Development of the online course learning adjustment scale for undergraduate

Abstract

Objective

Online learning differs substantially from traditional classroom learning in terms of instructional supervision, interaction, environmental distraction, and demands for self-regulation. However, instruments specifically designed to assess undergraduates’ adjustment to online course learning remain limited. This study aimed to develop and validate the Online Course Learning Adjustment Scale for Undergraduates.

Methods

An initial pool of 29 items was generated through a literature review, semi-structured interviews with 20 undergraduates, and evaluations by experts and graduate students in psychology and education. The initial questionnaire was administered to 217 undergraduates for item analysis and exploratory factor analysis. An independent sample of 532 undergraduates was then used for confirmatory factor analysis, criterion-related validity analysis, and reliability testing.

Results

Item analysis indicated that all initial items had adequate discriminatory power. Exploratory factor analysis resulted in a 22-item scale comprising four factors: online learning ability, online learning behavior, online learning attention, and online learning attitude. The four factors explained 57.83% of the total variance. Confirmatory factor analysis showed that the four-factor model provided a good fit to the data and performed better than the alternative three-factor model (χ²/df = 2.288, RMSEA = 0.049, CFI = 0.948, TLI = 0.941, and SRMR = 0.047). The total scale demonstrated good internal consistency reliability (Cronbach’s α = 0.894) and split-half reliability (0.763). The total score and four subscale scores were positively correlated with academic performance (r = 0.157–0.378, all p < 0.01), supporting criterion-related validity.

Conclusion

The Online Course Learning Adjustment Scale for Undergraduates demonstrates satisfactory factorial validity, criterion-related validity, and reliability. It may serve as a useful instrument for assessing undergraduates’ adjustment to online course learning and identifying areas in which students require additional educational support.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s40359-026-05491-5.

Introduction

With the globalization of internet-based education and the transformative impact of the COVID-19 pandemic on educational practices, online courses have emerged in recent years as a significant mode of learning in higher education institutions worldwide. Thanks to their flexibility, broad accessibility, and low entry barriers, online courses have gained widespread acceptance among students and educators across the globe. With the broad resumption of face-to-face teaching, online learning is no longer the emergency default adopted by higher education institutions during the COVID-19 pandemic. Nevertheless, it has not disappeared from university education. Instead, online components have become increasingly integrated into blended and hybrid teaching arrangements, learning management systems, massive open online courses, and other forms of digitally supported learning. The Organisation for Economic Co-operation and Development reported that most students, educators, and administrators expected higher education institutions to continue using blended and hybrid learning extensively after the pandemic [1]. Similarly, the 2023 Global Education Monitoring Report identified higher education as the education sector with the highest level of digital technology adoption and documented the extensive use of online platforms and courses [2]. Therefore, online learning in the post-pandemic period should be understood not merely as a temporary substitute for classroom teaching, but as a continuing and complementary component of contemporary higher education.

The continuing incorporation of online components into university courses makes students’ adjustment to online learning relevant even when face-to-face instruction remains the dominant teaching mode. Whenever students engage in digitally mediated course activities, they must adapt to learning conditions that differ from those of conventional classrooms. Although online courses provide flexibility and access to diverse learning resources, they may also create distinctive adjustment challenges for university students [3]. Many students need help with learning goals, insufficient foundational skills, and weak resistance to environmental distractions during online courses [4, 5]. Poor learning adaptability not only undermines instructional effectiveness and academic performance but may also lead to problems such as excessive anxiety, low self-esteem, and reduced ability to cope with stress [6]. Therefore, investigating university students’ adaptability to online learning holds great significance in the context of the increasing prevalence of online courses. Such research can help educators optimize instructional design, enhance students’ learning experiences, and provide strong support for improving the quality of higher education.

Learning adaptability refers to the ability of learners to achieve a harmonious psychological state and behavioral capacity with their environment by self-regulating to meet the demands of the learning context and content [7]. It typically encompasses multiple dimensions, such as learning skills, attitudes, and behaviors, as a key indicator of whether a learner can successfully engage in educational activities. Learning adaptability affects academic outcomes and has implications for psychological well-being and future development [6, 8]. International research has already developed several multidimensional instruments for assessing students’ preparedness for online learning. The Online Learning Readiness Scale (OLRS) assesses computer/Internet self-efficacy, self-directed learning, learner control, motivation for learning, and online communication self-efficacy [9], whereas the Student Online Learning Readiness (SOLR) instrument emphasizes technical competence, communication competence, and social competence with instructors and classmates [10]. Recent research has continued to refine this measurement field across populations and educational contexts. Studies have re-examined readiness dimensions among adult vocational learners [11], validated a five-factor version of the OLRS among Chinese nursing students [12], applied a seven-domain readiness assessment in Mexican higher education [13], and supported the six-dimensional Online Learning Readiness Self-Check across Turkish, Romanian, and Polish college students [14]. More recently, Koh et al., [15] developed an Online Learning Dexterity Survey covering self-directed, technological, access-related, asynchronous, synchronous, and collaborative competencies. Despite these advances in online learning readiness measurement, learning adjustment has been examined less systematically as a construct distinct from readiness, self-regulation, and online learning competence. While the core elements of learning adaptability—such as learning skills, attitudes, and behaviors—remain essential for adapting to online courses [16], the online learning environment introduces significant changes, particularly in areas like interaction and learning settings [17, 18]. These changes necessitate adjustments in the specific requirements for learning adaptability.

Firstly, teachers cannot directly supervise students’ learning processes in the online learning mode, and teacher-student interactions occur online. These shifts demand stronger self-regulation skills and more active student learning engagement [19]. Students must independently complete learning tasks, create study plans, and conduct self-assessments without direct supervision. Active learning engagement includes participating in discussions, completing assignments and projects, and seeking help proactively. Secondly, students’ attitudes toward learning and their perceptions and evaluations of the differences between online and traditional courses significantly influence their motivation and adaptability to online learning [20]. If students hold negative attitudes toward online courses, viewing them as lacking interactivity and authenticity, their motivation may decrease, hindering their adaptability and learning outcomes. Thus, fostering accurate perceptions and positive evaluations of online courses is crucial to improving adaptability. Additionally, online learning relies on electronic devices such as smartphones and computers, which, while convenient, also make students more susceptible to distractions from their physical surroundings and digital information [21, 22]. Regarding environmental adaptability, online learning places higher demands on students’ ability to maintain attention, requiring them to stay focused in environments filled with distractions and exercise adequate self-control. In summary, adaptability to online learning is a multifaceted concept. To enhance university students’ adaptability in online courses, efforts should focus on cultivating self-regulated learning abilities, fostering positive attitudes, strengthening online teacher-student interactions, and improving students’ ability to concentrate. By addressing these aspects, students can achieve better learning outcomes in online courses. Accordingly, the need to assess online course learning adjustment does not depend on online teaching replacing face-to-face instruction; rather, it arises from the continued incorporation of online learning components into contemporary university education [1, 2].

Although existing readiness and competency instruments have made important contributions, their construct focus does not fully coincide with online course learning adjustment. Online learning readiness generally concerns whether learners possess the predispositions, self-efficacy, resources, and competencies required before or at the beginning of online learning [23]. By contrast, learning adjustment concerns how students regulate and modify their cognition, behavior, attitude, and attention while participating in online courses. Existing instruments also tend to incorporate concentration and resistance to distraction within broader dimensions such as learner control, self-directed learning, or learning management, rather than treating attention as an independent component of adjustment [9, 14, 15].

Accordingly, the present study seeks to develop a complementary measure specifically focused on undergraduates’ ongoing adjustment to online course learning. Drawing on traditional learning adaptability research and the distinctive demands of online learning environments, the proposed scale assesses four interrelated domains: online learning ability, online learning behavior, online learning attitude, and online learning attention. The instrument is intended to support the identification of students’ adjustment difficulties and the development of more targeted educational support in online and blended courses.

Method

Conceptualization of the construct and proposed dimensions

Before generating the initial item pool, the target construct and its content domains were specified to establish the conceptual boundaries of the scale. Since the learning content and the learners have not changed, students still need to adjust their behaviors and psychological states during online learning to meet the demands of the knowledge being taught. Therefore, the fundamental dimensions of traditional learning adaptability remain essential for adapting to online learning. Regarding environmental adaptability, online learners are not only influenced by their physical surroundings. They are also highly susceptible to distractions from irrelevant information online, further underscoring attention’s importance. In light of this, after summarizing and analyzing the core dimensions of traditional learning adaptability, the present study aims to develop an evaluation tool for university students’ adaptability to online learning based on the actual conditions of online learning. This tool will focus on four proposed dimensions: online learning behavior, online learning ability, online learning attitude, and online learning attention. Online learning behavior refers to the actions that facilitate achieving learning goals during online learning. Due to the reduced direct supervision by teachers and increased student autonomy, this dimension places greater emphasis on self-directed learning than the traditional learning behavior dimension. Online learning ability refers to the individual’s capacity to absorb and understand the knowledge taught in online courses and their ability to utilize online resources for knowledge expansion. Online learning attitude involves students’ perceptions and evaluations of the effectiveness of online courses, their engagement with the learning process, and the differences between online and offline courses. Lastly, online learning attention is defined as the ability to focus one’s mental activity on the course content during online learning while resisting distractions from both the physical and online environments.

Collection of primary data

Based on the definition of online learning adaptability and the four proposed dimensions—online learning ability, online learning behavior, online learning attitude, and online learning attention—a semi-structured interview outline was developed to assess university students’ adaptability to online courses. The main content of the interview included the following questions:

1. What kinds of problems do you typically encounter during online learning?

2. How do the problems you face in online learning differ from those you experienced in traditional offline learning?

3. In your opinion, what learning abilities are essential for university students in online courses?

4. What learning behaviors or habits can help students achieve better outcomes in online learning?

5. How do you perceive online learning? What attitude should university students adopt toward online courses?

6. How would you assess your attention during online learning? What factors tend to distract you during the process of learning online?

A convenience sampling method was used to conduct semi-structured interviews with 20 university students (8 males, 12 females; 5 first-year students, 7 sophomores, and 4 students each from junior and senior years). These interviews were conducted offline.

Development of the initial questionnaire

An initial questionnaire was developed to assess online learning adaptability based on analyzing and synthesizing the behaviors and concepts related to university students’ adaptability to online learning collected from the semi-structured interviews. Subsequently, two psychology professors, two education professors, two graduate students in psychology, and two graduate students in education were invited to evaluate the content of the items, assessing the scale’s content validity. Items deemed inappropriate were modified or removed. As a result, a total of 29 initial items were obtained. All items were scored using a 5-point Likert scale, with responses ranging from “strongly disagree” (1) to “strongly agree” (5).

Participants

Sample 1

A convenience sampling method was used to recruit university students from across China to participate in the study, with online questionnaires distributed through the Wenjuanwang platform. 217 valid responses were received, with participants aged 18 to 26 years (M = 20.74 ± 1.39 years). Of the participants, 90 were male (41.5%) and 127 were female (58.5%); 17 were freshmen (7.8%), 78 were sophomores (35.9%), 88 were juniors (40.6%), and 34 were seniors (15.7%). Participants came from 28 provinces, autonomous regions, and municipalities across China, including Henan (23 participants, 10.6%), Guangdong (17 participants, 7.8%), Shaanxi (16 participants, 7.4%), and Sichuan (13 participants, 6.0%). Sample 1 was used for item analysis and exploratory factor analysis.

Sample 2

A convenience sampling method was also used to recruit university students from across China via the Wenjuanwang platform. 532 valid responses were received, with participants aged 18 to 25 years (M = 20.80 ± 1.61 years). Among the participants, 211 were male (39.7%), and 321 were female (60.3%); 61 were freshmen (11.5%), 241 were sophomores (45.3%), 133 were juniors (25.0%), and 97 were seniors (18.2%). Participants were from 29 provinces, autonomous regions, and municipalities in China, including Gansu (80 participants, 15.0%), Guangdong (53 participants, 10.0%), Henan (31 participants, 5.8%), and Jiangxi (29 participants, 5.5%). Sample 2 was used for confirmatory factor analysis, empirical validity, and internal consistency reliability analysis.

Statistical analysis

Data analysis and processing were conducted using SPSS 26 and AMOS 23. Data from Sample 1 were used for item analysis and exploratory factor analysis. Independent-samples t tests comparing the upper and lower 27% groups and item–total correlations were used to assess item discrimination. The Kaiser–Meyer–Olkin measure and Bartlett’s test of sphericity were first conducted to determine the suitability of the data for factor analysis. Exploratory factor analysis with principal component extraction and varimax rotation was then performed to identify the underlying factor structure and guide item retention. Data from Sample 2 were used for confirmatory factor analysis, criterion-related validity analysis, and reliability analysis. Confirmatory factor analysis evaluated the fit of the proposed four-factor model and compared it with an alternative three-factor model using χ²/df, RMSEA, CFI, TLI, and SRMR. Criterion-related validity was examined through correlations between the scale scores and academic performance, while internal consistency and split-half coefficients were calculated to evaluate reliability. Together, these analyses were used to determine the item quality, factorial structure, validity, and reliability of the scale.

Results

Item analysis

Using the data from Sample 1, participants were divided into high and low-score groups based on the distribution of total scores, with the top and bottom 27% of participants assigned to the high and low-score groups, respectively. The scores of the two groups on each item were compared to check for significant differences. Independent samples t-tests revealed significant differences between the two groups on all 29 initial items, with t-values ranging from 6.81 to 15.22 and p-values all less than 0.001. The Pearson correlation between each item and the total score was also calculated. The results showed that the correlation values were above 0.4, with p-values less than 0.001 for each item. These findings indicate that all items have good discriminatory power.

Validity analysis

Exploratory factor analysis

Exploratory factor analysis was conducted using the data from Sample 1. The results indicated that the KMO value was 0.876, and the Bartlett’s test of sphericity was 1930.49 (p < 0.001), suggesting that the sample data were suitable for exploratory factor analysis. Subsequently, principal component analysis (PCA) was performed on the 29 items, and the factor loading matrix was obtained using the maximum variance method for rotation. Factors were extracted based on eigenvalues and the scree plot. Items were deleted according to the following criteria, in combination with the content of the items and their relationship with the corresponding dimensions: commonalities less than 0.3, factor loadings below 0.4, items with multiple loadings (i.e., loading greater than 0.4 on two or more factors with a difference in loadings less than 0.2), and factors with fewer than three items. As a result, 7 items were removed, and 22 items that met all the above criteria were retained, resulting in 4 factors. The cumulative variance explained by these 4 factors was 57.8%. Based on the conceptual framework of the questionnaire and the meanings of the items, the four factors were named as follows: Factor 1 was named “Online Learning Ability,” consisting of 7 items; Factor 2 was named “Online Learning Behavior,” consisting of 5 items; Factor 3 was named “Online Learning Attention,” consisting of 5 items; Factor 4 was named “Online Learning Attitude,” consisting of 5 items. The factor loadings and communalities of each item are presented in Table 1.

Table 1.

Exploratory factor analysis: item loadings and communalities

items	Factors and loadings				Communalities
	Factor 1	Factor 2	Factor 3	Factor 4
V10: I am good at summarizing what I have learned in online classes.	0.789				0.675
V13: During online learning, I can identify some highly valuable research questions.	0.731				0.610
V15: I can complete the tasks assigned during online learning.	0.662				0.527
V12: For me, collecting the materials needed for learning through various channels is relatively easy	0.646				0.492
V11: During online learning, I can promptly understand the knowledge explained by the teacher.	0.631				0.520
V8: In online learning, I always try to delve deeper into the content I am learning.	0.626				0.584
V9: I maintain a high level of efficiency in online learning.	0.602				0.586
V4: During online learning, I actively engage and take detailed notes.		0.737			0.645
V5: When I encounter questions I don’t understand during online learning, I discuss them with classmates or seek help from teachers.		0.698			0.578
V3: I frequently participate actively in online discussions initiated by the instructor.		0.655			0.533
V2: I organize and follow a well-structured study plan during online learning.		0.646			0.600
V1: I prepare for online courses by completing necessary pre-class reviews.		0.568			0.509
*V18: I often find myself involuntarily doing other things during online learning.			0.765		0.630
*V20: Sometimes, I find it difficult to stay focused on the screen for an entire online class.			0.756		0.639
*V21: During online learning, I am frequently interrupted or distracted by new things or unexpected events			0.704		0.509
*V22: During online learning, I often fast-forward through the content.			0.660		0.451
*V16: I tend to do other things if the teacher doesn’t ask questions or require the camera to be on.			0.643		0.449
V26: Multimedia information in online courses enhances my understanding and retention of knowledge.				0.742	0.667
V27: Compared to offline courses, I feel more relaxed and enjoyable during online learning.				0.736	0.557
V25: Online learning can improve my academic performance.				0.693	0.564
V23: Compared to offline courses, I prefer learning through online courses.				0.689	0.574
V28: I will devote more time and effort to online learning.				0.628	0.602
Eigenvalues	7.119	2.231	2.003	1.148
Variance Explained(57.83%)	17.940	13.433	13.902	12.550

Note: *Indicates reverse scoring

Confirmatory factor analysis

Building upon the exploratory factor analysis results, confirmatory factor analysis was conducted using the data from Sample 2 to test the structural validity of the 4-factor model. The standardized path coefficients are presented in Fig. 1. In addition to the hypothesized four-factor model, a theoretically plausible three-factor model was tested. In this alternative model, online learning behavior and online learning attitude were combined into a single attitude–behavior factor. This alternative structure was theoretically motivated by research indicating that attitudes toward a behavior are related to behavioral intentions and subsequent actions [24], and that students’ attitudes toward online learning are associated with their intentions to continue online learning and their broader engagement in online learning environments [25, 26]. Nevertheless, attitude and behavior remain conceptually distinguishable: attitude reflects students’ evaluations, preferences, and willingness to invest effort, whereas behavior reflects the concrete learning actions they actually perform. The alternative model was therefore tested to determine whether the conceptual proximity between attitude and behavior could be represented more parsimoniously by a single factor. Online learning ability and online learning attention were retained as separate factors because they represent distinct cognitive and attentional capacities. The alternative three-factor model was then compared with the hypothesized four-factor model using multiple fit indices.

Fig. 1.

Standardized path diagram of the four-factor structure model. Note: F1 represents online learning ability, F2 represents online learning behavior, F3 represents online learning attention, F4 represents online learning attitude

As shown in Table 2, the 4-factor model demonstrated a better fit than the 3-factor model, indicating that the 4-factor model is more appropriate for capturing the dimensions of university students’ online learning adaptability. Accordingly, the present study developed a final version of the Online Course Learning Adjustment Scale for Undergraduate, comprising 22 items across four dimensions. The complete English version of the scale is provided in Supplementary File 1.

Table 2.

Confirmatory factor analysis fit indices for the 4-factor and 3-factor models

Model	χ2	df	χ2/df	RMSEA	CFI	TLI	SRMR
4-factor model	464.442	203	2.288	0.049	0.948	0.941	0.047
3-factor model	991.709	206	4.814	0.085	0.845	0.826	0.078

Empirical validity analysis

The academic performance of 532 participants from Sample 2 (based on grade ranking intervals) was selected as the criterion for empirical validity analysis. Academic performance was assessed using a single item: “How would you rank your academic performance within your class?” Responses were recorded on a five-point ordinal scale: 1 = near the bottom, 2 = below average, 3 = average, 4 = above average, and 5 = near the top. The selected response category was used directly as the academic performance score, with higher scores indicating better relative academic performance. Spearman’s rank-order correlation analysis indicated that the results indicated that the correlation coefficients between the scores of each factor and students’ academic performance ranged from 0.157 to 0.333, with p < 0.01. The correlation coefficient between the total score of the scale and academic performance was 0.378, with p < 0.01.

Reliability analysis

Using the data from Sample 2, internal consistency reliability and split-half reliability tests were conducted for the scale. The results showed that the internal consistency reliability of the College Students’ Online Learning Adaptability Scale was 0.894, and the split-half reliability was 0.763. The internal consistency and split-half reliability for each factor ranged from 0.814 to 0.853 and 0.737 to 0.824, respectively (see Table 3).

Table 3.

Reliability of factors

Internal Consistency Reliability	Online Learning Ability	Online Learning Behavior	Online Learning Attention	Online Learning Attitude
	0.849	0.814	0.853	0.838
Split-Half Reliability	0.824	0.737	0.772	0.788

Discussion

Based on the analysis and summary of existing research on online learning and previous questionnaires on general student learning adaptability and online learning adaptability, and considering the characteristics of online courses, this study developed an adaptability scale specifically for college students’ online course learning. The results show that this scale has good reliability and validity. The findings from exploratory factor analysis indicate that college students’ online course learning adaptability consists of four dimensions: online learning ability, online learning behavior, online learning attention, and online learning attitude.

The resulting four-factor structure both overlaps with and differs from established online learning readiness and competence instruments. Online learning ability and behavior partially correspond to self-directed learning and learner control in the OLRS, whereas online learning attitude is conceptually related to learning motivation and preferences [9]. However, the SOLR instrument places greater emphasis on technical, communication, and social competencies [10], while the OLRSC assesses learning, technology, space, interaction, and motivation management [14]. The more recent Online Learning Dexterity Survey focuses on students’ confidence in applying technological and pedagogical strategies across asynchronous, synchronous, and collaborative learning contexts [15]. By comparison, the present scale focuses more specifically on students’ ongoing cognitive, behavioral, attitudinal, and attentional adjustment during online course participation. It should therefore be regarded as complementary to, rather than a replacement for, existing readiness and competency measures.

Traditional learning adaptability emphasizes the importance of students’ fundamental learning abilities, such as summarizing, understanding, and expanding knowledge [7, 16]. Given the stability of learning content, these abilities are equally crucial for college students’ adaptation to online course learning. Therefore, the online course learning adaptability scale developed in this study also includes a dimension of online learning ability. Similarly, learning attitude plays a key role in online course learning adaptability. Learning attitude is a stable psychological tendency formed during learning, encompassing cognitive, emotional, and behavioral tendencies. College students with a positive learning attitude are more likely to understand the importance and value of online course learning, demonstrating tremendous enthusiasm and engagement, enabling them to adapt more smoothly to online learning [26]. This study focuses on students’ views and evaluations of the new learning model and examines their attitudes toward the differences between various learning modes. This design allows the scale to comprehensively assess college students’ adaptability to online course learning, providing educators with more accurate guidance and recommendations.

The dimension of online course learning behavior particularly emphasizes the self-directed learning behaviors exhibited by college students and the development of good study habits during their participation in online course learning. On one hand, good learning habits are crucial for achieving excellent results in traditional face-to-face learning. However, they also play an essential role in helping college students better adapt to online course learning. On the other hand, due to the differences in the interactive modes of online courses compared to traditional classrooms and the relatively reduced direct supervision by instructors [27], students are required to be more proactive and engaged in the learning process to ensure the effectiveness of the teaching activities. Furthermore, this study includes explicitly online course learning attention as a key dimension to assess students’ ability to adapt to online course learning, which needs to be adequately addressed or explored in previous learning adaptability scales and questionnaires. However, based on the findings of this study’s interviews and evidence from previous online learning research [28, 29], it is clear that students’ lack of attention during online courses is more prevalent than in traditional face-to-face settings. Therefore, in this study’s online course learning adaptability scale, we emphasize students’ focus during online learning, aiming to help students better adapt to the online learning environment and improve their learning efficiency and quality.

Our confirmatory factor analysis results indicate that the four-factor model of college students’ online course learning adaptability demonstrates a good fit for the observed data, supporting the findings from the exploratory factor analysis. Additionally, using academic performance as a criterion for the prediction of the scale developed in this study, we found a significant positive correlation between the scores of each factor and the total score of the online course learning adaptability scale and academic performance. This suggests that the scale has good criterion-related validity. However, it is worth noting that the correlation levels between the dimensions of online course learning ability, learning attitude, and learning behavior with academic performance are higher than the correlation between online course learning attention and academic performance. This indicates that, although online courses as a new learning mode emphasize the importance of students’ attention in learning adaptability, the fundamental factors covered by traditional offline learning adaptability continue to play a crucial role in individuals’ adaptation to online learning. The college students’ online course learning adaptability scale developed in this study, while drawing from past research on offline learning adaptability, highlights the critical role of attention in adapting to online learning based on the characteristics of online courses. The overall design of the scale is simple and concise, demonstrating good reliability and validity, making it an effective tool for assessing college students’ adaptability to online course learning.

Limitations and future research

First, both samples were recruited using convenience sampling through a single online survey platform. This recruitment approach may have introduced self-selection bias. Students who were more familiar with online platforms, more interested in online learning, or more willing to participate in research may have been more likely to complete the questionnaire. Therefore, the generalizability of the findings to the broader undergraduate population in China should be interpreted with caution. In addition, all the data collected in this study were from Chinese university students, so the generalizability of the results to a global context needs further validation. Future research could consider conducting cross-cultural studies to verify these findings. Second, the present study did not assess test–retest reliability; therefore, the temporal stability of the scale scores remains unknown. Future studies should administer the scale repeatedly over an appropriate interval to examine the test–retest reliability of the total scale and its four dimensions. Third, the present study used academic performance as the primary external criterion but did not examine convergent and discriminant validity using established measures of related constructs. Future research should further validate the scale by examining its relationships with constructs such as self-regulated learning and academic engagement, as well as theoretically distinct constructs. In addition, the dimensions of the learning adaptability scale we used need to be more comprehensive, and some of the items within specific dimensions, such as the ability and behavior dimensions, may have conceptual overlap. Future research could aim to provide a more thorough investigation and refinement of the dimensions included in online course learning adaptability. Lastly, this study focused on adaptability to university students’ online course learning. However, with the growing development of online education, other groups, such as high school students and workers receiving technical training, are increasingly exposed to online courses. Therefore, it is also essential to research online course learning adaptability for these groups. Future studies could assess the validity of the online course learning adaptability scale developed in this research across different populations. The primary focus of this study was to provide a preliminary tool for further exploration and development in this field. This tool still needs further refinement and revision in future research.

Conclusion

This study developed and preliminarily validated the Online Course Learning Adjustment Scale for Undergraduates. The final scale contains 22 items across four dimensions: online learning ability, online learning behavior, online learning attention, and online learning attitude. Exploratory and confirmatory factor analyses supported the proposed four-factor structure, while the reliability and criterion-related validity analyses provided further evidence for the quality of the scale scores. The scale offers a multidimensional instrument for assessing how undergraduates adjust to the cognitive, behavioral, attitudinal, and attentional demands of online course learning. It may assist researchers and educators in identifying students’ adjustment difficulties and providing more targeted support in online and blended learning environments.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

F.X.: Conceptualization, Methodology, Data curation, Project administration, Writing – original draft, editing. G.Z.: Supervision, Project administration.

Funding

The authors did not receive support from any organization for the submitted work.

Data availability

The raw data supporting the conclusions of this article will be made available by the authors without undue reservation.

Declarations

Ethics approval and consent to participate

This study was reviewed and approved by the Ethics Committee of the School of Psychology at Northwest Normal University. All procedures performed in this research were in accordance with the ethical standards of the institutional committee and with the Declaration of Helsinki and its later amendments. Written informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.