Learning Analytics for Tracking Student Progress in LMS

International Journal of Information and Education Technology, 2019

Nowadays, Information and Communication Technology (ICT) provides an opportunity to discover new knowledge and create a desirable learning environment. That is why the influence of ICT on education is irrefutable. Technology has changed the learning styles: the way people prefer to learn and improve the quality of their learning. Physical and online classes can be held concurrently so that lecturers and students can interact via learning management systems. A Learning Management System (LMS) is an application software that plays a significant role in educational technology. Such software can be designed to augment and facilitate instructional activities including registration and management of education courses, analyzing skill gaps, reporting, and delivery of electronic courses concurrently. Since all information and corresponding data are recorded and monitored in the LMS, it can provide an accurate insight into student's online behavior. In general, measuring student performance is an important part of the education system. The fields of learning analytics and educational data mining both emphasize the analysis of educational data in order to improve teaching and learning styles as well as to predict student performance. In the current study, we use data from the Moodle LMS from a collection of courses from a single institution to identify weak/strong students during the course. The result has to be interpretable and understandable as the aim is to give this information to lecturers, who may use the information to improve their course and identify students who may need special attention.

The evolution of learning technology and tools changed the way students access information and build their knowledge. Registering the interaction of students with these tools generates a large amount of data that, once critically analysed, can provide important clues about the students' learning progress. Nevertheless research has still to be conducted to fully understand how (and if) the students' interaction with the learning technologies relates to their learning success. In parallel, new analytical tools must be developed to allow teachers to fully exploit the information embedded in this data, in a friendly but flexible way. This article is a contribution to this effort and presents a study where the use of a learning management system (LMS) in a specific semester-long course in an Engineering school produced data that was analysed and correlated to the students' success. Results indicate that some correlation exists between the effective use of some of the tools integrated in the LMS and student success which points the way to buld specific applications to provide teachers with indicators of students in danger of failing.

2017

This paper focus on the potential contributes of Learning Analytics in the improvement students learning. The analysis of student's data collected from Virtual Learning Environments is important to ascertain student's engagement. This paper presents the analysis of data collected in the ambit of MatActiva project. Data was analysed with Google Analytics, Course Dedication and Moodle Reports. Promising results were obtained.

2017 12th Iberian Conference on Information Systems and Technologies (CISTI), 2017

Higher Education (HE) teachers are aware that the evolution of technology changed the way students build their knowledge. New forms of access to information and new means of communication allowed creating specialized learning tools. It is now important to understand how the students' interaction with learning technologies influences their learning success. For that purpose, data referring to the use of a learning management system (LMS) supporting a specific course was collected and analyzed. Results indicate that a minor correlation exists between the effective use of those tools and student success.

The research described in this paper aims to study how learning analytics methods can be used to impact on the process of learning. The novel method for data collection from different Learning systems is implemented. Using the data collected, various analyses and reports are presented and discussed, aiming to disclose important learner's behaviors and regularities during the educational process. Recommendations are made for further improvements of the learning processes. The paper concludes by enumerating some challenges and further works for creating effective Learning Analytics tools. INTRODUCTION The process of education implies building a complex set of relationships between teachers and learners. In an effort to hand over his knowledge and experience to students, the teacher asks himself a lot of questions, related to increasing the quality of his teaching. "Will my course be useful for the students?", "What is their background and are all of them at the same...

One of the characteristics of our world today is that it is continuously changing. Every aspect of our world has been transformed by the emerging technologies, ranging from our day to day activities to scientific and industrial developments. This progress has been made in the last few decades. It is also true for all aspects of education. The use of emerging technologies at all levels of education, such as intelligent tutoring systems, learning management systems, interactive learning environments, and online learning, gives access to vast volumes of data of the students. Student data collected automatically in online learning environments are usually not converted into useful information for teaching, although it is a good source of information. It is poorly used throughout the educational domain. A new concept has emerged to capture this educational big data called "Learning Analytics". LA is about discovering hidden patterns in the educational process, assess student learning, and make predictions, which in turn provide a better understanding of teaching, learning, and interpretation of student's data. Higher education institutes are adapting to the evolving concept of LA. It would help HEI's to facilitates students to improve academic performance and enable teachers to keep track of individual students, identify student at-risk, and provide timely interventions. This paper provides the bird's eye view of the research evidence on the usage of learning analytics in higher education across the world by reviewing the leading publications, research studies, and case studies in this area.

International Journal On Interactive Design And Manufacturing (ijidem), 2022

International Journal of Education and Practice

The use of technological platforms based online has become increasingly important in the formative activity of higher education. In this context, by its almost universal use in educational institutions, the LCMS (Learning Content Management Systems) stand out. From the activity of students and teachers in these platforms a huge amount of data ends up being recorded, with great potential for management, which are not used. The idea of the Learning Analytics is related to the organization and analysis of these data, transforming them into intelligible information so that people and bodies of Institutions of Higher Education (IHE) can control actions and make more informed decisions, in terms of the adoption strategy of LCMS and the discovery of patterns that enable statistical inferences The Learning Analytics in education is in its infancy at the theoretical level, which is reflected in the difficulty in maintaining a stable discourse, and, even more evident on the practical level of development and use of these systems. This paper presents the results of the work of design, development and operationalization of a Learning Analytics system to assess the integration of the LCMS in the teaching and learning process in higher education. The proposed system combines the reading of data from two sources: from the automated reporting platform and from a scale applied to students. As a methodological approach to the subject the Design Science Research Process model was followed. The results achieved were reflected in a backoffice of the extraction and analysis system within the LCMS and in the development and validation of a scale to assess the integration of the LCMS in the formative process in higher education.

International Journal of Emerging Technologies in Learning (iJET)

Educational structures have been evolving, that even so rapidly with the revolution of information technology and internet. Recent pandemic and its after effects are still looming over the globe, posing as challenge and an opportunity for educators. Online education was one such innovation, which has changed the dynamics of education around the world. The purpose of the paper is three-fold, first, to assess the levels of student engagement in the online learning environment, second, to examine how student engagement is related to their academic performance using learning analytic tools and third, to propose an integrated learning analytics framework. The study used, an exploratory research method and the data was collected from multiple sources; LMS Logs, self-administered questionnaires from students, and interviews with the instructor. The study was conducted at a course level in a private university. The finding suggests a positive relationship between student engagement and thei...

International Journal of Advanced Computer Science and Applications, 2019

The adaptive e-learning system (AE-LS) research has long focused on the learner model and learning activities to personalize the learner's experience. However, there are many unresolved issues that make it difficult for trainee teachers to obtain appropriate information about the learner's behavior. The evolution of the Learning Analytics (LA) offers new possibilities to solve problems of AE-LS. In this paper, we proposed a Business intelligence framework for AE-LS to monitor and manage the performance of the learner more effectively. The suggested architecture of the ALS proposes a data warehouse model that responds to these problems. It defines specifics measures and dimensions, which helps teachers and educational administrators to evaluate and analyze the learner's activities. By analyzing these interactions, the adaptive e-learning analytic system (AE-LAS) has the potential to provide a predictive view of upcoming challenges. These predictions are used to evaluate the adaptation of the content presentation and improve the performance of the learning process.