Technology is an inseparable element of the modern era, and its progressive evolution is inevitable. Over the decades, technology has been integrated into various industries, including education. Therefore, educational technology (EdTech) is a product of technological advancement. The effect of technology integration is amplified during the COVID-19 outbreak, as schools were closed, leaving a shocking impact on the educational community. As a result, the world is awash with countless technological solutions to address the new global trend (Ravichandran et al., 2024). It catalyses the transition of the education sector into the digital era. As a result, demand for edtech has surged since then.

In higher education, the means of knowledge transfer and delivery have changed due to the adoption of edtech. The restrictions during the pandemic unlocked the potential of edtech, enabling a transition from the traditional classroom to the virtual classroom, with edtech serving as an assistive tool that supports teachers in the virtual environment (Law et al., 2023; Lai et al., 2021). However, the practice of conventional teaching and learning can hardly be translated into virtual classroom settings. In the virtual environment, the interactions between teachers and students are hindered. Unlike in the conventional classroom, interactions and collaborations between teachers and students are challenging to occur virtually (Salarvand et al., 2023). Besides, teachers’ technological proficiency is also a concern due to the swift transition from traditional to virtual classroom environments, leaving them with limited digital pedagogical experience to cope with the sudden change (Lai et al., 2025; Teichert et al., 2023). Hence, concerns regarding teachers’ technology proficiency and the lack of interaction and collaboration between teachers and students arose due to the sudden shift in the teaching and learning environment.

Aside from the teaching and learning environment, students’ personality traits are among the factors that influence academic performance and can sometimes be overlooked. Personality traits play a vital role in education, shaping students’ levels of interaction and academic success (Yildiz Durak, 2023). On top of that, although previous studies showed that the relationship between personality traits and academic performance was robust (Lyons et al., 2017; Vasileva-Stojanovska et al., 2015), the topic required more findings as the educational trends change over time. The changes might affect the perspective on the relationship between personality traits and academic performance. Moreover, the rapid growth of virtual education during the pandemic opened more possibilities for conducting lessons. The scenario also indicates that education is no longer bound to fixed, or rather conventional methods, but is embracing various forms of technology integration to provide a more tailored and personalised environment for teaching and learning (Rahman et al., 2022; Weiling et al., 2025). It is more pronounced in virtual education. Therefore, it is essential to consider personality traits in the present study to examine their relationship with academic performance in modern education.

Speaking of modern education, conventional teaching methods are no longer sufficient to fulfil the needs of teaching and learning in the new norm. Thence, the pedagogical content knowledge (PCK) framework introduced by Shulman in 1986 is unable to address the additional technology component commonly integrated into modern pedagogy (Shulman, 1986). In 2006, Mishra and Koehler introduced the technological, pedagogical, and content knowledge (TPACK) framework to address the role of technology in modern education (Mishra & Koehler, 2006). TPACK framework. Therefore, the TPACK framework consists of 3 domains: (1) technological knowledge (TK), (2) pedagogical knowledge (PK), (3) content knowledge (CK), and 4 sub-domains: (1) technological pedagogical knowledge (TPK), (2) technological content knowledge (TCK), pedagogical content knowledge (PCK), (4) technological pedagogical content knowledge (TPCK). It is a conceptual framework that emphasises the assessment of pre-service teachers’ efficacy in integrating technology into their teaching. A questionnaire was designed with questions dedicated to each domain and subdomain, allowing pre-service teachers to assess themselves through self-reporting. However, the self-reporting questionnaire design has underlying bias concerns. Previous studies indicated that the teacher’s TPACK self-assessment questionnaire may introduce bias, leading to either under- or overestimation of results (Atmojo et al., 2025; Gonscherowski & Rott, 2025; Petridou et al., 2025; Su & Foulger, 2019). On top of that, students of modern education are involved in various technology integrations into their learning. Thus, it is vital to examine TPACK from their perspective, as their online learning experience is heavily dependent on their technology proficiency.

In light of the challenges outlined, the current research aims to further examine the stated issues by developing a framework that addresses students’ personality traits, continued use of technology, and collaborative learning in higher education.

Current educational research often focuses on a technology perspective, overlooking students’ personality traits. Their academic performance is often related to the teaching and learning environment, which is heavily integrated with pioneering technology. Oversight regarding how student personality traits influence academic performance can hinder the acquisition of valuable insights. Besides, the TPACK framework is well-known as a foundational framework for assessing pre-service teachers’ technology integration. However, previous research heavily favours the teachers’ perspective, overlooking that students are the primary users whose learning depends on technological integration. Furthermore, post-pandemic, academic institutions have shifted from physical to virtual classroom settings, mainly due to the educational continuity enabled by the virtual learning environment. Nonetheless, the conventional teaching practices and approaches are unlikely to be directly translated into the virtual environment without sacrificing the social components. Hence, research questions are drawn as follows:

The virtual classroom has become a common practice in modern education, and it is crucial to further investigate its potential in light of current educational trends. Consequently, a collaborative learning management system (C-LMS) is proposed and developed to cope with the current educational trend. Thus,

Collaborative learning is an umbrella term that encompasses a variety of educational approaches. It gained attention since the late 1950s, when research on the nature of collaboration emerged. In the past, students who engaged in collaborative learning were shown to outperform those who did not (Bruffee, 1987). It highlighted the edge of collaborative learning among students; however, it also defied the traditional classroom autonomy where the teacher is the centre of classroom activities. In other words, the classroom activities were conducted solely in a teacher-centred fashion. Due to traditional classroom autonomy constraints, teachers play a vital role in designing assignments and ensuring that activities and tasks involve student-to-student interaction (Arafat & Khshali, 2025). In addition, shifting teachers’ roles to facilitators shifted the focus of classroom activities from one-way lecturing to co-communication and mutual interaction among students. The teachers are responsible for structuring an environment that encourages student-to-student interaction while assisting them throughout the collaborative learning process (Bjelobaba et al., 2023). It promotes lifelong learning rather than traditional content memorisation, which has been practised for decades. As such, the mode of autonomy shifted from teacher in full control to partial autonomy, in which teachers do not interfere with their students’ work after their group is formed, and the task is assigned. It introduced a different form of collaboration in the virtual learning environment, especially given the limited presence of teachers. There are uncertainties underlying the swift transition and adoption of collaborative learning in the virtual learning environment that are worth further studying in the current research.

Furthermore, rapid technological evolution has led to the trend of integrating technology into education, serving as a supportive role in teaching and learning. In the meantime, it also enhanced the collaborative learning experience through modern technologies, including physical hardware devices and peripherals, as well as virtual applications and software (Holly et al., 2025). Therefore, collaborative learning is no longer limited to physical interactions between students, as technology enables a wide variety of new-era pedagogical approaches. Given the widespread use of computers nowadays, teachers and students have the luxury of choosing how classes are conducted. Besides, the means of achieving collaborative learning broaden with the introduction of computer-supported collaborative learning (CSCL) (Adhami & Taghizadeh, 2024). CSCL is a process that utilises computers, software, and applications to determine their learning preferences. It shifts classroom autonomy from teacher-centered to student-centered, granting students control over their learning pace and co-creation of knowledge. It enriches their learning experience by offering various types of learning materials that pique their interest. Hence, collaborative learning offers students vast opportunities to further discover and assimilate information, especially with the help of a technological perspective, which is a worthwhile research direction to be further explored.

Before the utilisation of LMS in education, teachers and admin relied solely on pen and paper for recording academic matters and storing them in a cabinet filing system. Aside from its ineffectiveness in maintaining data integrity and consistency, it does not provide any insightful information. As a result, educational institutions often adopt LMSs, replacing the legacy file system that has been in place for decades. The transition from legacy file systems to LMS adoption changes the means of teaching and learning, as well as the acquisition and management of academic records (AL-Nuaimi et al., 2024). According to Sulaiman et al. (2023), their study suggests that LMSs designed to be simple and accessible are often preferred by teachers, as they find them easy to use (Sulaiman et al., 2023). Additionally, unlike previous studies, Sulaiman et al.’s study revealed that the LMS’s usefulness and ease of use were merely affected by the availability of technical support. It contradicts the claim that LMS with adequate support lowers the learning curve, thereby helping teachers perform desired tasks more easily. However, the study focused solely on teachers’ perspectives, overlooking students’ thoughts and opinions, even though they are also users of the LMS.

According to Dahal and Manandhar’s (2024) study, LMS is well-suited for managing resources and academic content (Dahal & Manandhar, 2024). The LMS bridges technology and education, aligning with the modern education trend that treats technology as a core element in the presentation and delivery of knowledge. However, despite the remarkable benefits LMSs have offered, their study suggested that there is still room for improvement in the communicative features, engagement, and assessment. Those were the aspects that LMSs strive to outperform traditional classroom settings. Besides, according to Ahmad et al. (2023), limited computer literacy and limited support availability were among the major reasons teachers neglect to integrate and utilise LMS into their pedagogy. The issues are closely tied to the technology competency of teachers and students, as they are the primary users of LMSs. Thus, it is vital for current research to study the design and features of LMSs and identify their full potential, especially from an analytical perspective.

Within the vast field of education research, highlights often focus on pedagogical approaches, technology integration, and engagement. On top of that, the well-known TPACK framework was introduced by Mishra and Koehler in 2006 which is shown in Figure 1, focusing solely on assessing pre-service teachers’ efficacy in integrating technology into their teaching, which is insufficient to provide a clearer picture of current technology integration in education (Gatete, 2025).

As a result, students’ personality traits are generally overlooked, though they offer valuable insights for teachers to assess their students from different perspectives. In terms of personality traits, the Myers-Briggs Type Indicator (MBTI) and the Big Five Personality Traits (BFPT), also known as the Five-Factor Model, are among the most widely known measures, which are shown in Figure 2 and Figure 3, respectively.

In recent years, MBTI has become popular since the end of 2019. This phenomenon is more noticeable in Asia than in its origin, the United States. Although corporations adopt it as a marketing tool when seeking the young talent they desire, it remains a pseudoscience whose fundamental principles are incompatible with the scientific method (Ma, 2025). In other words, MBTI lacks empirical evidence, leading to confirmation bias and vague claims. With due reason, MBTI does not appeal to the scientific community because of its lack of scientific validity.

Unlike MBTI, which provides a narrative framework specifically for self-reflection, BFPT is more psychometrically stable, making it an ideal option for predictive social science research. The BFPT was used as the predictor of student academic performance. It is mainly due to its high reliability and statistical validity compared to MBTI (Shaninah & Mohd Noor, 2024; H. Wang et al., 2023). Moreover, each of the five dimensions of BFPT explicitly describes a person’s personality, which is often conceived as more independent and stable, and the model is psychologically grounded in this conception compared to MBTI. From an educational perspective, past studies have suggested that BFPT is closely related to educators’ effectiveness in their profession from various aspects, such as teaching performance and mental exhaustion level (Liu et al., 2022). As a result, the BFPT of oneself can affect the level of collaboration and the educational experience within the academic institution. Henceforth, the BFPT is further studied in current research from students’ perspectives, yielding valuable insights into the relationship between BFPT and students’ academic performance as well as continued use of technology.

To further explain the relationship between students’ BFPT and the technology integration framework, the current research extends the BFPT by integrating it with the technology integration framework. Although models such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) emphasise technology and its utility and features rather than pedagogical approaches (El Alfy & Kehal, 2024; Lotey et al., 2023), they do not address pedagogical approaches. Therefore, the current research adopted the TPACK framework, as the additional technological knowledge domain enables it to fit within modern education. In previous studies, TPACK has been used to assess pre-service teachers’ self-efficacy in integrating technology into their teaching (Bwalya & Rutegwa, 2023; Joshi, 2023; Thohir et al., 2023). However, these studies were solely focused on teachers’ perspectives, with results collected through self-assessment using a dedicated TPACK questionnaire. The classroom activities mainly involve two parties: teachers and students. It is essential to acquire input from students’ perspectives because it is as valuable as input from teachers’ perspectives, as the teachers’ efficacy directly affects the students’ learning experience. In the virtual environment, teachers and students are both users of technology. Hence, the current research examines TPACK from the students’ perspective, with BFPT as the predictor in the research framework.

The proposed research framework in the current research is shown in Figure 4. The research framework is developed with the proposed collaborative learning management system (C-LMS) as the primary environment for learning activities. The Big Five Personality Traits (BFPT) serve as the exogenous predictors that comprise: (1) openness to experience, (2) conscientiousness, (3) extraversion, (4) agreeableness, and (5) neuroticism. Besides, the TPACK domains and subdomains considerations in the current research are solely focusing on the technology-related ones, namely: (1) technological knowledge, (2) technological content knowledge, (3) technological pedagogical knowledge, and (4) technological pedagogical and content knowledge. The dependent variable in this research is continued use of technology.

The BFPT-TPACK framework is formulated in the current research, as shown in Figure 5. The framework adopts the BPFT and TPACK framework with an additional variable that inspects the influences on students’ continued use of technology. On top of that, the current research focuses on the technology-integrated collaborative learning environment, which explains the inclusion of only the technology-related domains and subdomains of the TPACK framework (Johnson et al., 2024). Henceforth, this research aims to establish a more in-depth understanding of the interconnection between the BFPT and TPACK, as well as of students’ continued use of technology from their perspective.

The hypotheses of the research are as follows:

In general, sampling techniques can be categorised into two major types, namely: (1) probability sampling, and (2) non-probability sampling (Farid Shamsudin et al., 2024). Non-probability convenience sampling was used in the current research, where the participants were assigned to different tutorial groups before the experiment began. The research involved 121 local private university students enrolled in the online course via the proposed C-LMS.

The proposed C-LMS is a site designed and developed with features that meet modern educational needs, specifically customised for the virtual learning environment. On the site, a dedicated online course is created and tailored for higher education students to foster collaborative learning in a virtual environment, with in-depth analytical insights. The existing LMS lacks the capability to support collaborative learning, which aligns with modern educational needs (Hemphill et al., 2023; Jaikrishin Belani et al., 2025; Saadati et al., 2023). Hence, the current research proposed a more direct approach to an online course with group-based activities such as group assignments, an open forum, and a glossary. These activities encourage students to interact with their peers and groupmates. The proposed C-LMS is designed to be a cross-platform web application that allows teachers and students to access the site on personal computers, smartphones, and tablets without distorting the user interface across platforms. The proposed C-LMS supports synchronous and asynchronous learning, accommodating students’ preferences for time management. It allows the students to access the learning materials and learn at their own pace. In the meantime, they can interact with their teachers and peers through dedicated spaces, such as real-time chat and forums, to share their thoughts and foster collaboration.

During the experiment phase, a group of local higher education students enrolled in the dedicated online course through the proposed C-LMS. The online course consists of three modules and a post-test assessment as the closure. The students are divided into small groups of 4. Before the students embark on their journey to explore and commit to the online course learning materials, they are required to complete a survey questionnaire about their BFPT. The questionnaire adopted the Big Five Inventory-20 (BFI-20), consisting of 20 self-reported items, with the 4 best-performing items for each personality trait selected from the 44-item Big Five Inventory (BFI-44). After completing the questionnaire, the students are open to accessing real-time chat, open discussion forums, and the learning materials of the first module. Students can proceed to the next module only after completing the first module. Each module offers students video and reading materials to choose from. Students must complete a pre-module knowledge-checking test before they are granted access to the module’s learning materials. The students are then required to work with their groupmate to create a glossary of what they have learnt from that particular module. The same steps are repeated for the remaining modules in the online course. Once all modules are completed, they are required to work in a group on a post-module course reflection, reflecting on what they have learnt throughout the course. After submitting the post-module course reflection, students need to complete a modified TPACK questionnaire. Upon submitting the modified TPACK questionnaire, students are considered to have completed the online course on the proposed C-LMS.

In terms of theoretical implications, the current research extends existing TPACK studies by incorporating BFPT, thereby validating students’ continued use of technology in the virtual collaborative learning environment. The previous studies showed that technology integration outcomes were not as promising; in fact, technology was mostly blamed. Although the TPACK framework has been shown to be highly relevant to modern education, it focuses solely on teachers’ understanding and proficiency in integrating technology into their teaching. Nevertheless, studies on the TPACK framework from students’ perspectives are limited. There are even fewer studies investigating students’ personality traits when the TPACK framework is adopted as the fundamental framework. Thence, the current research has taken the initiative to enrich the literature, indicating that the BFPT positively influenced the students’ TK, leading to a positive impact on the technology-related TPACK domains and subdomains, explaining that the continued use of technology is highly relevant to the students’ TPACK proficiency in the virtual collaborative learning environment. Furthermore, the current research’s findings contributed to the existing literature by addressing how the virtual collaborative learning environment enhanced students’ academic performance. The results also showed that the conventional beliefs and concepts in education, such as pedagogy and teachers’ autonomy, are not applicable in the virtual collaborative learning environment. Thus, the current research can be considered an early adopter of extending and assessing the relationship between the TPACK framework and BFPT from the student perspective, with limited literature.

Several limitations were considered in the current research. One of the most substantial limitations is the number of respondents involved in the research experiment. It might not be able to represent a more diverse demographic of respondents. A larger sample size is recommended in future studies for greater accuracy and reliability. Furthermore, the research experiment was conducted solely through the proposed C-LMS, a virtual collaborative learning environment. Hence, the findings of the current research may not directly reflect the physical collaborative learning environment, especially their BFPT relationship with TPACK. The students’ attitude towards their continued use of technology may differ as well. Moreover, the research focused solely on developing one dedicated system to encourage collaborative learning among students. Thence, it is very encouraging for developing a variety of systems with different classroom settings and activities for future studies. Hence, the collected findings and results offer greater value and insights for future educational technology development.

Zenodo: BFPT-TPACK-C-LMS. https://doi.org/10.5281/zenodo.20395339. (Lai, 2026a)

This project contains the following underlying data:

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

•  Ab Hamid MR, Sami W, Mohmad Sidek MH: Discriminant Validity Assessment: Use of Fornell & Larcker criterion versus HTMT Criterion. J. Phys. Conf. Ser. 2017; 890: 012163. Publisher Full Text
•  Adhami N, Taghizadeh M: Integrating inquiry-based learning and computer supported collaborative learning into flipped classroom: effects on academic writing performance and perceptions of students of railway engineering. Comput. Assist. Lang. Learn. 2024; 37(3): 521–557. Publisher Full Text
•  Ahmadi G, Mohammadi A, Asadzandi S, et al.: What Are the Indicators of Student Engagement in Learning Management Systems? A Systematized Review of the Literature. Int. Rev. Res. Open Distrib. Learn. 2023; 24(1): 117–136. Publisher Full Text
•  AL-Nuaimi MN, Al Sawafi OS, Malik SI, et al.: Extending the unified theory of acceptance and use of technology to investigate determinants of acceptance and adoption of learning management systems in the post-pandemic era: a structural equation modeling approach. Interact. Learn. Environ. 2024; 32(5): 1710–1736. Publisher Full Text
•  Arafat L, Khshali ZA: Unpacking Learner Autonomy: Instructor Insights, Sustainable Benefits, and Challenges in Tertiary EFL Context. Arab World English Journal. 2025; 16(3): 301–314. Publisher Full Text
•  Atmojo IRW, Isnantyo FD, Muzzazinah M, et al.: Analysis of HOTS-Based TPACK Ability Based on Self-Assessment of Teacher Professional Education Students. Mimbar Sekolah Dasar. 2025; 12(2): 326. Publisher Full Text
•  Bjelobaba G, Savić A, Tošić T, et al.: Collaborative Learning Supported by Blockchain Technology as a Model for Improving the Educational Process. Sustainability (Switzerland). 2023; 15(6). Publisher Full Text
•  Bruffee KA: The Art of Collaborative Learning. Change: The Magazine of Higher Learning. 1987; 19(2): 42–47. Publisher Full Text
•  Bwalya A, Rutegwa M: Technological pedagogical content knowledge self-efficacy of pre-service science and mathematics teachers: A comparative study between two Zambian universities. Eurasia Journal of Mathematics, Science and Technology Education. 2023; 19(2): em2222. Publisher Full Text
•  Cohen J: Statistical power analysis for the behavioural sciences/jacob cohen. Statistical Power Anaylsis for the Behavioral Sciences. 2nd ed.1988. Publisher Full Text
•  Cohen J: A power primer. Psychol. Bull. 1992; 112(1): 155–159. PubMed Abstract | Publisher Full Text
•  Dahal N, Manandhar NK: The reality of e-Learning: Success and failure of learning management system. Advances in Mobile Learning Educ. Res. 2024; 4(1): 903–910. Publisher Full Text
•  Dijkstra TK, Henseler J: Consistent partial least squares path modeling. MIS Quarterly: Management Information Systems. 2015; Vol. 39(2). Publisher Full Text
•  El Alfy S, Kehal M: Investigating the factors affecting educators’ adoption of learning analytics using the UTAUT model. The International Journal of Information and Learning Technology 2024; 41(3):280–303. Publisher Full Text
•  Farid Shamsudin M, Abu Hassim A, Abd Manaf S: Mastering Probability and Non-Probability Methods for Accurate Research Insights. Journal of Postgraduate Current Business Research. 2024; 9(1).
•  Gatete O: Revisiting TPACK: A Critical Review and Contextual Extension for the Digital Age. J. Educ. Technol. Syst. 2025; 54: 561–612. Publisher Full Text
•  Gonscherowski P, Rott B: A systematic review of the literature on TPACK instruments used with pre-service teachers from 2017 to 2023 focused on selecting digital resources. J. Comput. Educ. 2025. Publisher Full Text
•  Hemphill H, Hemphill L, Runquist R: Improving Completion in Online Education Systems: An Application of Systems Thinking. Learning, Design, and Technology: An International Compendium of Theory, Research, Practice, and Policy. 2023. Publisher Full Text
•  Holly M, Hildebrandt J, Pirker J: A Computer-Supported Collaborative Learning Environment for Computer Science Education. Communications in Computer and Information Science. 2025; 2271 CCIS. Publisher Full Text
•  Jaikrishin Belani A, Delsouz Bahri AR, Izhar A, et al.: Design and Implementation of an Enhanced Learning Management System: Addressing Modern Academic Challenges.2025. Publisher Full Text
•  Johnson F, Schmit J, Schneider C, et al.: Evaluating the Effectiveness of an Extracurricular Teacher Education Training Program for DigCompEdu Competences. Educ. Sci. 2024; 14(12). Publisher Full Text
•  Joshi SC: TPACK and Teachers’ Self-Efficacy: A Systematic Review. Can. J. Learn. Technol. 2023; 49(2): 1–23. Publisher Full Text
•  Lai D, Sook Ling L, Yin OS: Synchronous Display and Whiteboard-Like Freehand Writing App as Teaching Tool for Virtual Classroom amidst the Pandemic. F1000Res. 2021; 10. Publisher Full Text
•  Lai D, Lew SL, Ooi SY: Technological pedagogical and content knowledge with student course engagement framework for virtual classroom in Malaysia higher education. Front. Comp. Sci. 2025; 7. Publisher Full Text
•  Lai D: BFPT-TPACK-C-LMS. [Data set]. Zenodo. 2026a. Publisher Full Text
•  Lai D: BFPT-TPACK-CLMS-ExtData. Zenodo. 2026b. Publisher Full Text
•  Law VTS, Yee HHL, Ng TKC, et al.: Transition from Traditional to Online Learning in Hong Kong Tertiary Educational Institutions During COVID-19 Pandemic. Technol. Knowl. Learn. 2023; 28(3): 1425–1441. Publisher Full Text
•  Liu Z, Li Y, Zhu W, et al.: A Meta-Analysis of Teachers’ Job Burnout and Big Five Personality Traits. Frontiers in Education. 2022; 7. Publisher Full Text
•  López Martín E, Ardura Martínez D: The effect size in scientific publication. Educacion XX1. 2023; 26(1). Publisher Full Text
•  Lotey EK, Arthur YD, Gordon JF, Adu-Obeng B: Modeling basic school teachers acceptance of instructional technology in advancing mathematical pedagogy in Ghana. Contemporary Mathematics and Science Education. 2023; 4(1): ep23006. Publisher Full Text
•  Lyons M, Limniou M, Schermbrucker I, et al.: The big five, learning goals, exam preparedness, and preference for flipped classroom teaching: Evidence from a large psychology undergraduate cohort. Psychol. Learn. Teach. 2017; 16(1): 36–46. Publisher Full Text
•  Ma X: A Comparison of MBTI and Big Five Personality. Interdisciplinary Humanities and Communication Studies. 2025; 1(3). Publisher Full Text
•  Mishra P, Koehler MJ: Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record. 2006; Vol. 108(6). Publisher Full Text
•  Nallaluthan K, Kamaruddin S, Thurasamy R, et al.: Quantitative Data Analysis using PLS-SEM (SmartPLS): Issues and Challenges in Ethical Consideration. International Business Educ. J. 2024; 17(2): 41–54. Publisher Full Text
•  Petridou E, Molohidis A, Hatzikraniotis E: Contextual Influences on Self-Assessed TPACK: A Comparison of Physics Undergraduates and In-Service Science Teachers. Trends in Higher Education. 2025; 4(4). Publisher Full Text
•  Putu Gede Subhaktiyasa: PLS-SEM for Multivariate Analysis: A Practical Guide to Educational Research using SmartPLS. EduLine: Journal of Education and Learning Innovation. 2024; 4(3): 353–365. Publisher Full Text
•  Rahman MA, Sook Ling L, Yin OS: Interactive Learning System for Learning Calculus. F1000Res. 2022; 11: 10.12688/f1000research.73595.2. Publisher Full Text
•  Ravichandran K, Anita Virgin B, Edwin MRS, et al.: Moving towards a Blended Tomorrow: The Evolution of EdTech in Education. Libr. Prog. Int. 2024; 44(3).
•  Saadati Z, Zeki CP, Vatankhah Barenji R: On the development of blockchain-based learning management system as a metacognitive tool to support self-regulation learning in online higher education. Interact. Learn. Environ. 2023; 31(5): 3148–3171. Publisher Full Text
•  Salarvand S, Mousavi MS, Rahimi M: Communication and cooperation challenges in the online classroom in the COVID-19 era: a qualitative study. BMC Med. Educ. 2023; 23(1): 201. PubMed Abstract | Publisher Full Text | Free Full Text
•  Shaninah FSE, Mohd Noor MH: The impact of big five personality trait in predicting student academic performance. Journal of Applied Research in Higher Education. 2024; 16(2): 523–539. Publisher Full Text
•  Shmueli G, Sarstedt M, Hair JF, et al.: Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. Eur. J. Mark. 2019; 53(11): 2322–2347. Publisher Full Text
•  Shulman LS: Those Who Understand: Knowledge Growth in Teaching. Educ. Res. 1986; 15(2): 4–14. Publisher Full Text
•  Su M, Foulger TS: We Aren’t There Yet: A Progression of Literature on TPACK Measures to Assess Technology Integration. Proceedings of Society for Information Technology & Teacher Education International Conference. 2019. (September).
•  Sulaiman TT, Mahomed ASB, Rahman AA, et al.: Understanding Antecedents of Learning Management System Usage among University Lecturers Using an Integrated TAM-TOE Model. Sustainability (Switzerland). 2023; 15(3). Publisher Full Text
•  Teichert L, Piazza S, Hinga J: Teachers’ digital pedagogies and experiences in virtual classrooms. Improv. Sch. 2023; 26(1): 54–69. Publisher Full Text
•  Thohir MA, Ahdhianto E, Mas’ula S, et al.: The effects of TPACK and facility condition on preservice teachers’ acceptance of virtual reality in science education course. Contemporary Educ. Technol. 2023; 15(2). Publisher Full Text
•  Vasileva-Stojanovska T, Malinovski T, Vasileva M, et al.: Impact of satisfaction, personality and learning style on educational outcomes in a blended learning environment. Learn. Individ. Differ. 2015; 38: 127–135. Publisher Full Text
•  Wang B, Noor MS b M, Hussain N: Understanding How Environmental Self-Identity, Perceived Risk, and Frugal Innovation Shape Low-Carbon Behavior. Journal of Human, Earth, and Future. 2026; 7(1): 117–131. Publisher Full Text
•  Wang H, Liu Y, Wang Z, et al.: The influences of the Big Five personality traits on academic achievements: Chain mediating effect based on major identity and self-efficacy. Front. Psychol. 2023; 14. PubMed Abstract | Publisher Full Text | Free Full Text
•  Weiling CT, Ling LS, Yin OS: Personalised and Collaborative Learning Experience (PCLE) Framework for AI-driven Learning Management System (LMS). F1000Res. 2025; 14. Publisher Full Text
•  Yildiz Durak H: Role of personality traits in collaborative group works at flipped classrooms. Curr. Psychol. 2023; 42(15): 13093–13113. PubMed Abstract | Publisher Full Text | Free Full Text