Higher Education in China during the Pandemic: Analyzing Online Self-Learning Motivation Using Bayesian Networks

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper purports to analyse the online self-learning motivation of higher education students during the Covid-19 pandemic, based on a Bayesian analysis of data from an intelligent search platform. SEM models are put forward which show relationships between constructs such as gender, geographic location, age, etc. with online search frequency. Specific models are put forward for different genders and educational levels.

The fact is that the argument in the paper is very unclear. Several issues seem quite fundamental to the argument, but they are not addressed adequately at all:

 

·         How do the models address “online self-learning motivation”? The implication seems to be that “online search frequency” is being used to gauge motivation. But this is hardly a very obvious way of addressing the issue and requires justification. We need a more in-depth discussion of what is meant by “online self-learning motivation”, how this concept has been interrogated in previous literature, and why the paper focusses on it. We then need to understand the approach taken to conceptualising this issue in a way that is justified, rather than assumed, with the explanation setting up how the subsequent models will address it.

·         More generally, section 2 (Theoretical framework) needs to be re-written so that it justifies and defines the specific concepts used in the rest of the paper. At present, the section labelled “theoretical framework” does not really present a theoretical framework at all. Instead, it serves as a descriptive general overview of some prior literature.

·         Section 3.3 needs to show how Bayesian modelling was applied in this project, rather than giving a very generic description which seems to summarise textbooks on Bayesian modelling without referring to this project.

 

The above are all crucial for the success of the paper, in my view. If those points can be addressed, then some other issues with the paper will also be worth noting:

 

·         The abstract needs to be clearer about what is meant by “learning-incentive money”, which is not clear in the context of this text.

·         The Introduction section needs to make clear which area of academic literature this paper aims to contribute to.

·         The Introduction also needs to state the motivation for conducting this work. Why did the authors choose to pursue this problem in this way?

·         The Introduction also needs a clear and concise statement of the research problem (perhaps as one or more research questions).

·         Section 3 needs to discuss in *much* more detail the source, format and cleaning procedures for the data from the platform.

·         Section 3 needs to explain how BN approaches are viewed to be appropriate for addressing the *specific* research problem the paper addresses.

·         Section 4 should be renamed simply “Results”, since there is a separate Discussion section later. It should also start with some signposting: what will be covered in the section, and how will this allow the research problem to be addressed?

·         Section 5 should make clear how the results of the paper contribute to the specific academic literature to which the paper is addressed (and which I have asked the authors to clarify in section 1).

·         Section 7 needs to be clear how the future prospects outlined *relate to the actual results* of the paper.

Author Response

Reviewer(s)' Comments to Author:

Reviewer 1:

This paper purports to analyse the online self-learning motivation of higher education students during the Covid-19 pandemic, based on a Bayesian analysis of data from an intelligent search platform. SEM models are put forward which show relationships between constructs such as gender, geographic location, age, etc. with online search frequency. Specific models are put forward for different genders and educational levels. The fact is that the argument in the paper is very unclear. Several issues seem quite fundamental to the argument, but they are not addressed adequately at all:

Response: 

Thanks for the valuable comments. We have considered all the comments and suggestions carefully, and tried our best to revise the manuscript according to your comments.

For specific comments:

1.How do the models address “online self-learning motivation”? The implication seems to be that “online search frequency” is being used to gauge motivation. But this is hardly a very obvious way of addressing the issue and requires justification. We need a more in-depth discussion of what is meant by “online self-learning motivation”, how this concept has been interrogated in previous literature, and why the paper focusses on it. We then need to understand the approach taken to conceptualising this issue in a way that is justified, rather than assumed, with the explanation setting up how the subsequent models will address it.

Response: 

Thank you for your valuable advice.We have thoroughly considered your comments regarding "online self-learning motivation" and the use of "online search frequency" as its measure, and we have made the corresponding adjustments and additions to the manuscript. In the revised version, we have clarified the definition of "online self-learning motivation" and included a review of relevant literature to highlight the importance of this concept. We have referenced Self-Determination Theory (Ryan & Deci, 2000) to support our understanding of motivation, particularly the roles of intrinsic and extrinsic motivation in online learning environments. A detailed discussion of this theory has been added to Section 2 of the manuscript.(page  2, paragraph 5, and line 92.)

Regarding the rationale for using online search frequency as a measure of motivation, we have expanded the discussion in Section 3.1 of the revised manuscript. We have explained why online search frequency was selected as a key indicator of self-learning motivation, based on the following points: (1) Behavioral Expression: Online search frequency can be viewed as an external manifestation of learning motivation, where students with stronger motivation typically exhibit more proactive learning behaviors[14,16]. (2) Data Accessibility and Immediacy: Online search frequency data is easily accessible and provides real-time insights into students' learning motivation, making it a feasible and meaningful indicator for large-scale studies [29].(page 4, paragraph 7, and line 188.)

To enhance the explanatory power of our model, we have revised the theoretical framework in Section 2 and the methodology in Section 3. We have detailed the hypothesized pathways between these factors and learning motivation and have cited relevant literature to support the validity of these pathways.

The specific modifications are as follows:

Section 2:“Self-Determination Theory (SDT), developed by Ryan and Deci[14], is a widely recognized framework for understanding motivation within educational settings. SDT categorizes motivation into two types: intrinsic and extrinsic. Intrinsic motivation arises from the inherent enjoyment and interest in the activity itself, while extrinsic motivation is driven by external rewards or pressures. According to SDT, three basic psychological needs—autonomy, competence, and relatedness—must be satisfied to foster intrinsic motivation and optimal learning performance.

In the context of online autonomous learning, these needs are particularly critical. Autonomy refers to a student’s sense of control over their learning process, while competence refers to their confidence and ability to successfully complete online learning tasks. Research indicates that students with intrinsic or self-determined motivation typically achieve more positive learning outcomes [15, 16]; for example, they are better able to adapt to academic life, have lower levels of perceived stress, and can engage in more sustained learning [17, 18]. With the rapid advancement of technology, the manner in which people learn or acquire new knowledge has changed, and one major change is that an increasing number of students are choosing to receive education online [19]. Autonomy is a key feature in online learning [20, 21]; This surge in demand for online learning resources further underscores the importance of self-directed learning motivation in this new educational environment.”

Section 3:“In our study, we used the online search frequency of students on the learning platform as a key indicator of online self-directed learning motivation. Online search frequency refers to the rate at which students actively search for learning resources and information through the platform during their studies. This behavior reflects students' willingness and ability to actively acquire knowledge, making it an external manifestation of self-directed learning motivation[16, 29]. Furthermore, online search frequency data is easily accessible and can provide real-time insights into students' learning motivation, making it a feasible and meaningful indicator for large-scale studies.”

Reference:

[14] Ryan, R. M.; Deci, E. L., Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist 2000, 55, (1), 68-78.

[15] Pan, Y.; Gauvain, M., The continuity of college students’ autonomous learning motivation and its predictors: A three-year longitudinal study. Learning and Individual Differences 2012, 22, (1), 92-99.

[16] Keller, J. M., First principles of motivation to learn and e3-learning. Distance Education 2008, 29, (2), 175-185.

[17] Gottfried, A. E., Chapter Three - Academic Intrinsic Motivation: Theory, Assessment, and Longitudinal Research. In Advances in Motivation Science, Elliot, A. J., Ed. Elsevier: 2019; Vol. 6, pp 71-109.

[18] Lin, Y.G.; McKeachie, W. J.; Kim, Y. C., College student intrinsic and/or extrinsic motivation and learning. Learning and Individual Differences 2003, 13, (3), 251-258.

[19] Xu, Z.; Zhao, Y.; Liew, J.; Zhou, X.; Kogut, A., Synthesizing research evidence on self-regulated learning and academic achievement in online and blended learning environments: A scoping review. Educational Research Review 2023, 39, 100510.

[29] Wilson, T. D., Models in information behaviour research. Journal of Documentation 1999, 55, (3), 249-270.

 

2. More generally, section 2 (Theoretical framework) needs to be re-written so that it justifies and defines the specific concepts used in the rest of the paper. At present, the section labelled “theoretical framework” does not really present a theoretical framework at all. Instead, it serves as a descriptive general overview of some prior literature.

Response: 

Thank you for this valuable comment. We have thoroughly revised Section 2, reorganizing it into a logically coherent theoretical framework. This framework now comprehensively covers Self-Determination Theory and Economic Incentive Theory and appropriately applies these theories to the design of our research model.(page 2, paragraph 5, and line 92.)

The specific modifications are as follows:

Section 2:

“2.1. Self-Determination Theory and Online Learning Motivation

Self-Determination Theory (SDT), developed by Ryan and Deci[14], is a widely recognized framework for understanding motivation within educational settings. SDT categorizes motivation into two types: intrinsic and extrinsic. Intrinsic motivation arises from the inherent enjoyment and interest in the activity itself, while extrinsic motivation is driven by external rewards or pressures. According to SDT, three basic psychological needs—autonomy, competence, and relatedness—must be satisfied to foster intrinsic motivation and optimal learning performance.

In the context of online autonomous learning, these needs are particularly critical. Autonomy refers to a student’s sense of control over their learning process, while competence refers to their confidence and ability to successfully complete online learning tasks. Research indicates that students with intrinsic or self-determined motivation typically achieve more positive learning outcomes [15, 16]; for example, they are better able to adapt to academic life, have lower levels of perceived stress, and can engage in more sustained learning [17, 18]. With the rapid advancement of technology, the manner in which people learn or acquire new knowledge has changed, and one major change is that an increasing number of students are choosing to receive education online [19]. Autonomy is a key feature in online learning [20, 21]; This surge in demand for online learning resources further underscores the importance of self-directed learning motivation in this new educational environment.

2.2. The Impact of Personal Characteristics on Online Learning Motivation

Students' personal characteristics, such as gender, age, and educational level, are closely related to their learning motivation [22]. A study found significant gender differences in self-directed learning motivation [20], where girls usually show a higher autonomous motivation to learn [23]; this trend may contribute to their higher academic achievement. Ethnicity is associated with student autonomy, and during distance learning throughout the pandemic, curiosity among Latino students enrolled in ethnic studies courses improved during the school year, although students’ stress and motivation were lower. Additionally, research has indicated that as people age, they tend to develop a stronger sense of autonomy; older individuals are better at internalizing their goals and personal initiatives than younger individuals.

2.3. The Role of Academic Characteristics in the Formation of Learning Motivation

Academic characteristics, such as school atmosphere and reward systems[24], play distinct roles in influencing students' motivation and online learning behaviors. Economic Incentive Theory posits that monetary rewards can significantly influence behavior by increasing the perceived benefits of specific actions[25, 26]. In educational settings, economic incentives have been shown to enhance student engagement, particularly in environments where motivation may be low. In online learning, where immediate guidance and timely feedback are often challenging to provide, learning incentive funds can serve as an effective external motivator, enhancing students' extrinsic motivation and thereby promoting greater engagement in their studies[13]. It is worth noting that reward mechanisms in the form of external incentives may also lead to learner overdependence, with various implications for human behavior [27]. In addition, a correlation exists between academic climate and academic performance. Negative school belonging has been noted to negatively impact intrinsic motivation and perceived learning [28]. Higher-level schools offer a more comprehensive curriculum, better-quality educational resources, and a learning environment suitable for the development of self-directed learning skills. Such schools tend to encourage active inquiry and self-directed learning processes, and they may provide more external resources and opportunities to promote the development of self-learning skills.

2.4. The Impact of Academic Environment on Learning Motivation

The academic environment, including school type, geographic location, and economic background, often influences the resources and opportunities available for self-directed learning. Schools’ geographic location and economic level typically correlate with access to richer online learning resources. In areas of higher economic development, schools are likely to have more resources available for pedagogical innovation, thus providing students with a wealth of learning material and environments that enhance their self-directed learning. Conversely, in less-affluent economies, the investment capabilities of schools and families in education may be constrained, potentially inhibiting the promotion of self-directed learning. Students from lower socioeconomic backgrounds and rural communities may require additional support to overcome educational challenges—particularly those exacerbated by the COVID-19 pandemic. Furthermore, the nature of a school (i.e., whether it is academic, technical-vocational, or comprehensive) influences the development of self-learning capabilities. Academic-oriented schools may place greater emphasis on theoretical knowledge and research skill development, thereby facilitating self-directed learning, whereas technical vocational schools may prioritize skills-based and practical learning experiences. Overall, the evolution of self-learning is affected by multiple factors: a school’s type, nature, and geographic location can directly or indirectly impact the distribution of educational resources, selection of educational strategies, and improvement of educational quality, all of which, in turn, shape students’ capacity for self-directed learning.

This study integrates principles from Self-Determination Theory and Economic Incentive Theory to develop a comprehensive model that explains the factors influencing college students' motivation for self-directed online learning. The model posits the following:

(1)Learning incentive funds, as an external motivating factor, can enhance extrinsic motivation while potentially fostering self-directed learning motivation in an online setting.

(2)Personal characteristics (such as gender and age), academic characteristics (such as educational background and reward systems), and the academic environment (such as school type and geographic location) significantly impact students' learning motivation, with these factors being particularly crucial in online learning environments.

(3)The interaction among these variables is expected to reveal how different groups  respond to the challenges and rewards of online learning.”

Reference:

[14] Ryan, R. M.; Deci, E. L., Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist 2000, 55, (1), 68-78.

[15] Pan, Y.; Gauvain, M., The continuity of college students’ autonomous learning motivation and its predictors: A three-year longitudinal study. Learning and Individual Differences 2012, 22, (1), 92-99.

[16] Keller, J. M., First principles of motivation to learn and e3-learning. Distance Education 2008, 29, (2), 175-185.

[17] Gottfried, A. E., Chapter Three - Academic Intrinsic Motivation: Theory, Assessment, and Longitudinal Research. In Advances in Motivation Science, Elliot, A. J., Ed. Elsevier: 2019; Vol. 6, pp 71-109.

[18] Lin, Y.G.; McKeachie, W. J.; Kim, Y. C., College student intrinsic and/or extrinsic motivation and learning. Learning and Individual Differences 2003, 13, (3), 251-258.

[19] Xu, Z.; Zhao, Y.; Liew, J.; Zhou, X.; Kogut, A., Synthesizing research evidence on self-regulated learning and academic achievement in online and blended learning environments: A scoping review. Educational Research Review 2023, 39, 100510.

[20] Zhang, Z.; Maeda, Y.; Newby, T., Individual differences in preservice teachers’ online self-regulated learning capacity: A multilevel analysis. Computers & Education 2023, 207, 104926.

[21] Chiu, T. K. F., Applying the self-determination theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education 2022, 54, (sup1), S14-S30.

[22] Steinmayr, R.; Lazarides, R.; Weidinger, A. F.; Christiansen, H., Teaching and learning during the first COVID-19 school lockdown: Realization and associations with parent-perceived students' academic outcomes. Zeitschrift für Pädagogische Psychologie 2021, 35, (2-3), 85-106.

[23] Vantieghem, W.; Van Houtte, M., Differences in Study Motivation Within and Between Genders: An Examination by Gender Typicality Among Early Adolescents. Youth & Society 2015, 50, (3), 377-404.

[24] Brownback, A.; Sadoff, S., Improving College Instruction through Incentives. Journal of Political Economy 2020, 128, (8), 2925-2972.

[25] Murayama, K.; Kitagami, S., Consolidation power of extrinsic rewards: reward cues enhance long-term memory for irrelevant past events. Journal of experimental psychology. General 2014, 143, (1), 15-20.

[26] Murayama, K.; Matsumoto, M.; Izuma, K.; Matsumoto, K., Neural basis of the undermining effect of monetary reward on intrinsic motivation. Proceedings of the National Academy of Sciences of the United States of America 2010, 107, (49), 20911-6.

[27] Ding, X.-H.; He, Y.; Wu, J.; Cheng, C., Effects of positive incentive and negative incentive in knowledge transfer: carrot and stick. Chinese Management Studies 2016, 10, (3), 593-614.

[28] Ferreira, M.; Cardoso, A. P.; Abrantes, J. L., Motivation and Relationship of the Student with the School as Factors Involved in the Perceived Learning. Procedia - Social and Behavioral Sciences 2011, 29, 1707-1714.

 

3. Section 3.3 needs to show how Bayesian modelling was applied in this project, rather than giving a very generic description which seems to summarise textbooks on Bayesian modelling without referring to this project.

Response: 

Thank you for your valuable feedback. We acknowledge that the current discussion on Bayesian modeling is indeed somewhat general and lacks a detailed exploration of its specific application to this project. To address this, we have rewritten Section 3 to provide a precise description of how Bayesian Networks and multigroup structural equation modeling are constructed and applied in this project. We also incorporated actual data and the model reasoning process to demonstrate each step and present the analysis results in detail.(page 5, paragraph 3, and line 229.)

The specific modifications are as follows:

Section 3:

“3.2.1. Dependency establishment

Let  be the set of input points and a total of one variable which represent SEX,MON,FRE,LEN,EDU_L,AGE,SCH_L,NAT,ECO_L,LOC,TYP,SRN shown in Table 1. A BN on a set of variables U is a network structure which is a DAG on U and a set of probability tables[37]:

                        (1)

Where  is the set of the antecedents of  in BN and.

A BN represents joint probability distributions (2):

                        (2)

3.3. Multigroup structural equation model (SEM)

Where is a vector of latent variables, in this project,  is reflected in the key network which supports the implementation of the structural equation model;  is a vector of latent exogenous variables;  is a matrix of coefficients associated with latent endogenous variables;  is a matrix of coefficients associated with latent exogenous variables, and is a vector of error terms associated with endogenous variables.”

Reference:

[37] Kalisch, M.; Bühlmann, P., Estimating high-dimensional directed acyclic graphs with the PC-algorithm. Journal of Machine Learning Research 2007, 8, 613-636.

 

4. The abstract needs to be clearer about what is meant by “learning-incentive money”, which is not clear in the context of this text.

Response: 

Thank you for your valuable feedback. The term "learning-incentive money" refers to financial incentives such as rewards, subsidies, or scholarships. Given the complexity of this concept, I have provided detailed explanations in the introduction, Section 2.3, and Section 3.1 of the manuscript. These sections elaborate on the meaning of "learning-incentive money," its role in the study, and how it influences students' motivation for online self-directed learning. However, due to space limitations, it was challenging to fully elaborate on this concept within the abstract. Therefore, I have revised the abstract to improve clarity while maintaining conciseness. I hope this revision addresses your concerns, and I appreciate your understanding.(page 2, paragraph 2, and line 57.)

The specific modifications are as follows:

“While these studies have significantly contributed to understanding learning motivation in traditional educational settings, the factors driving university students' self-directed online learning, especially those involving economic incentives, have not been sufficiently explored. Economic incentives, such as learning-incentive money (which may include rewards, subsidies, or scholarships), are particularly relevant in online learning environments. These financial incentives can motivate students by providing tangible rewards for academic achievements, which, in turn, can enhance their self-directed learning motivation. For example, monetary incentives have been shown to increase students' engagement and effort in laboratory-based tasks, but their effectiveness in natural educational settings has yielded mixed results[12, 13]. Understanding how learning-incentive money interacts with other personal and academic factors to influence motivation is crucial for developing effective strategies in online education.”

“Economic Incentive Theory posits that monetary rewards can significantly influence behavior by increasing the perceived benefits of specific actions[25, 26]. In educational settings, economic incentives have been shown to enhance student engagement, particularly in environments where motivation may be low. In online learning, where immediate guidance and timely feedback are often challenging to provide, learning incentive funds can serve as an effective external motivator, enhancing students' extrinsic motivation and thereby promoting greater engagement in their studies[13]. It is worth noting that reward mechanisms in the form of external incentives may also lead to learner overdependence, with various implications for human behavior [27].”

“The platform also provides learning incentives based on students' academic performance, including rewards, subsidies, or scholarships. These economic incentives can be used by students to purchase learning resources, pay for course fees, or serve as rewards for completing specific learning tasks.”

Reference:

[12] O'Neil, J. H. F.; Sugrue, B.; Baker, E. L., Effects of Motivational Interventions on the National Assessment of Educational Progress Mathematics Performance. Educational Assessment 1995, 3, (2), 135-157.

[13] Schwab, J. F.; Somerville, L. H., Raising the Stakes for Online Learning: Monetary Incentives Increase Performance in a Computer-Based Learning Task Under Certain Conditions. Frontiers in Psychology 2022, 13.

[25] Murayama, K.; Kitagami, S., Consolidation power of extrinsic rewards: reward cues enhance long-term memory for irrelevant past events. Journal of experimental psychology. General 2014, 143, (1), 15-20.

[26] Murayama, K.; Matsumoto, M.; Izuma, K.; Matsumoto, K., Neural basis of the undermining effect of monetary reward on intrinsic motivation. Proceedings of the National Academy of Sciences of the United States of America 2010, 107, (49), 20911-6.

[27] Ding, X.-H.; He, Y.; Wu, J.; Cheng, C., Effects of positive incentive and negative incentive in knowledge transfer: carrot and stick. Chinese Management Studies 2016, 10, (3), 593-614.

 

5.The Introduction section needs to make clear which area of academic literature this paper aims to contribute to.

Response: 

Thank you for this valuable comment. In the introduction, we systematically explore the complexities of online learning motivation in higher education and clearly highlight the study's contributions to the fields of educational psychology and online learning motivation research, offering valuable insights for further research in this area.(page 2, paragraph 4, and line 84.)

The specific modifications are as follows:

“The findings of this study will not only provide new empirical support for the theoretical framework of online learning motivation but also contribute to the fields of educational psychology and online learning research. Furthermore, these insights will offer valuable information and recommendations for the development of future online education platforms, course design, and teaching strategies, particularly in promoting the sustainable development of online education in a globalized context.”

 

6. The Introduction also needs to state the motivation for conducting this work. Why did the authors choose to pursue this problem in this way?

Response: 

Thank you for this valuable comment. We have included a discussion of the research motivation in the introduction. (page 2, paragraph 2, and line 57.)

The specific modifications are as follows:

“While these studies have significantly contributed to understanding learning motivation in traditional educational settings, the factors driving university students' self-directed online learning, especially those involving economic incentives, have not been sufficiently explored.”

 “Most research has focused on small sample sizes and analyzed primarily offline learning models, making it difficult to generalize findings within the broader context of the global shift to online learning. Therefore, it is necessary to analyze data from large samples of university students during the COVID-19 pandemic to explore how personal characteristics, academic characteristics, and the academic environment influence online self-directed learning motivation. Additionally, conducting subgroup analyses among undergraduate, master’s, and doctoral students can provide deeper insights into how these variables affect different groups.”

 

7.The Introduction also needs a clear and concise statement of the research problem (perhaps as one or more research questions).

Response: 

Thank you for this valuable comment. The introduction has been revised to clearly articulate the research question, ensuring that readers can easily grasp the core concerns of the study. (page 2, paragraph 3, and line 78.)

The specific modifications are as follows:

“(1) to analyze how university students' personal characteristics, academic characteristics, and academic environment variables influence online self-directed learning motivation using large-scale sample data, and to explore the causal relationships between these factors; (2) to conduct subgroup analyses of undergraduates, master’s students, and doctoral candidates to gain a deeper understanding of how these variables influence online self-directed learning motivation across different groups. ”

 

8.Section 3 needs to discuss in *much* more detail the source, format and cleaning procedures for the data from the platform.

Response: 

Thank you for this valuable comment. In Section 3, we have provided a more comprehensive overview of the data cleansing process, detailing the data sources, their formats, and their applications. (page 4, paragraph 7, and line 188.)

The specific modifications are as follows:

“The survey data collected during the pandemic was provided by the Chinese educational technology company "Zhuolang Technology." This company offers a wide range of content across various academic disciplines, enabling both undergraduate and graduate students to easily access academic materials and learning resources. Additionally, the company provides learning incentives based on students' academic performance, including rewards, subsidies, or scholarships. These financial incentives can be used by students to purchase learning materials, pay for course fees, or serve as rewards for completing specific learning tasks.

The raw data were collected in the form of web search logs, which detailed students' search activities on the platform. Each log entry included information on search frequency, timestamps, and the types of content accessed. Additionally, demographic and academic information about the students, such as gender, age, ethnicity, educational level, and school characteristics, was included. To ensure the quality and accuracy of the data, we employed several data cleaning procedures. First, we removed any duplicate or incomplete entries from the dataset. Next, we standardized the format of the remaining data to ensure consistency across different variables. For categorical variables, such as search frequency, we categorized the data into five ordered levels based on the actual number of searches. Finally, we identified and appropriately handled any outliers that could impact the analysis, maintaining the integrity of the dataset. As a result, these data are highly reliable and representative, providing a solid foundation for analyzing and revealing differences and similarities between groups.”

 

9.Section 3 needs to explain how BN approaches are viewed to be appropriate for addressing the *specific* research problem the paper addresses.

Response: 

Thank you for this valuable comment. In Section 3, we have provided additional explanation as to why the Bayesian Network (BN) approach is particularly well-suited to addressing the specific research questions of this study.The specific modifications are as follows:(page 5, paragraph 3, and line 234.)

“BN can effectively handle large sample data to determine causal relationships among variables related to online self-directed learning motivation. BN is capable of dynamically adjusting its network based on the data presented or input, enabling it to successfully manage large and multivariate datasets, as well as handle incomplete or uncertain knowledge or information[31, 32]. However, BN is less robust in theoretical interpretation compared to SEM[33, 34]. Therefore, some studies combine these two models to establish causal relationships[35, 36].”

Reference:

[31] Huang, Z.; Yang, L.; Jiang, W., Uncertainty measurement with belief entropy on the interference effect in the quantum-like Bayesian Networks. Applied Mathematics and Computation 2019, 347, 417-428.

[32] Suter, P.; Kuipers, J.; Beerenwinkel, N., Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks. Briefings in Bioinformatics 2022, 23, (4), bbac219.

[33] Anderson, R. D.; Mackoy, R. D.; Thompson, V. B.; Harrell, G., A Bayesian Network Estimation of the Service-Profit Chain for Transport Service Satisfaction. Decision Sciences 2004, 35, (4), 665-689.

[34] Anderson, R. D.; Vastag, G., Causal modeling alternatives in operations research: Overview and application. European Journal of Operational Research 2004, 156, (1), 92-109.

[35] Gupta, S.; Kim, H. W., Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities. European Journal of Operational Research 2008, 190, (3), 818-833.

[36] Díez-Mesa, F.; de Oña, R.; de Oña, J., Bayesian networks and structural equation modelling to develop service quality models: Metro of Seville case study. Transportation Research Part A: Policy and Practice 2018, 118, 1-13.

 

10. Section 4 should be renamed simply “Results”, since there is a separate Discussion section later. It should also start with some signposting: what will be covered in the section, and how will this allow the research problem to be addressed?

Response: 

Thank you for your valuable advice. We have revised the title of the fourth section. Additionally, at the beginning of the "Results" section, we have added a brief paragraph that outlines the specific research results presented in this section and explains how these findings address the research questions posed in this paper. (page 7, paragraph 2, and line 289.)

The specific modifications are as follows:

“This section presents the main results of the study, with a detailed focus on how personal characteristics, academic factors, and academic environment variables influence college students' motivation for online self-study. By analyzing large-scale sample data, we identify key relationships between these variables and online learning motivation, and examine the differences across various groups (e.g., gender, education level). These findings will serve as the foundation for the subsequent discussion, where we will further explore how these results address the research questions posed in this paper.”

 

11.Section 5 should make clear how the results of the paper contribute to the specific academic literature to which the paper is addressed (and which I have asked the authors to clarify in section 1).

Response: 

Thank you for this valuable comment. We have clarified the contribution of the results to the academic literature in the conclusion section. (page 14, paragraph 3, and line 497.)

The specific modifications are as follows:

“Based on the study's findings, future online education platforms can offer personalized learning incentives tailored to students' gender, age, and educational background, thereby enhancing their self-directed learning motivation. This study not only expands the current understanding of how demographic characteristics, economic incentives, and educational factors influence learning motivation, providing a foundation for further research in educational psychology and online learning, but also contributes to the sustainable development of online education in a globalized context.”

 

12.Section 7 needs to be clear how the future prospects outlined *relate to the actual results* of the paper.

Response: 

Thank you for your valuable advice. We recognize the importance of clearly linking the future prospects discussed in Section 7 to the actual results of our research. To address this, we have made several revisions to Section 7 to ensure that our findings directly inform the proposed future developments of online education platforms. (page 14, paragraph 4, and line 504.)

The specific modifications are as follows:

“The study identified several key factors that influence students' self-directed learning motivation in online environments, including gender, age, educational background, and the impact of various incentives. These findings offer valuable insights for the design and development of future online education platforms.

7.1 Personalized Learning Incentives

Based on our research results, future online education platforms can implement personalized learning incentives tailored to the specific characteristics of students. For example, gender-specific motivational strategies can be developed to address the unique learning preferences and challenges faced by male and female students. This is particularly important given the study's findings that gender plays a significant role in self-directed learning motivation. Similarly, age-appropriate rewards can be designed to align with the developmental stages and cognitive needs of different age groups. Additionally, rewards suited to students' educational backgrounds can be created to support varying levels of academic preparedness and experience, thereby enhancing self-directed learning motivation. By adopting these targeted approaches, online education platforms can more effectively engage students and foster a more productive learning environment.

7.2 Tiered Learning Experiences

The study also highlights the different motivational needs across educational levels (undergraduate, master’s, doctoral). Considering the volume of learning and research tasks at each stage, course designers and instructors are advised to implement tiered online learning experiences for undergraduates. For example, a virtual research community could be created for master’s students, including additional learning sections on research submissions and writing, serving as a reliable source for academic support, enabling students to receive timely feedback from their peers.

7.3 Comprehensive Support Systems

Recognizing the importance of various academic characteristics and environmental factors identified in the study, system developers can create multiple channels to facilitate students' help-seeking behavior, such as online Q&A forums or collaborative help-seeking tools embedded in learning management systems[49, 51]. Platforms can offer different types of rewards, such as incentives for users to reopen the app, significantly increasing the likelihood of repeated use. These platforms could combine rewards like points, levels, badges, tasks, and leaderboards with visual feedback to encourage ongoing participation and enhance students' motivation for online self-directed learning[52].”

Reference:

[49] Zhang, L., Age matters for girls: School entry age and female graduate education. Economics of Education Review 2022, 86, 102204.

[50] Garaus, C.; Furtmüller, G.; Güttel, W. H., The Hidden Power of Small Rewards: The Effects of Insufficient External Rewards on Autonomous Motivation to Learn. Academy of Management Learning & Education 2015, 15, (1), 45-59.

[51] Zhao, F.; Zhang, Z.-H.; Bi, L.; Wu, X.-S.; Wang, W.-J.; Li, Y.-F.; Sun, Y.-H., The association between life events and internet addiction among Chinese vocational school students: The mediating role of depression. Computers in Human Behavior 2017, 70, 30-38.

[52] Ong, M.; Smith, J. M.; Ko, L. T., Counterspaces for women of color in STEM higher education: Marginal and central spaces for persistence and success. Journal of Research in Science Teaching 2018, 55, (2), 206-245.

Reviewer 2 Report

Comments and Suggestions for Authors

The topic of the research is interesting and deserves separate consideration. But there are many questions about how the authors study Self-Learning Motivation and "subjective learning motives". As can be understood from the studied parameters, subjective learning motivation is expressed by the authors through Online search frequency. Frankly, this is very far. Motivation is a very complex concept, it consists of many factors and it is very controversial to casually consider that motivation can be measured using online search. And when studying motivation, using only obvious socio-demographic and geographic parameters to determine it is somehow frivolous. I think it is necessary to more clearly formulate what exactly the authors are studying.

Figure 2 is almost illegible and can be divided into several separate stages.

More detailed comments on the drawings are needed.

Figure 3 shows both obvious and strange relationships. In Figure 3, does gender affect age?

Author Response

Reviewer(s)' Comments to Author:

Reviewer 2:

 The topic of the research is interesting and deserves separate consideration. But there are many questions about how the authors study Self-Learning Motivation and "subjective learning motives".

Response: 

Thanks for the valuable comments. We have considered all the comments and suggestions carefully, and tried our best to revise the manuscript according to your comments.

For specific comments:

1.As can be understood from the studied parameters, subjective learning motivation is expressed by the authors through Online search frequency. Frankly, this is very far. Motivation is a very complex concept, it consists of many factors and it is very controversial to casually consider that motivation can be measured using online search. And when studying motivation, using only obvious socio-demographic and geographic parameters to determine it is somehow frivolous. I think it is necessary to more clearly formulate what exactly the authors are studying.

Response: 

Thank you for your valuable advice. We have thoroughly considered your comments regarding "online self-learning motivation" and the use of "online search frequency" as its measure, and we have made the corresponding adjustments and additions to the manuscript. In the revised version, we have clarified the definition of "online self-learning motivation" and included a review of relevant literature to highlight the importance of this concept. We have referenced Self-Determination Theory (Ryan & Deci, 2000) to support our understanding of motivation, particularly the roles of intrinsic and extrinsic motivation in online learning environments. A detailed discussion of this theory has been added to Section 2 of the manuscript. (page 2, paragraph 5, and line 92.)

Regarding the rationale for using online search frequency as a measure of motivation, we have expanded the discussion in Section 3.1 of the revised manuscript. We have explained why online search frequency was selected as a key indicator of self-learning motivation, based on the following points: (1) Behavioral Expression: Online search frequency can be viewed as an external manifestation of learning motivation, where students with stronger motivation typically exhibit more proactive learning behaviors[14,16]. (2) Data Accessibility and Immediacy: Online search frequency data is easily accessible and provides real-time insights into students' learning motivation, making it a feasible and meaningful indicator for large-scale studies [29].(page 4, paragraph 7, and line 188.)

To enhance the explanatory power of our model, we have revised the theoretical framework in Section 2 and the methodology in Section 3. We have detailed the hypothesized pathways between these factors and learning motivation and have cited relevant literature to support the validity of these pathways.

In the theoretical framework, we refer to classical theories such as Self-Determination Theory and Economic Incentive Theory to justify the selection of gender, age, education level, school location, and learning incentives as research variables. Additionally, we have established a comprehensive model to explain the factors influencing college students' motivation for online autonomous learning. (page 2, paragraph 5, and line 92.)

The specific modifications are as follows:

Section 2:

“2.1. Self-Determination Theory and Online Learning Motivation

Self-Determination Theory (SDT), developed by Ryan and Deci[14], is a widely recognized framework for understanding motivation within educational settings. SDT categorizes motivation into two types: intrinsic and extrinsic. Intrinsic motivation arises from the inherent enjoyment and interest in the activity itself, while extrinsic motivation is driven by external rewards or pressures. According to SDT, three basic psychological needs—autonomy, competence, and relatedness—must be satisfied to foster intrinsic motivation and optimal learning performance.

In the context of online autonomous learning, these needs are particularly critical. Autonomy refers to a student’s sense of control over their learning process, while competence refers to their confidence and ability to successfully complete online learning tasks. Research indicates that students with intrinsic or self-determined motivation typically achieve more positive learning outcomes [15, 16]; for example, they are better able to adapt to academic life, have lower levels of perceived stress, and can engage in more sustained learning [17, 18]. With the rapid advancement of technology, the manner in which people learn or acquire new knowledge has changed, and one major change is that an increasing number of students are choosing to receive education online [19]. Autonomy is a key feature in online learning [20, 21]; This surge in demand for online learning resources further underscores the importance of self-directed learning motivation in this new educational environment.

2.2. The Impact of Personal Characteristics on Online Learning Motivation

Students' personal characteristics, such as gender, age, and educational level, are closely related to their learning motivation [22]. A study found significant gender differences in self-directed learning motivation [20], where girls usually show a higher autonomous motivation to learn [23]; this trend may contribute to their higher academic achievement. Ethnicity is associated with student autonomy, and during distance learning throughout the pandemic, curiosity among Latino students enrolled in ethnic studies courses improved during the school year, although students’ stress and motivation were lower. Additionally, research has indicated that as people age, they tend to develop a stronger sense of autonomy; older individuals are better at internalizing their goals and personal initiatives than younger individuals.

2.3. The Role of Academic Characteristics in the Formation of Learning Motivation

Academic characteristics, such as school atmosphere and reward systems[24], play distinct roles in influencing students' motivation and online learning behaviors. Economic Incentive Theory posits that monetary rewards can significantly influence behavior by increasing the perceived benefits of specific actions[25, 26]. In educational settings, economic incentives have been shown to enhance student engagement, particularly in environments where motivation may be low. In online learning, where immediate guidance and timely feedback are often challenging to provide, learning incentive funds can serve as an effective external motivator, enhancing students' extrinsic motivation and thereby promoting greater engagement in their studies[13]. It is worth noting that reward mechanisms in the form of external incentives may also lead to learner overdependence, with various implications for human behavior [27]. In addition, a correlation exists between academic climate and academic performance. Negative school belonging has been noted to negatively impact intrinsic motivation and perceived learning [28]. Higher-level schools offer a more comprehensive curriculum, better-quality educational resources, and a learning environment suitable for the development of self-directed learning skills. Such schools tend to encourage active inquiry and self-directed learning processes, and they may provide more external resources and opportunities to promote the development of self-learning skills.

2.4. The Impact of Academic Environment on Learning Motivation

The academic environment, including school type, geographic location, and economic background, often influences the resources and opportunities available for self-directed learning. Schools’ geographic location and economic level typically correlate with access to richer online learning resources. In areas of higher economic development, schools are likely to have more resources available for pedagogical innovation, thus providing students with a wealth of learning material and environments that enhance their self-directed learning. Conversely, in less-affluent economies, the investment capabilities of schools and families in education may be constrained, potentially inhibiting the promotion of self-directed learning. Students from lower socioeconomic backgrounds and rural communities may require additional support to overcome educational challenges—particularly those exacerbated by the COVID-19 pandemic. Furthermore, the nature of a school (i.e., whether it is academic, technical-vocational, or comprehensive) influences the development of self-learning capabilities. Academic-oriented schools may place greater emphasis on theoretical knowledge and research skill development, thereby facilitating self-directed learning, whereas technical vocational schools may prioritize skills-based and practical learning experiences. Overall, the evolution of self-learning is affected by multiple factors: a school’s type, nature, and geographic location can directly or indirectly impact the distribution of educational resources, selection of educational strategies, and improvement of educational quality, all of which, in turn, shape students’ capacity for self-directed learning.

This study integrates principles from Self-Determination Theory and Economic Incentive Theory to develop a comprehensive model that explains the factors influencing college students' motivation for self-directed online learning. The model posits the following:

(1)Learning incentive funds, as an external motivating factor, can enhance extrinsic motivation while potentially fostering self-directed learning motivation in an online setting.

(2)Personal characteristics (such as gender and age), academic characteristics (such as educational background and reward systems), and the academic environment (such as school type and geographic location) significantly impact students' learning motivation, with these factors being particularly crucial in online learning environments.

(3)The interaction among these variables is expected to reveal how different groups  respond to the challenges and rewards of online learning.”

Section 3:“In our study, we used the online search frequency of students on the learning platform as a key indicator of online self-directed learning motivation. Online search frequency refers to the rate at which students actively search for learning resources and information through the platform during their studies. This behavior reflects students' willingness and ability to actively acquire knowledge, making it an external manifestation of self-directed learning motivation[16, 29]. Furthermore, online search frequency data is easily accessible and can provide real-time insights into students' learning motivation, making it a feasible and meaningful indicator for large-scale studies.”

Reference:

[14] Ryan, R. M.; Deci, E. L., Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist 2000, 55, (1), 68-78.

[15] Pan, Y.; Gauvain, M., The continuity of college students’ autonomous learning motivation and its predictors: A three-year longitudinal study. Learning and Individual Differences 2012, 22, (1), 92-99.

[16] Keller, J. M., First principles of motivation to learn and e3-learning. Distance Education 2008, 29, (2), 175-185.

[17] Gottfried, A. E., Chapter Three - Academic Intrinsic Motivation: Theory, Assessment, and Longitudinal Research. In Advances in Motivation Science, Elliot, A. J., Ed. Elsevier: 2019; Vol. 6, pp 71-109.

[18] Lin, Y.G.; McKeachie, W. J.; Kim, Y. C., College student intrinsic and/or extrinsic motivation and learning. Learning and Individual Differences 2003, 13, (3), 251-258.

[19] Xu, Z.; Zhao, Y.; Liew, J.; Zhou, X.; Kogut, A., Synthesizing research evidence on self-regulated learning and academic achievement in online and blended learning environments: A scoping review. Educational Research Review 2023, 39, 100510.

[20] Zhang, Z.; Maeda, Y.; Newby, T., Individual differences in preservice teachers’ online self-regulated learning capacity: A multilevel analysis. Computers & Education 2023, 207, 104926.

[21] Chiu, T. K. F., Applying the self-determination theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education 2022, 54, (sup1), S14-S30.

[22] Steinmayr, R.; Lazarides, R.; Weidinger, A. F.; Christiansen, H., Teaching and learning during the first COVID-19 school lockdown: Realization and associations with parent-perceived students' academic outcomes. Zeitschrift für Pädagogische Psychologie 2021, 35, (2-3), 85-106.

[23] Vantieghem, W.; Van Houtte, M., Differences in Study Motivation Within and Between Genders: An Examination by Gender Typicality Among Early Adolescents. Youth & Society 2015, 50, (3), 377-404.

[24] Brownback, A.; Sadoff, S., Improving College Instruction through Incentives. Journal of Political Economy 2020, 128, (8), 2925-2972.

[25] Murayama, K.; Kitagami, S., Consolidation power of extrinsic rewards: reward cues enhance long-term memory for irrelevant past events. Journal of experimental psychology. General 2014, 143, (1), 15-20.

[26] Murayama, K.; Matsumoto, M.; Izuma, K.; Matsumoto, K., Neural basis of the undermining effect of monetary reward on intrinsic motivation. Proceedings of the National Academy of Sciences of the United States of America 2010, 107, (49), 20911-6.

[27] Ding, X.-H.; He, Y.; Wu, J.; Cheng, C., Effects of positive incentive and negative incentive in knowledge transfer: carrot and stick. Chinese Management Studies 2016, 10, (3), 593-614.

[28] Ferreira, M.; Cardoso, A. P.; Abrantes, J. L., Motivation and Relationship of the Student with the School as Factors Involved in the Perceived Learning. Procedia - Social and Behavioral Sciences 2011, 29, 1707-1714.

[29] Wilson, T. D., Models in information behaviour research. Journal of Documentation 1999, 55, (3), 249-270.

 

2.Figure 2 is almost illegible and can be divided into several separate stages.

Response: 

Thank you for this valuable comment. To improve clarity, we have divided Figure 2 into two separate stages, making the information easier to understand and interpret. The revised figures are now included in the updated manuscript.  (page 9, paragraph 2, and line 328.)

The specific modifications are as follows:

Figure 2. (a) An initial BN model using the PC algorithm; (b)An initial BN model using the HC algorithm

Figure 3. Key Networks

 

3.More detailed comments on the drawings are needed.

Response: 

Thank you for your valuable advice. We have added more detailed comments and explanations to Figures 2, 3, 4, 5, and 6. These additional details provide a clearer picture of the relationships and data presented in each figure, helping readers better understand the findings and their implications. 

 

4.Figure 3 shows both obvious and strange relationships. In Figure 3, does gender affect age?

Response: 

Thank you for this valuable comment. To clarify, the relationship between gender and age in the original chart was not intended to suggest a cause-and-effect relationship. However, we recognize that this may have caused confusion. We have revised Figure 3 (now Figure 4) to better represent the intended relationship and ensure that it accurately reflects the data.  (page 11, paragraph 1, and line 364.)

The specific modifications are as follows:

Figure 4. Diagram of the SEM

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I would like to thank the authors for the very diligent way in which they have addressed the previous round of comments. Many improvements have been made to the paper. But the most important ones are the clarification of the theoretical framework of the paper, and the justification of the key research measures based on this framework.

The previous version of the paper was based on a strong empirical analysis but had a rather unclear argument. The authors have *significantly* re-written several sections of their paper and now this version of the paper is much, much stronger. This new version addresses the many issues I had with the first version.

Congratulations to the authors!