Learning Analytics: A Way to Monitoring and Improving Students' Learning (original) (raw)
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Learning Analytics: Layers, Loops and Processes in a Virtual Learning Infrastructure
Handbook of Learning Analytics
Learning analytics (LA) is a term that refers to the use of digital data for analysis and feedback that generates actionable insights to improve learning. LA feedback can be used in two ways: 1) to improve the personal learning power of individuals and teams in the process of value creation; and 2) to respond more accurately to the learning needs of others. The human online communications; sensors that monitor more; as well as traditional survey data collected from sort of social and technical learning infrastructures that best support processes of improvement, adaptation and change. These challenges are located at the human/data interface and are as important for schools or universities whose purpose is to enhance learning and its outcomes as they are for corporate service or a product. Both depend on the capability of humans within their systems to be able to monitor, own learning journey to achieve a purpose of value. Learning analytics provides formative feedback at can be aggregated for individuals, teams, and whole
Learning Analytics for Student ’ s Motivation
2017
Methods of Learning Analytics are gaining ever broader usage and significance in the interpretation of big volumes of data related to the process of studying. Such data, recovered from backlogs of digital learning media, can assist monitoring the behavior of students in online platforms. Continuous analysis of this information allows the teaching stuff to evaluate the efficiency of existing teaching techniques and to formulate the requirements to new didactic methods that should improve the learning process and advance the learning success of students. Therefore the patterns of studies in learning analytics are usually specially tailored to serve the teaching stuff. However, for the students it can be nonetheless interesting to learn how, on the base of monitoring and interpretation of their own behavior and activities in online platforms, they can gain more information about the pace of their own learning compared to their classmates, and what they can and should do in order to rai...
Is Learning Analytics the Future of Online 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...
IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 2019
Learning Analytics (LA) has a significant impact in learning and teaching processes. These processes can be improved using the available data retrieved from students' activity inside the virtual classrooms of a learning management system (LMS). This process requires the development of a tool that allows one to handle the retrieved information properly. This paper presents a solution to this need, in the form of a development model and actual implementation of an LA tool. Four phases (Explanation, Diagnosis, Prediction and Prescription) are implemented in the tool, allowing a teacher to track students' activity in a virtual classroom via the Sakai LMS. It also allows for the identification of users who face challenges in their academic process and the initiation of personalised mentoring by the teacher or tutor. The use of the tool was tested on groups of students in an algorithms course in the periods 2017-1, 2017-2, 2018-1 and 2018-2, with a total of 90 studentsin parallel with the control groups in the same periods that totalled 95 studentsobtaining superior averages in the test groups versus the control groups, which evidenced the functionality and utility of the software.
Learning Analytics - New Flavor and Benefits for Educational Environments
Informatics in Education, 2018
Amount of educational data has been constantly increasing for years in all domains and kinds of education (formal or informal) and educational activities (teaching, learning, assessment, use of social media and collaboration and so on). Accordingly, Learning Analytics (LA) become a powerful mechanism for supporting learners, instructors, teachers, learning system designers and developers to better understand educational processes and predict learners' needs and performances. In this paper, we analyze the important dimensions and objectives of LA, application possibilities and some challenges to the beneficial exploitation of educational data. The required skills and capabilities that make meaningful use of LA techniques and technologies in this domain are considered and identified. Presented findings can act as a valuable guide for setting up LA services in support of educational practice. Also, they can be used as learner guidance, in quality assurance, curriculum development, and in improving learning process effectiveness and efficiency. Finally, this paper proposes the unavoidable constraints that affect LA technologies in education.
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.