Search

This study explored the concept of social capital in higher education contexts by investigating student discussion forum activity and academic performance. To address these aims online discussion forum logs, student marks and teaching delivery method (blended or fully online) data were extracted from the universities learning management system (LMS). Student social network centrality measures were then calculated from the course discussion activity and correlated against student academic performance for each delivery mode. Drawing on social capital and social network theories the analyses identified that in comparison to low performing students the high-performing group held more central positions in their networks and tended to establish dense social connections with students of a similar academic ability. It was also observed that the relationships formed in blended teaching units were of a greater intensity and reciprocity than those established in fully online teaching units indicating a higher level of social capital was reached. This difference in the amount of available social capital between the two teaching modes suggests that students in blended units have comparatively greater access to resources embedded within the network, which in turn can be mobilised to assist them in their academic endeavours.

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.

The increasing use of technology in education has brought new opportunities for the systematic collection of student data. Analyzing technology-mediated trace data, for example, has enabled researchers to bring new insights into student learning processes and the factors involved to support learning and teaching. However, many of these learning analytic studies have drawn conclusions from limited data sets that are derived from a single course or program of study. This impacts the generalizability of noted outcomes and calls for research on larger institutional data sets. The institutional adoption and analysis of learning technology can provide deeper insights into a wide range of learning contexts in practice. This study focused on examining how instructors used the learning tool, OnTask, to provide personalized feedback for students in large classes. We collected usage data from 99 courses and 19,385 students to examine how the instructors customized feedback to different groups of students. The findings reveal that there is a significant association between the topics of feedback and students with different performance. The results also demonstrated that instructors most frequently provided feedback related to student assessment. The study emphasizes the importance of teacher and student feedback literacy for creating effective feedback loops. [ABSTRACT FROM AUTHOR]

Learning analytics offers higher education valuable insights that can inform strategic decision-making regarding resource allocation for educational excellence. Research demonstrates that learning management systems (LMSs) can increase student sense of community, support learning communities and enhance student engagement and success, and LMSs have therefore become core enterprise component in many universities. We were invited to undertake a current state analysis of enterprise LMS use in a large research-intensive university, to provide data to inform and guide an LMS review and strategic planning process. Using a new e-learning analytics platform, combined with data visualization and participant observation, we prepared a detailed snapshot of current LMS use patterns and trends and their relationship to student learning outcomes. This paper presents selected data from this "current state analysis" and comments on what it reveals about the comparative effectiveness of this institution's LMS integration in the service of learning and teaching. More critically, it discusses the reality that the institutional planning process was nonetheless dominated by technical concerns, and made little use of the intelligence revealed by the analytics process. To explain this phenomenon we consider theories of change management and resistance to innovation, and argue that to have meaningful impact, learning analytics proponents must also delve into the socio-technical sphere to ensure that learning analytics data are presented to those involved in strategic institutional planning in ways that have the power to motivate organizational adoption and cultural change. (Contains 2 tables and 4 figures.)

Notions of what it is to be knowledgeable and skilled in one's profession have evolved in recent decades. For instance, medical practitioners are expected to think critically and creatively, communicate effectively, and to be a professional and community leader. While these attributes have always been well regarded, it is only relatively recently that higher education institutions are actively incorporating these skills and attributes into student admissions criteria. In parallel, methods of instruction and course delivery have also changed over time with respect to these driving social paradigms. Today's medical schools are expected to both select and develop students in terms of these qualities through socially based pedagogical practices. This paper investigates the admissions criteria that best predict student engagement in a social learning environment and thus the related attributes such as communication, creativity, and leadership. The paper frames this investigation in the scholarship related to 21st century skills and achievement orientations. (Contains 3 tables.)

Despite the burgeoning rhetoric from political, social and educational commentators regarding creativity and learning and teaching, there is a paucity of scalable and measurable examples of creativity-centric pedagogical practice. This paper makes an argument for the application of social network visualisations to inform and support creativity-enabling pedagogical practice. This paper first describes social networks and how they relate to creative capacities and learning as a social process. It then provides an initial case study of how social network analysis may be meaningfully applied to evaluate students' learning networks and creative capacities, and elaborates on how such an evaluative resource can allow educators to design and implement creativity-enabling pedagogical practice. In so doing, this paper contributes conceptual, methodological and empirical advances that can take learning and teaching for creativity, particularly in higher education, beyond rhetoric towards more observable and measurable mainstream pedagogical practice. (Contains 1 figure and 5 tables.)

The trend to adopt more online technologies continues unabated in the higher education sector. This paper elaborates the means by which such technologies can be employed for pedagogical purposes beyond simply providing virtual spaces for bringing learners together. It shows how data about student "movement" within and across a learning community can be captured and analysed for the purposes of making strategic interventions in the learning of "at risk" students in particular, through the application of social network analysis to the engagement data. The study that is set out in the paper indicates that online technologies bring with them an unprecedented opportunity for educators to visualise changes in student behaviour and their learning network composition, including the interventions teachers make in those networks over time. To date, these evaluative opportunities have been beyond the reach of the everyday practitioner--they can now be integrated into every teaching and learning plan.

Earlier studies have suggested that higher education institutions could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk students and allow for more timely pedagogical interventions. This paper confirms and extends this proposition by providing data from an international research project investigating which student online activities accurately predict academic achievement. Analysis of LMS tracking data from a Blackboard Vista-supported course identified 15 variables demonstrating a significant simple correlation with student final grade. Regression modelling generated a best-fit predictive model for this course which incorporates key variables such as "total number of discussion messages posted", "total number of mail messages sent", and "total number of assessments completed" and which explains more than 30% of the variation in student final grade. Logistic modelling demonstrated the predictive power of this model, which correctly identified 81% of students who achieved a failing grade. Moreover, network analysis of course discussion forums afforded insight into the development of the student learning community by identifying disconnected students, patterns of student-to-student communication, and instructor positioning within the network. This study affirms that pedagogically meaningful information can be extracted from LMS-generated student tracking data, and discusses how these findings are informing the development of a customizable dashboard-like reporting tool for educators that will extract and visualize real-time data on student engagement and likelihood of success. (Contains 5 figures and 6 tables.)

Purpose: This paper aims to examine how effective higher education institutions have been in harnessing the data capture mechanisms from their student information systems, learning management systems and communication tools for improving the student learning experience and informing practitioners of the achievement of specific learning outcomes. The paper seeks to argue that the future of analytics in higher education lies in the development of more comprehensive and integrated systems to value add to the student learning experience. Design/methodology/approach: Literature regarding the trend for greater accountability in higher education is reviewed in terms of its implications for greater "user driven" direction. In addition, IT usage within higher education and contemporary usage of data captured from various higher education systems is examined and compared to common commercial applications to suggest how higher education management and teachers can gain greater understanding of the student cohort and personalise and enhance the learning experience much as commercial entities have done for their client base. A way forward for higher education is proposed. Findings: If the multiple means that students engage with university systems are considered, it is possible to track individual activity throughout the entire student life cycle--from initial admission, through course progression and finally graduation and employment transitions. The combined data captured by various systems builds a detailed picture of the activities students, instructors, service areas and the institution as a whole undertake and can be used to improve relevance, efficiency and effectiveness in a higher education institution. Originality/value: The paper outlines how academic analytics can be used to better inform institutions about their students learning support needs. The paper provides examples of IT automation that may allow for student user-information to be translated into a personalised and semi-automated support system for students. (Contains 4 figures.)

This article explores the pedagogical significance of recent shifts in scholarly attention away from first generation and towards second generation understandings of creativity. First generation or big "C" creativity locates the creative enterprise as a complex set of behaviours and ideas exhibited by an individual, while second generation or small "c" creativity locates the creative enterprise in the processes and products of collaborative and purposeful activity. Second generation creativity is gaining importance for a number of reasons: its acknowledged significance as a driver in the new or digital economy; recent clarification of the notion of "creative capital"; the stated commitment of a growing number of universities to "more creativity" as part of their declared vision for their staff and students; and, the recognition that the creative arts does not have a monopoly on creative capability. We argue that this shift allows more space for engaging with creativity as an outcome of pedagogical work in higher education. The article builds on the project of connecting "creative capital" and university pedagogy that is already underway, assembling a number of principles from a wide range of scholarship, from computer modelling to social and cultural theorising. In doing so, it provides a framework for systematically orchestrating a "creativity-enhancing" learning environment in higher education.

As the integration of community-centred teaching practices intensifies, an understanding of the types of relationships that manifest in this network and the associated impact on student learning is required. This paper explores the relationship between a student's position in a classroom social network and their reported level of sense of community. Quantitative methods, such as Rovai's (2002b) Classroom Community Scale and social network centrality measures, were incorporated to evaluate an individual's level of sense of community and their position within the classroom social network. Qualitative methods such as discussion forum content analysis and student interviews were adopted to clarify and further inform this relationship. The results demonstrate that the centrality measures of "closeness" and "degrees" are positive predictors of an individual's reported sense of community whereas, "betweenness" indicates a negative correlation. Qualitative analyses indicate that an individual's pre-existing external social network influences the type of support and information exchanges an individual requires and therefore, the degree of sense of community ultimately experienced. The paper concludes by discussing future recommendations for teaching practices incorporating computer-mediated communications. (Contains 4 figures and 5 tables.)

Purpose: This paper demonstrates the need for the higher education sector to develop and implement scaleable, quantitative measures that evaluate community and establish organisational benchmarks in order to guide the development of future practices designed to enhance the student learning experience. Design/methodology/approach: Literature regarding contemporary Australian higher education policy and community development is critiqued to illustrate the need for universities to adopt scaleable quantitative measures to evaluate stated strategic imperatives and establish organisational benchmarks. The integration of organisational benchmarks guides the implementation of future practices designed to enhance the student learning experience. A current active exemplar methodology is discussed to demonstrate applicability to both higher education administrators and teaching staff across the various organisation levels. Findings: While universities are promoting and investing in the concept of community to enhance the student learning experience there are as yet, limited scaleable evaluative measures and performance indicators to guide practitioners. This paper proposes an effective measurement tool to benchmark current pedagogical performance standards and monitor the progress and achievement of future implemented practices designed to enhance the sense of community experienced by the student cohort. Originality/value: This paper identifies and addresses the current absence of effective scaleable evaluative measures to assess the achievement of stated strategic imperatives implemented as a consequence of reducing government financial support, increasing accountability, and increasing student expectations as result of educational consumerism. (Contains 3 tables and 3 figures.)

This study developed a quantitative methodology to ascertain lead indicators of student sense of community whilst undertaking a course of study. Study participants (N = 464) were drawn from students enrolled in undergraduate and postgraduate Education units within a large Australian metropolitan university. Through juxtaposing student online behaviours with an online survey, the data demonstrates that students and study units with greater frequencies of communication interactions possess stronger levels of sense of community as determined by Rovai's [Rovai, A.P. (2002b). "Development of an instrument to measure classroom community." "Internet and Higher Education," 5(3), 197-211.] Classroom Community Scale (CCS). As a result of the identification of this relationship utilising a quantitative process, education practitioners and managers now possess the formative evaluative tools and indicators necessary to gauge student sense of community on an ongoing basis. Therefore, education managers and practitioners have the capacity to monitor and alter the learning and teaching practices designed and implemented to promote community among the student cohort in a just-in-time environment. (Contains 5 tables.)

Given the current diversity of communication tools at an educator's disposal, what role (if any) does the discussion forum play in the development of a strong sense of community among students? This study sought to investigate the relationship between discussion forum interaction and perceived student sense of community. The results of the study demonstrate that while mere quantity of discussion forum postings is not an indicator of community development, a significant relationship is observed when contributions are codified into the various discussion interaction types (learner--learner; learner--content; system). An implication emerging from these findings is the ability for the institution to implement evaluative measures to gauge levels of student sense of community in a just in time environment. As discussion interactions are automatically captured and reported, the data provides an indication of the degree of community developing among the student population at a specific snapshot in time. As multiple snapshots provide an ongoing indicator of community development, practitioners have the capacity to develop intervention activities designed to promote further peer to peer discussion and therefore, facilitate the development of a strong sense of community. (Contains 3 tables.)