Stability and sensitivity of Learning Analytics based prediction models

Learning analytics seek to enhance the learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators. Track data from Learning Management Systems (LMS) constitute a main data source for learning analytics. This empirical contribution provides an application of Buckingham Shum and Deakin Crick’s theoretical framework of dispositional learning analytics: an infrastructure that combines learning dispositions data with data extracted from computer-assisted, formative assessments and LMSs. In two cohorts of a large introductory quantitative methods module, 2049 students were enrolled in a module based on principles of blended learning, combining face-to-face Problem-Based Learning sessions with e-tutorials. We investigated the predictive power of learning dispositions, outcomes of continuous formative assessments and other system generated data in modelling student performance and their potential to generate informative feedback. Using a dynamic, longitudinal perspective, computer-assisted formative assessments seem to be the best predictor for detecting underperforming students and academic performance, while basic LMS data did not substantially predict learning. If timely feedback is crucial, both use-intensity related track data from e-tutorial systems, and learning dispositions, are valuable sources for feedback generation

Exploring Learning Analytics In E-Learning: A Comprehensive Analysis of Student Characteristics and Behavior

This article aims to explore learning analytics in e-learning through a comprehensive analysis of student characteristics and behavior. E-learning has become increasingly significant in education, particularly due to the social situation influenced by the pandemic. The Learning Management System (LMS) has become a crucial tool for educators to track and record student learning in e-learning environments. Learning analytics can aid in understanding the context of students, ensuring that they receive a personalized learning experience aligned with learning objectives. However, educators often face challenges in conducting learning analytics for e-learning students, primarily due to the large number of students to analyze and limited data availability. This study seeks to provide a detailed description of e-learning students within the Open and Distance Education (ODE) system. ODE students exhibit high diversity in demographic profiles, learning behaviors, and competency backgrounds. To support this research, we utilize datasets containing student demographic profiles and learning activity data during e-learning sessions. The datasets are obtained from the academic system and LMS log data of Universitas Terbuka. The article employs Exploratory Data Analysis (EDA) and data science approaches as the foundation for predictive and prescriptive analytics of student learning outcomes. Relevant features are extracted from the dataset to build a robust predictive model. The analysis results present patterns and relationships between student characteristics, learning behaviors, and academic achievements. This research aims to provide valuable insights for the development of more effective and personalized e-learning strategies to enhance student learning outcomes in the context of distance education. Moreover, the analysis findings can serve as a basis for informed academic decision-making to improve the quality of e-learning environments

Improving workplace-based assessment and feedback by an E-portfolio enhanced with learning analytics

Electronic portfolios (E-portfolios) are crucial means for workplace-based assessment and feedback. Although E-portfolios provide a useful approach to view each learner’s progress, so far options for personalized feedback and potential data about a learner’s performances at the workplace often remain unexploited. This paper advocates that E-portfolios enhanced with learning analytics, might increase the quality and efficiency of workplace-based feedback and assessment in professional education. Based on a 5-phased iterative design approach, an existing E-portfolio environment was enhanced with learning analytics in professional education. First, information about crucial professional activities for professional domains and suited assessment instruments were collected (phase 1). Thereafter probabilistic student models were defined (phase 2). Next, personalized feedback and visualization of the personal development over time were developed (phase 3). Then the prototype of the E-portfolio—including the student models and feedback and visualization modules—were implemented in professional training-programs (phase 4). Last, evaluation cycles took place and 121 students and 30 supervisors from five institutes for professional education evaluated the perceived usefulness of the design (phase 5). It was concluded that E-portfolios with learning analytics were perceived to assist the development of students’ professional competencies and that the design is only successful when developed and implemented through the eyes of the users. Feedback and assessment methods based upon learning analytics can stimulate learning at the workplace in the long run. Practical, technological and ethical challenges are discussed

What Can Analytics Contribute to Accessibility in e-Learning Systems and to Disabled Students’ Learning?

This paper explores the potential of analytics for improving accessibility of e-learning and supporting disabled learners in their studies. A comparative analysis of completion rates of disabled and non-disabled students in a large five-year dataset is presented and a wide variation in comparative retention rates is characterized. Learning analytics enable us to identify and understand such discrepancies and, in future, could be used to focus interventions to improve retention of disabled students. An agenda for onward research, focused on Critical Learning Paths, is outlined. This paper is intended to stimulate a wider interest in the potential benefits of learning analytics for institutions as they try to assure the accessibility of their e-learning and provision of support for disabled students

Citizen Science: The Ring to Rule Them All?

There are many uncertainties about the future of e-Learning, but one thing is certain: e-Learning will be more data-driven in the future. The automation of data capturing, analysis and presentation, together with economic constraints that require evidence-based proof of impact, compels this data focus. On the other hand, the importance of community involvement in learning analytics and educational data mining is an accepted fact. Citizen science, at the nexus of community engagement, and data science can bridge the divide between data-driven and community-driven approaches to policy and content development. The rationale for this paper is the investigation of citizen science as an approach to collecting data for learning analytics in the field of e-Learning. Capturing data for policy and content development for learning analytics through citizen science projects is novel in the e-Learning field. Like any other new area, citizen science needs to be mapped in terms of the existing parent fields of data science and education so that differences and potential overlaps can be made explicit. This is important when considering conceptual or functional definitions, research tools and methodologies. A preliminary review of the literature has not provided any conceptual positioning of citizen science in relation to the research topics of learning analytics, data science, big data and visualisation in the e-Learning environment. The intent of this paper is firstly to present an overview of citizen science and the related research topics in the academic and practitioner literature based on a systematic literature review. Secondly, we propose a model that represents the relationship between citizen science and other salient concepts and shows how citizen science projects can be positioned in the e-Learning environment. Finally, we suggest research opportunities involving citizen science projects in the field of e-Learning.School of Computin

Online Learning Management System and Analytics using Deep Learning

During this pandemic we have seen rise in popularity of online learning platforms. In this paper, we are going to discuss E-Learning using analytics and deep learning focusing on mainly four objectives which are login systems for teachers and students, Gamification to engage learners, AR contents to increase the involvement of learners and learning analytics to develop competency. We will use Data Mining and Buisness Intelligence to extract high level knowledge from the raw data of students. To predict engagement of students we have used several ML algorithms. This study provides an introduction to the technology of AR and E-Learning systems. The main focus of this paper is to use research on augmented reality and integrate it with Buisness Intelligence and Data Mining(DM). Engaging student till the end of the course became really difficult for traditional E-Learning Platform. Therefore, Gamification in E-learning is good way to solve this problem

Applying an evolutionary approach for learning path optimization in the next-generation e-learning systems

Learning analytics is targeted to better understand and optimize the process of learning and its environments through the measurement, collection and analysis of learners' data and contexts. To advise people's learning in a specific subject, most intelligent e-learning systems would require course instructors to explicitly input some prior knowledge about the subject such as all the pre-requisite requirements between course modules. Yet human experts may sometimes have conflicting views leading to less desirable learning outcomes. In a previous study, we proposed a complete system framework of learning analytics to perform an explicit semantic analysis on the course materials, followed by a heuristic-based concept clustering algorithm to group relevant concepts before finding their relationship measures, and lastly employing a simple yet efficient evolutionary approach to return the optimal learning sequence. In this paper, we carefully consider to enhance the original evolutionary optimizer with the hill-climbing heuristic, and also critically evaluate the impacts of various experts' recommended learning sequences possibly with conflicting views to optimize the learning paths for the next-generation e-learning systems. More importantly, the integration of heuristics can make our proposed framework more self-adaptive to less structured knowledge domains with conflicting views. To demonstrate the feasibility of our prototype, we implemented a prototype of the proposed e-learning system framework for learning analytics. Our empirical evaluation clearly revealed many possible advantages of our proposal with interesting directions for future investigation. © 2013 IEEE.published_or_final_versio

Implementasi Pembelajaran Sinkronus pada Mata Kuliah Fisika 1 Menggunakan Discord Dipadukan dengan Google Jamboard dan Powerpoint

COVID-19 virus have affected educational activities to be carried out through e-learning. Discord and Google Jamboard offer advantages in delivering synchronous e-learning. The purpose of this research is to implement e-learning of Physics 1 course using Discord integrated with Google Jamboard and Powerpoint. In this research, we used qualitative-descriptive method which conducted in several steps, i.e: (1) Discord installation and Physics 1 Class preparation; (2) Virtual Tablet Server installation and its testing for mobile phone; (3) Learning materials preparation in Powerpoint; (4) Implementation of Physics 1 Class using Discord integrated with Google Jamboard and Powerpoint; (5) Evaluation of the results and (6) Surveying of student’s opinion about e-learning using using Discord integrated with Google Jamboard and Powerpoint. Discord can be used as an alternative e-learning tool because of its practical use as well as more powerful when integrated with Google Jamboard and Powerpoint, especially for mathematical-analytics subject such as Physics. This is supported by the results of a survey in which 34 out of 39 students said they wanted to use Discord and Google Jamboard for other mathematical-analytics subjects

Implementasi Pembelajaran Sinkronus pada Mata Kuliah Fisika 1 Menggunakan Discord Dipadukan dengan Google Jamboard dan Powerpoint

COVID-19 virus have affected educational activities to be carried out through e-learning. Discord and Google Jamboard offer advantages in delivering synchronous e-learning. The purpose of this research is to implement e-learning of Physics 1 course using Discord integrated with Google Jamboard and Powerpoint. In this research, we used qualitative-descriptive method which conducted in several steps, i.e: (1) Discord installation and Physics 1 Class preparation; (2) Virtual Tablet Server installation and its testing for mobile phone; (3) Learning materials preparation in Powerpoint; (4) Implementation of Physics 1 Class using Discord integrated with Google Jamboard and Powerpoint; (5) Evaluation of the results and (6) Surveying of student’s opinion about e-learning using using Discord integrated with Google Jamboard and Powerpoint. Discord can be used as an alternative e-learning tool because of its practical use as well as more powerful when integrated with Google Jamboard and Powerpoint, especially for mathematical-analytics subject such as Physics. This is supported by the results of a survey in which 34 out of 39 students said they wanted to use Discord and Google Jamboard for other mathematical-analytics subjects

Educational Data Analytics for Teachers and School Leaders

Educational Data Analytics (EDA) have been attributed with significant benefits for enhancing on-demand personalized educational support of individual learners as well as reflective course (re)design for achieving more authentic teaching, learning and assessment experiences integrated into real work-oriented tasks. This open access textbook is a tutorial for developing, practicing and self-assessing core competences on educational data analytics for digital teaching and learning. It combines theoretical knowledge on core issues related to collecting, analyzing, interpreting and using educational data, including ethics and privacy concerns. The textbook provides questions and teaching materials/ learning activities as quiz tests of multiple types of questions, added after each section, related to the topic studied or the video(s) referenced. These activities reproduce real-life contexts by using a suitable use case scenario (storytelling), encouraging learners to link theory with practice; self-assessed assignments enabling learners to apply their attained knowledge and acquired competences on EDL. By studying this book, you will know where to locate useful educational data in different sources and understand their limitations; know the basics for managing educational data to make them useful; understand relevant methods; and be able to use relevant tools; know the basics for organising, analysing, interpreting and presenting learner-generated data within their learning context, understand relevant learning analytics methods and be able to use relevant learning analytics tools; know the basics for analysing and interpreting educational data to facilitate educational decision making, including course and curricula design, understand relevant teaching analytics methods and be able to use relevant teaching analytics tools; understand issues related with educational data ethics and privacy. This book is intended for school leaders and teachers engaged in blended (using the flipped classroom model) and online (during COVID-19 crisis and beyond) teaching and learning; e-learning professionals (such as, instructional designers and e-tutors) of online and blended courses; instructional technologists; researchers as well as undergraduate and postgraduate university students studying education, educational technology and relevant fields