into 10 thematically structured units with scaffolded pre-, while-, and post-reading tasks that emphasize vocabulary development, comprehension, written production, and project work. The Listening and Speaking part used New Century College English: Listening and Speaking, Book 1 (2nd ed., Shanghai Foreign Language Education Press). It had eight units that incorporated vocabulary links, audio/video materials, situational dialogues, role-plays, and intercultural communication activities.
Two complementary online ecosystems supported instruction. For reading and writing, Ucampus (Unipus/FLTRP) offers a one-stop environment for learning, practice, testing, and analytics, with seamless PC-to-mobile synchronization. Core functions included assignment workflows (preview, practice, submission), formative assessment with auto-scoring and instant feedback, attendance/polling utilities, group activities with peer review, and data dashboards that surfaced accuracy, engagement, and error patterns to both students and instructors. For Listening and Speaking, WeLearn (Shanghai Foreign Language Education Press) integrated classroom interaction tools (mobile check-ins, polls, instant Q&A), community features (note sharing, leaderboards, peer evaluation), and AI-enabled engines for automated feedback on writing and oral tasks, enabling rapid diagnostic reporting and individualized remediation.
Student activity design followed a consistent cycle. In Reading and Writing, students completed vocabulary and structure previews, engaged in guided skimming and scanning, and analyzed paragraphs, producing written outputs (e.g., unit essays, translations, short reflections) that were submitted through Ucampus. In Listening and Speaking, students prepared targeted vocabulary, completed comprehension tasks (audio/video), practiced pronunciation/intonation, and participated in pair/group communicative tasks (e.g., interviews, role-plays), followed by brief written or oral summaries uploaded to WeLearn. Outside class, learners completed publisher item banks, reflective prompts, and extension listening/speaking tasks; platform data streams documented time-on-task, completion rates, and accuracy.
Quality assurance and feedback were multi-layered. Ucampus and WeLearn generated item-level analytics and formative progress reports (e.g., accuracy thresholds, error typologies), which instructors used to (a) flag at-risk learners, (b) assign targeted drills, and (c) provide timely qualitative comments. Intelligent scoring for writing/speaking reduced turnaround time and allowed for multiple feedback and revision cycles. In-class checkpoints (brief quizzes, group presentations, structured peer review) triangulated platform data with instructor judgments of discourse quality and strategic engagement.
To strengthen motivation and self-regulated learning, the course embedded explicit scaffolds aligned to the constructs measured in this study: (a) goal-setting prompts at the start of each unit (weekly micro-plans tied to concrete outputs, e.g., “master 20 key words,” “complete one oral practice set”); (b) structured peer learning via group tasks and peer review with accountability artifacts; (c) self-assessment and reflection after each unit (brief reflective writing plus auto-feedback quizzes to support monitoring and calibration); (d) gamified progression in Ucampus (accuracy gates to “unlock” subsequent modules); and (e) learning-community features in WeLearn (leaderboards, note-sharing, peer feedback) to leverage social motivation and persistence.
Beyond the publisher platforms, additional institutional supports were available. The School of Foreign Languages provided administrative and technical assistance (faculty training in digital pedagogy, troubleshooting, centralized platform oversight). The university maintained an Autonomous Language Learning Center (≈300 computer terminals, multimedia classrooms, video rooms, and staffed offices) to ensure access and reliability for both synchronous and independent study. To extend feedback capacity in writing, instructors also integrated Pigaiwang, an automated essay evaluation system that supplies sentence-level diagnostics (e.g., grammar, collocation), supports iterative revision/resubmission, and facilitates low-stakes competitions to enhance engagement while reinforcing academic integrity norms.

2.3. Instrument

To measure students’ motivation and self-regulated learning strategies in the context of online English language learning, we employed a validated instrument developed and validated by Tang et al. (2024). The instrument was designed to capture multidimensional constructs of motivation and SRL relevant to digitally mediated language learning environments.
The full scale consists of 29 items, covering two major domains: motivation (15 items) and SRL strategies (14 items). Within the motivation domain, three sub-factors are measured: (1) Online Learning Experience (3 items), (2) Cultural Interest (3 items), and (3) Goal Orientation (9 items). The SRL domain includes three sub-factors as well: (1) Goal Setting (4 items), (2) Environmental Structuring (4 items), and (3) Peer Learning (6 items). All items were rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
The psychometric properties of the instrument were rigorously evaluated in the initial validation study with the current sample (Tang et al., 2024). Confirmatory factor analysis supported the six-factor model, with satisfactory model fit indices (χ2 (390) = 1829.223, p < 0.001, CFI = 0.911, RMSEA = 0.074, SRMR = 0.056). The overall model has an acceptable internal consistency of 0.955, with subscales ranging from 0.878 to 0.928.
In addition to the 29 survey items, we also included four open-ended questions to gain insight into students’ reflections on their experiences with online English instruction. These questions asked participants to (1) evaluate whether online instruction enhanced their English learning and explain why; (2) describe any English learning strategies they employed during online study; (3) assess the learning environment, including teacher and peer interaction, platform usability, support received, and technical conditions; and (4) suggest improvements for the online course and instructor practices.

2.4. Data Analysis

To examine the multivariate relationship between students’ motivational characteristics and their self-regulated learning strategies in online English learning, a canonical correlation analysis (CCA) was conducted. CCA is appropriate when the objective is to explore the shared variance between two sets of conceptually related variables. In this study, the predictor set (Set 1) included three motivational variables (i.e., Online Learning Experience, Cultural Interest, and Goal Orientation), while the criterion set (Set 2) consisted of three self-regulatory strategies (i.e., Goal Setting, Environmental Structuring, and Peer Learning). Before analysis, data were screened for outliers and standardized to ensure comparability across scales.
The canonical solution was evaluated using Wilks’ Lambda, canonical correlations (Rc), squared canonical correlations (Rc2), and redundancy indices. The number of meaningful canonical functions retained was based on both statistical significance and interpretability. Structure coefficients and standardized canonical coefficients were examined to determine the relative contribution of each variable to the canonical variates. To identify distinct learner profiles based on students’ motivational and self-regulatory characteristics, a hierarchical cluster analysis (HCA) was conducted using standardized scores (z-scores) of six validated subscales: Online Learning Experience, Cultural Interest, Goal Orientation, Goal Setting, Environmental Structuring, and Peer Learning.
Hierarchical clustering was performed using Ward’s method and squared Euclidean distance as the similarity measure. This method was selected because it minimizes within-cluster variance and is commonly recommended for educational and psychological data. A dendrogram and agglomeration schedule were examined to determine the optimal number of clusters. A two-cluster solution was selected based on interpretability, theoretical coherence, and a substantial increase in the agglomeration coefficient.
Following the hierarchical procedure, a K-means cluster analysis was performed to refine the initial solution and assign cases to the final clusters. The cluster centers, distances between clusters, and cluster sizes were examined to interpret and validate the final grouping. Between-cluster differences across the six variables were further evaluated using one-way analyses of variance (ANOVA), with the understanding that significance tests are descriptive only, as cluster analysis inherently maximizes between-group differences by design.
To explore students’ perceptions and experiences in online English learning, responses to four open-ended questions were analyzed using thematic analysis (Braun & Clarke, 2006). The analysis aimed to identify patterns in student reflections regarding the perceived effectiveness of online instruction, strategy use, learning environments, and suggestions for improvement. An inductive–deductive coding strategy was employed: while themes were allowed to emerge from the data, cluster groupings—Engaged Strategic Learners (Cluster 1) and Disengaged or At-Risk Learners (Cluster 2)—served as a sensitizing framework to compare qualitative patterns across learner profiles.
Responses were first reviewed and open-coded to capture recurring ideas, which were then grouped into four overarching themes: (1) perceived effectiveness of online learning, (2) strategy use, (3) interaction and learning environment, and (4) suggestions for instructional improvement. Comparative analysis across clusters enabled identification of thematic divergences aligned with motivational and self-regulatory profiles derived from the cluster analysis. This mixed-methods integration provided a richer understanding of how students’ qualitative feedback aligned with or expanded upon their quantitative classification.

3. Results

RQ1. 
What is the multivariate relationship between motivational factors (Online Learning Experience, Cultural Interest, Goal Orientation) and self-regulatory strategies (Goal Setting, Environmental Structuring, Peer Learning) in students’ online English learning?
Canonical correlation analysis revealed a statistically significant relationship between motivational factors and self-regulated learning strategies, with Wilks’ Λ = 0.453 and F(9, 2662.66) = 113.66, p < 0.001 (see Table 1). The first canonical function was retained for interpretation, as it accounted for the largest proportion of shared variance and was statistically significant at p < 0.001. The first canonical function (Rc = 0.733, Rc2 = 53.7%, see Table 2) indicated that goal orientation was the strongest motivational predictor, while goal setting and environmental structuring were the strongest associated strategies. This result suggests that students who adopt personal learning goals and perceive English learning as important are more likely to apply deliberate learning strategies.
RQ2. 
What distinct profiles emerge from students based on their motivational and self-regulatory factors, and how do these groups differ in their online English learning experiences?
The cluster analysis revealed two distinct learner profiles with implications for online instructional design and learner support (see Table 3). Cluster 1 (n = 463), labeled Engaged Strategic Learners, was characterized by higher-than-average scores across all six dimensions—goal orientation, cultural interest, online learning experience, goal setting, environmental structuring, and peer learning. These students exhibit strong motivational dispositions and robust self-regulated learning strategies, suggesting they are well-positioned to succeed in online English learning environments that demand autonomy, initiative, and strategic engagement. In contrast, Cluster 2 (n = 637), labeled Disengaged or At-Risk Learners, demonstrated below-average scores across all six factors, indicating a lack of motivational and strategic learning behaviors. These learners may be more susceptible to disengagement and academic underperformance, particularly in online contexts that require self-directed learning. These findings highlight the importance of differentiated instructional strategies that both sustain the engagement of high-functioning learners and provide scaffolded support to those at risk. Tailoring interventions to these learner profiles, through structured guidance, peer mentorship, or targeted motivational supports, can enhance the inclusivity and effectiveness of online English instruction.
RQ3. 
How do students from different motivational and self-regulatory profiles describe their experiences and challenges in online English learning, and what improvements do they suggest for instructional design?
Analysis of the open-ended responses revealed meaningful differences in students’ perceptions of online English learning, learning strategy use, learning environments, and instructional needs based on their motivational and self-regulatory profiles. Across both clusters, four overarching themes emerged: perceived effectiveness of online learning, strategy use, interaction and learning environment, and suggestions for improvement.
Students classified as Engaged Strategic Learners (Cluster 1) generally viewed online English learning as effective and flexible. Many described the format as “convenient” or “resource-rich,” highlighting how it enabled them to manage their time effectively and access a wider range of materials. These students often demonstrated an active approach to learning, referencing strategies such as using vocabulary apps, watching English-language media (e.g., BBC, VOA), or taking detailed notes. One student shared, “I use Word software and listen to VOA to improve my vocabulary and listening skills,” reflecting a proactive engagement with multiple language modalities.
In contrast, Disengaged or At-Risk Learners (Cluster 2) frequently expressed skepticism or frustration regarding the effectiveness of online learning. Many responses included statements such as “I can’t concentrate” or “online classes are not as good as face-to-face,” indicating difficulties with sustained engagement and a preference for traditional learning environments. These students were also less likely to report using deliberate learning strategies, with several explicitly stating that they “don’t use any” or “don’t know how.” This lack of strategy awareness may partly explain their lower self-regulatory profiles in the quantitative analysis.
The quality of the online learning environment was another area of divergence. Cluster 1 students often described their platforms and instructor interactions positively, citing “good platform design” and “smooth network” as facilitators of learning. Conversely, Cluster 2 students commonly reported issues such as unstable internet connections, a lack of interaction, and insufficient feedback. One student noted, “There is not much interaction with the teacher, and it’s hard to stay motivated.”
When asked about areas for improvement, students from Cluster 1 tended to suggest enhancements such as expanding practice opportunities or offering more engaging content (e.g., “Add more activities like spelling competitions”). Cluster 2 students more frequently emphasized structural or motivational needs, requesting “more teacher presence,” “better engagement,” and “stable internet connections.” These responses reflect differing instructional needs: while high-functioning learners seek enrichment and autonomy, at-risk learners benefit more from structure, support, and guidance.

4. Discussion

The purpose of this study is to examine Chinese university students’ use of motivation and SRL strategies in the context of online English language learning. We identified a statistically significant relationship between students’ motivation and their use of self-regulated learning strategies. Further, two distinct learner profiles were identified based on students’ motivation and reported use of self-regulated learning strategies: the Engaged Strategic Learners and the Disengaged or At-Risk Learners. Quantitative and qualitative findings consistently revealed that Engaged Strategic Learners exhibited strong motivational dispositions and effective self-regulated learning strategies. They perceived the online learning environment as an effective medium for supporting their learning and employed intentional, adaptive approaches to manage their learning processes. In contrast, At-Risk Learners expressed reservations about online learning, which appeared to hinder their engagement and limit the strategic approaches they adopted to support their learning. Our discussion follows.
First, our findings are consistent with previous research in general, which suggests a reciprocal interrelationship between students’ motivation and the use of SRL interactions (Henry & Liu, 2024; Pintrich, 1999). In addition, among the motivation constructs, goal orientation emerged as the strongest predictor, aligning with prior research suggesting that learners with a mastery-oriented goal tend to demonstrate higher engagement and more effective self-regulated learning (T. Han et al., 2025; J. Li, 2024). As suggested by prior research emphasizing the value of examining SRL strategies in conjunction with motivational factors in academic contexts (Xu et al., 2023; Zhao et al., 2025), our study jointly used students’ reported motivation and use of self-regulated learning strategies to provide a more holistic understanding of students’ online learning behavior. Through this joint analysis, we identified two distinct groups of learners, highlighting the importance of providing them with customized and differentiated instruction to address their unique online learning needs.
Second, the use of effective SRL strategies has a positive impact on students’ foreign language learning and self-efficacy (Wang et al., 2022). At the same time, a fine-grained understanding of individual learners’ learning experience and characteristics is essential for promoting their capacity for self-regulated learning (Prasse et al., 2024). Our analysis revealed that the two identified groups of learners differed significantly across all six factors representing motivation and SRL constructs. Such differences highlight the importance of tailoring instructional approaches to address learners’ diverse needs and to foster both motivation and strategic learning skills in language learning within an online context.
Last, despite the significant and facilitative role of social presence in the online learning process (Cleveland-Innes & Campbell, 2012), we argue that greater attention should be given to the potential impact of teaching presence in designing effective online learning experiences that support students’ learning processes. Although the two identified learner groups expressed different needs, with Engaged Learners seeking more opportunities for practice and At-Risk Learners emphasizing the need for greater teacher facilitation and interaction, both sets of needs can be addressed through the intentional design of online courses that balance autonomy, support, and engagement. By recognizing students’ distinct learning characteristics and using completion rate and login frequency data from online learning platforms, teachers can tailor timely interventions to address specific learning needs rather than relying on uniform instructional approaches. For example, instructors could pair Disengated or At-Risk Learners with Engaged Learners to foster productive peer interactions and model effective learning strategies. Additionally, teachers may support Disengaged or At-Risk Learners by providing smaller, more manageable goals throughout the semester, such as weekly self-check-in prompts or end-of-month reflections, to help them monitor their progress and strengthen their self-regulation.

4.1. Limitations

This study has several limitations. First, our data were drawn from a single top-tier university in central China and one online College English program, which might limit the generalizability of the findings to other institutional contexts. Second, our analyses relied on self-report measures and de-identified archival testing data; individual-level platform log data (e.g., time-on-task, completion rates, or clickstream records) were not available. We recommend that future studies triangulate motivation and SRL profiles with fine-grained behavioral indicators. Third, the cross-sectional design and two-cluster solution provide an initial, data-driven snapshot of learner profiles, but they do not capture possible changes over time or alternative profile structures. Future research could integrate longitudinal designs, learning analytics, and mixed-methods approaches to examine how motivational-SRL profiles evolve and how they relate to achievement across diverse online language-learning settings.

4.2. Implications

Our findings highlighted the importance of addressing both motivational and self-regulatory dimensions when designing online English language instruction. Learners with strong goal orientation and cultural interest were more likely to adopt self-regulated strategies such as goal setting and environmental structuring, highlighting the reciprocal nature of motivation and learning behaviors (Henry & Liu, 2024). Differentiated instructional support is essential, as distinct learner profiles exhibit varying levels of engagement and strategy use. While some students thrive in flexible, autonomous environments, others require structured support, peer interaction, and motivational scaffolding. Integrating both motivational and SRL considerations can enhance learner engagement, inform adaptive design, and improve educational equity in online contexts.
These findings offer compelling evidence for the need to personalize online English learning experiences based on students’ motivational and self-regulatory profiles. A one-size-fits-all instructional model may unintentionally exacerbate disparities in student engagement and achievement. The emergence of clearly defined learner profiles reinforces theoretical frameworks such as Zimmerman’s social cognitive model of self-regulated learning and offers a practical basis for adaptive teaching strategies. Instructors and instructional designers should consider integrating tiered support systems, peer collaboration mechanisms, and learning analytics tools that monitor and respond to learners’ profiles in real time. Furthermore, future research could examine how these profiles evolve over time and whether they predict long-term academic outcomes, thereby informing scalable, data-informed interventions to promote equitable success in digital language learning environments.

Author Contributions

Conceptualization, S.T. and D.D.J.; methodology, S.T. and Z.W.; validation, Z.W. and M.J.; formal analysis, S.T. and Z.W.; investigation, L.Z.; data curation, S.T., Z.W., and L.Z.; writing—original draft, S.T., Z.W., M.J., D.D.J., and L.Z.; writing—review and editing, S.T., M.J., D.D.J., and L.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Office of the Texas A&M University-Commerce (protocol code 2378, with approval granted on 19 September 2022).

Informed Consent Statement

Patient consent was waived due to all data are deidentified.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Multivariate Tests for Canonical Functions Relating Motivational Factors and Self-Regulatory Strategies in Online English Learning.
Table 1. Multivariate Tests for Canonical Functions Relating Motivational Factors and Self-Regulatory Strategies in Online English Learning.
Canonical FunctionCanonical CorrelationSquared Canonical CorrelationWilks’ ΛFdf1df2p
10.7330.5370.453113.6692662.66<0.001
20.1320.0170.9795.8242190<0.001
30.060.0040.9963.94110960.047
Table 2. Structure Coefficients and Standardized Canonical Coefficients (Function 1), Variance Extracted, and Redundancy Indices.
Table 2. Structure Coefficients and Standardized Canonical Coefficients (Function 1), Variance Extracted, and Redundancy Indices.
VariableSetCorrelationCoefficient
Online Learning ExperienceSet 1 (Motivational)−0.706−0.287
Cultural InterestSet 1 (Motivational)−0.645−0.158
Goal OrientationSet 1 (Motivational)−0.893−0.736
Goal SettingSet 2 (Self-Regulatory)−0.794−0.494
Environmental StructuringSet 2 (Self-Regulatory)−0.689−0.377
Peer LearningSet 2 (Self-Regulatory)−0.512−0.259
Percent of Variance (Set 1) 0.49
Redundancy (Set 1) 0.26
Percent of Variance (Set 2) 0.52
Redundancy (Set 2) 0.28
Canonical Correlation 0.733
Table 3. Two-Cluster Learner Profiles: Standardized Factor Means and Between-Cluster F Tests on Motivational and Self-Regulatory Variables.
Table 3. Two-Cluster Learner Profiles: Standardized Factor Means and Between-Cluster F Tests on Motivational and Self-Regulatory Variables.
FactorsCluster 1 (n = 463)Cluster 2 (n = 637)Fp
Online Learning Experience0.62−0.45426.843<0.001
Cultural Interest0.58−0.42352.787<0.001
Goal Orientation0.73−0.53691.961<0.001
Goal Setting0.78−0.57866.183<0.001
Environmental Structuring0.75−0.55767.137<0.001
Peer Learning0.72−0.53677.779<0.001
Note: Cluster 1 = Engaged Strategic Learners; Cluster 2 = Disengaged or At-Risk Learners.
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Tang, S.; Wang, Z.; Jiang, M.; Jimenez, D.D.; Zhang, L. Motivation and Self-Regulated Learning Among Online English Learners: Profiles and Pedagogical Implications. Educ. Sci. 2025, 15, 1619. https://doi.org/10.3390/educsci15121619

AMA Style

Tang S, Wang Z, Jiang M, Jimenez DD, Zhang L. Motivation and Self-Regulated Learning Among Online English Learners: Profiles and Pedagogical Implications. Education Sciences. 2025; 15(12):1619. https://doi.org/10.3390/educsci15121619

Chicago/Turabian Style

Tang, Shifang, Zhuoying Wang, Mei Jiang, David D. Jimenez, and Lei Zhang. 2025. "Motivation and Self-Regulated Learning Among Online English Learners: Profiles and Pedagogical Implications" Education Sciences 15, no. 12: 1619. https://doi.org/10.3390/educsci15121619

APA Style

Tang, S., Wang, Z., Jiang, M., Jimenez, D. D., & Zhang, L. (2025). Motivation and Self-Regulated Learning Among Online English Learners: Profiles and Pedagogical Implications. Education Sciences, 15(12), 1619. https://doi.org/10.3390/educsci15121619

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