Recommendations for the Selection of Methods for the Analysis of eCollaboration based on a Systematic Literature Review

Learning Analytics plays an increasing role in the analysis of virtual learning activities. This article addresses the gap between educational needs and technical supply. By means of a Systematic Literature Review of the LAK conferences the authors extracted observations, methods and tools which represent potential solutions for a given eCollaboration scenario. Based on three prioritised examples of an observation sheet, methods are derived and recommendations for the use of Learning Analytics tools are given. The result is a catalogue that enable users to select suitable methods and tools for an implementation. The (semi-) automation can increase the efficiency of Community Managers in monitoring the participants and hence make real-time intervention feasible

Student participation model in e-learning

Collaborative learning becomes a popular method to be a potential approach in meaningful learning and motivate student’s participation. However, its use has yet to be implemented in the current education system. E-learning is a platform that promises the full usage of ICT and technology in learning process and contains social media tools that support collaborative learning. However, the usage of e-learning and student’s participations are low, where the social media tools for collaborative learning are not fully utilized. In fact, the e-learning is only being used for disseminating learning. Interactions among students in elearning are still very poor and the learning process is not effective. It is also found that students fail to stimulate their participations until the end of the course. Other issues in collaborative learning assessment is analyzing the log to measure student’s participation that gives equal grade of performance to each student. This measurement is unfair because normally each student has different participation levels. This research proposes a Student Participation Model (SPM) that contains three main components which are social learning component, collaborative learning process component and collaborative learning assessment component. The main purpose of this model is to increase student’s participation and the usage of e-learning as well as to improve collaborative learning assessment. The social components include the Person: namely instructors, students and collaborators, the Behavior of a person’s action: namely Sharing, Exchanging and Exploring, and the Environment which is the e-learning technology. Meanwhile, the collaborative learning process of SPM is based on the Task-oriented collaborative learning model. In addition, the collaborative learning assessment for students is the log analysis that includes the students’ attitude and their participation levels. The model is able to generate students’ work progress result, participation percentage (individually and group) and also students’ attitude in e-learning (passive and active). This model is verified using expert validation method. The expert validation method is used to validate the effectiveness of model. It is found that the experts agreed that the model is relevant to apply, understandable and able to identify students’ participation

eCollaboration in der Hochschullehre: Bewertung mittels Learning Analytics

Wissen bekommt in unserer Gesellschaft eine immer stärkere Bedeutung und stellt Individuen und Organisationen vor verschiedenste Herausforderungen. Der gesamtwirtschaftliche Anteil materieller Güter wird gegenüber wissensintensiven Dienstleistungen zurückgehen, d.h. es bedarf zukünftig immer stärker gut aus- und weitergebildeter Experten, die miteinander die Innovationskraft von Organisationen steigern und kundenspezifische Lösungen entwickeln. Ausgehend von den für Experten benötigten Kompetenzen sogenannter Wissensarbeiter fokussiert diese Arbeit die Begleitung und Bewertung von kollaborativer Teamarbeit. Als Forschungsobjekt dienen Virtual Collaborative Learning-Veranstaltungen, in denen die zukünftigen Experten als Lernende teilnehmen. Sie werden in virtuellen Klassenräumen von Lernbegleitern beobachtet und bewertet, um sie bei der Entwicklung neuer Kompetenzen zu unterstützen und so die Erreichung der Lernziele zu gewährleisten. Da die Lernbegleitung bislang manuell durchgeführt wird, ist eine zeitnahe Beurteilung und Intervention nicht möglich. Mit Learning Analytics sollen Lösungen vorgeschlagen werden, die die Lernbegleiter in ihrer Arbeit unterstützen und den Aufwand reduzieren. Hierzu thematisiert diese Dissertation in sechs kumulativen Beiträgen, wie die zugrundeliegenden Daten verfügbar gemacht werden können und welche Beobachtungen anhand welcher Kriterien mittels ausgewählter Methoden der Learning Analytics durchgeführt werden sollten. Im Rahmen des Design Science Paradigmas werden verschiedene qualitative und quantitative Forschungsmethoden zur Datenerhebung und -auswertung angewendet. Im Ergebnis entsteht ein beispielhafter Katalog von Learning Analytics Methoden, die sich auf spezifische Erkenntnisziele der Beobachtungen von kollaborativer Gruppenarbeit beziehen. Ebenso wird der Einsatz einer ausgewählten Methode evaluiert. Die gewonnenen Erkenntnisse können zukünftig auf vergleichbare kollaborative Lehrangebote in der Ausbildung von Wissensarbeitern, aber auch zur Analyse und Unterstützung der virtuellen Zusammenarbeit im geschäftlichen Umfeld transferiert werden

e-Portfolio assessment in networked learning based communities

There is a substantial body of research suggesting the advantages of using e- Portfolios in higher education assessment, where work is collated by individuals to record their learning. The use of learning communities in this context is an under-researched area, despite the number of e-Portfolios that implement a social component. This work develops an alternative e-Portfolio approach by using a networked learning based pedagogy, which brings richer descriptions of both artifacts and the structure of the underlying community. Action research and free/open source development principles have been aligned over two cycles, where students have participated as both co-researchers and co-developers. Evolving the nature and presentation of assessment artifacts, participants have determined how these are best shared and reused, and the ways in which larger contextual information about the community can improve both the learning and the knowledge of the learning taking place. A multi-method research framework is used to show what artifacts are created, who is interacting with whom and why participants act as they do. Data has been collected using interviews, focus groups and from analytics from the e-Portfolio itself. The findings suggest that the types of artifacts created are influenced by both the community and by the nature of the material being learnt. Artifacts reveal the sources that students use for their work and although participants can be reluctant to reveal incorrect or incomplete work to the community, this can be encouraged by a carefully constructed induction, reinforcing the importance of the role of teacher as tutor. Expertise is quickly associated with some participants by the quality and regularity of their artifact construction, who become more central and influential to the community, with their work becoming increasingly visible through search activities. This work presents the framework, an analysis of the results, conclusions and recommendations along with a reference implementation

Multikonferenz Wirtschaftsinformatik (MKWI) 2016: Technische Universität Ilmenau, 09. - 11. März 2016; Band I

Übersicht der Teilkonferenzen Band I: • 11. Konferenz Mobilität und Digitalisierung (MMS 2016) • Automated Process und Service Management • Business Intelligence, Analytics und Big Data • Computational Mobility, Transportation and Logistics • CSCW & Social Computing • Cyber-Physische Systeme und digitale Wertschöpfungsnetzwerke • Digitalisierung und Privacy • e-Commerce und e-Business • E-Government – Informations- und Kommunikationstechnologien im öffentlichen Sektor • E-Learning und Lern-Service-Engineering – Entwicklung, Einsatz und Evaluation technikgestützter Lehr-/Lernprozess