The relationship between student motivation and academic performance: the mediating role of online learning behavior

Xiangju Meng, Zhenfang Hu

Quality Assurance in Education

ISSN: 0968-4883

Article publication date: 13 May 2022

Issue publication date: 10 January 2023

2936

Abstract

Purpose

This paper aims to use a quantitative approach to explore the role of online learning behavior in students’ academic performance during the COVID-19 pandemic. Specifically, the authors probe its mediating effect in the relationship between student motivation (extrinsic and intrinsic) and academic performance in a blended learning context.

Design/methodology/approach

Survey data were collected from 148 students taking an organizational behavior course at one Chinese university. The data were paired and analyzed through regression analysis.

Findings

The results show that students should actively engage in online learning behavior to maximize the effects of blended learning. Extrinsic motivation was found to positively influence academic performance both directly and indirectly through online learning behavior, while intrinsic motivation affected academic performance only indirectly.

Originality/value

Through paired data on extrinsic and intrinsic motivation, online learning behavior and academic performance, this study provides a more nuanced understanding of how online learning behavior affects the focal relationship, and it advances research on the mechanisms underlying the focal relationship. Practitioners should enhance students’ online learning behavior to boost blended learning effects during the COVID-19 pandemic.

Keywords

Citation

Meng, X. and Hu, Z. (2023), "The relationship between student motivation and academic performance: the mediating role of online learning behavior", Quality Assurance in Education, Vol. 31 No. 1, pp. 167-180. https://doi.org/10.1108/QAE-02-2022-0046

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited


Introduction

The COVID-19 pandemic has had a global impact on all walks of life. Many countries shut down cities and borders to prevent its spread, instructing citizens to stay home, avoid close physical contact and maintain social distancing. In such an emergency, many educational institutions were forced to transfer to emergency remote teaching on a large scale (). This shift sparked a crisis response in which teachers used the same course design, assessment and teaching strategies that are used in face-to-face (F2F) learning (). It was hoped that this state of emergency would end quickly, and students could then return to campus to resume their studies as usual. However, with no end in sight to the pandemic, online teaching must continue to support educational activities. At the same time, many problems have arisen, such as online classes being poorly organized, students having a diminished feeling of connection, and a lack of effective interaction (). Nevertheless, COVID-19 made online learning central to current education strategies, as schools and universities have drawn on the significant potential of online learning for education. Blended learning is believed to be “an effective approach for accommodating an increasingly diverse student population in higher education and enriching the learning environment by incorporating online teaching resources” (, p. 273). An increasing number of institutions and lecturers have resorted to a blended learning mode, combining online learning with traditional offline learning. Consequently, practitioners are concerned about ensuring the quality of education with a blended learning mode and maximize its outcomes.

Previous studies have indicated that a variety of factors influence the implementation of blended learning. These factors are mainly categorized into three groups: those that influence teachers’ decision about the adoption of blended learning; those that influence students’ acceptance of blended learning; and those that concern the design of blended learning and its supportive technology. In each of these three domains, various factors have been examined, e.g. the adoption attitude of teachers (), appropriate technology and design and students’ characteristics and digital literacy (). Earlier research yielded valuable insights into understanding and implementing blended learning. Nevertheless, the underlying mechanism of blended learning in relation to student academic performance remains unclear.

To address this gap, the current study sought to determine the role of online learning behavior in improving academic performance and found a mediating role in online learning behavior in the relationship between students’ motivation and academic performance in blended learning. “Online learning behavior” refers to students’ logging into the online learning platform provided by the blended learning teacher. Academic performance in this paper refers to final paper-based exam results. Extrinsic and intrinsic motivations are also distinguished. “Intrinsic motivation” refers to a motive stemming from the activity itself and emerging from within the person (; ). By contrast, “extrinsic motivation” is driven by a motive originating elsewhere, anywhere except from the activity itself ().

This study makes three contributions to the relevant literature.

First, from a theoretical standpoint, it demonstrates that extrinsic motivation influences academic performance both directly and indirectly in blended learning, while intrinsic motivation affects academic performance only indirectly. These results shed new light on the differing effects of intrinsic and extrinsic motivations, thus contributing to both the motivation and blended learning literature.

Second, we provide evidence that online learning behavior acts as a bridge to connect motivation and academic performance. Without online learning behavior, it is impossible for intrinsic motivation to influence academic performance. By unveiling the underlying mechanism, our study offers a more nuanced understanding of the relationship between motivation and academic performance in blended learning, which also contributes to the literature on academic success. Finally, from a practical perspective, the study was empirically conducted in a blended learning context to explore how and why some students achieve excellent performance while others fail. Our findings can present feasible guidelines for practitioners.

Literature review

A huge shift to online learning during COVID-19 pandemic

Web-based learning (WBL), also referred as online learning or e-learning, provides online course content with all possible educational interventions, including discussion forums, videoconferencing and live lectures (). This technology-enabled model creates a virtual learning space outside the traditional physical classroom with both synchronous and asynchronous interactions available (). Teachers and students can thus be spatially and temporally dispersed ().

Due to the sudden outbreak of COVID-19 worldwide in 2020 and the severe threat it poses to human lives, emergency remote learning (ERL) was implemented to facilitate safe education. Its application has spread widely in the absence of an alternative. ERL differs from the earlier WBL in that the latter had well-planned course designs, assessment tools and teaching strategies (). All teachers were rushed through a crash course on using open resources and different platforms to support students’ study remotely (), as this allowed students to remain safe and healthy during COVID-19, while giving students a sense of stability and purpose, and an outlet during quarantine ().

Blended learning in China

The early definition of blended learning referred to a mix of traditional synchronous F2F learning and technology-powered asynchronous text-based online material (). Further qualifications have been added to this definition to yield a variety of strong and weak blends, referring to a combination of in-person sessions and online experiences that incorporate technology to facilitate the learning process (). We characterize blended learning as having two necessary key components:

  1. compulsory regular synchronous F2F learning; and

  2. an online platform providing resources that can be used and accessed asynchronously ().

Online platforms offer better visual and dynamic explanation or development to facilitate students’ learning (). These interactive capabilities have contributed greatly to the success of blended learning in modern education (). As a supplement to F2F learning in blended learning, online learning provides asynchronous learning and assessment that afford students autonomy regarding when and how to finish their tasks. During COVID-19, blended learning showed other potential: a quick response, both temporal and spatial, to sudden and emerging crises, enabling teachers and students to continue their interrupted courses beyond the physical limitations of F2F learning.

At the beginning of 2020, almost all universities in China shifted to ERL by demand of the authorities. As teachers familiarized themselves with online teaching, they discovered the value of the online learning platform Chaoxing, where teachers upload their course materials and manage students’ activities, such as group discussions, questionnaires, homework and quizzes. Teachers have continued to use the online platform during offline F2F teaching – that is, blended learning spontaneously became important in China.

Motivation and academic performance of students

Research on motivation is abundant and spans multiple disciplines. Motivation denotes people’s internal thrust, which encourages them to take various actions to realize their needs and objectives (). According to self-determination theory (), motivation is usually categorized into intrinsic motivation, deriving from genuine interests and extrinsic motivation, arising from an expected gain or a separable outcome (). Intrinsic motivation can be broadly defined as the motive stemming from the activity itself and emerging from within the person (). Students are intrinsically motivated when they seek enjoyment, interest, satisfaction of curiosity, self‐expression or personal challenge in the work (). In contrast, extrinsic motivation is any motive originating outside of the activity (). This definition in turn yields various categorizations of extrinsic motivation (). introduced two types of extrinsic motivation: compensation orientation and outward orientation. Compensation orientation emphasizes external rewards, while outward orientation emphasizes comparisons to other people. Under self-determination theory, extrinsic motivation could be classified into at least four types of regulations (; ). Our study focuses on extrinsic motivation that rewards, similar to compensation orientation. Such extrinsic motivation compels students to study, while intrinsic motivation allows students to enjoy themselves and gain satisfaction from the course.

Student academic performance is typically perceived as the knowledge acquired, the abilities developed at school, and the capacity to complete an assigned task to obtain ideal results (). Its measurement could be multidimensional. However, student academic performance is traditionally assessed by the final exam marks at the end of the semester in China. As a result, this study adopts final exam marks as the proxy of academic performance.

When students are highly motivated, they seem to be more easily involved in a specific activity or task. In particular, intrinsic motivation is a fundamental driving force of human actions (), obtaining enjoyment from the activity itself. An intrinsically motivated student may engage in learning out of curiosity, interest or enjoyment to achieve their own scholarly or personal goals (). Thus, we posit:

H1.

Intrinsic motivation is positively related to students’ academic performance.

Extrinsic motivation stems from rewards and influences received from external authorities or controls over activities (). These rewards and influences give students concrete goals or expectations. If they see the rewards or influences as meaningful for their short-term and long-term goals, they will have the incentive to complete the relevant tasks. While extrinsic motivation caused by external stimuli can be temporary, it can change into internal motivation when prolonged (). Researchers (; ) have shown that the connection between extrinsic motivation and academic performance is significant. Therefore, we hypothesize that:

H2.

Extrinsic motivation is positively related to students’ academic performance.

Potential mediating effects of online learning behavior

Motivation is closely related to online learning behavior. Efforts to construct motivation theories have led to four prominent contemporary theories in educational psychology: self-efficacy theory, attribution theory, self-worth theory, and achievement goal theory (). These four theories explain how motivation is influenced to yield different future behaviors. First, in terms of self-efficacy, students who have confidence in their own capabilities are more likely to behave in an adaptive and masterful way than those who have less confidence in themselves (). Blended learning is new to students. Self-efficacious students can adapt more easily to this change, as they believe themselves capable of completing the online learning task.

Second, attribution is the perceived cause of an outcome (). In the process of learning, students intend to look for the causes of their academic success and failures. These causes may be themselves (their knowledge, efforts, strategies, etc)., teachers (teachers’ mistakes, ability, mood), environment (the exam site, friends, classmates, etc). or luck. Online learning has nullified many previous failure factors. For example, a student may attribute academic failure to the teaching pace or lack of illustrations in class, but online learning offers additional opportunities for studying after class, which may increase online learning behavior.

Third, self-worth is the self-judgment of one’s sense of value and dignity (). A person with strong sense of self-worth is more likely to undertake difficult tasks. Blended learning aims to improve F2F learning by adding more tasks or resources to the online platform. Students with a strong sense of self-worth are more likely to perform online learning tasks, as they are not satisfied with easy or common tasks. Finally, achievement goal theory attributes student academic motivation to the attempts to achieve goals. Students’ behaviors are seen as a function of their desire to achieve their goals of learning (). Online learning behavior is obviously a positive behavioral option for them to achieve their academic goals.

Online learning behavior is positively related to academic performance. As mentioned above, our study used final exam scores to measure academic performance. The final exam was time-limited (two hours) and was intended to assess proficiency in knowledge, theories, and their application. Certain theories must be memorized to achieve good scores, and online learning behavior could aid memory. Memory is a complex phenomenon involving a number of distinct systems and processes, and implicit memory is distinguished from explicit memory ().

Within the study of explicit memory, three stages are typically considered. The first stage is encoding. Encoding of the information refers to the initial processing of the stimuli or events and their transfer into long-term memory by means of active processes such as elaboration or organization (). F2F could aid the encoding stage. The second stage is storage, and refers to the storage of learned information over a period. However, the mnemonic traces are susceptible to decay over time (). Forgetting, and its related phenomena, are attributed to a failure of the retrieval process, which is the third stage. Retrieval of information refers to accessing previously encoded memories (). The forgetting rate is thus usually used to assess storage efficiency. With the increase in online learning behavior, and the constant reviewing of knowledge, students’ forgetting rate might be lower, resulting in better storage of information and thus better retrieval of knowledge. Such excellent memory could result in good exam scores because the final is a closed-book exam. Thus, we hypothesize:

H3a.

Online learning behavior mediates the effect of intrinsic motivation on students’ performance.

H3b.

Online learning behavior mediates the effect of extrinsic motivation on students’ performance.

Methodology

Method

To test the proposed hypotheses (), a questionnaire was designed in 2021. This survey method is suitable because it enables the generalizability of outcomes, can be replicated easily, and allows the simultaneous investigation of multiple factors (). We mainly deployed existing scales. To ensure the quality of translation into Chinese, the questionnaire was back-translated into English and the consistencies were confirmed. The questionnaire included two independent variables (intrinsic and extrinsic motivation) as well as several control variables (except past performance).

Intrinsic and extrinsic motivations were measured by multi-item scales reported in previous research. First, the existing measurements of these constructs in the literature were reviewed. Next, the validity of the initial scales was evaluated by conducting several personal interviews with experts who have expertise in motivation research to ensure the content validity, clarity, and accuracy of the survey. All items were operationalized on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).

Intrinsic motivation was assessed through three items adapted from . For intrinsic motivation, we mainly assessed students’ academic interest in the course. A sample item is, “I get a sense of satisfaction from this course.” Cronbach’s α in this study is 0.814.

Extrinsic motivation was conceptualized as the extent to which students emphasize the importance of higher education and good grades for future career development and study. Currently, educational background and grade point average (GPA) are crucial for students either to find a job or to gain admission to a master’s program in China. Put differently, students are compelled to study hard to cope with employment pressure. We thus used three items adapted from previous research (), a sample of which is, “Do you agree that you go to university because education is important for getting a job later on?” Cronbach’s α in this study is 0.893.

Online learning behavior was measured using online study frequency, following earlier research (). The data were retrieved from the online learning platform Chaoxing, on which students were enrolled. The data showed each student’s total study times during the course; the cut-off date was exam day.

Academic performance was measured by the final exam, scored by the course teacher; scores denoted the students’ overall mastery of knowledge, theories, and applications to practice, which are commonly used measures of academic performance (; ; ).

Control variables were set. Following previous studies (; ), we controlled for several variables. Sex was controlled for as a dummy variable (1 = male; 0 = female) because the literature suggests significant sex differences in school engagement (), which is critical to student educational success (). We also controlled for socioeconomic status, i.e. parental education (1 = vocational and technical school, 2 = high school, 3 = college/higher vocational college, 4 = undergraduate, 5 = master’s or above) and family annual income (1 = 120,000 RMB and below, 2 = 120,001–300,000, 3 = 300,000 and above). Following previous research (), past performance is also controlled, with data from students’ previous marks.

Sample and demographics

Questionnaires were distributed F2F to all 149 students who attended a compulsory course called organizational behavior, selected for convenience, via the online platform Chaoxing. These students are junior undergraduates majoring in human resource management in one mainland Chinese university. After obtaining students’ consent, 149 students’ responses were received and paired with their online learning behavior data from Chaoxing. We rejected one invalid response due to incomplete answers, resulting in 148 useable questionnaires. The valid rate was 99.3% that was especially high because the students wished to earn course credit. Of the 148 respondents, 85 were female.

Data analysis

Common method variance biases, and the reliability and validity of the data, were checked first. To assess the reliability, convergent validity and discriminant validity, we performed confirmatory factor analysis (CFA) via AMOS 22.0 software. Correlational analysis and multiple regressions analysis were conducted to investigate relationships among variables using SPSS 22.0.

Findings

Data validation

Common method variance test. We followed suggestions by to reduce common method errors. We used procedural control measures like randomly arranging items in the scale and ensuring the academic use of the data. In this study, only intrinsic and extrinsic motivations were self-reported. Online learning behavior and academic performance were either retrieved from the online platform or from the course teacher. Consequently, common method bias might not be a serious problem in this study.

Reliability and validity test. The CFA fit indices were χ2/df = 2.43, RMSEA = 0.099, CF I= 0.978, TLI = 0.959, GFI = 0.957. In general, a value of χ2/df below 3 implies a good fit (), and CFI and TLI above 0.95, RMSEA close to or less than 0.06, and GFI above 0.9 are desirable (). Our fit indices met most of the criteria, indicating convergent validity. Convergent validity was also supported by the significant standardized loadings for our constructs () ().

For discriminant validity, each square root of the average variance extracted (AVE) of a construct was greater than its correlations with other constructs (), suggesting good discriminant validity ().

Reliability was assessed using both CR and AVE. All CRs were above 0.70, and all AVEs exceeded 0.50, supporting adequate reliability (). Additionally, Cronbach’s α exceeded 0.70, which further confirmed good reliability ().

Tests of the hypotheses

shows the means, standard deviations and correlation matrices. As indicated in , academic performance was positively associated with both extrinsic motivation (r = 0.205, p < 0.05) and online learning behavior (r = 0.246, p < 0.01). However, intrinsic motivation was not correlated with academic performance (r = 0.056, p > 0.05), indicating that there may be mediating factors between the two.

Regression analysis was conducted to estimate the relationships among variables. The variance inflation factors range from 1.030 to 2.302, well below the acceptable level of 10 (). Therefore, multicollinearity does not seem to be a concern. The regression results are presented in . Models 1 and 4 serve as the base models, including control variables only.

H1 predicted that intrinsic motivation was positively related to academic performance. However, as shown in Model 5, intrinsic motivation was positive but not significantly related to academic performance (β = 0.092, p > 0.05). Thus, H1 is rejected. Model 7 showed that extrinsic motivation was significantly and positively related to academic performance (β = 0.234, p < 0.01), supporting H2. Next, as shown in Model 2, we found that intrinsic motivation was positively associated with online learning behavior (β = 0.153, p < 0.05). Model 6 added the intrinsic motivation and online learning behavior variables together. The relationship between online learning behavior and academic performance was positive (β = 0.262, p < 0.01). Such preliminary results evidenced that the effect of intrinsic motivation on academic performance was fully mediated by online learning behavior. H3a is thus supported. For extrinsic motivation, Model 3 showed that it is positively associated with online learning behavior (β = 0.160, p < 0.05). When both extrinsic motivation and online learning behavior were entered in Model 8, the relationship between extrinsic motivation and academic performance was still significant (β = 0.197, p < 0.05). In addition, online learning behavior (β = 0.234, p < 0.01) was significantly related to academic performance. Online learning behavior thus partially mediated the relationship between extrinsic motivation and academic performance, supporting H3b. We further used the bootstrap method and PROCESS macros () to confirm mediation and determine the direct and indirect effects of motivation on academic performance. The upper and lower limits of the bootstrapping yielded 95% confidence intervals for the indirect effects that do not include 0 (results are available from the authors upon request), further supporting H3a and H3b.

Discussion of results

Main findings and theoretical implications

Multiple studies have explored the experiences and expectations of online learning after the COVID-19 outbreak (; ). Our research further extended the current knowledge of blended learning by focusing on the influence of motivation on academic performance. In this study, we investigated the relationship among intrinsic motivation, extrinsic motivation, online learning behavior and academic performance in blended learning. Our results make several theoretical contributions to the blended learning literature and yield further insight into ways to promote academic performance in blended learning.

First, our results revealed that intrinsic motivation did not contribute directly to student academic performance. This surprising finding is contrary to previous research in pedagogy (). It can be explained from two aspects. First, this study was conducted in a Chinese context. One previous study in the same context showed that cultural differences affect the multiplicative effects of extrinsic and intrinsic motivations (). Second, although intrinsic motivation has numerous benefits, its drawbacks should not be neglected. Intrinsic motivation can lead one to engage in a challenging activity in a state of complete absorption, focus, and concentration (). However, such intense engagement can make students unaware of time and space limits and ignore other responsibilities or task requirements (). Academic performance in this study was measured by the final exam scores. Exams are time-sensitive tasks that constitute a learning outcome, rather than a challenging new task. Students with higher intrinsic motivation enjoy the learning process during class sessions, but may pay less attention to the parts that do not interest them. In fact, some researchers have defined intrinsic motivation as the task being interesting (). A student’s intrinsic motivation toward a certain task is premised on the task being the source of interest and enjoyment, which may only be maintained during the task period. It remains to be tested whether intrinsic motivation remains once the task is finished. Furthermore, intrinsically motivated students are less effectively motivated if the topics are not interesting (). To be constantly intrinsically motivated, a student must find interesting or enjoyable elements in later tasks (). It is also generally believed that inappropriate external rewards or requirements undermine intrinsic motivation and are therefore harmful to the final outcome (), because they make students feel forced or controlled, while intrinsic motivation is based on free choice.

Second, our findings suggest that extrinsic motivation has a positive influence on academic performance. External motivations are mainly a sum of recognition, grades, and competition in learning, social aspects and extrinsic rewards () in educational settings. According to achievement goal theory, students’ behaviors reflect their desires to achieve their goals of learning (). Unlike intrinsic motivation for learning, students with extrinsic motivation receive concrete standards for their goals. On the one hand, these goals may lead to an impoverished form of extrinsic motivation accompanied by actions performed with resentment, resistance and disinterest. On the other hand, extrinsic motivation may exert a positive influence. Appropriate goals arouse active, agentic states with an attitude of willingness that reflect an inner acceptance of the value or utility of a task (), making learning useful and necessary to achieve these goals. External motivation also produces expectations about outcome. From the behavioral perspective, external incentives are reinforcers for the outcome performance (). Externally motivated students hope for success, rewards and recognition in the future. In their minds, the realization of these expectations brings about important changes, ensures meaningful things or state for their lives, and serves as a bridge to their desired future. By recognizing the differing effects of intrinsic and extrinsic motivations on academic performance in blended learning, this study contributes to the motivation literature.

Finally, our study demonstrates that online learning behavior mediate the effect of motivation on student academic performance. Although no direct association existed between intrinsic motivation and academic performance, intrinsic motivation could positively influence online learning behavior, paving the way for academic performance. This result demonstrates the value of blended learning, revealing ways to enhance students’ performance. The online platform provides flexible and personalized curricula, and responds to students’ diversity with dynamic and interactive materials with which they may learn at their own pace (). Intrinsically motivated students feel autonomous, competent and self-determined (), which increases their acceptance of blended learning. Moreover, online course resources are retained on the platform throughout the course, making it easy for the students to consult and check them whenever they wish, thereby eliminating the time limits that persist in traditional F2F. In addition, intrinsically motivated people tend to enjoy interesting tasks. The online learning platform could transform the learning content from words and static pictures in the textbook to other forms, providing numerous ways of learning. Such special learning experiences may keep them interested for a longer time. The influence of extrinsic motivation on student academic performance was also mediated by online learning behavior. Extrinsically motivated students have clear goals to achieve. They would like to achieve higher academic performance to gain external rewards like admission to a postgraduate program or better employment opportunities. They do not enjoy the content as much as intrinsically motivated students do, but they are more willing to repeat their online learning behavior to achieve their pre-set goals. The online learning platform serves as another way to review and summarize the material. In addition, online platforms offer practice tests with real-time feedback. Thus, to achieve ideal results, students may value the extra chances to test themselves ahead of their real final exam, for if they are not satisfied, they can repeat the review, summary and tests. By explicating the role of online learning behavior in the relationship between motivation and academic performance, we shed light on the pivotal role of blended learning in optimizing academic performance. Blended learning makes online learning behavior possible, which acts as a bridge to connect motivation and academic performance. Without online learning behavior, it is impossible for intrinsic motivation to influence academic performance. By revealing the underlying mechanism, our study offers a more nuanced understanding of blended learning; it also contributes to the literature on academic success.

Practical implications

COVID-19 has pushed online learning to the forefront of education. Ensuring the quality of students’ studies has become a core concern of practitioners. Our study holds several implications for practitioners. First, practitioners should encourage students to increase the use of an online learning platform, because such behavior is directly related to their academic performance. Practitioners should manage and monitor online learning behavior to keep better track of students’ learning status, and teachers should properly organize the online learning platform to facilitate students’ use. Second, practitioners should seek to evoke stronger student motivation, as intrinsic motivation positively affects academic performance in an indirect way, while extrinsic motivation has both direct and indirect effects. Even in post-COVID-19 education, as long as blended learning is implemented, online learning behavior remains key to maximizing its value.

Limitations of the current study and suggestions for future research

There are several limitations to this study. First, we only collected data for students in a single major at one university in mainland China; generalizing the results should be careful to account for cultural factors. Future research should justify the ubiquity of blended learning. Second, although we implemented several activities to reduce the risk of common method variance bias, we still could not guarantee the complete exclusion of such a problem. Further research should adopt a longitudinal design and span a longer period. Third, we only hypothesized online learning behavior as mediating between motivation and academic performance in blended learning. We encourage future studies to probe other key factors in online learning platforms. Finally, F2F is a major part of our research setting, one that is merely supplemented by online learning. As there may be a more optimal match in blended learning, a few adjustments might be needed to its traditional aspects to improve the effectiveness of blended learning. Future studies can focus on possible measures to better combine the two forms.

Conclusion

Blended learning has become widespread since the outbreak of COVID-19. Practitioners thus have an urgent need to know its impact on academic performance. Our research question addresses concerns such as, “What is the role of online learning behavior in improving academic performance?” The results of this study reveal that online learning behavior acts as a bridge to connect the motivation and academic performance of the students. Extrinsic motivation positively influences academic performance both directly and indirectly through online learning behavior, while intrinsic motivation only contributes to the success of student academic performance via online learning behavior. Finally, the current study collected empirical evidence to identify an effective pattern to maximize the outcome of blended learning.

Figures

Research model

Figure 1.

Research model

Scales and items

Item Loading
Intrinsic motivation (α = 0.814; AVE = 0.619; CR = 0.828)
I get a sense of satisfaction from this course 0.897
I have tried very hard in this course 0.718
Compared to other courses, this course is interesting 0.734
Extrinsic motivation (α = 0.893; AVE = 0.743; CR = 0.896)
Do you agree that you go to university because education is important for getting a job later on 0.805
Good grades are important to you because they might influence master’s program admission 0.910
Good grades are important to you because they might affect employment opportunities 0.868

Descriptive statistics and correlation matrix

Variable M SD 1 2 3 4 5 6 7 8
1 Sex 0.426 0.50
2 Family income 1.81 0.64 –0.130
3 Mother education 2.64 1.01 0.149 0.270**
4 Father education 2.74 1.05 0.126 0.330** 0.732**
5 Past performance 90.3 9.79 –0.042 0.034 –0.197* –0.132
6 Intrinsic motivation 4.33 0.53 0.037 0.056 0.160 0.140 0.012 (0.787)
7 Extrinsic motivation 4.36 0.62 0.036 0.035 0.131 0.133 0.088 0.681** (0.862)
8 Online learning behavior 131.97 60.52 –0.186* –0.081 –0.188* –0.160 –0.305** 0.100 0.086
9 Academic performance 78.62 6.91 –0.080 –0.047 –0.230** –0.162* 0.119 0.056 0.205* 0.246**
Notes:

N = 148. On the diagonal the square root of the AVE in parenthesis.

Each square root of AVE is larger than its correlation coefficients with other constructs. *p < 0.05, **p < 0.01

Results of regression

Online learning behavior Academic performance
Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
1 Sex −0.171* −0.173* −0.173* −0.046 −0.047 −0.002 −0.049 −0.009
2 Family income −0.027 −0.028 −0.024 −0.001 −0.002 0.006 0.004 0.009
3 Mother education −0.207 −0.227* −0.222* −0.219 −0.231 −0.172 −0.242* −0.190
4 Father education −0.025 −0.032 −0.038 0.015 0.010 0.019 −0.004 0.004
5 Past performance −0.355*** −0.362*** −0.374*** 0.076 0.071 0.166 0.047 0.135
6 Intrinsic motivation 0.153* 0.092 0.052
7 Extrinsic motivation 0.160* 0.234** 0.197*
8 Online learning behavior 0.262** 0.234**
Adjusted R2 0.156 0.174 0.176 0.028 0.029 0.080 0.076 0.115
ΔR2 0.185 0.023 0.025 0.061 0.008 0.054 0.053 0.043
F 6.452*** 6.169*** 6.244*** 1.837 1.742 2.814** 3.016** 3.726**
Notes:

N = 148. Standardized coefficients are shown. *p < 0.05, **p < 0.01, ***p < 0.001

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Acknowledgements

This research was funded by Research Project on Comprehensive Education and Teaching Reform of Sino Foreign Cooperation in Running Schools in Henan University of Technology (GJXY202117), Research Project on Education and Teaching Reform of School of Management of Henan University of Technology (GLXY2021YB02) and Education Research Fund of North China University of Water Resources and Electric Power.

Corresponding author

Zhenfang Hu can be contacted at: huzhenfang@ncwu.edu.cn

About the authors

Xiangju Meng is an Associate Professor in management at Henan University of Technology, China since 2013. Currently, her main research areas are human resource management, organizational behavior, and teaching reform.

Zhenfang Hu is a lecturer in North China University of Water Resources and Electric Power since 2018. Her main research fields are teaching reforms, pedagogy and foreign language teaching.

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