Association between Learning Management System assessment tools and academic decision making: A Case of the Faculty of Management Studies and Commerce in a Selected State University in Sri Lanka.

Association between Learning Management System assessment tools and academic decision making: A Case of the Faculty of Management Studies and Commerce in a Selected State University in Sri Lanka. | 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 Association between Learning Management System assessment tools and academic decision making: A Case of the Faculty of Management Studies and Commerce in a Selected State University in Sri Lanka. Nadumi Nimasha Sellahewa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6391834/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The integration of Learning Management Systems in higher education has significantly transformed traditional teaching, learning, and assessment methods. LMS platforms provide various assessment tools, such as quizzes, assignments, and feedback mechanisms, which are increasingly used to support academic decision-making. This study examines the association between LMS assessment tools and academic decision-making within the Faculty of Management Studies and Commerce at a selected state university in Sri Lanka. The research aims to explore faculty members' utilization of LMS assessment tools, their perceived usefulness and ease of use, and how these tools influence key academic decisions, including curriculum development, student evaluation, and instructional improvement. A quantitative research approach was adopted, utilizing structured questionnaires distributed to 150 faculty members. Data analysis, including correlation and regression techniques, revealed that LMS assessment tools play a significant role in shaping academic decisions. The study also found that perceived usefulness and perceived ease of use are critical factors influencing faculty adoption of these tools. Challenges such as system complexity and inadequate faculty training hinder effective utilization. The findings highlight the need for improving LMS usability, enhancing faculty training programs, and integrating more data-driven analytics to optimize academic decision-making processes. This study contributes to the growing body of research on educational technology and offers practical recommendations for enhancing LMS implementation in Sri Lankan universities. Academic decision-making Assessment tools Learning Management System perceived ease of use perceived usefulness Figures Figure 1 Figure 2 1. Introduction 1.1 BACKGROUND OF THE STUDY Traditional teaching and learning approaches have experienced a substantial transformation as a result of the use of technology in education. At the forefront of this transformation is the implementation of Learning Management Systems (LMS), which have become a fundamental component in the management and delivery of educational content. An LMS is a software application designed to facilitate the administration, documentation, tracking, reporting, automation, and delivery of educational courses, training programs, or learning and development programs. It serves as a central hub where educators and students can interact, access course materials, participate in assessments, and track academic progress (Mitra, 2022) Learning Management Systems have evolved over the past few decades, becoming increasingly sophisticated and user-friendly. Initially designed to support distance learning programs, LMS platforms now offer a wide array of features that cater to both traditional and online educational environments. These systems enable institutions to provide a consistent and standardized educational experience, regardless of the physical location of the learners and educators. Assessment tools are particularly significant among the various features of LMS platforms as they directly influence academic decision-making. These tools include quizzes, assignments, exams, grading systems, and analytics dashboards, providing educators with real-time student performance data. 1.1.1 Importance of LMS in Higher Education LMS are becoming essential tools for higher education institutions in the modern educational environment. Numerous features, including communication tools, assessment procedures, and course content distribution, are available on LMS platforms. By giving instructors and students alike a consolidated and easily accessible platform, these solutions are intended to improve the teaching and learning process. Higher education in Sri Lanka is increasingly viewing the incorporation of LMS as a calculated step toward bettering student outcomes and harmonizing with international educational norms. Assessment practices in higher education have also changed, moving away from accountability-based evaluation and toward improvement-based evaluation. Assessment is more inclusive of institutional professors and staff, more useful to colleges and universities, and more focused on improving instruction and learning when it is done for improvement. We have a culture of assessment in contemporary learning environments where measurement and instruction are combined.(de Jesús Araiza and García, 2021). LMS assessment tools can help lecturers save time throughout the evaluation process and give students feedback more quickly (Barkand, 2017). LMS is increasingly providing digital tools that could aid in assessment by partially automating numerous monotonous tasks. Since the LMS facilitates regulated and documented teaching and learning as well as feedback cycles for ongoing improvement, assessment of the system also offers quality assurance for accreditation ( Atkinson & Lim, 2013). Access to student performance data that could influence pedagogical practice through LA, which evaluates and acts upon the data to maximize learning, is one advantage of using the LMS. 1.1.2 LMS Assessment Tools and their Role in Education Moodle LMS offers a diverse selection of robust assessment tools that accommodate different methodologies of teaching and learning, encompassing both formative and summative evaluations. Assignments The Assignment activity allows students to submit work digitally for grading. Text posted online or in the form of files can be submitted. educators can use annotations, comments, and even rubric-based grading to give feedback. Because it integrates plagiarism detection and allows collaborative work, it is adaptable to a variety of task kinds (Moodle Org., 2024). Choice With the Choice activity, learners may pose a question and offer several possible answers using a straightforward survey instrument. Students are free to choose how they respond, and the outcomes can be shared publicly or kept confidential. It's a simple, informal way to get feedback or direct class decisions (Moodle Org, 2024). Feedback Educators can construct surveys with unique questions using the input activity to get student input. Feedback is a great tool for getting course feedback because it allows you to design non-graded questionnaires, unlike the Survey tool (Moodle Org, 2024). Lesson With the use of the lesson activity, educators can design learning pathways that are either branching or linear and allow students to make decisions that take them through various course materials. Instructors can incorporate (Moodle Org, 2024). Quiz One of Moodle's most flexible features is the Quiz activity, which offers a large range of question kinds (such as multiple-choice, true/false, and short answer) along with configurable grading and feedback (Moodle Org, 2024). Survey Standard, pre-built surveys like the COLLES (Constructivist Online Learning Environment Survey) and ATTLS (Attitudes to Thinking and Learning Survey), which are available through Moodle's Survey activity, help collect (Moodle Org, 2024). Workshop The Workshop activity provides for peer evaluation of student work. Students submit assignments for peer review using teacher-set criteria. Both contributions and peer reviews are graded, which encourages collaboration and critical thinking (Moodle Org, 2024). 1.1.3 Role of LMS in Academic Decision-Making Processes LMS plays an important role in academic decision-making by facilitating curriculum development, student evaluation, and instructional improvement via data-driven insights and streamlined processes (Mitra, 2022). Curriculum development LMS platforms give complete data on student performance and engagement, allowing instructors to identify effective courses and areas for change. Quizzes, assignments, and surveys are useful tools for determining how well pupils understand specific topics. This information assists curriculum planners in tailoring content to better satisfy learning outcomes and ensure alignment with academic standards. Furthermore, the flexibility of LMS enables the inclusion of multimedia, making curricular content more engaging and adaptive to varied learning demands. Student Evaluation LMS systems provide a single platform for measuring student performance using a variety of approaches, such as quizzes, peer assessments, and assignments. With built-in analytics, teachers may monitor student progress in real-time, discovering individual and class-wide learning gaps. Educators rely on this data to make informed decisions about interventions, support, and enrichment opportunities for students. Furthermore, LMS enables the use of rubrics and thorough feedback methods, which standardize evaluations and increase transparency. Instructional Improvement LMS platforms offer ongoing feedback loops for both students and teachers. Educators can improve their educational practices by examining data from exams, surveys, and engagement measures. Features like discussion forums and peer assessments promote collaboration and reflection, allowing teachers to experiment with alternative instructional methodologies and teaching approaches. LMS solutions also promote teacher growth through self-assessment and professional learning communities, establishing a culture of ongoing instructional improvement (Geddes, 2009). 1.2 PROBLEM STATEMENT LMS in higher education institutions has transformed traditional teaching and learning methods, offering new opportunities for enhancing educational experiences through technology. Among the various features of LMS platforms, assessment tools such as quizzes, assignments, exams, grading systems, and analytics dashboards are crucial in facilitating student evaluation and academic decision-making. However, despite the widespread adoption of LMS in universities globally, there is a pressing need for more understanding of how these assessment tools impact academic decision-making processes, particularly in the context of Sri Lankan higher education. At the Faculty of Management Studies and Commerce in a selected state university in Sri Lanka, where LMS adoption has increased in response to the growing demand for flexible and data-driven educational practices, the effectiveness of LMS assessment tools in influencing academic decisions such as curriculum development, instructional strategies, and student support services remains underexplored. Furthermore, challenges such as inadequate training for faculty, varying levels of digital literacy among educators and students, and limited access to advanced LMS features may hinder the optimal use of these tools for informed decision-making. This study investigates the Impact of Learning Management System Assessment Tools on Academic Decision Making within this context. By examining how educators and administrators utilize data from LMS assessment tools to make key academic decisions, the study seeks to identify factors that affect the effectiveness of these tools and provide insights for enhancing their use in Sri Lankan higher education institutions. 1.3 RESEARCH QUESTIONS How do faculty members use LMS assessment tools? How do LMS assessment tools influence academic decision-making? How useful are LMS assessment tools in academic decision-making? How easy are LMS assessment tools in academic decision-making? 1.4 RESEARCH OBJECTIVES General Objective To examine the association between Learning Management System assessment tools and academic decision-making. Specific Objectives To investigate the ways faculty members, utilize LMS assessment tools. To determine the influence of LMS assessment tools on academic decision-making. To identify the usefulness of LMS assessment tools for academic decision-making. To identify the ease of use of LMS assessment tools for academic decision-making. 2. Literature Review Higher education institutions are increasingly utilizing innovative methods of student assessment and learning enhancement in response to the increased demand for accountability and transparency on the quality of student learning (Natasha A. Jankowski, 2012). Professionals in higher education widely acknowledge that assessments enable us to evaluate students' learning. However, it is rather uncommon to use evaluation data to alter teaching methods and curriculum (Natasha A. Jankowski, 2012), (Carless, 2009). With access to student data through the LMS assessment tools and the ability to analyze the data, educational institutions are using experimental predictive analyses to detect areas of instruction. The use of technology in evaluation could raise students' self-efficacy, learning engagement, performance, and achievement (Chen and Zhang, 2017). A Learning Management System is desired by the majority of contemporary universities to manage teaching and learning activities. Offering students access to online lecture materials via the Internet at any time and from any location is vital in some way (A. Nkhoma et al. , 2020). All Sri Lankan universities have started using learning management systems because they recognize the importance of these demands and think that distant learning would only grow in importance within the educational system. According to (Kommerell and Klein, 2020), learning management systems are crucial for managing online programs and developing material. The ability to create a learning and teaching environment free from time or location constraints is one of the most crucial aspects of LMS (Didam Markus, 2015). 2.1 LMS Currently, the use of ICT in education varies in different countries and educational institutions. LMS and E-Learning are generally useful for students, particularly for those who are ill or have somewhere to stay away from the educational institution, as learning never stops regardless of time or distance limitations (Aldiab et al. , 2019). The ability to assist in the creation of standardized content is one benefit of online learning. However, the drawbacks of online learning are their rigid, unchanging frameworks that do not allow for customized adjustments to meet the needs of individual learners (Ueda et al. , 2018). LMS also called digital learning environment, online learning environment, course management system, or virtual learning environment, is a web-based platform that allows teachers to create online courses (Aikina and Bolsunovskaya, 2020). Currently, most of the universities in the world are using several commercial and open sources available LMS packages such as Moodle, Blackboard, Canvas, and D2L. A short descriptions of each LMS are given below. Moodle Martin Dougiamas founded Moodle in 1999, and the initial iteration, known as Moodle 1.0, was released in 2002. The acronym for Modular Object-Oriented Dynamic Learning Environment is Moodle. The server in use at the time was located at Curtin University of Technology's Science and Mathematics Education Centre in Perth, Western Australia. Moodle 3.6.1 is the most recent version of this program (December 2018). There is no registration or annual renewal charge for Moodle, an open-source, free learning management system.(Moodle Org, 2024). Blackboard In 1997, Michael Chasen and Matthew Pittinsky founded Blackboard LLC. Between 1998 and 2004, Blackboard LLC combined with other rivals like Course Info LLC and WebCT and acquired various businesses in the same industry, including MadDuck and Prometheus. In contrast to Moodle, Blackboard is a for-profit learning management system that charges a registration cost and an annual renewal fee (Bradford et al. , 2007), Additionally, certain features may require money in order to be activated. Canvas Josh Coates founded Canvas in 2008, and the first Canvas was unveiled in 2011. In 2012, Canvas Network was established. In the past, Canvas was called Instructor, but its founders eventually changed the name. It is believed that Canvas is an open-source program.(Instructure, 2024). D2L In 1999, John Baker founded D2L (Desire2Learn). It is an open-source program that runs on the cloud. D2L has offices for official representatives in numerous nations worldwide. Since its inception, this LMS has accomplished a number of noteworthy milestones. Among the most noteworthy is the fact that D2L was the second partner after Target Corporation and the first LMS to be admitted into the National Federation of the Blind's (NFB) new Strategic Nonvisual Access Partnership (SNAP) initiative in 2016. (D2L Corporation, 2024). 2.2 LMS Assessment Tools Assessment, defined as “a systematic process for gathering data about student achievement,” is an essential component of teaching (Dhindsa, Omar and Waldrip, 2007). The evaluation of student outcomes is process-improvement-oriented, goal-driven, scientifically grounded, and concentrated on the learning outcomes of specific students (Natasha A. Jankowski, 2012), (Al-Fraihat et al. , 2020). Students who receive assessments are better able to focus on their areas of weakness and have more chances to achieve their goals. It also gives academics the chance to evaluate their instructional strategies and modify the curricula of their courses and programs. Creating a meaningful and ongoing assessment of student learning and success also gives faculty members the chance to collaborate and share ideas with colleagues in their discipline as well as with other stakeholders like student services, library staff, and administration (Mitra, 2022), (Hajjej, Hlaoui and Ayed, 2015). Determining the true level of student LMS usage requires significant attention in colleges that use LMSs. Even if trends are moving toward learning tools that are more focused on the needs of students, research has revealed that teachers are still the primary users of learning management systems. When one considers how difficult it is to determine how much an LMS is genuinely utilized inside an institution, it becomes clear that there is a need for an accurate indicator of the level of LMS utilization. Numerous research have examined a variety of LMS adoption, implementation, support, and usage-related topics (Murshitha and Wickramarachchi, 2016). 2.3 Faculty Role in Assessment The purpose of assessments has changed from being a required activity to a collaborative endeavor between faculty members to enhance student learning (Mello et al. , 2016). Faculty play a vital role in supporting, empowering, and advancing the academic success and intellectual independence of the students (Acceptance, 2009). One main feature of high-order assessment is high-level instruction, complementing instruction and evaluation (Waleed Mustafa Eyadat a, 2010). (Grandgirard et al. , 2002) state that, ideally, assessment “enhances learning, provides feedback about student progress, builds self-confidence and self-esteem, and develops skills in evaluation”. In addition, they argue that when instruction, assessment, and outcomes are all in line, learning happens effectively. Therefore, assessment plays a crucial role in learning because of its strong relationship to instruction and learning outcomes (Waleed Mustafa Eyadat a, 2010). Research has also shown that faculty need to be engaged to advance assessment and to use the assessment data as evidence to guide improvement (Banta and Blaich, 2010). Different types of assessment techniques are implemented, for instance, to raise equity or improve performance, albeit their effectiveness relies on how educators use them (Sivanand, 2017), (Strandler, 2016). Additionally, faculty must take calculated risks by implementing cutting-edge teaching strategies and creative forms of evaluation that improve student learning (Lock et al. , 2018). There is still much faculty can do in their classrooms and programs to harness the power of technological convergence in ways that benefit student learning (Acceptance, 2009). More research in established best practices is needed in this field. 2.4 Learning Management Systems in Sri Lankan Universities The rapid advancement of ICT infrastructures in Sri Lanka encourages all educational establishments to utilize the internet as a means of student communication. the efficient and successful acquisition of educational resources made possible by the ideas and practices of technology-based learning. Using e-learning resources more often makes them an invaluable resource for academic institutions. Higher education has made extensive use of LMS because of its many benefits, which include endless remote learning opportunities and flexible scheduling of lectures (Kommerell and Klein, 2020). The open-source Moodle platform provides the LMS in the majority of state universities in Sri Lanka. Some of the currently in-use learning management system interfaces at Sri Lankan university systems are displayed in Figure 1. Well-managed Moodle learning management systems should have at least the following characteristics, according to Sri Lankan universities: The lecturers' and students' registration on the learning portal. Planning and scheduling the course's schedule and structure. Provide a delivery method or make the course available to users who have registered. 2.5 Why Moodle in Sri Lanka Moodle, which stands for Modular Object Oriented Developmental Learning Environment, is an online course management system that is also known as a Virtual Learning Environment (VLE) or an LMS. Teachers can utilize this free online learning environment as a model for successful online learning systems. In this way, it can serve as an example of successful online education initiatives. One of the main benefits is that it is open-source, meaning that anyone with programming skills can use it and customize the environment to suit their needs. Any number of servers can have it installed for free, and updating doesn't require any maintenance fees. Universities, communities, schools, instructors, courses, and even businesspeople use this learning platform all over the world. Universities in Sri Lanka also adjust to this. Socio-constructivist pedagogy served as the foundation for Moodle's design (Kommerell and Klein, 2020). This indicates that its objective is to provide a set of resources that support an approach to online learning that is built on inquiry and discovery. 2.6 Technology-Acceptance Model The technology acceptance model (TAM), put out by (Venkatesh and Davis, 2000), is one of the most well-known models of technology and its application in blended learning and e-learning. TAM can be used to understand the factors that influence technology usage and to learn about the beliefs and actions of users on their preferred methods of using information (Buchanan, Sainter and Saunders, 2013). This paradigm assists in the explanation of how a user's acceptance or rejection of technology is influenced by perceived usefulness and perceived ease of use (PEOU). According to (Amornkitpinyo and Piriyasurawong, 2017), perceived usefulness is the extent to which one believes a certain innovation is efficient and well-executed, potentially improving job performance. One of the main motivators for TAM and a secondary source is perceived ease of use. PEOU stands for perceived ease of use (Users' belief in the practicality of a certain system's use(Amornkitpinyo and Piriyasurawong, 2017). The TAM also includes a behavioral intention to use and attitude, two additional variables that influence technology adoption. As to the TAM, attitude is the correlation between a system's utility and ease of use. The users' general ideas about utilizing technology impact their inclination to accept a prospective technology. Additionally, TAM suggests that through mediated effects on perceived utility and perceived ease of use, external variables influence intention and actual use (Venkatesh and Davis, 2000). 3. Methodology The research methodology is a systematic way to solve the research problem. There have two main approaches are used for research studies such as quantitative and qualitative. This research is deductive and I will use a quantitative approach for data collection. The quantitative research approach is based on the measurement of quantity or amount. It is used when one begins with a hypothesis and tests for confirmation and disconfirmation of that hypothesis. 3.1 CONCEPTUAL FRAMEWORK This conceptual framework aims to investigate the relationship between LMS assessment tools and academic decision-making within a selected Sri Lankan state university. The framework posits those four key independent variables Convenience , Accessibility , Simplicity , and Benefits influence academic decision-making, which serves as the dependent variable. These variables are critical to understanding how LMS assessment tools are perceived and utilized by academic decision-makers, thereby impacting decisions related to curriculum development, student evaluation, and instructional improvement. In this framework, Academic Decision-Making is considered the dependent variable, encompassing three major areas; Curriculum Development , Student Evaluation , and Instructional Improvement . These decisions rely heavily on the information provided by LMS assessment tools, which offer data that is timely, relevant, and reflective of student performance. The quality of these decisions is directly correlated with the degree to which the assessment tools are perceived to meet certain criteria, which are captured in the independent variables of the framework. The independent variables, Convenience , Accessibility , Simplicity , and Benefits are derived from the core components of the TAM, which has been widely used to understand user acceptance of technology. Convenience refers to the ease with which users can engage with LMS assessment tools and integrate them into their daily workflows. Accessibility pertains to the extent to which these tools are available and usable across various devices, platforms, and environments. Simplicity involves the user-friendly design of the tools, which ensures that they do not introduce unnecessary complexity or barriers to their use. Finally, Benefits encompass the perceived advantages that users derive from the use of LMS assessment tools, such as improved efficiency, better student engagement, and enhanced academic outcomes. The TAM , developed by Davis (1989), serves as the theoretical foundation for this framework. TAM posits that two key beliefs, PU and PEOU are fundamental in explaining technology acceptance and usage. PU is defined as the degree to which a person believes that using a particular system will enhance their job performance. In the context of LMS assessment tools, this refers to the belief that these tools will improve academic decision-making processes, such as enhancing the accuracy of student evaluation or informing curriculum adjustments. PEOU refers to the degree to which a person believes that using a particular system will be free of effort. For LMS assessment tools, this involves the perceived ease with which these tools can be utilized by educators, without requiring significant additional effort or learning curves. The theoretical foundation of PU and PEOU is rooted in the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB) , both of which emphasize the role of beliefs in shaping attitudes, which in turn influence intentions and behaviors (Ajzen, 1991). According to these theories, individuals' beliefs about the outcomes of using a system such as its perceived usefulness and ease of use directly shape their attitudes toward the system. These attitudes, in turn, influence their intentions to adopt the system and ultimately their actual usage behavior. By focusing on PU and PEOU , TAM simplifies the complex process of technology adoption. It identifies key intervention points where efforts can be directed to enhance users’ perceptions of the system, thereby improving their acceptance and usage behavior. In the context of this research, understanding how Convenience , Accessibility , Simplicity , and Benefits influence PU and PEOU will provide valuable insights into the factors that drive academic decision-makers’ adoption of LMS assessment tools. By enhancing these perceptions, universities can improve the effectiveness of LMS tools, leading to more informed and data-driven academic decision-making processes. 3.2 HYPOTHESES H0 - There is no significant relationship between convenience and academic decision-making. H1 - There is a significant relationship between convenience and academic decision-making. H0 - There is no significant relationship between accessibility and academic decision-making. H2 - There is a significant relationship between accessibility and academic decision-making. H0 - There is no significant relationship between simplicity and academic decision-making. H3 - There is a significant relationship between simplicity and academic decision-making. H0 - There is no significant relationship between benefits and academic decision-making. H4 - There is a significant relationship between benefits and academic decision-making. 3.3 RESEARCH DESIGN The purpose of this study is to identify the impact of Learning Management System assessment tools and academic decision-making. There are two types of data. Primary data and secondary data. The Researcher has planned to use primary data for this study. Primary data will be collected by questionnaires. Primary data will be collected from responders of the Faculty of Management Studies and Commerce in a selected state universities in Sri Lanka. 3.4 RESEARCH APPROACH (Kamolson, 2007) Suggests that quantitative research is suited when specific hypotheses are tested in the research study. The purpose of this study is to prove the specific hypotheses. Also, this study uses statistical data analysis methods such as correlation and regression analysis to test the hypotheses by using numerical values. This research utilizes a Deductive approach, which is consistent with its aim of testing predetermined hypotheses and investigating the correlations among variables. A deductive approach formulates hypotheses based on a theoretical framework or well-established models, such as the TAM. This research investigates the perceived usefulness and ease of use of LMS assessment tools and their impact on academic decision-making. The study collects and analyzes actual data in an attempt to verify or disprove these hypotheses using the deductive method. This approach ensures a disciplined and methodical assessment of how LMS tools influence decision-making in areas like curriculum development and instructional improvement. 3.5 TECHNIQUES AND PROCEDURES 3.5.1 Research population A selected state university in Sri Lanka related to my research has 10 faculties, encompassing diverse academic disciplines. The total number of the Faculty of Management Studies and Commerce in academic staff members is around 244. This population forms the basis of our study, focusing on how LMS assessment tools impact academic decision-making processes. 3.5.2 Research sample Developed by Krejcie and Morgan in 1970, the Morgan Table is a statistical tool used to calculate sample sizes for research projects. It gives researchers a way to determine the right sample size based on the size of the population, guaranteeing statistical significance and representativeness. The Morgan Table recommends 150 sample sizes for a population of 244. Standard criteria in educational and social scientific research include a 95% confidence level and a 5% margin of error, which are assumed in this computation. The Morgan Table's underlying formula accounts for the necessity of striking a compromise between practical limitations like time and resource availability and the accuracy of the data. A simple random sampling method is employed within the faculty to select individual participants. From the total population, a sample size of 150 faculty members is randomly chosen, ensuring that each individual has an equal chance of being included in the study. 3.6 METHOD OF THE DATA COLLECTION Secondary data Some important data were collected from different secondary data sources. The previous studies were based on LMS assessment tools and academic decision-making. Its related areas provided the researcher with a comprehensive understanding of the conceptualization of the research study. Also, in the previous studies the researcher used journals and articles that are related to the LMS assessment tools. Accordingly, previous studies, articles, and journals helped the researcher to get an insight into the sample size, construction of the questionnaire, and scaling procedures of the study. Primary data The primary data was collected by using a structured questionnaire for independent variables, and dependent variables. The questionnaires were distributed to the respondents using a Google Form and distributed through emails. 3.7 OPERATIONALIZATION OF THE STUDY 4. Analysis 4.1 INTRODUCTION The approach by which academic activities are managed in higher education has been completely transformed by the introduction of Learning Management Systems (LMS), especially in using evaluation tools. These technologies, integrated into LMS systems, give lecturers creative ways to evaluate student performance and make accurate academic decisions. The relationship between LMS assessment tools and academic decision-making is examined in this chapter, with a particular focus on the opinions of faculty members at the selected state university in Sri Lanka. Knowing how LMS assessment tools affect learning and instruction is essential as more and more higher education institutions rely on digital technologies. A lot of academic decision-making, including curriculum development, student evaluation, and instructional improvement, depends on precise, timely, and useful data. Those decisions could be greatly impacted by LMS assessment tools, which are intended to offer such insights. However, as suggested by the study's hypotheses, perceived usefulness and perceived ease of use, are two important elements that determine how effective they are. 4.2 DEMOGRAPHIC ANALYSIS 4.2.1 University Positions in the sample Diverse representation across academic levels is revealed by the demographic analysis of university positions. The largest percentage of replies (36.0%) were from Temporary Assistants Lecturers which made up 54 individuals. Lecturer (Probationary), who made up 29.3% of the sample (44 individuals), came next. With a 14.0% share in Grade I (21 individuals) and a 2.7% percentage in Grade II (4 individuals), senior lecturers made up a sizeable portion. With 10.0% of the respondents being professors (15 individuals) and only 1.3% being associate professors (2 individuals), professors and associate professors together accounted for a lesser percentage of the respondents. This distribution indicates that the sample's academic workforce is youthful, with a significant proportion of early-career academics, especially probationary and temporary assistant lecturers. 4.2.2 University Positions and Assessment Tools Crosstabulation Assignments, lessons, and quizzes are the most often utilized assessment tools of the Faculty of Management Studies and Commerce according to the crosstabulation, demonstrating their significance in academic evaluations. With minimal usage of tools like Feedback, Choice, Workshop, and Survey. The Faculty of Management Studies and Commerce mainly rely on assignments and quizzes. 4.3 VALIDITY AND RELIABILITY OF THE RESEARCH Reliability According to previous research, it is generally accepted that the value of Cronbach’s Alpha value is between 0. 6 and 0.7, if Cronbach’s Alpha value is between 0.7 and 0.8 Strong if Cronbach’s Alpha value is between 0.8 and 1 Very strong. The researcher has calculated Cronbach’s Alpha value of independent variables and dependent variables. According to the calculations, the researcher could conclude that the set of questions that are used for the variance of attitude was reliable. Validity To satisfy the convergent validity, three conditions should be satisfied. Kaiser- Meyer-Olkin Measure (KMO) value should be greater than 0.5 Sig value of Bartlett’s Test of Sphericity should be less than 0.05 Average Variance Explained (AVE) value should be greater than 0.5 4.4 DESCRIPTIVE STATISTICS The mean values for all variables, Assessment Tools (4.6333), Academic Decision (4.4978), Convenience (4.4867), Accessibility (4.5700), Simplicity (4.4453), and Benefit (4.4500) are above 3 on a 5-point Likert scale. This indicates that respondents generally provided positive evaluations, reflecting agreement or strong agreement with the usability statements in the questionnaire. If the mean value had been below 3, it would have indicated disagreement or strong disagreement with the statements. Therefore, the results support the validity of the questionnaire. Additionally, the standard deviation values for all variables are less than 1 (Assessment Tools = 0. 47886, Academic Decision = 0. 30162, Convenience = 0.38183, Accessibility = 0.40666, Simplicity = 0.35983, and Benefit = 0.34271), suggesting minimal variation in responses. This indicates that respondents share similar attitudes toward the given variables. A standard deviation greater than 1 would have implied greater variability in responses. Thus, the low standard deviations further confirm consistency and agreement among respondents. 4.5 CORRELATION ANALYSIS The correlation analysis explores the relationships between academic decision-making and four key factors: convenience, accessibility, simplicity, and benefit. The results indicate that all these factors have a strong and statistically significant positive correlation with academic decision-making, suggesting that improvements in these aspects of the LMS can enhance academic choices. Among these, simplicity has the highest correlation (0.770), indicating that a user-friendly and easy-to-navigate LMS substantially impacts academic decisions. Convenience (0.718), benefit (0.680), and accessibility (0.662) also show strong correlations, emphasizing their role in shaping students’ academic choices. The interrelationships among these variables suggest that enhancing one factor, such as accessibility, is likely to improve others, such as convenience and benefit. These findings highlight the importance of designing LMS platforms that prioritize simplicity, accessibility, and user convenience to support effective academic decision-making in Sri Lankan higher education institutions. 4.6 REGRESSION ANALYSIS A statistical technique for analyzing the relationship between one or more independent variables (predictors) and a dependent variable (outcome) is regression analysis. Researchers can learn how changes in independent factors affect or forecast the dependent variable through approach. In order to find patterns, test hypotheses, and make predictions, regression analysis is frequently used in research in a variety of fields, including business, education, social sciences, and economics. When two or more independent variables have a strong correlation with one another, this is known as multicollinearity in regression analysis. This indicates that one independent variable may be significantly predicted from the others using a linear model. A certain amount of correlation between variables is normal, but too much multicollinearity can cause issues for statistical modeling. By raising the standard errors of the predictors' coefficients, multicollinearity compromises the statistical significance of individual predictors. Even if the model as a whole may still have good predictive ability, this makes it difficult to ascertain the actual impact of each variable on the dependent variable. It is crucial to determine whether multicollinearity among the independent variables exists before moving further with regression analysis. High levels of correlation between independent variables can lead to multicollinearity, which can make the regression coefficients unstable and make it more difficult to interpret the results. The following standards are applied in order to assess multicollinearity. Pearson Correlation Coefficient - Multicollinearity may be indicated by a high correlation (> 0.8) between independent variables. Tolerance - Multicollinearity is suggested by a tolerance value below 0.1. The formula for tolerance is 1−R 2 , where 𝑅 2 is the percentage of variance that can be accounted for by the other independent variables. Variance Inflation Factor (VIF) - Significant multicollinearity is indicated by a VIF value larger than 5. VIF calculates the extent to which correlations with other predictors increase a coefficient's variance. There is less chance of severe multicollinearity since, according to the correlation table, none of the independent variable pairings show abnormally high correlations (above 0.8). The researcher should, however, verify this further by looking at the regression output's tolerance and VIF values. 4.6.1 Tolerance and VIF In relationship to the dependent variable, Academic Decision, the table shows the collinearity statistics, such as Tolerance and Variance Inflation Factor (VIF), for the independent variables: Convenience, Accessibility, Simplicity, and Benefit. All of the tolerance values, which fall between 0.372 and 0.499, are significantly over the 0.1 threshold, suggesting that there is no significant multicollinearity among the independent variables. The absence of multicollinearity is further supported by the fact that all VIF values, which vary from 2.006 to 2.690, are below the 5-point threshold. These findings show that the assumption of no multicollinearity is met and that there is not an excessive correlation between the independent variables. Therefore, it is appropriate to proceed with the regression analysis without any concerns regarding multicollinearity. 4.6.2 Model summary The model summary provides insights into the effectiveness of the regression model in explaining the variation in academic decision-making based on the predictors: benefit, accessibility, simplicity, and convenience. The R value (0.742) indicates a strong positive correlation between the independent variables and academic decision-making. The Adjusted R Square value (0.738) suggests that 73% of the variance in academic decision-making can be explained by the given predictors, demonstrating a high level of explanatory power. The standard error of the estimate (0.20503) represents the average deviation of the observed values from the predicted values, indicating the model’s accuracy. These results confirm that benefit, accessibility, simplicity, and convenience are strong predictors of academic decision-making, reinforcing the importance of these factors in influencing students' choices within the LMS context. 4.6.3 Analysis of variance (ANOVA) The ANOVA table evaluates the overall significance of the regression model in explaining the variance in academic decision-making. The regression sum of squares is 7.460, while the residual sum of squares is 6.095, leading to a total sum of squares of 13.555. The degrees of freedom (df) for regression are 4, corresponding to the number of predictors (Benefit, Accessibility, Simplicity, and Convenience), while the residual df is 145, representing the remaining variance not explained by the model. The mean square for regression is 1.865, whereas the mean square for residuals is 0.042. The F-statistic, which measures the overall fit of the model, is 44.364, indicating a strong explanatory power of the predictors. The significance value (Sig.) is .000, suggesting that the regression model is statistically significant at the 0.05 level. This implies that the predictors collectively have a significant impact on academic decision-making, justifying the use of LMS assessment tool-related factors in influencing decision-making processes. 4.7 COEFFICIENT The coefficient table provides evidence for testing the study's hypotheses regarding the relationship between LMS Convenience, Accessibility, Simplicity, Benefit, and academic decision-making. 4.8 TEST HYPOTHESIS 5. Conclusion and Recommendations Along with two other significant features, PU and PEOU, the current study aimed to investigate the relationship between academic decision-making and assessment tools from LMS. The study's goals were to investigate how faculty members use LMS assessment tools, ascertain how they impact academic decision-making, and evaluate the practicality and usability of these tools in aiding decision-making. This study uses regression analysis to provide insight on how these factors work together to influence academic decision-making within the framework of education. 5.1 IMPLICATIONS OF THE STUDY The Implications of the Study provided effectively reflect the significance of LMS tools in enhancing academic decision-making and offer valuable insights for the future of educational technology. The key takeaways you've outlined are highly relevant for both theory and practice. Below is a refined version of these implications with some additional suggestions for a more comprehensive approach: 1. The Role of LMS Assessment Tools LMS tools have undeniably become an integral part of academic operations. The study underscores the critical role these tools play in assessing student progress, facilitating academic decisions, and streamlining workflows. Faculty members' positive reception of LMS tools highlights the need for continued investment in their development. Educational institutions should prioritize regular updates, ensuring these platforms remain relevant and capable of meeting the ever-evolving needs of faculty and students. Additionally, integrating advanced analytics features can further assist instructors in tracking student performance and making data-driven decisions, thus improving overall academic management. 2. Perceived Usefulness Drives Decision-Making The study emphasizes that PU is a crucial determinant of faculty adoption and effective use of LMS tools in academic decision-making. Faculty members are more likely to integrate LMS technologies into their teaching and evaluation when they perceive a clear benefit, such as increased productivity and enhanced student learning outcomes. Institutions must therefore ensure that the features of LMS platforms are tailored to provide tangible, measurable benefits that align with faculty goals, such as streamlined grading, efficient student feedback mechanisms, and improved course management. 5.2 FUTURE RESEARCH 1. Addressing Response Bias and Data Skewness The researcher has correctly identified potential biases or imbalances in the responses due to data skewness. While data transformation techniques could help deal with non-normality, it's also important to consider alternative methods, Stratified Sampling ensure more representative samples across different faculty disciplines, demographic groups, or levels of technological expertise. This could reduce bias by ensuring that all faculty members, regardless of their background, are adequately represented. 2. Cross-Sectional vs. Longitudinal Design The researcher has pointed out the limitation of the cross-sectional design, which captures opinions at a single point in time. This limitation can be addressed by employing a longitudinal approach in future studies. A longitudinal design would allow researchers to track changes in perceptions over time, providing insight into the evolution of faculty attitudes and the long-term impact of LMS tools. Measure the impact of any interventions (such as new training programs or system updates) on the usage and effectiveness of LMS tools. Declarations The author has no relevant financial or non-financial interests to disclose. The author has no conflicts of interest to declare that are relevant to the content of this article. The author certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The author has no financial or proprietary interests in any material discussed in this article. DATA AVAILABILITY STATEMENT The data utilized in this study were obtained through a survey conducted at a selected university. To maintain confidentiality and uphold ethical standards, the name of the university will not be disclosed at any stage of this research. All relevant data supporting the findings of this study are included in the manuscript, specifically in Table 2 to Table 11. Further inquiries regarding the dataset can be directed to the corresponding author. CONSENT TO PARTICIPATE All participants involved in this study were informed about the purpose, procedures, and potential implications of the research. Participation was voluntary, and informed consent was obtained from all individuals before data collection. Participants were also informed of their right to withdraw from the study at any time without penalty. Confidentiality and anonymity were ensured throughout the research process. FUNDING STATEMENT The author did not receive support from any organization for the submitted work. No funding was received to assist with the preparation of this manuscript. No funding was received for conducting this study. No funds, grants, or other support was received CONFLICTS OF INTEREST/COMPETING INTERESTS The author has no relevant financial or non-financial interests to disclose. The author has no conflicts of interest to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The author has no financial or proprietary interests in any material discussed in this article. CLINICAL TRIAL NUMBER Clinical trial number: not applicable. ETHICS APPROVAL All procedures performed in studies involving human participants were in accordance with the ethical standards of the university. The study involving participants was approved by the ethical standards of the university. Prior to data collection, informed consent was obtained from all participants. Participation was voluntary, and anonymity and confidentiality of responses were ensured. References A. Nkhoma, C. et al. (2020) ‘The Role of Rubrics in Learning and Implementation of Authentic Assessment: A Literature Review’, Proceedings of the 2020 InSITE Conference , (October), pp. 237–276. Available at: https://doi.org/10.28945/4606. Acceptance, T. (2009) ‘Purdue University滞在記’, Journal of the Atomic Energy Society of Japan , 51(6), pp. 503–503. Available at: https://doi.org/10.3327/jaesjb.51.6_503. 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(2017) ‘The concept framework of structural equation model of mobile cloud learning acceptance for higher education students in the 21st Century’, TEM Journal , 6(3), pp. 464–468. Available at: https://doi.org/10.18421/TEM63-05. Banta, T.W. and Blaich, C. (2010) ‘Closing the Assessment Loop’, Change: The Magazine of Higher Learning , 43(1), pp. 22–27. Available at: https://doi.org/10.1080/00091383.2011.538642. Bradford, P. et al. (2007) ‘The Blackboard Learning System: The Be All and End All in Educational Instruction?’, Journal of Educational Technology Systems , 35(3), pp. 301–314. Available at: https://doi.org/10.2190/x137-x73l-5261-5656. Buchanan, T., Sainter, P. and Saunders, G. (2013) ‘WestminsterResearch implications for models of technology adoption’, Journal of Computing in Higher Education [Preprint]. Carless, D. 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(2015) ‘Innovative use of Blackboard for Improving Laboratory Reporting in Electronics: Case Study in the’, (January), p. 135. Available at: https://www.researchgate.net/publication/312595128. Geddes, D. (2009) ‘How am i doing? Exploring on-line gradebook monitoring as a self-regulated learning practice that impacts academic achievement’, Academy of Management Learning and Education , 8(4), pp. 494–510. Available at: https://doi.org/10.5465/AMLE.2009.47785469. Grandgirard, J. et al. (2002) ‘Costs of secondary parasitism in the facultative hyperparasitoid Pachycrepoideus dubius: Does host size matter?’, Entomologia Experimentalis et Applicata , 103(3), pp. 239–248. Available at: https://doi.org/10.1023/A. Hajjej, F., Hlaoui, Y.B. and Ayed, L.J. Ben (2015) ‘Personalized and generic e-assessment process based on cloud computing’, Proceedings - International Computer Software and Applications Conference , 3, pp. 387–392. Available at: https://doi.org/10.1109/COMPSAC.2015.250. Instructure, I. (2024) Canvas . Available at: https://www.canvaslms.com/about-us/. de Jesús Araiza, M. and García, M. (2021) ‘Impact of an LMS Platform on the Academic Performance of Postgraduate Students: A Study from Data Analytics’, International Journal of Technologies in Learning , 28(1), pp. 75–91. Available at: https://doi.org/10.18848/2327-0144/CGP/V28I01/75-91. Kamolson, S. (2007) ‘Fundamentals of quantitative research Suphat Sukamolson, Ph.D. Language Institute Chulalongkorn University’, Language Institute , p. 20. Available at: http://www.culi.chula.ac.th/e-Journal/bod/Suphat Sukamolson.pdf%5Cnhttp://isites.harvard.edu/fs/docs/icb.topic1463827.files/2007_Sukamolson_Fundamentals of Quantitative Research.pdf. King, W.R. and He, J. (2006) ‘A meta-analysis of the technology acceptance model’, Information and Management , 43(6), pp. 740–755. Available at: https://doi.org/10.1016/j.im.2006.05.003. Kommerell, G. and Klein, U. (2020) ‘Usability Evaluation of Learning Management Systems in Sri Lankan Universities’, Neuro-Ophthalmology , 6(2), pp. 101–107. Available at: https://doi.org/10.3109/01658108608997334. Lescevica, M., Ginters, E. and Mazza, R. (2013) ‘Unified theory of acceptance and use of technology (UTAUT) for market analysis of FP7 CHOReOS products’, Procedia Computer Science , 26(December), pp. 51–68. Available at: https://doi.org/10.1016/j.procs.2013.12.007. Lock, J. et al. (2018) ‘Navigating the Tensions of Innovative Assessment and Pedagogy in Higher Education’, The Canadian Journal for the Scholarship of Teaching and Learning , 9(1). Available at: https://doi.org/10.5206/cjsotl-rcacea.2018.1.8. Mello, B. et al. (2016) ‘Assessment and NCA’s student learning outcomes’, Communication Education , 65(4), pp. 485–487. Available at: https://doi.org/10.1080/03634523.2016.1203001. Mitra, S. (2022) ‘AN ACTION RESEARCH STUDY ON LMS ASSESSMENT TOOLS AND FACULTY PRACTICE IN ENGLISH COMPOSITION COURSES’. Moodle Org. (2024) Moodle Doc . Available at: https://docs.moodle.org/404/en/Assignment_activity. Moodle Org (2024) Moodle Doc . Available at: https://docs.moodle.org/404/en/Choice_activity. Murshitha, S.M. and Wickramarachchi, A.P.R. (2016) ‘An LMS usage assessment among students in blended learning environment’, 1(2), pp. 1–7. Natasha A. Jankowski (2012) ‘MAPPING THE TOPOGRAPHY OF THE EVIDENCE USE TERRAIN IN ASSESSMENT OF U.S. HIGHER EDUCATION: A MULTIPLE CASE STUDY APPROACH’. Osabutey, E.L.C., Senyo, P.K. and Bempong, B.F. (2024) ‘Evaluating the potential impact of online assessment on students’ academic performance’, Information Technology and People , 37(1), pp. 152–170. Available at: https://doi.org/10.1108/ITP-05-2021-0377. Sivanand, A. (2017) ‘SUPPORTING POST-SECONDARY EDUCATIONAL DATA USAGE IN THE ASSESSMENT PROCESS WITH INFORMATION VISUALIZATION by’. Strandler, O. (2016) ‘Equity Through Assessment? Teachers’ Mediation of Outcome-Focused Reforms in Socioeconomically Different Schools’, Scandinavian Journal of Educational Research , 60(5), pp. 538–553. Available at: https://doi.org/10.1080/00313831.2015.1062414. Ueda, H. et al. (2018) ‘SCORMAdaptiveQuiz: Implementation of Adaptive e-Learning for Moodle’, Procedia Computer Science , 126, pp. 2261–2270. Available at: https://doi.org/10.1016/j.procS.2018.07.223. Venkatesh, V. and Davis, F.D. (2000) ‘Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies’, Management Science , 46(2), pp. 186–204. Available at: https://doi.org/10.1287/mnsc.46.2.186.11926. Waleed Mustafa Eyadat a, Y.A.E. (2010) ‘World Journal on Educational Technology’, World Journal on Educational Technology , 2(2), pp. 87–99. Available at: www.world?education?center.org/index.php/wjet. Tables Tables 1 to 12 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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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-6391834","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464786159,"identity":"a7193673-c699-4f86-9df4-b5594637802c","order_by":0,"name":"Nadumi Nimasha Sellahewa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYHACAxDiATLYGBgqgBQzcwMpWs6AtDASowUC2BgY20A0AS3m7Ie3Sd0ouCOj23782YOf82qj+duBWn5UbMOpxbInrUw6x+AZj9mZHHPD3m3Hc2ccZmxg7DlzG7erDuSYAbUc5jE7kMMmwbvtWG4DUAszYxseLeffQLWcf/5M8u+cY7nzCWq5AbPlRoKZNG9DTe4GwlqeFVtDtACtkzl2IHcjUMtBvH45n7zxds6fw/Zm59OfSb6pqcudd/7wwQc/KnBrQQeHweQBotUDQR0pikfBKBgFo2CEAAAusFyuWysObQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Sri Jayewardenepura","correspondingAuthor":true,"prefix":"","firstName":"Nadumi","middleName":"Nimasha","lastName":"Sellahewa","suffix":""}],"badges":[],"createdAt":"2025-04-07 08:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6391834/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6391834/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83897527,"identity":"de9a4fed-3797-4400-a95d-bb3caf13747c","added_by":"auto","created_at":"2025-06-04 08:59:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eSome of the currently in-use learning management system interfaces at Sri Lankan university systems.\u003c/p\u003e","description":"","filename":"placeholderimage.png","url":"https://assets-eu.researchsquare.com/files/rs-6391834/v1/4d170d636ab4bf8611e8832d.png"},{"id":83897528,"identity":"37e51beb-b834-41fc-9e54-fbdf018b76e0","added_by":"auto","created_at":"2025-06-04 08:59:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41387,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConceptual Framework\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource: Developed by Researcher\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6391834/v1/0279f95b8a09ecb87eca9e38.png"},{"id":86654721,"identity":"15493288-537e-4974-8da8-d4b189c1b6d1","added_by":"auto","created_at":"2025-07-14 10:09:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1094732,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6391834/v1/b6930d1e-c4f3-4c3c-ad61-271ece4726d4.pdf"},{"id":83897529,"identity":"fe7b972e-5124-4419-b47c-1721a4e7ba42","added_by":"auto","created_at":"2025-06-04 08:59:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":105828,"visible":true,"origin":"","legend":"","description":"","filename":"Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-6391834/v1/37cce42cf20eca3bc83b88b6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Learning Management System assessment tools and academic decision making: A Case of the Faculty of Management Studies and Commerce in a Selected State University in Sri Lanka.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003cstrong\u003e1.1 BACKGROUND OF THE STUDY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTraditional teaching and learning approaches have experienced a substantial transformation as a result of the use of technology in education. At the forefront of this transformation is the implementation of Learning Management Systems (LMS), which have become a fundamental component in the management and delivery of educational content. An LMS is a software application designed to facilitate the administration, documentation, tracking, reporting, automation, and delivery of educational courses, training programs, or learning and development programs. It serves as a central hub where educators and students can interact, access course materials, participate in assessments, and track academic progress (Mitra, 2022)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLearning Management Systems have evolved over the past few decades, becoming increasingly sophisticated and user-friendly. Initially designed to support distance learning programs, LMS platforms now offer a wide array of features that cater to both traditional and online educational environments. These systems enable institutions to provide a consistent and standardized educational experience, regardless of the physical location of the learners and educators.\u003c/p\u003e\n\u003cp\u003eAssessment tools are particularly significant among the various features of LMS platforms as they directly influence academic decision-making. These tools include quizzes, assignments, exams, grading systems, and analytics dashboards, providing educators with real-time student performance data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1.1 Importance of LMS in Higher Education\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLMS are becoming essential tools for higher education institutions in the modern educational environment. Numerous features, including communication tools, assessment procedures, and course content distribution, are available on LMS platforms. By giving instructors and students alike a consolidated and easily accessible platform, these solutions are intended to improve the teaching and learning process. Higher education in Sri Lanka is increasingly viewing the incorporation of LMS as a calculated step toward bettering student outcomes and harmonizing with international educational norms.\u003c/p\u003e\n\u003cp\u003eAssessment practices in higher education have also changed, moving away from accountability-based evaluation and toward improvement-based evaluation. \u0026nbsp;Assessment is more inclusive of institutional professors and staff, more useful to colleges and universities, and more focused on improving instruction and learning when it is done for improvement. \u0026nbsp;We have a culture of assessment in contemporary learning environments where measurement and instruction are combined.(de Jesús Araiza and García, 2021).\u003c/p\u003e\n\u003cp\u003eLMS assessment tools can help lecturers save time throughout the evaluation process and give students feedback more quickly (Barkand, 2017). LMS is increasingly providing digital tools that could aid in assessment by partially automating numerous monotonous tasks. Since the LMS facilitates regulated and documented teaching and learning as well as feedback cycles for ongoing improvement, assessment of the system also offers quality assurance for accreditation ( Atkinson \u0026amp; Lim, 2013). Access to student performance data that could influence pedagogical practice through LA, which evaluates and acts upon the data to maximize learning, is one advantage of using the LMS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1.2 LMS Assessment Tools and their Role in Education\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMoodle LMS offers a diverse selection of robust assessment tools that accommodate different methodologies of teaching and learning, encompassing both formative and summative evaluations.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eAssignments\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe Assignment activity allows students to submit work digitally for grading. Text posted online or in the form of files can be submitted. educators can use annotations, comments, and even rubric-based grading to give feedback. Because it integrates plagiarism detection and allows collaborative work, it is adaptable to a variety of task kinds (Moodle Org., 2024).\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003eChoice\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWith the Choice activity, learners may pose a question and offer several possible answers using a straightforward survey instrument. Students are free to choose how they respond, and the outcomes can be shared publicly or kept confidential. It's a simple, informal way to get feedback or direct class decisions (Moodle Org, 2024).\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003eFeedback\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eEducators can construct surveys with unique questions using the input activity to get student input. Feedback is a great tool for getting course feedback because it allows you to design non-graded questionnaires, unlike the Survey tool (Moodle Org, 2024).\u003c/p\u003e\n\u003col start=\"4\"\u003e\n \u003cli\u003eLesson\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWith the use of the lesson activity, educators can design learning pathways that are either branching or linear and allow students to make decisions that take them through various course materials. Instructors can incorporate (Moodle Org, 2024).\u003c/p\u003e\n\u003col start=\"5\"\u003e\n \u003cli\u003eQuiz\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eOne of Moodle's most flexible features is the Quiz activity, which offers a large range of question kinds (such as multiple-choice, true/false, and short answer) along with configurable grading and feedback (Moodle Org, 2024).\u003c/p\u003e\n\u003col start=\"6\"\u003e\n \u003cli\u003eSurvey\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eStandard, pre-built surveys like the COLLES (Constructivist Online Learning Environment Survey) and ATTLS (Attitudes to Thinking and Learning Survey), which are available through Moodle's Survey activity, help collect (Moodle Org, 2024).\u003c/p\u003e\n\u003col start=\"7\"\u003e\n \u003cli\u003eWorkshop\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe Workshop activity provides for peer evaluation of student work. Students submit assignments for peer review using teacher-set criteria. Both contributions and peer reviews are graded, which encourages collaboration and critical thinking (Moodle Org, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1.3 Role of LMS in Academic Decision-Making Processes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLMS plays an important role in academic decision-making by facilitating curriculum development, student evaluation, and instructional improvement via data-driven insights and streamlined processes (Mitra, 2022).\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eCurriculum development\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eLMS platforms give complete data on student performance and engagement, allowing instructors to identify effective courses and areas for change. Quizzes, assignments, and surveys are useful tools for determining how well pupils understand specific topics. This information assists curriculum planners in tailoring content to better satisfy learning outcomes and ensure alignment with academic standards. Furthermore, the flexibility of LMS enables the inclusion of multimedia, making curricular content more engaging and adaptive to varied learning demands.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003eStudent Evaluation\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eLMS systems provide a single platform for measuring student performance using a variety of approaches, such as quizzes, peer assessments, and assignments. With built-in analytics, teachers may monitor student progress in real-time, discovering individual and class-wide learning gaps. Educators rely on this data to make informed decisions about interventions, support, and enrichment opportunities for students. Furthermore, LMS enables the use of rubrics and thorough feedback methods, which standardize evaluations and increase transparency.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003eInstructional Improvement\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eLMS platforms offer ongoing feedback loops for both students and teachers. Educators can improve their educational practices by examining data from exams, surveys, and engagement measures. Features like discussion forums and peer assessments promote collaboration and reflection, allowing teachers to experiment with alternative instructional methodologies and teaching approaches. LMS solutions also promote teacher growth through self-assessment and professional learning communities, establishing a culture of ongoing instructional improvement (Geddes, 2009).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 PROBLEM STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLMS in higher education institutions has transformed traditional teaching and learning methods, offering new opportunities for enhancing educational experiences through technology. Among the various features of LMS platforms, assessment tools such as quizzes, assignments, exams, grading systems, and analytics dashboards are crucial in facilitating student evaluation and academic decision-making. However, despite the widespread adoption of LMS in universities globally, there is a pressing need for more understanding of how these assessment tools impact academic decision-making processes, particularly in the context of Sri Lankan higher education.\u003c/p\u003e\n\u003cp\u003eAt the Faculty of Management Studies and Commerce in a\u0026nbsp;selected state university in Sri Lanka, where LMS adoption has increased in response to the growing demand for flexible and data-driven educational practices, the effectiveness of LMS assessment tools in influencing academic decisions such as curriculum development, instructional strategies, and student support services remains underexplored. Furthermore, challenges such as inadequate training for faculty, varying levels of digital literacy among educators and students, and limited access to advanced LMS features may hinder the optimal use of these tools for informed decision-making.\u003c/p\u003e\n\u003cp\u003eThis study investigates the Impact of Learning Management System Assessment Tools on Academic Decision Making\u0026nbsp;within this context. By examining how educators and administrators utilize data from LMS assessment tools to make key academic decisions, the study seeks to identify factors that affect the effectiveness of these tools and provide insights for enhancing their use in Sri Lankan higher education institutions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 RESEARCH QUESTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eHow do faculty members use LMS assessment tools?\u003c/li\u003e\n \u003cli\u003eHow do LMS assessment tools influence academic decision-making?\u003c/li\u003e\n \u003cli\u003eHow useful are LMS assessment tools in academic decision-making?\u003c/li\u003e\n \u003cli\u003eHow easy are LMS assessment tools in academic decision-making?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 RESEARCH OBJECTIVES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneral Objective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the association between Learning Management System assessment tools and academic decision-making.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpecific Objectives\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eTo investigate the ways faculty members, utilize LMS assessment tools.\u003c/li\u003e\n \u003cli\u003eTo determine the influence of LMS assessment tools on academic decision-making.\u003c/li\u003e\n \u003cli\u003eTo identify the usefulness of LMS assessment tools for academic decision-making.\u003c/li\u003e\n \u003cli\u003eTo identify the ease of use of LMS assessment tools for academic decision-making.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eHigher education institutions are increasingly utilizing innovative methods of student assessment and learning enhancement in response to the increased demand for accountability and transparency on the quality of student learning (Natasha A. Jankowski, 2012). \u0026nbsp; Professionals in higher education widely acknowledge that assessments enable us to evaluate students' learning. However, it is rather uncommon to use evaluation data to alter teaching methods and curriculum (Natasha A. Jankowski, 2012), (Carless, 2009). With access to student data through the LMS assessment tools and the ability to analyze the data, educational institutions are using experimental predictive analyses to detect areas of instruction. The use of technology in evaluation could raise students' self-efficacy, learning engagement, performance, and achievement (Chen and Zhang, 2017).\u003c/p\u003e\n\u003cp\u003eA Learning Management System is desired by the majority of contemporary universities to manage teaching and learning activities. Offering students access to online lecture materials via the Internet at any time and from any location is vital in some way (A. Nkhoma \u003cem\u003eet al.\u003c/em\u003e, 2020). All Sri Lankan universities have started using learning management systems because they recognize the importance of these demands and think that distant learning would only grow in importance within the educational system. According to (Kommerell and Klein, 2020), learning management systems are crucial for managing online programs and developing material. The ability to create a learning and teaching environment free from time or location constraints is one of the most crucial aspects of LMS (Didam Markus, 2015).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1 LMS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCurrently, the use of ICT in education varies in different countries and educational institutions. LMS and E-Learning are generally useful for students, particularly for those who are ill or have somewhere to stay away from the educational institution, as learning never stops regardless of time or distance limitations (Aldiab \u003cem\u003eet al.\u003c/em\u003e, 2019). The ability to assist in the creation of standardized content is one benefit of online learning. However, the drawbacks of online learning are their rigid, unchanging frameworks that do not allow for customized adjustments to meet the needs of individual learners (Ueda \u003cem\u003eet al.\u003c/em\u003e, 2018). LMS also called digital learning environment, online learning environment, course management system, or virtual learning environment, is a web-based platform that allows teachers to create online courses (Aikina and Bolsunovskaya, 2020).\u003c/p\u003e\n\u003cp\u003eCurrently, most of the universities in the world are using several commercial and open sources available LMS packages such as Moodle, Blackboard, Canvas, and D2L. A short descriptions of each LMS are given below.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eMoodle\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eMartin Dougiamas founded Moodle in 1999, and the initial iteration, known as Moodle 1.0, was released in 2002. \u0026nbsp;The acronym for Modular Object-Oriented Dynamic Learning Environment is Moodle. \u0026nbsp;The server in use at the time was located at Curtin University of Technology's Science and Mathematics Education Centre in Perth, Western Australia. \u0026nbsp;Moodle 3.6.1 is the most recent version of this program (December 2018). \u0026nbsp;There is no registration or annual renewal charge for Moodle, an open-source, free learning management system.(Moodle Org, 2024).\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003eBlackboard\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIn 1997, Michael Chasen and Matthew Pittinsky founded Blackboard LLC. Between 1998 and 2004, Blackboard LLC combined with other rivals like Course Info LLC and WebCT and acquired various businesses in the same industry, including MadDuck and Prometheus. In contrast to Moodle, Blackboard is a for-profit learning management system that charges a registration cost and an annual renewal fee (Bradford \u003cem\u003eet al.\u003c/em\u003e, 2007), \u0026nbsp;Additionally, certain features may require money in order to be activated.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003eCanvas\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eJosh Coates founded Canvas in 2008, and the first Canvas was unveiled in 2011. \u0026nbsp;In 2012, Canvas Network was established. \u0026nbsp;In the past, Canvas was called Instructor, but its founders eventually changed the name. \u0026nbsp; It is believed that Canvas is an open-source program.(Instructure, 2024).\u003c/p\u003e\n\u003col start=\"4\"\u003e\n \u003cli\u003eD2L\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIn 1999, John Baker founded D2L (Desire2Learn). \u0026nbsp;It is an open-source program that runs on the cloud. \u0026nbsp; D2L has offices for official representatives in numerous nations worldwide. \u0026nbsp;Since its inception, this LMS has accomplished a number of noteworthy milestones. \u0026nbsp;Among the most noteworthy is the fact that D2L was the second partner after Target Corporation and the first LMS to be admitted into the National Federation of the Blind's (NFB) new Strategic Nonvisual Access Partnership (SNAP) initiative in 2016. (D2L Corporation, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 LMS Assessment Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssessment, defined as “a systematic process for gathering data about student achievement,” is an essential component of teaching (Dhindsa, Omar and Waldrip, 2007). The evaluation of student outcomes is process-improvement-oriented, goal-driven, scientifically grounded, and concentrated on the learning outcomes of specific students (Natasha A. Jankowski, 2012), (Al-Fraihat \u003cem\u003eet al.\u003c/em\u003e, 2020). Students who receive assessments are better able to focus on their areas of weakness and have more chances to achieve their goals. It also gives academics the chance to evaluate their instructional strategies and modify the curricula of their courses and programs. Creating a meaningful and ongoing assessment of student learning and success also gives faculty members the chance to collaborate and share ideas with colleagues in their discipline as well as with other stakeholders like student services, library staff, and administration (Mitra, 2022), (Hajjej, Hlaoui and Ayed, 2015).\u003c/p\u003e\n\u003cp\u003eDetermining the true level of student LMS usage requires significant attention in colleges that use LMSs. Even if trends are moving toward learning tools that are more focused on the needs of students, research has revealed that teachers are still the primary users of learning management systems. When one considers how difficult it is to determine how much an LMS is genuinely utilized inside an institution, it becomes clear that there is a need for an accurate indicator of the level of LMS utilization. Numerous research have examined a variety of LMS adoption, implementation, support, and usage-related topics (Murshitha and Wickramarachchi, 2016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Faculty Role in Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe purpose of assessments has changed from being a required activity to a collaborative endeavor between faculty members to enhance student learning (Mello \u003cem\u003eet al.\u003c/em\u003e, 2016). Faculty play a vital role in supporting, empowering, and advancing the academic success and intellectual independence of the students (Acceptance, 2009). One main feature of high-order assessment is high-level instruction, complementing instruction and evaluation (Waleed Mustafa Eyadat a, 2010).\u003c/p\u003e\n\u003cp\u003e(Grandgirard \u003cem\u003eet al.\u003c/em\u003e, 2002) state that, ideally, assessment “enhances learning, provides feedback about student progress, builds self-confidence and self-esteem, and develops skills in evaluation”. In addition, they argue that when instruction, assessment, and outcomes are all in line, learning happens effectively. Therefore, assessment plays a crucial role in learning because of its strong relationship to instruction and learning outcomes (Waleed Mustafa Eyadat a, 2010).\u003c/p\u003e\n\u003cp\u003eResearch has also shown that faculty need to be engaged to advance assessment and to use the assessment data as evidence to guide improvement (Banta and Blaich, 2010). Different types of assessment techniques are implemented, for instance, to raise equity or improve performance, albeit their effectiveness relies on how educators use them (Sivanand, 2017), (Strandler, 2016). Additionally, faculty must take calculated risks by implementing cutting-edge teaching strategies and creative forms of evaluation that improve student learning (Lock \u003cem\u003eet al.\u003c/em\u003e, 2018). There is still much faculty can do in their classrooms and programs to harness the power of technological convergence in ways that benefit student learning (Acceptance, 2009). More research in established best practices is needed in this field.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Learning Management Systems in Sri Lankan Universities\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe rapid advancement of ICT infrastructures in Sri Lanka encourages all educational establishments to utilize the internet as a means of student communication. the efficient and successful acquisition of educational resources made possible by the ideas and practices of technology-based learning. Using e-learning resources more often makes them an invaluable resource for academic institutions. Higher education has made extensive use of LMS because of its many benefits, which include endless remote learning opportunities and flexible scheduling of lectures (Kommerell and Klein, 2020).\u003c/p\u003e\n\u003cp\u003eThe open-source Moodle platform provides the LMS in the majority of state universities in Sri Lanka. Some of the currently in-use learning management system interfaces at Sri Lankan university systems are displayed in Figure 1. Well-managed Moodle learning management systems should have at least the following characteristics, according to Sri Lankan universities:\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe lecturers' and students' registration on the learning portal.\u003c/li\u003e\n \u003cli\u003ePlanning and scheduling the course's schedule and structure.\u003c/li\u003e\n \u003cli\u003eProvide a delivery method or make the course available to users who have registered.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Why Moodle in Sri Lanka\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMoodle, which stands for Modular Object Oriented Developmental Learning Environment, is an online course management system that is also known as a Virtual Learning Environment (VLE) or an LMS. \u0026nbsp;Teachers can utilize this free online learning environment as a model for successful online learning systems. \u0026nbsp;In this way, it can serve as an example of successful online education initiatives. \u0026nbsp;One of the main benefits is that it is open-source, meaning that anyone with programming skills can use it and customize the environment to suit their needs. \u0026nbsp; Any number of servers can have it installed for free, and updating doesn't require any maintenance fees.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Universities, communities, schools, instructors, courses, and even businesspeople use this learning platform all over the world. Universities in Sri Lanka also adjust to this. Socio-constructivist pedagogy served as the foundation for Moodle's design (Kommerell and Klein, 2020). This indicates that its objective is to provide a set of resources that support an approach to online learning that is built on inquiry and discovery.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Technology-Acceptance Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe technology acceptance model (TAM), put out by (Venkatesh and Davis, 2000), is one of the most well-known models of technology and its application in blended learning and e-learning. TAM can be used to understand the factors that influence technology usage and to learn about the beliefs and actions of users on their preferred methods of using information (Buchanan, Sainter and Saunders, 2013). This paradigm assists in the explanation of how a user's acceptance or rejection of technology is influenced by perceived usefulness and perceived ease of use (PEOU).\u003c/p\u003e\n\u003cp\u003eAccording to (Amornkitpinyo and Piriyasurawong, 2017), perceived usefulness is the extent to which one believes a certain innovation is efficient and well-executed, potentially improving job performance. One of the main motivators for TAM and a secondary source is perceived ease of use. PEOU stands for perceived ease of use (Users' belief in the practicality of a certain system's use(Amornkitpinyo and Piriyasurawong, 2017). The TAM also includes a behavioral intention to use and attitude, two additional variables that influence technology adoption. As to the TAM, attitude is the correlation between a system's utility and ease of use. The users' general ideas about utilizing technology impact their inclination to accept a prospective technology. Additionally, TAM suggests that through mediated effects on perceived utility and perceived ease of use, external variables influence intention and actual use (Venkatesh and Davis, 2000).\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThe research methodology is a systematic way to solve the research problem. There have two main approaches are used for research studies such as quantitative and qualitative. This research is deductive and I will use a quantitative approach for data collection. The quantitative research approach is based on the measurement of quantity or amount. It is used when one begins with a hypothesis and tests for confirmation and disconfirmation of that hypothesis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 CONCEPTUAL FRAMEWORK\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis conceptual framework aims to investigate the relationship between LMS assessment tools and academic decision-making within a selected Sri Lankan state university. The framework posits those four key independent variables \u003cstrong\u003eConvenience\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAccessibility\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSimplicity\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003eBenefits\u003c/strong\u003e influence academic decision-making, which serves as the dependent variable. These variables are critical to understanding how LMS assessment tools are perceived and utilized by academic decision-makers, thereby impacting decisions related to curriculum development, student evaluation, and instructional improvement.\u003c/p\u003e\n\u003cp\u003eIn this framework, \u003cstrong\u003eAcademic Decision-Making\u003c/strong\u003e is considered the dependent variable, encompassing three major areas; \u003cstrong\u003eCurriculum Development\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStudent Evaluation\u003c/strong\u003e, and \u003cstrong\u003eInstructional\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eImprovement\u003c/strong\u003e. These decisions rely heavily on the information provided by LMS assessment tools, which offer data that is timely, relevant, and reflective of student performance. The quality of these decisions is directly correlated with the degree to which the assessment tools are perceived to meet certain criteria, which are captured in the independent variables of the framework.\u003c/p\u003e\n\u003cp\u003eThe independent variables, \u003cstrong\u003eConvenience\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAccessibility\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSimplicity\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003eBenefits\u003c/strong\u003e are derived from the core components of the TAM, which has been widely used to understand user acceptance of technology. \u003cstrong\u003eConvenience\u003c/strong\u003e refers to the ease with which users can engage with LMS assessment tools and integrate them into their daily workflows. \u003cstrong\u003eAccessibility\u003c/strong\u003e pertains to the extent to which these tools are available and usable across various devices, platforms, and environments. \u003cstrong\u003eSimplicity\u003c/strong\u003e involves the user-friendly design of the tools, which ensures that they do not introduce unnecessary complexity or barriers to their use. Finally, \u003cstrong\u003eBenefits\u003c/strong\u003e encompass the perceived advantages that users derive from the use of LMS assessment tools, such as improved efficiency, better student engagement, and enhanced academic outcomes.\u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eTAM\u003c/strong\u003e, developed by Davis (1989), serves as the theoretical foundation for this framework. TAM posits that two key beliefs, \u003cstrong\u003ePU\u003c/strong\u003e and \u003cstrong\u003ePEOU\u0026nbsp;\u003c/strong\u003eare fundamental in explaining technology acceptance and usage. \u003cstrong\u003ePU\u003c/strong\u003e is defined as the degree to which a person believes that using a particular system will enhance their job performance. In the context of LMS assessment tools, this refers to the belief that these tools will improve academic decision-making processes, such as enhancing the accuracy of student evaluation or informing curriculum adjustments. \u003cstrong\u003ePEOU\u003c/strong\u003e refers to the degree to which a person believes that using a particular system will be free of effort. For LMS assessment tools, this involves the perceived ease with which these tools can be utilized by educators, without requiring significant additional effort or learning curves.\u003c/p\u003e\n\u003cp\u003eThe theoretical foundation of \u003cstrong\u003ePU\u003c/strong\u003e and \u003cstrong\u003ePEOU\u003c/strong\u003e is rooted in the \u003cstrong\u003eTheory of Reasoned Action (TRA)\u003c/strong\u003e and the \u003cstrong\u003eTheory of Planned Behavior (TPB)\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e both of which emphasize the role of beliefs in shaping attitudes, which in turn influence intentions and behaviors (Ajzen, 1991). According to these theories, individuals\u0026apos; beliefs about the outcomes of using a system such as its perceived usefulness and ease of use directly shape their attitudes toward the system. These attitudes, in turn, influence their intentions to adopt the system and ultimately their actual usage behavior.\u003c/p\u003e\n\u003cp\u003eBy focusing on \u003cstrong\u003ePU\u003c/strong\u003e and \u003cstrong\u003ePEOU\u003c/strong\u003e, TAM simplifies the complex process of technology adoption. It identifies key intervention points where efforts can be directed to enhance users\u0026rsquo; perceptions of the system, thereby improving their acceptance and usage behavior. In the context of this research, understanding how \u003cstrong\u003eConvenience\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAccessibility\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSimplicity\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003eBenefits\u003c/strong\u003einfluence \u003cstrong\u003ePU\u003c/strong\u003e and \u003cstrong\u003ePEOU\u003c/strong\u003e will provide valuable insights into the factors that drive academic decision-makers\u0026rsquo; adoption of LMS assessment tools. By enhancing these perceptions, universities can improve the effectiveness of LMS tools, leading to more informed and data-driven academic decision-making processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 HYPOTHESES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH0\u003cstrong\u003e\u0026nbsp;-\u0026nbsp;\u003c/strong\u003eThere is no significant relationship between convenience and academic decision-making.\u003c/p\u003e\n\u003cp\u003eH1 - There is a significant relationship between convenience and academic decision-making.\u003c/p\u003e\n\u003cp\u003eH0\u003cstrong\u003e\u0026nbsp;-\u003c/strong\u003eThere is no significant relationship between accessibility and academic decision-making.\u003c/p\u003e\n\u003cp\u003eH2 - There is a significant relationship between accessibility and academic decision-making.\u003c/p\u003e\n\u003cp\u003eH0\u003cstrong\u003e\u0026nbsp;-\u0026nbsp;\u003c/strong\u003eThere is no significant relationship between simplicity and academic decision-making.\u003c/p\u003e\n\u003cp\u003eH3 - There is a significant relationship between simplicity and academic decision-making.\u003c/p\u003e\n\u003cp\u003eH0\u003cstrong\u003e\u0026nbsp;-\u0026nbsp;\u003c/strong\u003eThere is no significant relationship between benefits and academic decision-making.\u003c/p\u003e\n\u003cp\u003eH4 - There is a significant relationship between benefits and academic decision-making.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 RESEARCH DESIGN\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe purpose of this study is to identify\u0026nbsp;the impact of Learning Management System assessment tools and academic decision-making.\u003c/p\u003e\n\u003cp\u003eThere are two types of data. Primary data and secondary data. The Researcher has planned to use primary data for this study. Primary data will be collected by questionnaires. Primary data will be collected from responders of the Faculty of Management Studies and Commerce in a selected state universities in Sri Lanka.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 RESEARCH APPROACH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(Kamolson, 2007) Suggests that quantitative research is suited when specific hypotheses are tested in the research study. The purpose of this study is to prove the specific hypotheses. Also, this study uses statistical data analysis methods such as correlation and regression analysis to test the hypotheses by using numerical values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research utilizes a Deductive approach, which is consistent with its aim of testing predetermined hypotheses and investigating the correlations among variables. A deductive approach formulates hypotheses based on a theoretical framework or well-established models, such as the TAM. This research investigates the perceived usefulness and ease of use of LMS assessment tools and their impact on academic decision-making. The study collects and analyzes actual data in an attempt to verify or disprove these hypotheses using the deductive method. This approach ensures a disciplined and methodical assessment of how LMS tools influence decision-making in areas like curriculum development and instructional improvement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 TECHNIQUES AND PROCEDURES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.1 Research population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA selected state university in Sri Lanka related to my research has 10 faculties, encompassing diverse academic disciplines. The total number of\u0026nbsp;the Faculty of Management Studies and Commerce in\u0026nbsp;academic staff members is around 244. This population forms the basis of our study, focusing on how LMS assessment tools impact academic decision-making processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.2 Research sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDeveloped by Krejcie and Morgan in 1970, the Morgan Table is a statistical tool used to calculate sample sizes for research projects. It gives researchers a way to determine the right sample size based on the size of the population, guaranteeing statistical significance and representativeness. The Morgan Table recommends 150 sample sizes for a population of 244. Standard criteria in educational and social scientific research include a 95% confidence level and a 5% margin of error, which are assumed in this computation. The Morgan Table\u0026apos;s underlying formula accounts for the necessity of striking a compromise between practical limitations like time and resource availability and the accuracy of the data.\u003c/p\u003e\n\u003cp\u003eA simple random sampling method is employed within the faculty to select individual participants. From the total population, a sample size of 150 faculty members is randomly chosen, ensuring that each individual has an equal chance of being included in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 METHOD OF THE DATA COLLECTION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSecondary data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSome important data were collected from different secondary data sources. The previous studies were based on LMS assessment tools and academic decision-making. Its related areas provided the researcher with a comprehensive understanding of the conceptualization of the research study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlso, in the previous studies the researcher used journals and articles that are related to the LMS assessment tools. \u0026nbsp;Accordingly, previous studies, articles, and journals helped the researcher to get an insight into the sample size, construction of the questionnaire, and scaling procedures of the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary data was collected by using a structured questionnaire for independent variables, and dependent variables. The questionnaires were distributed to the respondents using a Google Form and distributed through emails.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 OPERATIONALIZATION OF THE STUDY\u003c/strong\u003e\u003c/p\u003e"},{"header":"4.\tAnalysis","content":"\u003cp\u003e\u003cstrong\u003e4.1 INTRODUCTION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe approach by which academic activities are managed in higher education has been completely transformed by the introduction of Learning Management Systems (LMS), especially in using evaluation tools. These technologies, integrated into LMS systems, give lecturers creative ways to evaluate student performance\u0026nbsp;and make accurate academic decisions. The relationship between LMS assessment tools and academic decision-making is examined in this chapter, with a particular focus on the opinions of faculty members at the selected state university in Sri Lanka.\u003c/p\u003e\n\u003cp\u003eKnowing how LMS assessment tools affect learning and instruction is essential as more and more higher education institutions rely on digital technologies. A lot of academic decision-making, including curriculum development, student evaluation, and instructional improvement, depends on precise, timely, and useful data. Those decisions could be greatly impacted by LMS assessment tools, which are intended to offer such insights. However, as suggested by the study\u0026apos;s hypotheses, perceived usefulness and perceived ease of use,\u0026nbsp;are two important elements that determine how effective they are.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 DEMOGRAPHIC ANALYSIS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.1 University Positions in the sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiverse representation across academic levels is revealed by the demographic analysis of university positions. The largest percentage of replies (36.0%) were from Temporary Assistants Lecturers which made up 54 individuals. Lecturer (Probationary), who made up 29.3% of the sample (44 individuals), came next. With a 14.0% share in Grade I (21 individuals) and a 2.7% percentage in Grade II (4 individuals), senior lecturers made up a sizeable portion. With 10.0% of the respondents being professors (15 individuals) and only 1.3% being associate professors (2 individuals), professors and associate professors together accounted for a lesser percentage of the respondents. This distribution indicates that the sample\u0026apos;s academic workforce is youthful, with a significant proportion of early-career academics, especially probationary and temporary assistant lecturers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.2 University Positions and Assessment Tools Crosstabulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssignments, lessons, and quizzes are the most often utilized assessment tools of the Faculty of Management Studies and Commerce according to the crosstabulation, demonstrating their significance in academic evaluations. With minimal usage of tools like Feedback, Choice, Workshop, and Survey. The Faculty of Management Studies and Commerce mainly rely on assignments and quizzes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 VALIDITY AND RELIABILITY OF THE RESEARCH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReliability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to previous research, it is generally accepted that the value of Cronbach\u0026rsquo;s Alpha value is between 0. 6 and 0.7, if Cronbach\u0026rsquo;s Alpha value is between 0.7 and 0.8 Strong if Cronbach\u0026rsquo;s Alpha value is between 0.8 and 1 Very strong. The researcher has calculated Cronbach\u0026rsquo;s Alpha value of independent variables and dependent variables. According to the calculations, the researcher could conclude that the set of questions that are used for the variance of attitude was reliable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo satisfy the convergent validity, three conditions should be satisfied.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eKaiser- Meyer-Olkin Measure (KMO) value should be greater than 0.5\u003c/li\u003e\n \u003cli\u003eSig value of Bartlett\u0026rsquo;s Test of Sphericity should be less than 0.05\u003c/li\u003e\n \u003cli\u003eAverage Variance Explained (AVE) value should be greater than 0.5\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 DESCRIPTIVE STATISTICS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean values for all variables, Assessment Tools (4.6333), Academic Decision (4.4978), Convenience (4.4867), Accessibility (4.5700), Simplicity (4.4453), and Benefit (4.4500) are above 3 on a 5-point Likert scale. This indicates that respondents generally provided positive evaluations, reflecting agreement or strong agreement with the usability statements in the questionnaire. If the mean value had been below 3, it would have indicated disagreement or strong disagreement with the statements. Therefore, the results support the validity of the questionnaire.\u003c/p\u003e\n\u003cp\u003eAdditionally, the standard deviation values for all variables are less than 1 (Assessment Tools = 0. 47886, Academic Decision = 0. 30162, Convenience = 0.38183, Accessibility = 0.40666, Simplicity = 0.35983, and Benefit = 0.34271), suggesting minimal variation in responses. This indicates that respondents share similar attitudes toward the given variables. A standard deviation greater than 1 would have implied greater variability in responses. Thus, the low standard deviations further confirm consistency and agreement among respondents.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e4.5 CORRELATION ANALYSIS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe correlation analysis explores the relationships between academic decision-making and four key factors: convenience, accessibility, simplicity, and benefit. The results indicate that all these factors have a strong and statistically significant positive correlation with academic decision-making, suggesting that improvements in these aspects of the LMS can enhance academic choices. Among these, simplicity has the highest correlation (0.770), indicating that a user-friendly and easy-to-navigate LMS substantially impacts academic decisions. Convenience (0.718), benefit (0.680), and accessibility (0.662) also show strong correlations, emphasizing their role in shaping students\u0026rsquo; academic choices. The interrelationships among these variables suggest that enhancing one factor, such as accessibility, is likely to improve others, such as convenience and benefit. These findings highlight the importance of designing LMS platforms that prioritize simplicity, accessibility, and user convenience to support effective academic decision-making in Sri Lankan higher education institutions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6 REGRESSION ANALYSIS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA statistical technique for analyzing the relationship between one or more independent variables (predictors) and a dependent variable (outcome) is regression analysis. Researchers can learn how changes in independent factors affect or forecast the dependent variable through approach. In order to find patterns, test hypotheses, and make predictions, regression analysis is frequently used in research in a variety of fields, including business, education, social sciences, and economics.\u003c/p\u003e\n\u003cp\u003eWhen two or more independent variables have a strong correlation with one another, this is known as multicollinearity in regression analysis. This indicates that one independent variable may be significantly predicted from the others using a linear model. A certain amount of correlation between variables is normal, but too much multicollinearity can cause issues for statistical modeling. By raising the standard errors of the predictors\u0026apos; coefficients, multicollinearity compromises the statistical significance of individual predictors. Even if the model as a whole may still have good predictive ability, this makes it difficult to ascertain the actual impact of each variable on the dependent variable.\u003c/p\u003e\n\u003cp\u003eIt is crucial to determine whether multicollinearity among the independent variables exists before moving further with regression analysis. High levels of correlation between independent variables can lead to multicollinearity, which can make the regression coefficients unstable and make it more difficult to interpret the results.\u003c/p\u003e\n\u003cp\u003eThe following standards are applied in order to assess multicollinearity.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003ePearson Correlation Coefficient - Multicollinearity may be indicated by a high correlation (\u0026gt; 0.8) between independent variables.\u003c/li\u003e\n \u003cli\u003eTolerance - Multicollinearity is suggested by a tolerance value below 0.1. The formula for tolerance is 1\u0026minus;R\u003csup\u003e2\u003c/sup\u003e, where 𝑅\u003csup\u003e2\u003c/sup\u003e is the percentage of variance that can be accounted for by the other independent variables.\u003c/li\u003e\n \u003cli\u003eVariance Inflation Factor (VIF) - Significant multicollinearity is indicated by a VIF value larger than 5. VIF calculates the extent to which correlations with other predictors increase a coefficient\u0026apos;s variance.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThere is less chance of severe multicollinearity since, according to the correlation table, none of the independent variable pairings show abnormally high correlations (above 0.8). The researcher should, however, verify this further by looking at the regression output\u0026apos;s tolerance and VIF values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6.1 Tolerance and VIF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn relationship to the dependent variable, Academic Decision, the table shows the collinearity statistics, such as Tolerance and Variance Inflation Factor (VIF), for the independent variables: Convenience, Accessibility, Simplicity, and Benefit.\u003c/p\u003e\n\u003cp\u003eAll of the tolerance values, which fall between 0.372 and 0.499, are significantly over the 0.1 threshold, suggesting that there is no significant multicollinearity among the independent variables.\u003c/p\u003e\n\u003cp\u003eThe absence of multicollinearity is further supported by the fact that all VIF values, which vary from 2.006 to 2.690, are below the 5-point threshold.\u003c/p\u003e\n\u003cp\u003eThese findings show that the assumption of no multicollinearity is met and that there is not an excessive correlation between the independent variables. Therefore, it is appropriate to proceed with the regression analysis without any concerns regarding multicollinearity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6.2 Model summary\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe model summary provides insights into the effectiveness of the regression model in explaining the variation in academic decision-making based on the predictors: benefit, accessibility, simplicity, and convenience. The R value (0.742) indicates a strong positive correlation between the independent variables and academic decision-making. The Adjusted R Square value (0.738) suggests that 73% of the variance in academic decision-making can be explained by the given predictors, demonstrating a high level of explanatory power. The standard error of the estimate (0.20503) represents the average deviation of the observed values from the predicted values, indicating the model\u0026rsquo;s accuracy. These results confirm that benefit, accessibility, simplicity, and convenience are strong predictors of academic decision-making, reinforcing the importance of these factors in influencing students\u0026apos; choices within the LMS context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6.3 Analysis of variance (ANOVA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ANOVA table evaluates the overall significance of the regression model in explaining the variance in academic decision-making. The regression sum of squares is 7.460, while the residual sum of squares is 6.095, leading to a total sum of squares of 13.555. The degrees of freedom (df) for regression are 4, corresponding to the number of predictors (Benefit, Accessibility, Simplicity, and Convenience), while the residual df is 145, representing the remaining variance not explained by the model. The mean square for regression is 1.865, whereas the mean square for residuals is 0.042. The F-statistic, which measures the overall fit of the model, is 44.364, indicating a strong explanatory power of the predictors. The significance value (Sig.) is .000, suggesting that the regression model is statistically significant at the 0.05 level. This implies that the predictors collectively have a significant impact on academic decision-making, justifying the use of LMS assessment tool-related factors in influencing decision-making processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.7 COEFFICIENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe coefficient table provides evidence for testing the study\u0026apos;s hypotheses regarding the relationship between LMS Convenience, Accessibility, Simplicity, Benefit, and academic decision-making.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.8 TEST HYPOTHESIS\u003c/strong\u003e\u003c/p\u003e"},{"header":"5.\tConclusion and Recommendations","content":"\u003cp\u003eAlong with two other significant features, PU and PEOU, the current study aimed to investigate the relationship between academic decision-making and assessment tools from LMS. The study\u0026apos;s goals were to investigate how faculty members use LMS assessment tools, ascertain how they impact academic decision-making, and evaluate the practicality and usability of these tools in aiding decision-making. This study uses regression analysis to provide insight on how these factors work together to influence academic decision-making within the framework of education.\u003c/p\u003e\n\u003cp id=\"_Toc188004952\"\u003e\u003cstrong\u003e5.1 IMPLICATIONS OF THE STUDY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Implications of the Study provided effectively reflect the significance of LMS tools in enhancing academic decision-making and offer valuable insights for the future of educational technology. The key takeaways you\u0026apos;ve outlined are highly relevant for both theory and practice. Below is a refined version of these implications with some additional suggestions for a more comprehensive approach:\u003c/p\u003e\n\u003cp\u003e1. The Role of LMS Assessment Tools\u003c/p\u003e\n\u003cp\u003eLMS tools have undeniably become an integral part of academic operations. The study underscores the critical role these tools play in assessing student progress, facilitating academic decisions, and streamlining workflows. Faculty members\u0026apos; positive reception of LMS tools highlights the need for continued investment in their development. Educational institutions should prioritize regular updates, ensuring these platforms remain relevant and capable of meeting the ever-evolving needs of faculty and students. Additionally, integrating advanced analytics features can further assist instructors in tracking student performance and making data-driven decisions, thus improving overall academic management.\u003c/p\u003e\n\u003cp\u003e2. Perceived Usefulness Drives Decision-Making\u003c/p\u003e\n\u003cp\u003eThe study emphasizes that PU is a crucial determinant of faculty adoption and effective use of LMS tools in academic decision-making. Faculty members are more likely to integrate LMS technologies into their teaching and evaluation when they perceive a clear benefit, such as increased productivity and enhanced student learning outcomes. Institutions must therefore ensure that the features of LMS platforms are tailored to provide tangible, measurable benefits that align with faculty goals, such as streamlined grading, efficient student feedback mechanisms, and improved course management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 FUTURE RESEARCH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Addressing Response Bias and Data Skewness\u003c/p\u003e\n\u003cp\u003eThe researcher has correctly identified potential biases or imbalances in the responses due to data skewness. While data transformation techniques could help deal with non-normality, it\u0026apos;s also important to consider alternative methods, Stratified Sampling ensure more representative samples across different faculty disciplines, demographic groups, or levels of technological expertise. This could reduce bias by ensuring that all faculty members, regardless of their background, are adequately represented.\u003c/p\u003e\n\u003cp\u003e2. Cross-Sectional vs. Longitudinal Design\u003c/p\u003e\n\u003cp\u003eThe researcher has pointed out the limitation of the cross-sectional design, which captures opinions at a single point in time. This limitation can be addressed by employing a longitudinal approach in future studies. A longitudinal design would allow researchers to track changes in perceptions over time, providing insight into the evolution of faculty attitudes and the long-term impact of LMS tools. Measure the impact of any interventions (such as new training programs or system updates) on the usage and effectiveness of LMS tools.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe author has no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eThe author has no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003eThe author certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003eThe author has no financial or proprietary interests in any material discussed in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data utilized in this study were obtained through a survey conducted at a selected university. To maintain confidentiality and uphold ethical standards, the name of the university will not be disclosed at any stage of this research. All relevant data supporting the findings of this study are included in the manuscript, specifically in Table 2 to Table 11. Further inquiries regarding the dataset can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT TO PARTICIPATE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants involved in this study were informed about the purpose, procedures, and potential implications of the research. Participation was voluntary, and informed consent was obtained from all individuals before data collection. Participants were also informed of their right to withdraw from the study at any time without penalty. Confidentiality and anonymity were ensured throughout the research process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003eNo funds, grants, or other support was received\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICTS OF INTEREST/COMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author has no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eThe author has no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003eAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003eThe author has no financial or proprietary interests in any material discussed in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCLINICAL TRIAL NUMBER\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the university. The study involving participants was approved by the ethical standards of the university. Prior to data collection, informed consent was obtained from all participants. Participation was voluntary, and anonymity and confidentiality of responses were ensured.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eA. Nkhoma, C. \u003cem\u003eet al.\u003c/em\u003e (2020) \u0026lsquo;The Role of Rubrics in Learning and Implementation of Authentic Assessment: A Literature Review\u0026rsquo;, \u003cem\u003eProceedings of the 2020 InSITE Conference\u003c/em\u003e, (October), pp. 237\u0026ndash;276. Available at: https://doi.org/10.28945/4606.\u003c/li\u003e\n\u003cli\u003eAcceptance, T. (2009) \u0026lsquo;Purdue University滞在記\u0026rsquo;, \u003cem\u003eJournal of the Atomic Energy Society of Japan\u003c/em\u003e, 51(6), pp. 503\u0026ndash;503. Available at: https://doi.org/10.3327/jaesjb.51.6_503.\u003c/li\u003e\n\u003cli\u003eAikina, T.Y. and Bolsunovskaya, L.M. 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(2000) \u0026lsquo;Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies\u0026rsquo;, \u003cem\u003eManagement Science\u003c/em\u003e, 46(2), pp. 186\u0026ndash;204. Available at: https://doi.org/10.1287/mnsc.46.2.186.11926.\u003c/li\u003e\n\u003cli\u003eWaleed Mustafa Eyadat a, Y.A.E. (2010) \u0026lsquo;World Journal on Educational Technology\u0026rsquo;, \u003cem\u003eWorld Journal on Educational Technology\u003c/em\u003e, 2(2), pp. 87\u0026ndash;99. Available at: www.world?education?center.org/index.php/wjet.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 12 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Academic decision-making, Assessment tools, Learning Management System, perceived ease of use, perceived usefulness","lastPublishedDoi":"10.21203/rs.3.rs-6391834/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6391834/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The integration of Learning Management Systems in higher education has significantly transformed traditional teaching, learning, and assessment methods. LMS platforms provide various assessment tools, such as quizzes, assignments, and feedback mechanisms, which are increasingly used to support academic decision-making. This study examines the association between LMS assessment tools and academic decision-making within the Faculty of Management Studies and Commerce at a selected state university in Sri Lanka. The research aims to explore faculty members' utilization of LMS assessment tools, their perceived usefulness and ease of use, and how these tools influence key academic decisions, including curriculum development, student evaluation, and instructional improvement. A quantitative research approach was adopted, utilizing structured questionnaires distributed to 150 faculty members. Data analysis, including correlation and regression techniques, revealed that LMS assessment tools play a significant role in shaping academic decisions. The study also found that perceived usefulness and perceived ease of use are critical factors influencing faculty adoption of these tools. Challenges such as system complexity and inadequate faculty training hinder effective utilization. The findings highlight the need for improving LMS usability, enhancing faculty training programs, and integrating more data-driven analytics to optimize academic decision-making processes. This study contributes to the growing body of research on educational technology and offers practical recommendations for enhancing LMS implementation in Sri Lankan universities.","manuscriptTitle":"Association between Learning Management System assessment tools and academic decision making: A Case of the Faculty of Management Studies and Commerce in a Selected State University in Sri Lanka.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-04 08:59:39","doi":"10.21203/rs.3.rs-6391834/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"45082977-10c7-474e-9b49-a655547620d3","owner":[],"postedDate":"June 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-14T10:09:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-04 08:59:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6391834","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6391834","identity":"rs-6391834","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}