Digital communities: context for leading learning into the future?

In 2011, a robust, on-campus, three-element Community of Practice model consisting of growing community, sharing of practice and building domain knowledge was piloted in a digital learning environment. An interim evaluation of the pilot study revealed that the three-element framework, when used in a digital environment, required a fourth element. This element, which appears to happen incidentally in the face-to-face context, is that of reflecting, reporting and revising. This paper outlines the extension of the pilot study to the national tertiary education context in order to explore the implications for the design, leadership roles, and selection of appropriate technologies to support and sustain digital communities using the four-element model

How to design for persistence and retention in MOOCs?

Design of educational interventions is typically carried out following a design cycle involving phases of investigation, conceptualization, prototyping, implementation, execution and evaluation. This cycle can be applied at different levels of granularity e.g. learning activity, module, course or programme. In this paper we consider an aspect of learner behavior that can be critical to the success of many MOOCs i.e. their persistence to study, and the related theme of learner retention. We reflect on the impact that consideration of these can have on design decisions at different stages in the design cycle with the aim of en-hancing MOOC design in relation to learner persistence and retention, with particular attention to the European context

A LEARNER INTERACTION STUDY OF DIFFERENT ACHIEVEMENT GROUPS IN MPOCS WITH LEARNING ANALYTICS TECHNIQUES

The purpose of this study was to conduct data-driven research by employing learning analytics methodology and Big Data in learning management systems (LMSs), and then to identify and compare learners’ interaction patterns in different achievement groups through different course processes in Massive Private Online Courses (MPOCs). Learner interaction is the foundation of a successful online learning experience. However, the uncertainties about the temporal and sequential patterns of online interaction and the lack of knowledge about using dynamic interaction traces in LMSs have prevented research on ways to improve interactive qualities and learning effectiveness in online learning. Also, most research focuses on the most popular online learning organization form, Massive Open Online Courses (MOOCs), and little online learning research has been conducted to investigate learners’ interaction behaviors in another important online learning organization form: MPOCs. To fill these needs, the study pays attention to investigate the frequent and effective interaction patterns in different achievement groups as well as in different course processes, and attaches importance to LMS trace data (log data) in better serving learners and instructors in online learning. Further, the learning analytics methodology and techniques are introduced here into online interaction research. I assume that learners with different achievements express different interaction characteristics. Therefore, the hypotheses in this study are: 1) the interaction activity patterns of the high-achievement group and the low-achievement group are different; 2) in both groups, interaction activity patterns evolve through different course processes (such as the learning process and the exam process). The final purpose is to find interaction activity patterns that characterize the different achievement groups in specific MPOCs courses. Some learning analytics approaches, including Hidden Markov models (HMMs) and other related measures, are taken into account to identify frequently occurring interaction activity sequence patterns of High/Low achievement groups in the Learning/Exam processes under MPOCs settings. The results demonstrate that High-achievement learners especially focused on content learning, assignments, and quizzes to consolidate their knowledge construction in both Learning and Exam processes, while Low-achievement learners significantly did not perform the same. Further, High-achievement learners adjusted their learning strategies based on the goals of different course processes; Low-achievement learners were inactive in the learning process and opportunistic in the exam process. In addition, despite achievements or course processes, all learners were most interested in checking their performance statements, but they engaged little in forum discussion and group learning. In sum, the comparative analysis implies that certain interaction patterns may distinguish the High-achievement learners from the Low-achievement ones, and learners change their patterns more or less based on different course processes. This study provides an attempt to conduct learner interaction research by employing learning analytics techniques. In the short term, the results will give in-depth knowledge of the dynamic interaction patterns of MPOCs learners. In the long term, the results will help learners to gain insight into and evaluate their learning, help instructors identify at-risk learners and adjust instructional strategies, help developers and administrators to build recommendation systems based on objective and comprehensive information, all of which in turn will help to improve the achievements of all learner groups in specific MPOC courses

Learning networks and moodle use in online courses: a social network analysis study

Dissertação para obtenção do Grau de Doutor em Ciências da Educação Especialidade em Tecnologias, Redes e Multimédia na Educação e FormaçãoThis research presents a case study on the interactions between the participants of the forums of four online undergraduate courses from the perspective of social network analysis (SNA). Due to lack of studies on social networks in online learning environments in higher education in Portugal we have choose a qualitative structural analysis to address this phenomenon. The context of this work was given by the new experiences in distance education (DE) that many institutions have been making. Those experiences are a function of the changes in educational paradigms and due to a wider adoption of Information and Communication Technologies (ICT) from schools as well as to the competitive market. Among the technologies adopted by universities are the Learning Management Systems (LMSs) that allow recording, storing and using large amounts of relational data about their users and that can be accessed through Webtracking. We have used this information to construct matrices that allowed the SNA. In order to deepen knowledge about the four online courses we were studying we have also collect data with questionnaires and interviews and we did a content analysis to the participations in the forums. The three main sources of data collection led us to three types of analysis: SNA, statistical analysis and content analysis. These types of analysis allowed, in turn, a three-dimensional study on the use of the LMS: 1) the relational dimension through the study of forums networks and patterns of interaction among participants in those networks, 2) the dimension relative to the process of teaching and learning through content analysis of the interviews; 3) and finally the dimension related to the participants' perceptions about the use of LMS for educational purposes and as a platform for creating social networks through the analysis of questionnaires.With the results obtained we carried out a comparative study between the four courses and tried to present a reflection on the Online Project of the University as well as possible causes that led to what was observed. We have finished with a proposal of a framework for studying the relational aspects of online learning networks aimed at possible future research in this area

Assessing learners’ satisfaction in collaborative online courses through a big data approach

none4noMonitoring learners' satisfaction (LS) is a vital action for collecting precious information and design valuable online collaborative learning (CL) experiences. Today's CL platforms allow students for performing many online activities, thus generating a huge mass of data that can be processed to provide insights about the level of satisfaction on contents, services, community interactions, and effort. Big Data is a suitable paradigm for real-time processing of large data sets concerning the LS, in the final aim to provide valuable information that may improve the CL experience. Besides, the adoption of Big Data offers the opportunity to implement a non-intrusive and in-process evaluation strategy of online courses that complements the traditional and time-consuming ways to collect feedback (e.g. questionnaires or surveys). Although the application of Big Data in the CL domain is a recent explored research area with limited applications, it may have an important role in the future of online education. By adopting the design science research methodology, this article describes a novel method and approach to analyse individual students' contributions in online learning activities and assess the level of their satisfaction towards the course. A software artefact is also presented, which leverages Learning Analytics in a Big Data context, with the goal to provide in real-time valuable insights that people and systems can use to intervene properly in the program. The contribution of this paper can be of value for both researchers and practitioners: the former can be interested in the approach and method used for LS assessment; the latter can find of interest the system implemented and how it has been tested in a real online course.openElia G.; Solazzo G.; Lorenzo G.; Passiante G.Elia, G.; Solazzo, G.; Lorenzo, G.; Passiante, G

The Utilization of Data Analysis Techniques in Predicting Student Performance in Massive Open Online Courses (MOOCs)

The growth of the Internet has enabled the popularity of open online learning platforms to increase over the years. This has led to the inception of Massive Open Online Courses (MOOCs) that enrol, millions of people, from all over the world. Such courses operate under the concept of open learning, where content does not have to be delivered via standard mechanisms that institutions employ, such as physically attending lectures. Instead learning occurs online via recorded lecture material and online tasks. This shift has allowed more people to gain access to education, regardless of their learning background. However, despite these advancements in delivering education, completion rates for MOOCs are low. In order to investigate this issue, the paper explores the impact that technology has on open learning and identifies how data about student performance can be captured to predict trend so that at risk students can be identified before they drop-out. In achieving this, subjects surrounding student engagement and performance in MOOCs and data analysis techniques are explored to investigate how technology can be used to address this issue. The paper is then concluded with our approach of predicting behaviour and a case study of the eRegister system, which has been developed to capture and analyse data. Keywords: Open Learning; Prediction; Data Mining; Educational Systems; Massive Open Online Course; Data Analysi

Representation of virtual choreographies in learning dashboards of interoperable LMS analytics

Learning management systems (LMS) collect a large amount of data from user interaction, and it isn't easy to analyze this data in a reliable and context-independent manner. This research seeks to comprehend how virtual choreographies can be represented in interoperable LMS analytics dashboards. In order to gain a better understanding of the problem, this objective has been divided into three sub-goals: determining which interactions can be gathered from LMS contexts, identifying virtual choreographies from LMS logs, and representing virtual choreographies in learning dashboards. To achieve these objectives, we first conducted a Systematic Literature Review to comprehend the behaviors and interactions other authors have investigated in LMS contexts. Then, by applying these findings to this dissertation's case study, a methodical procedure for extracting valuable choreographies from the logs was outlined. The Design Science Research methodology was then applied to transforming logs into virtual choreographies and their representation in learning dashboards. It was implemented two services: one responsible for identifying virtual choreographies from data logs and transforming the logs into statements, recipes, and choreographies, following xAPI specification elements; and the other translates the information from the backend service into dashboard visualizations, allowing the user to view representations for statements, recipes, choreographies, and various visualizations. These artifacts provide a new flexible and cost-efficient solution for the identification of virtual choreographies, thereby facilitating the widespread adoption of their use