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Cluster Analysis of Learning Management System (LMS) Performance : A Diagnostic Assessment
Dr. Hector John T. Manaligod,Rogelio V. del Cano,Jelica Enriquez ASCONS 2022 IJASC Vol.4 No.3
Background/Objective. The COVID-19 has accelerated the conduct of online classes around the world. To administer distant learning, schools are now relying on learning management systems. With the problems of the abrupt shift to an online modality, there must be measures in place to cluster students’ characteristics in order to assist their learning requirements, struggles, and academic performances. Methods/Statistical Analysis. Based on Delone and McLean’s IS Success model, this study used quantitative approaches and exploratory techniques to explain the higher education learners’ clusters. From March to April 2021, 303 samples were randomly selected from students participating in online programs at a higher education school on Taft Avenue in Manila. Sex, school, frequency of use, time duration, and experience with the LMS were all factors in the study. Both Hierarchical Cluster Analysis and K-Means Cluster Analysis were used to classify the samples. Findings. The results revealed that there are four clusters formed which are labeled as: service-oriented, system quality-oriented, holistic-oriented, and LMS-averse. Based on the results, information quality has the greatest influence. Since the clustering analysis is non-inferential and it is used as an exploratory technique, the researchers do not guarantee a unique solution as this depends on the elements of the subjects and the variables used. Improvements/Applications. The academic stakeholders can use the cluster analysis to make the appropriate interventions to improve the online course content and enhance the students’ academic performance. Some of these appropriate interventions include creating course policies, planning the necessary training of teachers and students, and developing the online delivery of the lessons.
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Hector John T. Manaligod,Rogelio V. del Cano,Jelica R. Enriquez ASCONS 2019 INTERNATIONAL JOURNAL OF EMERGING MULTIDISCIPLINAR Vol.3 No.1
This study examined the relationship of university students’ course performance in the regular block and irregular sections taking advanced programming course and the mediating effect of motivation. The respondents of the study were information systems students who were enrolled in major subjects. The independent variable was the student’s course performance. The dependent variable was the section classification (i.e., regular block or irregular section). Motivation was included in the model to test whether it influences the variance found students’ performance as to their sections. Logistic regression was used as statistical test for course performance and sectioning. Omnibus Test of Model Coefficient and Cox & Snelll and Nagelkerke statistics were used for the coefficient of determination. The study concludes that sectioning significantly relates to academic performance and grade have a modest explanatory power to sectioning. Motivation did not influence the students’ grades. Certain factors of motivation such as intrinsic, self-efficacy for learning and test anxiety suggest a significant relationship with grade not necessarily based on the effect of sectioning.
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Hector John T. Manaligod,Rogelio V. del Cano,Jelica R. Enriquez ASCONS 2019 IJASC Vol.1 No.1
Background/Objectives: The study sought to investigate technology adoption and use of learning management system (LMS) in a university. The study included antecedents of behavioral intention (BI) namely: performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC) and its relationship with actual use LMS. Methods/Statistical analysis: The research used self-administered on-line survey method and actual use based on data registers from LMS. Random samples were drawn from the faculty population who are using LMS. A multivariate statistical analysis and factor analysis were applied to find structural relationship among the variables and latent constructs (structural equation modeling). The instrument registered high internal consistency and validity. Findings: The results revealed that PE, EE, SI significantly explained the variation in BI. The BI and FC are not significantly related to LMS actual usage. Overall, there is a strong positive inter-correlation among the BI,PE, EE, and SI. Moderating effects were found in age and experience in the relationship between FI and actual usage; between EE and BI. Improvements/Applications: Data suggested a strong faculty behavioral intention to use LMS, but this did not translate into a substantial level of actual usage.
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