Unveiling Key E-Learning Ingredients for Enhancing Higher-Order Thinking Skills

Unveiling Key E-Learning Ingredients for Enhancing Higher-Order Thinking Skills | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unveiling Key E-Learning Ingredients for Enhancing Higher-Order Thinking Skills Yan Piaw Chua, Fung Ying Loo, Fung Chiat Loo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6061211/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The existing e-learning models for higher-order thinking skills (HOTS) enhancement focus more on technology and e-learning methods, ignoring the important roles of human factors such as e-leadership, collaboration, and readiness. This exploratory sequential mixed-methods design study aimed to identify significant factors for e-learning practices that enhance HOTS. Semi-structured interviews were conducted with school administrators, teachers, students, parents, and school software experts, and the transcripts were analyzed using ATLAS.ti. Seven core factors emerged from the study: collaboration, readiness, e-leadership, personal factors, strategies, practices, and organizational factors. Their associations were verified through a quantitative survey involving 430 secondary school teachers. The quantitative data was analyzed using PLS-SEM in SMARTPLS 4, resulting in five sub-models defining a HOTS enhancement framework for schools e-learning. E-learning strategy appeared to be the most important factor for HOTS enhancement, followed by organizational factors, collaboration, personal factors, e-leadership and readiness. Besides that, the Necessary Condition Analysis and Importance-performance Map analysis revealed that the current role of e-leadership in schools is only 31%, although 96% e-leadership is required to maximize HOTS enhancement. This highlights the critical role of school leaders and teachers in leveraging e-leadership within e-learning platforms. This research provides a model that educational leaders, policymakers, and educators can adopt to enhance HOTS in e-learning. e-learning higher order thinking skills e-learning practices secondary school quality education Figures Figure 1 Figure 2 Figure 3 1. Introduction The increasing usage of e-learning platforms in schools provides opportunities for students to improve their higher-order thinking skills (HOTS) [ 4 ]. Although e-learning in schools is capable of improving student knowledge [ 34 ], its effectiveness in fostering HOTS remains limited and unclear [ 23 ], as the existing e-learning models focus more on technological tools and teaching methods, which often ignore the important roles related to human factors such as e-leadership, collaboration, and readiness [ 20 ]. This situation has resulted in the inconsistent effectiveness of e-learning in fostering HOTS, where e-leadership, collaboration, and readiness factors of administrators, teachers, students, and parents are less emphasized, leading to the practice of e-learning not being maximized [ 30 ]. Although the cultivation of students’ HOTS has been the main focus in schools, the limited understanding of school practitioners, administrators, teachers, parents, and students about the factors that drive HOTS in e-learning platforms is insufficient due to the lack of a framework that links all factors that can foster students' higher-order thinking skills [ 15 ]. Without comprehensive knowledge, educators and policymakers have difficulty designing e-learning strategies that can effectively maximize HOTS among students. Therefore, this study aims to address the above knowledge gap by investigating the key factors that can foster HOTS in e-learning from the perspective of various parties who are directly and indirectly involved with e-learning platforms in schools. The results of this study would suggest actions for educational policymakers, school leaders, and e-learning platform developers to provide a more conducive e-learning environment for fostering HOTS among students. 2. Literature review Several e-learning models has been used to guide e-learning process. These models integrate teaching with technology to enhance e-learning. For instances, the SAMR model, the TPACK model, and the PTEACES model. The models focus more on integrating teaching methods and the use of technology [ 29 ], and less emphasis on the dynamics between schools, leaders, teachers and students, which is highly important for the development of students’ high order thinking [ 5 ]. For example, the SAMR model [ 32 ] emphasizes linking technology to pedagogy but does not address how technology can be utilized to enhance higher-order thinking skills [ 6 ]. The TPACK model [ 28 ] focuses on the intersection of technological knowledge, pedagogical knowledge and content knowledge but overlooks human factors, such as students’ motivation, attitude, engagement and commitment which influence cognitive and mental development in e-learning [ 40 ]. Similarly, the PTEACES model [ 25 ] focuses on e-learning environment itself. Although this model integrates technology with teaching methods and is student-centered, the model neglects the role of e-leaders and organizational readiness in developing higher-order thinking skills among students. Meanwhile, the focuses of most recent studies (see Table 1 ) include the impact of discussion forum in e-learning [ 14 ], the integration of gamification in e-learning project [ 17 ], the effect of peer online teaching tools [ 31 ], the role of multimedia tools [ 26 , 42 ], the impacts of project-based learning [ 10 , 41 ], the uses of collaborative tools [ 2 ] and the effectiveness of interactive platforms [ 24 ]. Besides the limitations of small sample sizes, demography constraints and subjective data collection methods, the previous studies fail to address the knowledge gaps, to provide a larger picture for our understanding on how higher-order thinking skills be cultivated through e-learning. Table 1 Recent research on enhancing higher order thinking skills through e-learning Authors (year) Research method and sampling Findings Firdaus et al. (2024) [ 14 ] Quasi-experimental design; third-year university students; cluster random sampling. Discussion forums in e-learning improved critical thinking. Engagement and collaboration are significant factors for e-learning. Gejandran and Abdullah (2024) [ 17 ] Reviewed 37 studies published in the past decade, between 2014 and 2023. Gamification in e-learning improved student motivation and engagement levels. Game elements enhance HOTS. Zunaidah and Asih (2024) [ 42 ] Design and development research; 52 English learners (N = 52); convenience sampling E-learning with multimedia tools improved student engagement and critical thinking in an online English language course. Yu (2024) [ 41 ] An extensive literature review on Project-based learning, including e-learning. Project-based e-learning modules significantly enhanced both creative and critical thinking. Ekayana et al. (2024) [ 13 ] Quasi-experimental pretest-post-test non-equivalent control group design; 150 university engineering students; purposive sampling. High academic self-efficacy in e-learning have higher learning achievement and creative thinking skills than students with low academic self-efficacy. Irwan (2024) [ 24 ] Experimental study; vocational high school students; convenience sampling. Interactive video-based e-learning improved critical thinking skills. Alharbi et al. (2022) [ 2 ] Experimental study; 200 female students of kindergarten department in a university; random assigned into experimental and control groups. E-collaborative learning environment had significant and positive effect on development of critical thinking. Page (2022) [ 31 ] Survey; participants were pre-service teacher education students enrolled in an online university course (N = 625); purposive sampling. Peer online teaching tool enhanced HOTS in students. Cortazar et al. (2021) [ 10 ] Experimental study; 834 students at an engineering school Online project-based learning fostered the development of critical thinking. Lee & Choi (2017) [ 27 ] Survey study, 487 undergraduates; random sampling. Readiness in terms of epistemological beliefs, approach to learning, and attitudes toward technology use affected higher-order thinking in technology-enhanced learning environments. Kassim (2013) [ 26 ] Quasi-experimental study with one group pre-test post-test design; Mechanical engineering undergraduates (N = 32; 97% male). Multimedia learning tool enhanced creative thinking of active, reflective, intuitive and high visual students. 3. The study The aim of this exploratory sequential mixed methods design study was to develop a model of e-learning practices for enhancing higher order thinking skills among secondary school students in e-learning environments. The study was first carried out with a qualitative study. Semi-structured interviews were conducted on school administrators, teachers, students, parents, and software experts to gather information about the implementation and practices of e-learning in schools. The qualitative transcripts were then analyzed using the ATLAS.TI software to identify significant indicators and themes, and their associations for HOTS enhancement in e-learning. The study was then followed by a quantitative survey on 430 school teachers to validate the model emerged from the qualitative study. The model was analyzed to examine the importance and performances of the variables in the model, as well as the levels of each variable necessary for achieving quality outcome for HOTS enhancement. The quantitative data was analyzed with PLS-SEM and Necessary Condition Analysis using SMARTPLS 4 software. 3.1 The qualitative study 3.1.1 Participants The study used theoretical sampling to select key participants with direct and indirect experience in a school e-learning platform. The sample included five school administrators, five teachers, five students, five parents, and five software experts. This diverse groups were chosen to provide rich insights into the e-learning practices in secondary schools in enhancing HOTS. 3.1.2 Research Instruments Three inventories were created for data collection: The School Administrators Inventory which focused on e-leadership and management roles, the Teacher, Student, and Parent Inventory , focused on personal and organizational factors, and the Software Expert Inventory , focused on technical aspects of the e-learning platforms. They covered planning, implementation, support, challenges, collaboration and recommendations for improving e-learning practices in schools through the e-learning platforms to enhance higher order thinking skills in students. 3.1.3 Qualitative Data Analysis The transcripts of the interview data were first coded by open coding using the ATLAS.ti software and break down into manageable indicators. Axial coding was then used to identify similarities and differences between the indicators to investigate how the data are gathered in categories of data and associated into themes, to explain the data in a meaningful way. Through the axial coding process, eight key themes emerged from thirty-eight indicators in the qualitative transcripts: collaboration, e-learning readiness, e-leadership, personal factors, e-learning strategies, e-learning practices, organizational factors and quality outcomes (see Table 2 ). Selective coding was then used to identify the associations between the eight themes. By referring to the Spradley’s semantic relations criteria [ 38 ], i.e. , strict inclusion (A is a property of B), spatial (A is part of B), cause-effect (A is cause of B), and rationale (A is an outcome of B), the associations between the themes were identified (see Fig. 1 ). 3.2 The quantitative study 3.2.1 Participants Quantitative survey data were gathered from 430 secondary school teachers who are the instructors cum practitioners of the e-learning platforms in schools, with an average age of 39.2 years. Using teachers as participants has advantages because teachers implement the e-learning platforms, assess the progress and behaviors of students in e-learning, and they also act as mediators between school principals, students and parents in e-learning platforms. 3.2.2 Survey questionnaire The survey questionnaire utilized in this study was divided into two sections: one focusing on demographic variables and the other addressing eight key variables in the model developed from interview data. The questionnaire comprised a total of 38 items, which were derived from the themes identified during the semi-structured interviews, as detailed in Table 1 . For instance, the first item related to Collaboration (CO1) states, “ Design collaborative e-learning curriculum-related activities for fostering HOTS .” The questionnaire utilized a continuous measurement scale ranging from 0 to 10, where “0” represented “completely disagree” and “10” indicated “completely agree” with each statement related to enhancing higher order thinking skills through e-learning. This scale was selected for its precision, allowing for mathematical operations essential for PLS-SEM analysis [ 21 ]. Cohen, et al. [ 8 ] suggested using a continuous 0 to 10 interval scale for confirmatory factor analysis to establish validity and reliability of the items, while Awang [ 3 ] supported the use of an 11-point scale, which meets the requirements for ratio measures, ensuring valid and reliable statistical analysis. The items were validated by three experts in educational psychology research. Thereafter a pilot study involved 100 respondents who did not participate in the actual study was conducted. Their responses were analyzed using exploratory factor analysis (EFA) to organize the items into constructs. Employing principal component analysis with varimax rotations, the 38 items were arranged into the eight constructs. Additionally, internal consistency reliability analysis was conducted, revealing high reliability coefficients for the constructs (Cronbach's alpha: collaboration = 0.82, readiness = 0.89, e-leadership = 0.87, personal factors = 0.91, strategies = 0.89, practices = 0.85, organizational factors = 0.85, and quality outcomes = 0.84). 3.2.3 Quantitative Data analysis Data analysis for the quantitative study was conducted using SMARTPLS 4 for: (1) confirmatory factor analysis (CFA), (2) model fit analysis, (3) importance-performance map analysis (IPMA), and (4) necessary condition analysis (NCA). CFA was used to examine the validity and reliability of the items of the eight variables; Model fit analysis was used to identify whether the relationships between the variables in the model are valid, and to determine whether further analysis is needed. IPMA was used to identify the importance and performance of the variables for HOTS enhancement through e-learning, and NCA was used to examine the levels of each of the variables needed for maximizing the quality outcome of HOTS enhancement. 4. Results 4.1 Confirmatory factor analysis Confirmatory factor analysis (CFA) was conducted using PLS-SEM algorithm to assess convergent validity, construct validity, construct reliability, discriminant validity, and multicollinearity of the eight measurement models to ensure the items accurately represented the model's constructs. The results in Table 3 show that all indicator loadings met the benchmark of convergent validity (loadings ≥ 0.70). Besides that, construct validity (AVE > 0.50, ranging from 0.51 to 0.68) and construct reliability (Cronbach's alpha and composite reliability > 0.70, ranging from 0.821 to 0.913) were achieved for all the eight reflective measurement models. Table 3 Construct Reliability and Construct Validity Construct reliability Construct validity Cronbach's alpha Composite reliability Average variance extracted (AVE) Quality outcome 0.85 0.85 0.54 Readiness 0.82 0.82 0.64 Personal factors 0.91 0.91 0.68 Collaboration 0.82 0.82 0.51 E-Learning Practices 0.84 0.85 0.58 E-Learning Strategies 0.89 0.89 0.60 E-leadership 0.87 0.87 0.59 Organizational factors 0.85 0.85 0.59 Discriminant validity analysis was then conducted. HTMT assesses the true correlations between the measurement models. It is a reliable tool for assessing discriminant validity [ 21 ]. In this case, discriminant validity achieved in which no overlapping of concepts were found between the eight measurement models, with HTMT values < 0.90, ranging from 0.25 to 0.74 (see Table 4 ). The results ascertained that discriminant validity achieved for all the constructs in the model. Table 4 Heterotrait-monotrait Ratio (HTMT) 1 2 3 4 5 6 7 1. Collaboration 2. E-Learning Practices 0.59 3. E-Learning Strategies 0.54 0.65 4. E-leadership 0.69 0.55 0.57 5. Organizational factors 0.71 0.64 0.55 0.61 6. Personal factors 0.40 0.51 0.44 0.36 0.53 7. Quality outcome 0.47 0.51 0.50 0.67 0.40 0.26 8. Readiness 0.67 0.50 0.57 0.74 0.60 0.25 0.52 Furthermore, collinearity analysis was conducted to assess whether multi-collinearity occurs among the indicators in each measurement model. The results show that multicollinearity did not occur among all indicators in each of the eight measurement models, with VIF values < 5.0, ranging from 2.11 to 4.19. 4.2 Model fit Model fit analysis was then analyzed to examine whether the relationships in the model are valid, that is, whether the model fits the data collected from sample of the study. The results show in Table 5 that model fit was confirmed, with SRMR = 0.061, D ULS = 0.342, D G = 1.228 and NFI = 0.913. Table 5 Model fit indices and the results of model fit testing Model fit indices Benchmarks Estimated model Model fit testing SRMR ≤ 0.08 0.061 Model fit achieved D ULS ≥ 0.05 0.342 Model fit achieved D G ≥ 0.05 1.228 Model fit achieved NFI ≥ 0.90 0.913 Model fit achieved Note: Model fit indexes were generated using PLS-SEM in SMARTPLS 4. 4.3 Effects between the variables in the model After ascertained that all the indicators of the eight measurement models achieved construct validity, construct reliability and discriminant validity; multi-collinearity does not occur between the models and among the indicators; and model fit achieved, PLS-SEM bootstrapping analysis was conducted to examine the relationships between the variables. The results in Fig. 2 show that all path coefficients in the model are significant (p < 0.01). The results support the findings of the qualitative study. The final model depicted in Fig. 2 consists of the quality outcome for HOTS enhancement variable with its seven inter-related core factors. Quality outcome is directly influenced by e-learning practices, where a one-unit change in e-learning practices would cause a 0 514-unit increase or 26.4% of variance change in quality outcome (β = 0.514, p < 0.01; R 2 = 0.264). Furthermore, e-learning practices plays the role of a full mediator in creating the indirect effects of its three core factors, namely, e-learning strategies, organizational factors and collaboration on quality outcome. This means with right e-learning strategies (β = 0.422, p < 0.01), positive organizational factors (β = 0.252, p < 0.01) and excellent collaboration between the stakeholders (β = 0.212, p < 0.05), e-learning practices would be maximized to nearly 53.4% (R 2 = 0.534, large effect size). In addition, e-learning strategies also has three direct factors, namely, e-leadership (β = 0.208, p < 0.01), personal factors (β = 0.271, p < 0.01) and readiness (β = 0.384, p < 0.01), where readiness plays the main role, followed by personal factors and e-leadership, and the three factors contribute 42.2% of e-learning strategies (R 2 = 0.422, large effect size). This means to improve e-learning strategies, readiness of schools, teachers and students for enhancing HOTS through e-learning, personal factors, and e-leadership of school leaders in enhancing HOTS through e-learning are needed to maximize e-learning strategies and to stimulate a conducive culture. The following are five sub-models derived from the results in Fig. 2 . These sub-models are the basics of the e-learning HOTS enhancement model. The sub-models serve as useful guides for improving higher order thinking skills in schools through e-learning platforms. Sub-model 1 : Quality outcome - HOTS enhancement = 0.514 E-learning practices R 2 = 0.264 (large effect) Sub-model 2 : E-learning practices = 0.422 E-learning strategies + 0.252 Organizational factors + 0.212 Collaboration R 2 = 0.534 (large effect) Sub-model 3 : E-learning strategies = 0.384 Readiness + 0.271 Personal factors + 0.208 E-leadership R 2 = 0.422 (large effect) Sub-model 4 : Collaboration = 0.524 E-leadership + 0.283 E-learning strategies R 2 = 0.466 (large effect) Sub-model 5 : Readiness = 0.251 Personal factors R 2 = 0.053 (small effect) Note Effect sizes for social science data: R 2 : 0.01 = small effect, 0.09 = medium effect, 0.25 = large effect (Source: Gravetter & Wallnau, 2009) After the model was finalized, the next steps were to assess the importance, performances and the levels of the factors that needed to maximize e-learning HOTS enhancement. 4.4 Importance and performance map analysis The importance and performance of the six direct and indirect factors of e-learning practices towards HOTS enhancement was conducted by using the Importance-Performance Map Analysis (IPMA) . IPMA highlights the total effects, representing the importance of the six factors of e-learning practices in influencing quality outcome for HOTS enhancement in the model, with the average scores indicating their performance [ 21 ]. Through IPMA, the performance scores of all variables in the model are rescaled to standardized scores of 0 to 100% for comparisons. The results in Table 6 show that the most important factor for e-learning practices for HOTS enhancement is e-learning strategies (0.376), which is located at the rightmost side in Fig. 3 . It was followed by organizational factors (0.188), collaboration (0.176), personal factors (0.150), e-leadership (0.070) and finally readiness (0.016). Furthermore, the current performances of the constructs are between 31.021% (e-leadership) to 52.045% (personal factors). This shows that there is ample room to improve e-learning strategies for HOTS enhancement. The performances of the six constructs can be improved by 47.955–68.979%. Table 6 Construct importance and performance for e-learning practices Importance (effect) Performance (%) E-Learning Strategies 0.376 39.004 Organizational factors 0.188 45.865 Collaboration 0.176 44.605 Personal factors 0.150 52.045 E-leadership 0.070 31.021 Readiness 0.016 37.468 4.5 Necessary condition analysis Since there were ample rooms for enhancing the factors of HOTS enhancement, the final step is to determine the minimum levels of each of the factors needed for the maximizing quality outcome of HOTS enhancement. It can be examined with the bottleneck table of necessary condition analysis [ 12 , 35 , 36 ]. The bottleneck table presents the necessity relationship of the minimum levels of the necessary conditions or the factors which are necessary for achieving the levels of quality outcome. By reading the bottleneck table row by row from left to right, the necessary conditions for any levels of the outcome variable can be easily identified. On the left column in Table 7 , the levels of quality outcome to be achieved are expressed as percentages of the range between 0 to 100. Table 7 Bottleneck table for HOTS enhancement Quality outcome for HOTS enhancement Collaboration E-leadership Organizational factors Personal factors Practices Strategies Readiness 0% NN NN NN NN NN NN NN 10% NN NN NN NN NN NN NN 20% NN NN NN NN NN NN NN 30% NN NN NN NN NN NN NN 40% 3.578 NN NN NN 4.143 5.838 NN 50% 6.591 8.475 NN NN 9.228 11.488 NN 60% 12.618 33.898 2.637 NN 16.384 17.891 5.461 70% 17.137 57.627 8.286 4.331 23.917 31.262 13.936 80% 25.612 76.836 16.573 10.923 32.203 45.574 23.540 90% 32.957 87.571 26.930 19.021 43.503 56.685 35.970 100% 40.678 95.857 42.750 31.450 55.556 67.797 46.893 Note: NN = not necessary Table 7 shows that to maximize quality outcome for HOTS enhancement to 100%, the following minimum criteria must be met: 40.678% of collaboration, 95.857% of e-leadership, 42.750% of organizational factors, 31.450% of personal factors, 55.556% of e-learning practices, 67.797% of e-learning strategies and 46.893% of readiness. In this case, HOTS enhancement will not achieve the maximum of 100% if any of the seven necessary conditions is not achieved. 5. Discussion The e-learning HOTS enhancement model generated from the study has a limited scope and is not intended to establish a universal standard. However, it can serve as a reference for schools using e-learning platforms to enhance e-leadership practices for higher order thinking skills enhancement. Practically, this research offers a framework that educational leaders, policymakers, and educators can adopt to enhance HOTS in e-learning. Schools can leverage findings on collaboration, personal and organizational factors, readiness, and e-leadership to design programs and policies that encourage higher order thinking skills in e-learning. Training programs for teachers and administrators can focus on e-leadership and innovative e-learning strategies, ensuring institutional support and structured development of thinking skills. Additionally, the research highlights the importance of creating supportive policies and partnerships with external organizations, which can provide resources and expertise to overcome e-learning obstacles. The study contributes to existing theories by expanding on the Community of Inquiry model [ 16 ], which focuses on cognitive, social, and teaching presence, but neglects institutional and e-leadership factors critical for successful e-learning. It accomplishes the TPACK model [ 28 ], the SAMR model [ 32 ] and the PTEACES model [ 25 ] by addressing personal, organizational and collaboration factors. Integrating these factors provides a more holistic model for developing HOTS in e-learning. By addressing these additional factors, this model fills theoretical gaps in understanding the key factors and their relationships for enhancing HOTS in e-learning platforms. Five sub-models emerged from the data, defining the model of HOTS enhancement through e-learning in schools. To maximize the quality of e-learning HOTS enhancement, schools should maximize the use of discussions and forums to enhance HOTS, incorporate multiple simulations in e-learning platforms to develop HOTS, use varied assessment methods focusing on HOTS, apply continuous assessments in e-learning practices for HOTS enhancement, and share best e-learning practices for developing HOTS among teachers. (refer to Sub-model 1) To maximize e-learning practices for HOTS enhancement, schools should implement effective e-learning strategies that include using inquiry-based approaches in e-learning to foster higher-order thinking skills, accepting different views in e-learning to enhance HOTS, integrating creative multimedia resources in e-learning to enhance HOTS, and developing interdisciplinary e-learning projects focusing on HOTS. Besides that, in terms of organizational factors, schools should provide sufficient technical support for improving HOTS in e-learning, provide sufficient institutional backing for professional development focused on HOTS, provide HOTS online resources for teachers, cultivate community engagement to support HOTS initiatives, implement supportive policies from educational authorities to promote HOTS among students, and develop partnerships with external organizations that facilitate students’ HOTS development. Furthermore, schools must try to maximize collaboration between stakeholders, including maximizing feedback loops to promote HOTS in students, designing group problem-based e-learning projects that enhance HOTS, designing collaborative e-learning curriculum-related activities for fostering HOTS, and establishing e-learning working committees focused on HOTS. (refer to Sub-model 2) To maximize e-learning strategies for HOTS enhancement, schools must ensure that the readiness of schools, their e-learning facilities, and their stakeholders are at the highest levels by implementing the following: enhancing students’ knowledge of HOTS in e-learning, regularly evaluating school technology infrastructures for HOTS enhancement, creating school policies that promote HOTS through e-learning, implementing training courses focused on fostering HOTS, encouraging mindset changes among students, teachers, and school administrators, and cultivating a positive attitude among students, teachers, and school administrators towards e-learning. Besides that, schools must consider personal factors, including promoting positive attitudes through interactive HOTS activities, addressing students’ needs related to HOTS development, encouraging students’ involvement in HOTS participation, fostering teachers’ commitment to e-learning platforms that promote a culture of higher-order thinking skills, and encouraging HOTS through self-directed e-learning among students. Finally, e-leadership in schools should be maximized by providing e-leadership training for administrators and teachers to improve their knowledge of enhancing HOTS through e-learning, encouraging innovative practices to foster HOTS, developing a mission and vision for integrating HOTS into e-learning, and promoting leadership initiatives that support HOTS. (refer to Sub-model 3) To maximize collaboration between stakeholders, e-leadership should be implemented to its maximum by developing a mission and vision for integrating HOTS into e-learning, providing e-leadership training, promoting leadership initiatives that support HOTS, and encouraging innovative practices to foster HOTS. Besides that, schools should implement the following e-learning strategies: integrating creative multimedia resources in e-learning to enhance HOTS, using inquiry-based approaches in e-learning to foster HOTS, accepting different views in e-learning to enhance HOTS, and developing interdisciplinary e-learning projects focusing on HOTS. (refer to Sub-model 4) To effectively enhance e-learning readiness, schools, teachers, and parents must fully consider students’ needs related to HOTS development, promote positive attitudes through interactive HOTS activities, encourage students’ involvement in HOTS participation, foster teachers’ commitment to e-learning platforms that cultivate a culture of HOTS, and encourage HOTS through self-directed e-learning among students. (refer to Sub-model 5) An interesting and significant finding from the necessary condition analysis is that at least 95.85% of e-leadership is required to achieve the maximum 100% of HOTS enhancement in e-learning. This underscores the critical roles of school leaders and teachers in utilizing e-leadership within e-learning platforms to enhance HOTS. Most importantly, the results of IPMA analysis showed that the current level of e-leadership in schools is only at 31%, highlighting an urgent need to maximize e-leadership efforts in e-learning platforms to reach optimal levels. Nevertheless, the NCA results suggested that all the seven factors are directly or indirectly interrelated, and none of them can be ignored in maximizing HOTS among students in e-learning. HOTS enhancement will not reach its maximum if one of the seven necessary factors is not achieved. 6. Conclusion In conclusion, this research provides a comprehensive picture for enhancing higher order thinking skills among students in e-learning platforms. Collaboration, e-learning readiness, e-leadership, personal and organizational factors, supports and readiness emerged as essential factors for fostering HOTS, with practical and theoretical contributions that expand existing educational frameworks. By addressing institutional, personal, and practical dimensions of e-learning, this study provides an actionable framework for enhancing HOTS in secondary schools. Future research could explore the application of this model in other contexts to validate its adaptability and effectiveness in diverse educational settings. Declarations Acknowledgements Special thanks to the Institute of Research Management and Monitoring, University of Malaya, for expertly managing the grant. Funding This work was funded by the Fundamental Research Grant Scheme (FRGS), Ministry of Higher Education, Malaysia. Grant number: FP023-2018A. The authors declare no conflicts of interest related to this project. Author information Authors and Affiliations Chua Yan Piaw , PhD, Professor “Corresponding Author” Affiliations: Faculty of Social Sciences and Liberal Arts, UCSI University, Kuala Lumpur, Malaysia. Email: [email protected] Faculty of Education, Universiti Malaya, Kuala Lumpur, Malaysia. Email: [email protected] Loo Fung Ying , PhD Associate professor and Deputy Dean (Postgraduate Studies, Research & Innovation) Affiliation: Faculty of Creative Arts, Universiti Malaya. Kuala Lumpur, Malaysia. Email: [email protected] Loo Fung Chiat , PhD, Associate professor Affiliation: Faculty of Human Ecology, Universiti Putra Malaysia. Email: [email protected] Author contribution statement Chua Yan Piaw contributed to the study conception and design. The draft of the manuscript was written by Chua Yan Piaw. Loo Fung Ying and Loo Fung Chiat commented on the manuscript. All authors approved the final manuscript. Corresponding author Chua Yan Piaw Ethics declarations Ethics approval and consent to participate This study was ethically approved for its general characteristics the Ethics Committee of the University of Malaya, Malaysia. All methods were carried out in accordance with relevant guidelines and regulations. Informed consent Informed consent was obtained from all individual participants included in the study. Competing interests The authors declare no competing interests. Data Availability Statement The datasets of the current study are not publicly available due to funding restrictions but may be available upon reasonable request. References Akpen, C.N., Asaolu, S., Atobatele, S., Okagbue, H., & Sampson, S. (2024). Impact of online learning on student's performance and engagement: a systematic review. Discovery Education, 3 , 205. https://doi.org/10.1007/s44217-024-00253-0 Alharbi, S. M., Elfeky, A. S., & Ahmed, E.S. (2022). The effect of e-collaborative learning environment on development of critical thinking and higher order thinking skills. Journal of Positive School Psychology, 6 (6), 6848–6854. https://journalppw.com/index.php/jpsp/article/view/8692 Awang, Z. (2014). A handbook on structural equation modeling . MPWS Rich Resources. Barikzai, S., Bharathi S, V., & Perdana, A. (2024). Challenges and strategies in e-learning adoption in emerging economies: a scoping review. Cogent Education, 11 (1). https://doi.org/10.1080/2331186X.2024.2400415 Chiu, Y. Y. (2021). Effectiveness of Online Learning Relies on the Dynamic Between Teachers and Students. Journal of E-Learning Research, 1 (4), 61–81. https://doi.org/10.33422/jelr.v1i4.237 Chris, B., Michelle, M., & Shaun, N. (2022). A scoping review of the application of the SAMR model in research. Computers and Education Open, 3 : 100093. https://doi.org/10.1016/j.caeo.2022.100093 Chua, Y. P., Tie, F. H., & Don, Z. M. (2013). Creating an Education Research Acculturation Theory for Research Implementation in School. Education and Urban Society, 45 (4), 506–526. https://doi.org/10.1177/0013124511413124 Cohen, L., Manion, L., & Morrison, K. (2018). Research Methods in Education , (8th ed.). Routledge. Corbin, J., & Strauss, A. (2008). Basics of qualitative research: techniques and procedures for developing grounded theory (3rd ed .). Sage Cortazar, C., Nussbaum, M., Harcha, J., Alvares, D., Lopez, F., Goni, J., & Cabezas, V. (2021). Promoting critical thinking in an online, project-based course. Computers in Human Behavior, 119, Article 106705. https://doi.org/10.1016/j.chb.2021.106705 Creswell, J. W., & Guetterman, T. (2024). Educational research: Planning, conducting, and evaluating quantitative and qualitative research , (7th ed.) . Pearson. Dul, J. (2015). Necessary Condition Analysis (NCA): Logic and methodology of necessary but not sufficient causality. Organizational Research Methods, 19 (1), 10–52. https://doi.org/10.1177/1094428115584005 Ekayana, A. A. D., Parwati, N. N., Agustini,K., & Ratnaya, I. G. (2024). Enhancing creative thinking skills and student achievement: An innovative approach through integrating project-based learning with STEAM and self-efficacy. Pegem Journal of Education and Instruction, 14 (4), 19–29. https://doi.org/10.47750/pegegog.14.04.03 Firdaus, M., Mukhtar, M., & Darari, M. (2024). Effectiveness of case-based interactive videos in online discussion forums to improve critical thinking skills of prospective mathematics teachers. Cognizance journal, 4 (1), 26–38. https://doi.org/10.47760/cognizance.2024.v04i01.003 Fu, P., Gao, C., Chen, X., Zhang, Z., Chen, J., & Yang, D. (2024). Proactive personality and its impact on online learning engagement through positive emotions and learning motivation. Scientific Reports , 14 , 28144 (2024). https://doi.org/10.1038/s41598-024-79776-3 Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. American Journal of Distance Education, 15 (1), 7–23. https://doi.org/10.1080/08923640109527071 Gejandran, P., & Abdullah, N. (2024). Gamification in e-learning: A systematic review of benefits, challenges, and future possibilities, Journal of Logistics, Informatics and Service Science , 11 (2), 84–104. https://www.aasmr.org/liss/Vol.11/ No.2/Vol.11.No.2.06.pdf Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research . Aldine. https://doi.org/10.1097/00006199-196807000-00014 Gravetter, F.J., & Wallnau, L.B. (2009). Statistics for the behavioral sciences , (8th ed.). Cengage Learning. H. Elneel, D. A. (2023). The factors influenced by stakeholder identification in E-learning systems: A survey. Journal of King Saud University - Science, 35 (3), 102566. https://doi.org/10.1016/j.jksus.2023.102566 Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM), (3rd ed.) . Sage. Hendriyani, M. E., Rifqiawati, I., & Lestari, D. (2022). Online learning videos to develop creative thinking skills of students. Research and Development in Education, 2 (2), 67–75. https://doi.org/10.22219/raden.v2i2.20035 Hague, C. (2024). Fostering higher-order thinking skills online in higher education: A scoping review. OECD Education Working Papers , 306. https://doi.org/10.1787/84f7756a-en Irwan, I. (2024). Introduction to Interactive Video-Based E-Learning to Improve Critical Thinking Skills in Vocational High School Students. Unram Journal of Community Service. Corpus ID: 273195357. https://doi.org/10.29303/ujcs.v5i3.705 Isaias, P., Issa, T., & Pena, N. (2014). Promoting Higher Order Thinking Skills via IPTEACES e-Learning Framework in the Learning of Information Systems Units. Journal of Information Systems Education, 25(1), 45–60. https://jise.org/volume25/n1/JISEv25n1p45.html Kassim, H. (2013). The Relationship between learning styles, creative thinking performance and multimedia learning materials. Procedia - Social and Behavioral Sciences, 97 , 229–237 https://doi.org/10.1016/j.sbspro.2013.10.227 Lee, J., & Choi, H. (2017). What affects learners' higher-order thinking in technology-enhanced learning environments? Computers & Education. 115 , 143–152. https://doi.org/10.1016/j.compedu.2017.06.015 Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108 (6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x Mohd Basar, Z., Zulaikha, M. B., Mansor, A, N., & Jamaludin, K. A., Alias, B. S. (2021). The effectiveness and challenges of online learning for secondary school students: A case study. Asian Journal of University Education, 17 , 120–129. https://doi.org/10.24191/ajue.v17i3.14514 Nurul Syahirah, A., Ahmad, N. A. A., & Md Fauzi, A. (2024). The challenges of e-learning readiness among UTHM students towards COVID-19 Situation. Research in Management of Technology and Business, 5 (1), 1067–1077 https://doi.org/10.30880/rmtb.2024.05.01.073 Page, A. (2022). Using online tools to develop higher order learning among tertiary students. Online Learning, 26 (3), 221–235. Puentedura, R. R. (2006). Transformation, technology, and education . http://hippasus.com/resources/tte/ Ratniece, D. (2018). Cognitive development in active elearning. International Journal of Engineering & Technology, 7 (2.28), 53–57. https://doi.org/10.14419/ijet.v7i2.28.12881 Rawashdeh, A.Z., Mohammed, E.Y., Arab, A.R., Alara, M., & Al-Rawashdeh, B.Z. (2021). Advantages and disadvantages of using e-learning in university education: Analyzing students’ perspectives. The Electronic Journal of e-Learning, 19 (2), 107–117. https://doi.org/10.34190/ejel.19.3.2168 Richter, N.F., Schubring, S., Hauff, S., Ringle, C. M. & Sarstedt, M. (2020). When predictors of outcomes are necessary: guidelines for the combined use of PLS-SEM and NCA. Industrial Management & Data Systems, 120 (12), 2243–2267. https://doi.org/10.1108/IMDS-11-2019-0638 Richter, N. F., & Hauff, S. (2022). Necessary conditions in international business research: advancing the field with a new perspective on causality and data analysis. Journal of World Business, 57 (5), Article 101310. https://doi.org/10.1016/j.jwb.2022.101310 Salarvand, S., Mousavi, M. S., & Rahimi, M. (2023). Communication and cooperation challenges in the online classroom in the COVID-19 era: a qualitative study. BMC Medical Education, 23 (1), 201. https://doi.org/10.1186/s12909-023-04189-1 Spradley J. P. (1980). Participant observation . Holt, Rinehart & Winston. Strauss, A., & Corbin, J. M. (1990). Basics of qualitative research: Grounded theory procedures and techniques . Sage. Trigueros, G., & María, I. (2018). New learning of geography with technology: the TPACK model. European Journal of Geography, 9 (1), 38–48. https://www.eurogeojournal.eu/index.php/egj/article/view/104 Yu, H. (2024). Enhancing creative cognition through project-based learning: An in-depth scholarly exploration. Heliyon, 10 (6): 27706. https://doi.org/10.1016/j.heliyon.2024.e27706 Zunaidah, A., & Asih, R. (2024). Tapping into The Interactive Online Classroom: The Use of Multimedia in E-Learning. 2024 2nd International Conference on Software Engineering and Information Technology (ICoSEIT) , 13–18. https://doi.org/10.1109/ICoSEIT60086.2024.10497472 Tables Table 2 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table2.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Apr, 2025 Reviewers agreed at journal 07 Apr, 2025 Reviews received at journal 07 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviewers agreed at journal 01 Apr, 2025 Reviewers invited by journal 01 Apr, 2025 Editor assigned by journal 24 Mar, 2025 Submission checks completed at journal 21 Mar, 2025 First submitted to journal 21 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6061211","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441580119,"identity":"f41a2c46-5b1c-466d-9961-b9acb18aeb3e","order_by":0,"name":"Yan Piaw Chua","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABIklEQVRIiWNgGAWjYDACCTBpA8SMDQwMBgxQBozErSUNRUtjAxFaDqOIMeLVwj+7x/Az747zif39h9ukCwoOy5n3H25/zMNgI7vhAHeaBDZL7pwxluY9cztxxo3ENukZBoeNZW4kNjbzMKQZbzjAuw2bFgOJHANp3rbbiRskGNukeQzSEmdIMIK0HE7Eo8X4N2/bucQN/AfBWupn8B8EafmPT4sZ0JYDiRsYEkFabBIkGMAOO4BTi8SNtDLLuWeSjYF+abYGajGcIZHYOHOOQbLxzMO8my2whdiM5M033u6wk+3vP/7wNs8fCXkJ/uMPPrypsJPtO9678QaOgGbibUAX4QHFKTMDCzaHgQDjT3QtjD8gNPMHHFpGwSgYBaNgRAEADdtmjmBEFNIAAAAASUVORK5CYII=","orcid":"","institution":"UCSI University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"Piaw","lastName":"Chua","suffix":""},{"id":441580120,"identity":"db6948a8-0c23-4862-b7bd-cf05ac0d9623","order_by":1,"name":"Fung Ying Loo","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Fung","middleName":"Ying","lastName":"Loo","suffix":""},{"id":441580121,"identity":"717aad03-b6b9-4bd6-8a17-8057bf89d6c7","order_by":2,"name":"Fung Chiat Loo","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Fung","middleName":"Chiat","lastName":"Loo","suffix":""}],"badges":[],"createdAt":"2025-02-19 06:38:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6061211/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6061211/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81143026,"identity":"6839811f-7b7c-4166-b700-d2d6507fd548","added_by":"auto","created_at":"2025-04-22 17:11:06","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":206114,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe associations between the variables were generated from the selective coding approach and semantics relations method\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6061211/v1/0cf07043058f7509d01dfc73.jpeg"},{"id":81143301,"identity":"ea98d12d-d39f-40a0-8ca6-64bf665beaf1","added_by":"auto","created_at":"2025-04-22 17:19:06","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":298426,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe results of the quantitative study support the e-learning HOTS enhancement model generated from the qualitative study\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6061211/v1/f1b8358bba080c2d2fa9361f.jpeg"},{"id":81143299,"identity":"e095c4c4-c28f-4b11-b681-652cca7f202e","added_by":"auto","created_at":"2025-04-22 17:19:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":132813,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eImportance performance map for factors of e-learning practices for HOTS enhancement\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6061211/v1/bddeb7fe6f9c0e2d87dcf03a.png"},{"id":81144261,"identity":"8d600bd6-62cc-4409-8775-d08152d6ea11","added_by":"auto","created_at":"2025-04-22 17:35:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1670754,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6061211/v1/bb65689a-5cc6-40e2-96be-8c63bc302abe.pdf"},{"id":81143017,"identity":"5dfc346a-293a-4d49-8cc5-ea158dc7995d","added_by":"auto","created_at":"2025-04-22 17:11:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18149,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6061211/v1/aac129a0ff9c1f862dea9561.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling Key E-Learning Ingredients for Enhancing Higher-Order Thinking Skills","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe increasing usage of e-learning platforms in schools provides opportunities for students to improve their higher-order thinking skills (HOTS) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although e-learning in schools is capable of improving student knowledge [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], its effectiveness in fostering HOTS remains limited and unclear [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], as the existing e-learning models focus more on technological tools and teaching methods, which often ignore the important roles related to human factors such as e-leadership, collaboration, and readiness [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This situation has resulted in the inconsistent effectiveness of e-learning in fostering HOTS, where e-leadership, collaboration, and readiness factors of administrators, teachers, students, and parents are less emphasized, leading to the practice of e-learning not being maximized [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough the cultivation of students\u0026rsquo; HOTS has been the main focus in schools, the limited understanding of school practitioners, administrators, teachers, parents, and students about the factors that drive HOTS in e-learning platforms is insufficient due to the lack of a framework that links all factors that can foster students' higher-order thinking skills [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Without comprehensive knowledge, educators and policymakers have difficulty designing e-learning strategies that can effectively maximize HOTS among students.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to address the above knowledge gap by investigating the key factors that can foster HOTS in e-learning from the perspective of various parties who are directly and indirectly involved with e-learning platforms in schools. The results of this study would suggest actions for educational policymakers, school leaders, and e-learning platform developers to provide a more conducive e-learning environment for fostering HOTS among students.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eSeveral e-learning models has been used to guide e-learning process. These models integrate teaching with technology to enhance e-learning. For instances, the SAMR model, the TPACK model, and the PTEACES model. The models focus more on integrating teaching methods and the use of technology [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and less emphasis on the dynamics between schools, leaders, teachers and students, which is highly important for the development of students\u0026rsquo; high order thinking [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. For example, the SAMR model [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] emphasizes linking technology to pedagogy but does not address how technology can be utilized to enhance higher-order thinking skills [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The TPACK model [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] focuses on the intersection of technological knowledge, pedagogical knowledge and content knowledge but overlooks human factors, such as students\u0026rsquo; motivation, attitude, engagement and commitment which influence cognitive and mental development in e-learning [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Similarly, the PTEACES model [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] focuses on e-learning environment itself. Although this model integrates technology with teaching methods and is student-centered, the model neglects the role of e-leaders and organizational readiness in developing higher-order thinking skills among students.\u003c/p\u003e \u003cp\u003eMeanwhile, the focuses of most recent studies (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) include the impact of discussion forum in e-learning [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], the integration of gamification in e-learning project [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the effect of peer online teaching tools [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], the role of multimedia tools [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], the impacts of project-based learning [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], the uses of collaborative tools [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and the effectiveness of interactive platforms [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Besides the limitations of small sample sizes, demography constraints and subjective data collection methods, the previous studies fail to address the knowledge gaps, to provide a larger picture for our understanding on how higher-order thinking skills be cultivated through e-learning.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eRecent research on enhancing higher order thinking skills through e-learning\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthors (year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResearch method and sampling\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFindings\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirdaus et al. (2024) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuasi-experimental design; third-year university students; cluster random sampling.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiscussion forums in e-learning improved critical thinking. Engagement and collaboration are significant factors for e-learning.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGejandran and Abdullah (2024) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReviewed 37 studies published in the past decade, between 2014 and 2023.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGamification in e-learning improved student motivation and engagement levels. Game elements enhance HOTS.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZunaidah and Asih (2024) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDesign and development research; 52 English learners (N\u0026thinsp;=\u0026thinsp;52); convenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE-learning with multimedia tools improved student engagement and critical thinking in an online English language course.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYu (2024) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAn extensive literature review on Project-based learning, including e-learning.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProject-based e-learning modules significantly enhanced both creative and critical thinking.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEkayana et al. (2024) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuasi-experimental pretest-post-test non-equivalent control group design; 150 university engineering students; purposive sampling.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh academic self-efficacy in e-learning have higher learning achievement and creative thinking skills than students with low academic self-efficacy.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrwan (2024) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental study; vocational high school students; convenience sampling.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInteractive video-based e-learning improved critical thinking skills.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlharbi et al. (2022) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental study; 200 female students of kindergarten department in a university; random assigned into experimental and control groups.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE-collaborative learning environment had significant and positive effect on development of critical thinking.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePage (2022) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurvey; participants were pre-service teacher education students enrolled in an online university course (N\u0026thinsp;=\u0026thinsp;625); purposive sampling.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeer online teaching tool enhanced HOTS in students.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCortazar et al. (2021) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental study; 834 students at an engineering school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOnline project-based learning fostered the development of critical thinking.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLee \u0026amp; Choi (2017) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurvey study, 487 undergraduates; random sampling.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReadiness in terms of epistemological beliefs, approach to learning, and attitudes toward technology use affected higher-order thinking in technology-enhanced learning environments.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKassim (2013) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuasi-experimental study with one group pre-test post-test design; Mechanical engineering undergraduates (N\u0026thinsp;=\u0026thinsp;32; 97% male).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultimedia learning tool enhanced creative thinking of active, reflective, intuitive and high visual students.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"3. The study","content":"\u003cp\u003eThe aim of this \u003cem\u003eexploratory sequential mixed methods\u003c/em\u003e design study was to develop a model of e-learning practices for enhancing higher order thinking skills among secondary school students in e-learning environments. The study was first carried out with a qualitative study. Semi-structured interviews were conducted on school administrators, teachers, students, parents, and software experts to gather information about the implementation and practices of e-learning in schools. The qualitative transcripts were then analyzed using the \u003cem\u003eATLAS.TI\u003c/em\u003e software to identify significant indicators and themes, and their associations for HOTS enhancement in e-learning.\u003c/p\u003e \u003cp\u003eThe study was then followed by a quantitative survey on 430 school teachers to validate the model emerged from the qualitative study. The model was analyzed to examine the importance and performances of the variables in the model, as well as the levels of each variable necessary for achieving quality outcome for HOTS enhancement. The quantitative data was analyzed with \u003cem\u003ePLS-SEM\u003c/em\u003e and \u003cem\u003eNecessary Condition Analysis\u003c/em\u003e using SMARTPLS 4 software.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The qualitative study\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Participants\u003c/h2\u003e \u003cp\u003eThe study used theoretical sampling to select key participants with direct and indirect experience in a school e-learning platform. The sample included five school administrators, five teachers, five students, five parents, and five software experts. This diverse groups were chosen to provide rich insights into the e-learning practices in secondary schools in enhancing HOTS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Research Instruments\u003c/h2\u003e \u003cp\u003eThree inventories were created for data collection: The \u003cem\u003eSchool Administrators Inventory\u003c/em\u003e which focused on e-leadership and management roles, the \u003cem\u003eTeacher, Student, and Parent Inventory\u003c/em\u003e, focused on personal and organizational factors, and the \u003cem\u003eSoftware Expert Inventory\u003c/em\u003e, focused on technical aspects of the e-learning platforms. They covered planning, implementation, support, challenges, collaboration and recommendations for improving e-learning practices in schools through the e-learning platforms to enhance higher order thinking skills in students.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Qualitative Data Analysis\u003c/h2\u003e \u003cp\u003eThe transcripts of the interview data were first coded by \u003cem\u003eopen coding\u003c/em\u003e using the ATLAS.ti software and break down into manageable indicators. \u003cem\u003eAxial coding\u003c/em\u003e was then used to identify similarities and differences between the indicators to investigate how the data are gathered in categories of data and associated into themes, to explain the data in a meaningful way. Through the axial coding process, eight key themes emerged from thirty-eight indicators in the qualitative transcripts: collaboration, e-learning readiness, e-leadership, personal factors, e-learning strategies, e-learning practices, organizational factors and quality outcomes (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eSelective coding\u003c/em\u003e was then used to identify the associations between the eight themes. By referring to the Spradley\u0026rsquo;s \u003cem\u003esemantic relations\u003c/em\u003e criteria [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], \u003cem\u003ei.e.\u003c/em\u003e, strict inclusion (A is a property of B), spatial (A is part of B), cause-effect (A is cause of B), and rationale (A is an outcome of B), the associations between the themes were identified (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The quantitative study\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Participants\u003c/h2\u003e \u003cp\u003eQuantitative survey data were gathered from 430 secondary school teachers who are the instructors cum practitioners of the e-learning platforms in schools, with an average age of 39.2 years. Using teachers as participants has advantages because teachers implement the e-learning platforms, assess the progress and behaviors of students in e-learning, and they also act as mediators between school principals, students and parents in e-learning platforms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Survey questionnaire\u003c/h2\u003e \u003cp\u003eThe survey questionnaire utilized in this study was divided into two sections: one focusing on demographic variables and the other addressing eight key variables in the model developed from interview data.\u003c/p\u003e \u003cp\u003eThe questionnaire comprised a total of 38 items, which were derived from the themes identified during the semi-structured interviews, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For instance, the first item related to Collaboration (CO1) states, \u0026ldquo;\u003cem\u003eDesign collaborative e-learning curriculum-related activities for fostering HOTS\u003c/em\u003e.\u0026rdquo; The questionnaire utilized a continuous measurement scale ranging from 0 to 10, where \u0026ldquo;0\u0026rdquo; represented \u0026ldquo;completely disagree\u0026rdquo; and \u0026ldquo;10\u0026rdquo; indicated \u0026ldquo;completely agree\u0026rdquo; with each statement related to enhancing higher order thinking skills through e-learning. This scale was selected for its precision, allowing for mathematical operations essential for PLS-SEM analysis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Cohen, et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] suggested using a continuous 0 to 10 interval scale for confirmatory factor analysis to establish validity and reliability of the items, while Awang [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] supported the use of an 11-point scale, which meets the requirements for ratio measures, ensuring valid and reliable statistical analysis.\u003c/p\u003e \u003cp\u003eThe items were validated by three experts in educational psychology research. Thereafter a pilot study involved 100 respondents who did not participate in the actual study was conducted. Their responses were analyzed using \u003cem\u003eexploratory factor analysis\u003c/em\u003e (EFA) to organize the items into constructs. Employing principal component analysis with varimax rotations, the 38 items were arranged into the eight constructs. Additionally, internal consistency reliability analysis was conducted, revealing high reliability coefficients for the constructs (Cronbach's alpha: collaboration\u0026thinsp;=\u0026thinsp;0.82, readiness\u0026thinsp;=\u0026thinsp;0.89, e-leadership\u0026thinsp;=\u0026thinsp;0.87, personal factors\u0026thinsp;=\u0026thinsp;0.91, strategies\u0026thinsp;=\u0026thinsp;0.89, practices\u0026thinsp;=\u0026thinsp;0.85, organizational factors\u0026thinsp;=\u0026thinsp;0.85, and quality outcomes\u0026thinsp;=\u0026thinsp;0.84).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Quantitative Data analysis\u003c/h2\u003e \u003cp\u003eData analysis for the quantitative study was conducted using SMARTPLS 4 for: (1) confirmatory factor analysis (CFA), (2) model fit analysis, (3) importance-performance map analysis (IPMA), and (4) necessary condition analysis (NCA). CFA was used to examine the validity and reliability of the items of the eight variables; Model fit analysis was used to identify whether the relationships between the variables in the model are valid, and to determine whether further analysis is needed. IPMA was used to identify the importance and performance of the variables for HOTS enhancement through e-learning, and NCA was used to examine the levels of each of the variables needed for maximizing the quality outcome of HOTS enhancement.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Confirmatory factor analysis\u003c/h2\u003e \u003cp\u003eConfirmatory factor analysis (CFA) was conducted using \u003cem\u003ePLS-SEM algorithm\u003c/em\u003e to assess convergent validity, construct validity, construct reliability, discriminant validity, and multicollinearity of the eight measurement models to ensure the items accurately represented the model's constructs. The results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show that all indicator loadings met the benchmark of convergent validity (loadings\u0026thinsp;\u0026ge;\u0026thinsp;0.70). Besides that, construct validity (AVE\u0026thinsp;\u0026gt;\u0026thinsp;0.50, ranging from 0.51 to 0.68) and construct reliability (Cronbach's alpha and composite reliability\u0026thinsp;\u0026gt;\u0026thinsp;0.70, ranging from 0.821 to 0.913) were achieved for all the eight reflective measurement models.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eConstruct Reliability and Construct Validity\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eConstruct reliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConstruct validity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCronbach's alpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComposite reliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage variance extracted (AVE)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReadiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollaboration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE-Learning Practices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE-Learning Strategies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE-leadership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganizational factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDiscriminant validity analysis was then conducted. HTMT assesses the true correlations between the measurement models. It is a reliable tool for assessing discriminant validity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this case, discriminant validity achieved in which no overlapping of concepts were found between the eight measurement models, with HTMT values\u0026thinsp;\u0026lt;\u0026thinsp;0.90, ranging from 0.25 to 0.74 (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results ascertained that discriminant validity achieved for all the constructs in the model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eHeterotrait-monotrait Ratio (HTMT)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Collaboration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. E-Learning Practices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. E-Learning Strategies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. E-leadership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Organizational factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. Personal factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. Quality outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. Readiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurthermore, collinearity analysis was conducted to assess whether multi-collinearity occurs among the indicators in each measurement model. The results show that multicollinearity did not occur among all indicators in each of the eight measurement models, with VIF values\u0026thinsp;\u0026lt;\u0026thinsp;5.0, ranging from 2.11 to 4.19.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Model fit\u003c/h2\u003e \u003cp\u003eModel fit analysis was then analyzed to examine whether the relationships in the model are valid, that is, whether the model fits the data collected from sample of the study. The results show in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e that model fit was confirmed, with SRMR\u0026thinsp;=\u0026thinsp;0.061, D ULS\u0026thinsp;=\u0026thinsp;0.342, D G\u0026thinsp;=\u0026thinsp;1.228 and NFI\u0026thinsp;=\u0026thinsp;0.913.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eModel fit indices and the results of model fit testing\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel fit indices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenchmarks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimated model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel fit testing\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026nbsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel fit achieved\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD ULS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel fit achieved\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel fit achieved\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel fit achieved\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Model fit indexes were generated using PLS-SEM in SMARTPLS 4.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Effects between the variables in the model\u003c/h2\u003e \u003cp\u003eAfter ascertained that all the indicators of the eight measurement models achieved construct validity, construct reliability and discriminant validity; multi-collinearity does not occur between the models and among the indicators; and model fit achieved, PLS-SEM \u003cem\u003ebootstrapping\u003c/em\u003e analysis was conducted to examine the relationships between the variables. The results in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show that all path coefficients in the model are significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The results support the findings of the qualitative study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe final model depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e consists of the quality outcome for HOTS enhancement variable with its seven inter-related core factors. Quality outcome is directly influenced by e-learning practices, where a one-unit change in e-learning practices would cause a 0 514-unit increase or 26.4% of variance change in quality outcome (β\u0026thinsp;=\u0026thinsp;0.514, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.264).\u003c/p\u003e \u003cp\u003eFurthermore, e-learning practices plays the role of a full mediator in creating the indirect effects of its three core factors, namely, e-learning strategies, organizational factors and collaboration on quality outcome. This means with right e-learning strategies (β\u0026thinsp;=\u0026thinsp;0.422, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), positive organizational factors (β\u0026thinsp;=\u0026thinsp;0.252, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and excellent collaboration between the stakeholders (β\u0026thinsp;=\u0026thinsp;0.212, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), e-learning practices would be maximized to nearly 53.4% (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.534, large effect size).\u003c/p\u003e \u003cp\u003eIn addition, e-learning strategies also has three direct factors, namely, e-leadership (β\u0026thinsp;=\u0026thinsp;0.208, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), personal factors (β\u0026thinsp;=\u0026thinsp;0.271, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and readiness (β\u0026thinsp;=\u0026thinsp;0.384, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), where readiness plays the main role, followed by personal factors and e-leadership, and the three factors contribute 42.2% of e-learning strategies (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.422, large effect size). This means to improve e-learning strategies, readiness of schools, teachers and students for enhancing HOTS through e-learning, personal factors, and e-leadership of school leaders in enhancing HOTS through e-learning are needed to maximize e-learning strategies and to stimulate a conducive culture.\u003c/p\u003e \u003cp\u003eThe following are five sub-models derived from the results in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These sub-models are the basics of the \u003cem\u003ee-learning HOTS enhancement\u003c/em\u003e model. The sub-models serve as useful guides for improving higher order thinking skills in schools through e-learning platforms.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eSub-model 1\u003c/span\u003e:\u003c/p\u003e \u003cp\u003eQuality outcome - HOTS enhancement\u0026thinsp;=\u0026thinsp;0.514 E-learning practices\u003c/p\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.264 (large effect)\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eSub-model 2\u003c/span\u003e:\u003c/p\u003e \u003cp\u003eE-learning practices\u0026thinsp;=\u0026thinsp;0.422 E-learning strategies\u0026thinsp;+\u0026thinsp;0.252 Organizational factors\u0026thinsp;+\u0026thinsp;0.212 Collaboration\u003c/p\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.534 (large effect)\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eSub-model 3\u003c/span\u003e:\u003c/p\u003e \u003cp\u003eE-learning strategies\u0026thinsp;=\u0026thinsp;0.384 Readiness\u0026thinsp;+\u0026thinsp;0.271 Personal factors\u0026thinsp;+\u0026thinsp;0.208 E-leadership\u003c/p\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.422 (large effect)\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eSub-model 4\u003c/span\u003e:\u003c/p\u003e \u003cp\u003eCollaboration\u0026thinsp;=\u0026thinsp;0.524 E-leadership\u0026thinsp;+\u0026thinsp;0.283 E-learning strategies\u003c/p\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.466 (large effect)\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eSub-model 5\u003c/span\u003e:\u003c/p\u003e \u003cp\u003eReadiness\u0026thinsp;=\u0026thinsp;0.251 Personal factors\u003c/p\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.053 (small effect)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eEffect sizes for social science data: R\u003csup\u003e2\u003c/sup\u003e: 0.01\u0026thinsp;=\u0026thinsp;small effect, 0.09\u0026thinsp;=\u0026thinsp;medium effect, 0.25\u0026thinsp;=\u0026thinsp;large effect (Source: Gravetter \u0026amp; Wallnau, 2009)\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAfter the model was finalized, the next steps were to assess the importance, performances and the levels of the factors that needed to maximize e-learning HOTS enhancement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Importance and performance map analysis\u003c/h2\u003e \u003cp\u003eThe importance and performance of the six direct and indirect factors of e-learning practices towards HOTS enhancement was conducted by using the \u003cem\u003eImportance-Performance Map Analysis (IPMA)\u003c/em\u003e. IPMA highlights the total effects, representing the importance of the six factors of e-learning practices in influencing quality outcome for HOTS enhancement in the model, with the average scores indicating their performance [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Through IPMA, the performance scores of all variables in the model are rescaled to standardized scores of 0 to 100% for comparisons.\u003c/p\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e show that the most important factor for e-learning practices for HOTS enhancement is e-learning strategies (0.376), which is located at the rightmost side in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. It was followed by organizational factors (0.188), collaboration (0.176), personal factors (0.150), e-leadership (0.070) and finally readiness (0.016). Furthermore, the current performances of the constructs are between 31.021% (e-leadership) to 52.045% (personal factors). This shows that there is ample room to improve e-learning strategies for HOTS enhancement. The performances of the six constructs can be improved by 47.955\u0026ndash;68.979%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConstruct importance and performance for e-learning practices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImportance (effect)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerformance (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE-Learning Strategies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganizational factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollaboration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE-leadership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReadiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Necessary condition analysis\u003c/h2\u003e \u003cp\u003eSince there were ample rooms for enhancing the factors of HOTS enhancement, the final step is to determine the minimum levels of each of the factors needed for the maximizing quality outcome of HOTS enhancement. It can be examined with the \u003cem\u003ebottleneck\u003c/em\u003e table of \u003cem\u003enecessary condition analysis\u003c/em\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The bottleneck table presents the necessity relationship of the minimum levels of the necessary conditions or the factors which are necessary for achieving the levels of quality outcome. By reading the bottleneck table row by row from left to right, the necessary conditions for any levels of the outcome variable can be easily identified. On the left column in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the levels of quality outcome to be achieved are expressed as percentages of the range between 0 to 100.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eBottleneck table for HOTS enhancement\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality outcome for\u003c/p\u003e \u003cp\u003eHOTS enhancement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollaboration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE-leadership\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrganizational factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePersonal factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePractices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStrategies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eReadiness\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35.970\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e67.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: NN\u0026thinsp;=\u0026thinsp;not necessary\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows that to maximize quality outcome for HOTS enhancement to 100%, the following minimum criteria must be met: 40.678% of collaboration, 95.857% of e-leadership, 42.750% of organizational factors, 31.450% of personal factors, 55.556% of e-learning practices, 67.797% of e-learning strategies and 46.893% of readiness. In this case, HOTS enhancement will not achieve the maximum of 100% if any of the seven necessary conditions is not achieved.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe e-learning HOTS enhancement model generated from the study has a limited scope and is not intended to establish a universal standard. However, it can serve as a reference for schools using e-learning platforms to enhance e-leadership practices for higher order thinking skills enhancement. Practically, this research offers a framework that educational leaders, policymakers, and educators can adopt to enhance HOTS in e-learning. Schools can leverage findings on collaboration, personal and organizational factors, readiness, and e-leadership to design programs and policies that encourage higher order thinking skills in e-learning. Training programs for teachers and administrators can focus on e-leadership and innovative e-learning strategies, ensuring institutional support and structured development of thinking skills. Additionally, the research highlights the importance of creating supportive policies and partnerships with external organizations, which can provide resources and expertise to overcome e-learning obstacles.\u003c/p\u003e \u003cp\u003eThe study contributes to existing theories by expanding on the \u003cem\u003eCommunity of Inquiry\u003c/em\u003e model [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], which focuses on cognitive, social, and teaching presence, but neglects institutional and e-leadership factors critical for successful e-learning. It accomplishes the \u003cem\u003eTPACK\u003c/em\u003e model [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], the \u003cem\u003eSAMR\u003c/em\u003e model [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and the \u003cem\u003ePTEACES\u003c/em\u003e model [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] by addressing personal, organizational and collaboration factors. Integrating these factors provides a more holistic model for developing HOTS in e-learning. By addressing these additional factors, this model fills theoretical gaps in understanding the key factors and their relationships for enhancing HOTS in e-learning platforms.\u003c/p\u003e \u003cp\u003eFive sub-models emerged from the data, defining the model of HOTS enhancement through e-learning in schools.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo maximize the quality of e-learning HOTS enhancement, schools should maximize the use of discussions and forums to enhance HOTS, incorporate multiple simulations in e-learning platforms to develop HOTS, use varied assessment methods focusing on HOTS, apply continuous assessments in e-learning practices for HOTS enhancement, and share best e-learning practices for developing HOTS among teachers. (refer to Sub-model 1)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo maximize e-learning practices for HOTS enhancement, schools should implement effective e-learning strategies that include using inquiry-based approaches in e-learning to foster higher-order thinking skills, accepting different views in e-learning to enhance HOTS, integrating creative multimedia resources in e-learning to enhance HOTS, and developing interdisciplinary e-learning projects focusing on HOTS. Besides that, in terms of organizational factors, schools should provide sufficient technical support for improving HOTS in e-learning, provide sufficient institutional backing for professional development focused on HOTS, provide HOTS online resources for teachers, cultivate community engagement to support HOTS initiatives, implement supportive policies from educational authorities to promote HOTS among students, and develop partnerships with external organizations that facilitate students\u0026rsquo; HOTS development. Furthermore, schools must try to maximize collaboration between stakeholders, including maximizing feedback loops to promote HOTS in students, designing group problem-based e-learning projects that enhance HOTS, designing collaborative e-learning curriculum-related activities for fostering HOTS, and establishing e-learning working committees focused on HOTS. (refer to Sub-model 2)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo maximize e-learning strategies for HOTS enhancement, schools must ensure that the readiness of schools, their e-learning facilities, and their stakeholders are at the highest levels by implementing the following: enhancing students\u0026rsquo; knowledge of HOTS in e-learning, regularly evaluating school technology infrastructures for HOTS enhancement, creating school policies that promote HOTS through e-learning, implementing training courses focused on fostering HOTS, encouraging mindset changes among students, teachers, and school administrators, and cultivating a positive attitude among students, teachers, and school administrators towards e-learning. Besides that, schools must consider personal factors, including promoting positive attitudes through interactive HOTS activities, addressing students\u0026rsquo; needs related to HOTS development, encouraging students\u0026rsquo; involvement in HOTS participation, fostering teachers\u0026rsquo; commitment to e-learning platforms that promote a culture of higher-order thinking skills, and encouraging HOTS through self-directed e-learning among students. Finally, e-leadership in schools should be maximized by providing e-leadership training for administrators and teachers to improve their knowledge of enhancing HOTS through e-learning, encouraging innovative practices to foster HOTS, developing a mission and vision for integrating HOTS into e-learning, and promoting leadership initiatives that support HOTS. (refer to Sub-model 3)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo maximize collaboration between stakeholders, e-leadership should be implemented to its maximum by developing a mission and vision for integrating HOTS into e-learning, providing e-leadership training, promoting leadership initiatives that support HOTS, and encouraging innovative practices to foster HOTS. Besides that, schools should implement the following e-learning strategies: integrating creative multimedia resources in e-learning to enhance HOTS, using inquiry-based approaches in e-learning to foster HOTS, accepting different views in e-learning to enhance HOTS, and developing interdisciplinary e-learning projects focusing on HOTS. (refer to Sub-model 4)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo effectively enhance e-learning readiness, schools, teachers, and parents must fully consider students\u0026rsquo; needs related to HOTS development, promote positive attitudes through interactive HOTS activities, encourage students\u0026rsquo; involvement in HOTS participation, foster teachers\u0026rsquo; commitment to e-learning platforms that cultivate a culture of HOTS, and encourage HOTS through self-directed e-learning among students. (refer to Sub-model 5)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eAn interesting and significant finding from the necessary condition analysis is that at least 95.85% of e-leadership is required to achieve the maximum 100% of HOTS enhancement in e-learning. This underscores the critical roles of school leaders and teachers in utilizing e-leadership within e-learning platforms to enhance HOTS. Most importantly, the results of IPMA analysis showed that the current level of e-leadership in schools is only at 31%, highlighting an urgent need to maximize e-leadership efforts in e-learning platforms to reach optimal levels.\u003c/p\u003e \u003cp\u003eNevertheless, the NCA results suggested that all the seven factors are directly or indirectly interrelated, and none of them can be ignored in maximizing HOTS among students in e-learning. HOTS enhancement will not reach its maximum if one of the seven necessary factors is not achieved.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eIn conclusion, this research provides a comprehensive picture for enhancing higher order thinking skills among students in e-learning platforms. Collaboration, e-learning readiness, e-leadership, personal and organizational factors, supports and readiness emerged as essential factors for fostering HOTS, with practical and theoretical contributions that expand existing educational frameworks. By addressing institutional, personal, and practical dimensions of e-learning, this study provides an actionable framework for enhancing HOTS in secondary schools. Future research could explore the application of this model in other contexts to validate its adaptability and effectiveness in diverse educational settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecial thanks to the Institute of Research Management and Monitoring, University of Malaya, for expertly managing the grant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Fundamental Research Grant Scheme (FRGS), Ministry of Higher Education, Malaysia. Grant number: FP023-2018A. The authors declare no conflicts of interest related to this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChua Yan Piaw\u003c/strong\u003e, PhD, Professor “Corresponding Author”\u003c/p\u003e\n\u003cp\u003eAffiliations:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFaculty of Social Sciences and Liberal Arts, UCSI University, Kuala Lumpur, Malaysia. Email: \u0026nbsp;[email protected]\u003c/p\u003e\n\u003cp\u003eFaculty of Education, Universiti Malaya, Kuala Lumpur, Malaysia. Email: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLoo Fung Ying\u003c/strong\u003e, PhD Associate professor and Deputy Dean (Postgraduate Studies, Research \u0026amp; Innovation)\u003c/p\u003e\n\u003cp\u003eAffiliation: Faculty of Creative Arts, Universiti Malaya. Kuala Lumpur, Malaysia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLoo Fung Chiat\u003c/strong\u003e, PhD, Associate professor\u003c/p\u003e\n\u003cp\u003eAffiliation: Faculty of Human Ecology, Universiti Putra Malaysia. Email: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChua Yan Piaw contributed to the study conception and design. The draft of the manuscript was written by Chua Yan Piaw. Loo Fung Ying and Loo Fung Chiat commented on the manuscript. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChua Yan Piaw\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was ethically approved for its general characteristics the Ethics Committee of the University of Malaya, Malaysia. All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets of the current study are not publicly available due to funding restrictions but may be available upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkpen, C.N., Asaolu, S., Atobatele, S., Okagbue, H., \u0026amp; Sampson, S. (2024). Impact of online learning on student's performance and engagement: a systematic review. Discovery Education, \u003cem\u003e3\u003c/em\u003e, 205. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s44217-024-00253-0\u003c/span\u003e\u003cspan address=\"10.1007/s44217-024-00253-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlharbi, S. M., Elfeky, A. S., \u0026amp; Ahmed, E.S. (2022). The effect of e-collaborative learning environment on development of critical thinking and higher order thinking skills. Journal of Positive School Psychology, \u003cem\u003e6\u003c/em\u003e(6), 6848\u0026ndash;6854. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://journalppw.com/index.php/jpsp/article/view/8692\u003c/span\u003e\u003cspan address=\"https://journalppw.com/index.php/jpsp/article/view/8692\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAwang, Z. (2014). \u003cem\u003eA handbook on structural equation modeling\u003c/em\u003e. MPWS Rich Resources.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarikzai, S., Bharathi S, V., \u0026amp; Perdana, A. (2024). Challenges and strategies in e-learning adoption in emerging economies: a scoping review. Cogent Education, \u003cem\u003e11\u003c/em\u003e(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/2331186X.2024.2400415\u003c/span\u003e\u003cspan address=\"10.1080/2331186X.2024.2400415\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiu, Y. Y. (2021). Effectiveness of Online Learning Relies on the Dynamic Between Teachers and Students. Journal of E-Learning Research, \u003cem\u003e1\u003c/em\u003e(4), 61\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.33422/jelr.v1i4.237\u003c/span\u003e\u003cspan address=\"10.33422/jelr.v1i4.237\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChris, B., Michelle, M., \u0026amp; Shaun, N. (2022). A scoping review of the application of the SAMR model in research. Computers and Education Open, \u003cem\u003e3\u003c/em\u003e: 100093. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.caeo.2022.100093\u003c/span\u003e\u003cspan address=\"10.1016/j.caeo.2022.100093\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChua, Y. P., Tie, F. H., \u0026amp; Don, Z. M. (2013). Creating an Education Research Acculturation Theory for Research Implementation in School. Education and Urban Society, \u003cem\u003e45\u003c/em\u003e(4), 506\u0026ndash;526. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0013124511413124\u003c/span\u003e\u003cspan address=\"10.1177/0013124511413124\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen, L., Manion, L., \u0026amp; Morrison, K. (2018). \u003cem\u003eResearch Methods in Education\u003c/em\u003e, \u003cem\u003e(8th ed.).\u003c/em\u003e Routledge.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorbin, J., \u0026amp; Strauss, A. (2008). \u003cem\u003eBasics of qualitative research: techniques and procedures for developing grounded theory (3rd ed\u003c/em\u003e.). Sage\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCortazar, C., Nussbaum, M., Harcha, J., Alvares, D., Lopez, F., Goni, J., \u0026amp; Cabezas, V. (2021). Promoting critical thinking in an online, project-based course. Computers in Human Behavior, 119, Article 106705. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chb.2021.106705\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2021.106705\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCreswell, J. W., \u0026amp; Guetterman, T. (2024). \u003cem\u003eEducational research: Planning, conducting, and evaluating quantitative and qualitative research\u003c/em\u003e, \u003cem\u003e(7th ed.)\u003c/em\u003e. Pearson.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDul, J. (2015). Necessary Condition Analysis (NCA): Logic and methodology of necessary but not sufficient causality. Organizational Research Methods, \u003cem\u003e19\u003c/em\u003e(1), 10\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1094428115584005\u003c/span\u003e\u003cspan address=\"10.1177/1094428115584005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEkayana, A. A. D., Parwati, N. N., Agustini,K., \u0026amp; Ratnaya, I. G. (2024). Enhancing creative thinking skills and student achievement: An innovative approach through integrating project-based learning with STEAM and self-efficacy. Pegem Journal of Education and Instruction, \u003cem\u003e14\u003c/em\u003e(4), 19\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.47750/pegegog.14.04.03\u003c/span\u003e\u003cspan address=\"10.47750/pegegog.14.04.03\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFirdaus, M., Mukhtar, M., \u0026amp; Darari, M. (2024). Effectiveness of case-based interactive videos in online discussion forums to improve critical thinking skills of prospective mathematics teachers. Cognizance journal, \u003cem\u003e4\u003c/em\u003e(1), 26\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.47760/cognizance.2024.v04i01.003\u003c/span\u003e\u003cspan address=\"10.47760/cognizance.2024.v04i01.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu, P., Gao, C., Chen, X., Zhang, Z., Chen, J., \u0026amp; Yang, D. (2024). Proactive personality and its impact on online learning engagement through positive emotions and learning motivation. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e, 28144 (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-024-79776-3\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-79776-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarrison, D. R., Anderson, T., \u0026amp; Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. American Journal of Distance Education, \u003cem\u003e15\u003c/em\u003e(1), 7\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/08923640109527071\u003c/span\u003e\u003cspan address=\"10.1080/08923640109527071\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGejandran, P., \u0026amp; Abdullah, N. (2024). Gamification in e-learning: A systematic review of benefits, challenges, and future possibilities, \u003cem\u003eJournal of Logistics, Informatics and Service Science\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), 84\u0026ndash;104. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.aasmr.org/liss/Vol.11/\u003c/span\u003e\u003cspan address=\"https://www.aasmr.org/liss/Vol.11/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eNo.2/Vol.11.No.2.06.pdf\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlaser, B. G., \u0026amp; Strauss, A. L. (1967). \u003cem\u003eThe discovery of grounded theory: Strategies for qualitative research\u003c/em\u003e. Aldine. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/00006199-196807000-00014\u003c/span\u003e\u003cspan address=\"10.1097/00006199-196807000-00014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGravetter, F.J., \u0026amp; Wallnau, L.B. (2009). \u003cem\u003eStatistics for the behavioral sciences\u003c/em\u003e, (8th ed.). Cengage Learning.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. Elneel, D. A. (2023). The factors influenced by stakeholder identification in E-learning systems: A survey. Journal of King Saud University - Science, \u003cem\u003e35\u003c/em\u003e(3), 102566. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jksus.2023.102566\u003c/span\u003e\u003cspan address=\"10.1016/j.jksus.2023.102566\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHair, J. F., Hult, G. T. M., Ringle, C. M., \u0026amp; Sarstedt, M. (2022). \u003cem\u003eA primer on partial least squares structural equation modeling (PLS-SEM), (3rd ed.)\u003c/em\u003e. Sage.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHendriyani, M. E., Rifqiawati, I., \u0026amp; Lestari, D. (2022). Online learning videos to develop creative thinking skills of students. Research and Development in Education, \u003cem\u003e2\u003c/em\u003e(2), 67\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.22219/raden.v2i2.20035\u003c/span\u003e\u003cspan address=\"10.22219/raden.v2i2.20035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHague, C. (2024). Fostering higher-order thinking skills online in higher education: A scoping review. \u003cem\u003eOECD Education Working Papers\u003c/em\u003e, 306. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1787/84f7756a-en\u003c/span\u003e\u003cspan address=\"10.1787/84f7756a-en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIrwan, I. (2024). Introduction to Interactive Video-Based E-Learning to Improve Critical Thinking Skills in Vocational High School Students. Unram Journal of Community Service. Corpus ID: 273195357. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.29303/ujcs.v5i3.705\u003c/span\u003e\u003cspan address=\"10.29303/ujcs.v5i3.705\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsaias, P., Issa, T., \u0026amp; Pena, N. (2014). Promoting Higher Order Thinking Skills via IPTEACES e-Learning Framework in the Learning of Information Systems Units. Journal of Information Systems Education, 25(1), 45\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jise.org/volume25/n1/JISEv25n1p45.html\u003c/span\u003e\u003cspan address=\"https://jise.org/volume25/n1/JISEv25n1p45.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKassim, H. (2013). The Relationship between learning styles, creative thinking performance and multimedia learning materials. Procedia - Social and Behavioral Sciences, \u003cem\u003e97\u003c/em\u003e, 229\u0026ndash;237 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.sbspro.2013.10.227\u003c/span\u003e\u003cspan address=\"10.1016/j.sbspro.2013.10.227\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, J., \u0026amp; Choi, H. (2017). What affects learners' higher-order thinking in technology-enhanced learning environments? Computers \u0026amp; Education. \u003cem\u003e115\u003c/em\u003e, 143\u0026ndash;152. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.compedu.2017.06.015\u003c/span\u003e\u003cspan address=\"10.1016/j.compedu.2017.06.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMishra, P., \u0026amp; Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, \u003cem\u003e108\u003c/em\u003e(6), 1017\u0026ndash;1054. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1467-9620.2006.00684.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1467-9620.2006.00684.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohd Basar, Z., Zulaikha, M. B., Mansor, A, N., \u0026amp; Jamaludin, K. A., Alias, B. S. (2021). The effectiveness and challenges of online learning for secondary school students: A case study. Asian Journal of University Education, \u003cem\u003e17\u003c/em\u003e, 120\u0026ndash;129. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24191/ajue.v17i3.14514\u003c/span\u003e\u003cspan address=\"10.24191/ajue.v17i3.14514\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNurul Syahirah, A., Ahmad, N. A. A., \u0026amp; Md Fauzi, A. (2024). The challenges of e-learning readiness among UTHM students towards COVID-19 Situation. Research in Management of Technology and Business, \u003cem\u003e5\u003c/em\u003e(1), 1067\u0026ndash;1077 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.30880/rmtb.2024.05.01.073\u003c/span\u003e\u003cspan address=\"10.30880/rmtb.2024.05.01.073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePage, A. (2022). Using online tools to develop higher order learning among tertiary students. Online Learning, \u003cem\u003e26\u003c/em\u003e(3), 221\u0026ndash;235.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePuentedura, R. R. (2006). \u003cem\u003eTransformation, technology, and education\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hippasus.com/resources/tte/\u003c/span\u003e\u003cspan address=\"http://hippasus.com/resources/tte/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRatniece, D. (2018). Cognitive development in active elearning. International Journal of Engineering \u0026amp; Technology, \u003cem\u003e7\u003c/em\u003e(2.28), 53\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14419/ijet.v7i2.28.12881\u003c/span\u003e\u003cspan address=\"10.14419/ijet.v7i2.28.12881\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRawashdeh, A.Z., Mohammed, E.Y., Arab, A.R., Alara, M., \u0026amp; Al-Rawashdeh, B.Z. (2021). Advantages and disadvantages of using e-learning in university education: Analyzing students\u0026rsquo; perspectives. The Electronic Journal of e-Learning, \u003cem\u003e19\u003c/em\u003e(2), 107\u0026ndash;117. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.34190/ejel.19.3.2168\u003c/span\u003e\u003cspan address=\"10.34190/ejel.19.3.2168\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichter, N.F., Schubring, S., Hauff, S., Ringle, C. M. \u0026amp; Sarstedt, M. (2020). When predictors of outcomes are necessary: guidelines for the combined use of PLS-SEM and NCA. Industrial Management \u0026amp; Data Systems, \u003cem\u003e120\u003c/em\u003e (12), 2243\u0026ndash;2267. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/IMDS-11-2019-0638\u003c/span\u003e\u003cspan address=\"10.1108/IMDS-11-2019-0638\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichter, N. F., \u0026amp; Hauff, S. (2022). Necessary conditions in international business research: advancing the field with a new perspective on causality and data analysis. Journal of World Business, \u003cem\u003e57\u003c/em\u003e(5), Article 101310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jwb.2022.101310\u003c/span\u003e\u003cspan address=\"10.1016/j.jwb.2022.101310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalarvand, S., Mousavi, M. S., \u0026amp; Rahimi, M. (2023). Communication and cooperation challenges in the online classroom in the COVID-19 era: a qualitative study. BMC Medical Education, \u003cem\u003e23\u003c/em\u003e(1), 201. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12909-023-04189-1\u003c/span\u003e\u003cspan address=\"10.1186/s12909-023-04189-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpradley J. P. (1980). \u003cem\u003eParticipant observation\u003c/em\u003e. Holt, Rinehart \u0026amp; Winston.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrauss, A., \u0026amp; Corbin, J. M. (1990). \u003cem\u003eBasics of qualitative research: Grounded theory procedures and techniques\u003c/em\u003e. Sage.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrigueros, G., \u0026amp; Mar\u0026iacute;a, I. (2018). New learning of geography with technology: the TPACK model. European Journal of Geography, \u003cem\u003e9\u003c/em\u003e(1), 38\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eurogeojournal.eu/index.php/egj/article/view/104\u003c/span\u003e\u003cspan address=\"https://www.eurogeojournal.eu/index.php/egj/article/view/104\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu, H. (2024). Enhancing creative cognition through project-based learning: An in-depth scholarly exploration. Heliyon, \u003cem\u003e10\u003c/em\u003e(6): 27706. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2024.e27706\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e27706\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZunaidah, A., \u0026amp; Asih, R. (2024). Tapping into The Interactive Online Classroom: The Use of Multimedia in E-Learning. \u003cem\u003e2024 2nd International Conference on Software Engineering and Information Technology (ICoSEIT)\u003c/em\u003e, 13\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/ICoSEIT60086.2024.10497472\u003c/span\u003e\u003cspan address=\"10.1109/ICoSEIT60086.2024.10497472\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"e-learning, higher order thinking skills, e-learning practices, secondary school, quality education","lastPublishedDoi":"10.21203/rs.3.rs-6061211/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6061211/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe existing e-learning models for higher-order thinking skills (HOTS) enhancement focus more on technology and e-learning methods, ignoring the important roles of human factors such as e-leadership, collaboration, and readiness. This exploratory sequential mixed-methods design study aimed to identify significant factors for e-learning practices that enhance HOTS. Semi-structured interviews were conducted with school administrators, teachers, students, parents, and school software experts, and the transcripts were analyzed using ATLAS.ti. Seven core factors emerged from the study: collaboration, readiness, e-leadership, personal factors, strategies, practices, and organizational factors. Their associations were verified through a quantitative survey involving 430 secondary school teachers. The quantitative data was analyzed using PLS-SEM in SMARTPLS 4, resulting in five sub-models defining a HOTS enhancement framework for schools e-learning. E-learning strategy appeared to be the most important factor for HOTS enhancement, followed by organizational factors, collaboration, personal factors, e-leadership and readiness. Besides that, the Necessary Condition Analysis and Importance-performance Map analysis revealed that the current role of e-leadership in schools is only 31%, although 96% e-leadership is required to maximize HOTS enhancement. This highlights the critical role of school leaders and teachers in leveraging e-leadership within e-learning platforms. This research provides a model that educational leaders, policymakers, and educators can adopt to enhance HOTS in e-learning.\u003c/p\u003e","manuscriptTitle":"Unveiling Key E-Learning Ingredients for Enhancing Higher-Order Thinking Skills","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-22 17:11:01","doi":"10.21203/rs.3.rs-6061211/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-22T17:31:30+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"96979134451261059115142034177724872980","date":"2025-04-08T02:44:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-07T06:38:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96853557450908366831534437806664356332","date":"2025-04-06T10:20:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280467707034379581598735808675500663770","date":"2025-04-01T11:09:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T08:58:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-24T19:00:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-21T08:38:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Education","date":"2025-03-21T08:37:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"911159a8-4a09-44c1-acd9-57de02834f65","owner":[],"postedDate":"April 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-03T08:23:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-22 17:11:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6061211","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6061211","identity":"rs-6061211","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}