How should we measure online learning activity?

The proliferation of Web-based learning objects makes finding and evaluating resources a considerable hurdle for learners to overcome. While established learning analytics methods provide feedback that can aid learner evaluation of learning resources, the adequacy and reliability of these methods is questioned. Because engagement with online learning is different from other Web activity, it is important to establish pedagogically relevant measures that can aid the development of distinct, automated analysis systems. Content analysis is often used to examine online discussion in educational settings, but these instruments are rarely compared with each other which leads to uncertainty regarding their validity and reliability. In this study, participation in Massive Open Online Course (MOOC) comment forums was evaluated using four different analytical approaches: the Digital Artefacts for Learning Engagement (DiAL-e) framework, Bloom's Taxonomy, Structure of Observed Learning Outcomes (SOLO) and Community of Inquiry (CoI). Results from this study indicate that different approaches to measuring cognitive activity are closely correlated and are distinct from typical interaction measures. This suggests that computational approaches to pedagogical analysis may provide useful insights into learning processes.

Nurturing global collaboration and networked learning in higher education

We consider the principles of communities of practice (CoP) and networked learning in higher education, illustrated with a case study. iCollab has grown from an international community of practice connecting students and lecturers in seven modules across seven higher education institutions in six countries, to a global network supporting the exploration and evaluation of mobile web tools to engage in participatory curriculum development and supporting students in developing international collaboration and cooperation skills. This article explores the interplay of collaboration and cooperation, CoP and networked learning; describes how this interplay has operated in iCollab; and highlights opportunities and challenges of learning, teaching and interacting with students in networked publics in higher education.

A reflexive evaluation of technology-enhanced learning

This article explores the lived experiences of two academics in a UK Higher Education Institution who have embedded digital learning approaches within their curriculum delivery. Achieving student excellence can be impeded by a lack of engagement and sense of identity on large courses. Digital learning strategies can offer opportunities to overcome these challenges by empowering students to engage self-confidently. Through an evaluation of the authors’ own experiences of using social media, polling and web-conferencing software, the article shows how interacting with students via a range of learning technologies can create more inclusive and engaging learning environments. Including feedback from students within this article provides evidence that diversification of communication within teaching and learning practice gives students more choice and opportunity to interact with both their peers and teaching staff. The article concludes with recommendations for embedding technology, whilst acknowledging the well-established value of face-to-face interaction.

A review of predictive factors of student success in and satisfaction with online learning

Students perceive online courses differently than traditional courses. Negative perceptions can lead to unfavourable learning outcomes including decreased motivation and persistence. Throughout this review, a broad range of factors that affect performance and satisfaction within the online learning environment for adult learners will be examined including learning outcomes, instructional design and learner characteristics, followed by suggestions for further research, and concluding with implications for online learning pertinent to administrators, instructors, course designers and students. Online learning may not be appropriate for every student. Identifying particular characteristics that contribute to online success versus failure may aid in predicting possible learning outcomes and save students from enrolling in online courses if this type of learning environment is not appropriate for them. Furthermore, knowing these learner attributes may assist faculty in designing quality online courses to meet students’ needs. Adequate instructional methods, support, course structure and design can facilitate student performance and satisfaction.