Learning Analytics: A Way to Monitoring and Improving Students' Learning (original) (raw)
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.
2019
Learning Analytics is a new field of techniques widely used in a number of communities. Some of them are Statistics, Business Intelligence, Web analytics or Operational research. The use of the Analytics approaches in the context of the learning process is called Learning Analytics (LA). A widely accepted definition of LA, provided at the 2012 International Conference on Learning Analytics and Knowledge, describes the field as "the measurement, collection, analysis and reporting of data about learners and their contexts, for the purpose of understanding and optimizing learning and the environments in which it occurs" (Siemens & Baker, 2012). The rise of LA comes from the chance of observing and tracking the learners' activities through log files. Logged data describes who the students are, which activities they carried out and when, and sometimes how and where, they worked. Such intensive data collection produces the so-called Big Data that facilitates the use of data analysis procedures (de-la-Fuente-Valentín et al., 2015). Non-intrusive measurement and collection is difficult to achieve in the learning context. The most popular method is to capture web interactions in a Learning Management System (LMS), but the captured data may not be fully representative of the student activities and other monitoring methods are required. Methods include social network analysis, collaborative filtering, clustering, neural networks, just to mention some. LA attempts to discover the factors that affect learning in a certain context, so that instructors and learners reflect on these factors and improve their experience. LA will explore continuous monitoring of learner progresses and traces of skill development of individual learners as well as learning groups, both within and across programs and institutions. It will discuss issues concerning
Journal of e-learning and knowledge society, 2019
Learners have different needs and abilities; teachers have the ambition to intervene before it is too late. How may e-learning systems support this? Learning Analytics may be the answer but there is not a general-purpose model to adopt. Many learning analytics tools examine data related to the activities of learners in on-line systems. Research efforts in learning analytics tried to examine data coming from LMS tracks in order to define predictive model of students’ performances and failure risks and to intervene to improve the learning outcomes. The analytical methods are widely used but no theoretical references are clear. In this paper, we tried to define a prediction model for learning analytics. In particular, we adopted a Moodle-based LMS in a blended course and collected all data of more than 400 undergraduate students in terms of resource accesses and exam performances. The model we defined was able to identify the learners at risk during their learning processes only by ana...
Let's not forget: Learning analytics are about learning
The analysis of data collected from the interaction of users with educational and information technology has attracted much attention as a promising approach for advancing our understanding of the learning process. This promise motivated the emergence of the new research field, learning analytics, and its closely related discipline, educational data mining. This paper first introduces the field of learning analytics and outlines the lessons learned from well-known case studies in the research literature. The paper then identifies the critical topics that require immediate research attention for learning analytics to make a sustainable impact on the research and practice of learning and teaching. The paper concludes by discussing a growing set of issues that if unaddressed, could impede the future maturation of the field. The paper stresses that learning analytics are about learning. As such, the computational aspects of learning analytics must be well integrated within the existing educational research.
Journal of Physics: Conference Series, 2019
Learning Analytics (LA) is evolving learning into a new era of analyzing student's participation and engagement in order to gain some insights. The implementation of LA in a university helps the administration and faculty associates to observe the progress of the students alongside their rate of success. The purpose of this study is to develop a student's engagement model for holistic involvement in the Learning Management System (LMS). The model was developed from an initial model that was derived from the review of literature and existing model of engagement in LMS. The data were collected from the online learning management system of one public University in Malaysia. From the data analysis, it was found that the strong engagement and interaction between the students, lecturers and the content in LMS, led to boost up the usage of the LMS as long as the student participation in the learning environment is accepted, which in return prepared the students to be evaluated anytime. The model that will be developed from this study can help increase the interaction and engagement between lecturers and students in LMS. Unlike the engagement of students in higher education LMS, which has been discussed already in the literature, this research integrated the role of trace data in shaping the learning environment communication and participation of the users.
International Journal of Education and Information Technologies, 2020
This paper aims to demonstrate the ongoing work of developing a framework that will allows to improve performance to students. The framework combines use of the open source virtual worlds, the Sloodle module and a learning analytics tool, in order to facilitate the execution of the collaborative learning techniques and improved the performance to students through of analytics learning tool monitorization. This framework is still in the design phase and will later be tested in a classroom context. The target public will be students of the fifth year of basic education, with aim of improve the learning mathematics.
Learning Analytics Architecture to Scaffold Learning Experience through Technology-based Methods
International Journal of Serious Games, 2015
The challenge of delivering personalized learning experiences is often increased by the size of classrooms and online learning communities. Serious Games (SGs) are increasingly recognized for their potential to improve education. However, the issues related to their development and their level of effectiveness can be seriously affected when brought too rapidly into growing online learning communities. Deeper insights into how the students are playing is needed to deliver a comprehensive and intelligent learning framework that facilitates better understanding of learners' knowledge, effective assessment of their progress and continuous evaluation and optimization of the environments in which they learn. This paper discusses current SOTA and aims to explore the potential in the use of games and learning analytics towards scaffolding and supporting teaching and learning experience. The conceptual model (ecosystem and architecture) discussed in this paper aims to highlight the key considerations that may advance the current state of learning analytics, adaptive learning and SGs, by leveraging SGs as an suitable medium for gathering data and performing adaptations.
Learning Analytics Lens: Improving Quality of Higher Education
World Academy of Research in Science and Engineering, 2020
With digital revolution expansion and a rapid change in the technologies, educational data is increasing at a swift pace. Learning analytics (LA) turns out to be a powerful tool for improving learning and teaching practices. Learning analytics uncover hidden patterns, correlation, and other insights about learners and educators in educational big data that leads them to stay agile, better outcomes, and employability. This literature review aims to categorize such measures of data-driven improvement. A comprehensive review of Learning Analytics (LA) and Educational Data Mining (EDM) and significant techniques in higher education was conducted. Analysis of the research questions, methodology, techniques, learning environment, associated projects, and findings of various published papers is done and is accordingly categorized. Analysis of various reviews on the development and growth of LA in higher education in various countries is also done. The results provide a comprehensive background for understanding current knowledge on LA and EDM and its impact on both learner and instructor in the various learning environment. The results showed that in HEIs, where LA has been implemented aimed at better assessing and predicting learner's performance. It has also helped in monitoring and motivating them, discovering undesirable learning behaviors and their emotional states, help educators and administrators to unlock big data potentials, and making quicker data-driven decisions.
Learning Analytics: an Updated View
2020
Learning analytics has been a hot topic in the education industry for several years. Given its diverse roots (business intelligence, web analytics, educational data mining, etc.), researchers and practitioners have approached learning analytics from a range of perspectives. Although many studies concerning learning analytics have highlighted its great potential for improving learning practice, there has not been much success in the transfer of the suggested potential into the practice of higher education. This clearly indicates a need for rethinking not only the goals that can be achieved using learning analytics, but also what must be done in order to attain these goals. The aim of the descriptive research presented in this paper is to provide an honest and updated view of where we are, the real benefits, and the challenges to be overcome when using learning analytics as educational technology. This paper emphasizes that a deeper understanding of students’ learning experiences as w...
e-Learning Analytics for Improvement of Education
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...