Toward a Quantitative Analysis of Online Communities

In flexible learning environments there has been an increased focus on developing resources that promote and facilitate the emergence of online communities. The formation of, and active participation in, a learning community has been suggested to facilitate the learning process (Rovai, 2002; Palloff & Pratt, 1999). Current literature examining the formation and development of online communities has predominantly centred on the qualitative analysis of posted messages (within an asynchronous discussion forum) as evidence for community attainment and sustainment (Brook & Oliver, 2003; Hew & Cheung, 2003). The search for key words and phrases is conducted regardless of timing and position within the threaded discussion. Hence, analysis of the postings often occurs in a manner that de-contextualises the discourse throughout the delivery of a subject (Misanchuk & Dueber, 2001). Furthermore, as analysis is limited to a few disparate units of study, an overall picture of the extent to which the online communities formed in individual units are supporting the strategic goals of the university is not formed. Investment in online technologies and development of learning and teaching strategies is conducted at an enterprise level. However, current methodologies evaluating the development and sustainment of online communities have been focussed at a localised level. This paper proposes a scaleable quantitative approach to identify the degree of learner interactions occurring in specific subject-based forums for further qualitative analysis. It is proposed that the examination of data derived from the wider University context better positions and informs staff undertaking subject-based forums in order to align with University strategic goals

Eight unique basal bodies in the multi-flagellated diplomonad Giardia lamblia.

Giardia lamblia is an intestinal parasitic protist that causes significant acute and chronic diarrheal disease worldwide. Giardia belongs to the diplomonads, a group of protists in the supergroup Excavata. Diplomonads are characterized by eight motile flagella organized into four bilaterally symmetric pairs. Each of the eight Giardia axonemes has a long cytoplasmic region that extends from the centrally located basal body before exiting the cell body as a membrane-bound flagellum. Each basal body is thus unique in its cytological position and its association with different cytoskeletal features, including the ventral disc, axonemes, and extra-axonemal structures. Inheritance of these unique and complex cytoskeletal elements is maintained through basal body migration, duplication, maturation, and their subsequent association with specific spindle poles during cell division. Due to the complex composition and inheritance of specific basal bodies and their associated structures, Giardia may require novel basal body-associated proteins. Thus, protists such as Giardia may represent an undiscovered source of novel basal body-associated proteins. The development of new tools that make Giardia genetically tractable will enable the composition, structure, and function of the eight basal bodies to be more thoroughly explored

Length-dependent disassembly maintains four different flagellar lengths in Giardia.

With eight flagella of four different lengths, the parasitic protist Giardia is an ideal model to evaluate flagellar assembly and length regulation. To determine how four different flagellar lengths are maintained, we used live-cell quantitative imaging and mathematical modeling of conserved components of intraflagellar transport (IFT)-mediated assembly and kinesin-13-mediated disassembly in different flagellar pairs. Each axoneme has a long cytoplasmic region extending from the basal body, and transitions to a canonical membrane-bound flagellum at the 'flagellar pore'. We determined that each flagellar pore is the site of IFT accumulation and injection, defining a diffusion barrier functionally analogous to the transition zone. IFT-mediated assembly is length-independent, as train size, speed, and injection frequencies are similar for all flagella. We demonstrate that kinesin-13 localization to the flagellar tips is inversely correlated to flagellar length. Therefore, we propose a model where a length-dependent disassembly mechanism controls multiple flagellar lengths within the same cell

Curriculum analytics: Application of social network analysis for improving strategic curriculum decision-making in a research-intensive university

This paper provides insight into the use of curriculum analytics to enhance learning-centred curricula in diverse higher education contexts. Engagement in evidence-based practice to evaluate and monitor curricula is vital to the success and sustainability of efforts to reform undergraduate and graduate programs. Emerging technology-enabled inquiry methods have enormous potential to inform evidence-based practice in complex curriculum settings. For example, curriculum analytics, including data from student learning outcomes, graduate qualities, course selection and assessment activities, can be mined from various student learning systems and analysed to inform curriculum renewal strategies and demonstrate impact at both the program and course level. Curriculum analytics can serve to foster a culture of inquiry and scholarship regarding program improvements that is characterised by information sharing within and across disciplinary borders. This paper presents an innovative technology that draws on social network methodologies for assessing and visualising the integration and linkages across individual courses that ultimately form a student’s complete program of study. Insights are grounded in the literature and curriculum leadership experiences in a Canadian research-intensive university setting

Monitoring student creative capacity: using network visualisation to evaluate pedagogical practice

This paper explores how research in the fields of Social Network Analysis (SNA) and Business can be applied to monitoring the development of student creative capacity

Second-year student perceptions and use of technology during emergency remote teaching to connect with peers and instructors

Learning is a social experience and having meaningful connections with peers and instructors is important for student learning. The interpersonal relationships between students and their instructor can positively influence students’ well-being, motivation and self-efficacy (Aguilera-Hermida, 2020; Almendingen et al., 2021; Gillis & Krull, 2020; Kim & Sax, 2009; Marković et al., 2021; Parpala et al., 2021; Pitsick, 2018). Creating productive interpersonal relationships with peers contributes to students’ beliefs of being supported, respected, and valued, and increases the likelihood of students asking their peers for help (Mäkitalo-Siegl & Fischer, 2011). When students feel connected to their peers they are more likely to engage with their peers in ways that support their learning and deepen their knowledge as a result (Shim et al., 2013). Interaction with instructors can also positively influence learning outcomes and student well-being (Pitsick, 2018), and instructors can be a valuable source of help and guidance (Ryan et al., 2001). However, during the COVID-19 pandemic and the shift to emergency remote teaching and learning, students’ relationship with peers was significantly impacted (Motz et al., 2022) and forcing peer-to-peer interaction through mandating camera feeds on during live synchronous video classes disproportionately affected students from disadvantaged backgrounds and those experiencing anxiety or depression (Castelli & Sarvary, 2021). As students were adapting to learn during the pandemic, they increased their reliance on their instructor and highly ranked instructor engagement as a factor that positively influenced their motivation (Nguyen, 2021). As motivation increases, so does self-efficacy, and when students feel supported, engaged, connected and valued by their peers and instructors, they are more likely to be successful students (Zepke, 2018)

Teaching smarter: how mining ICT data can inform and improve learning and teaching practice

The trend to greater adoption of online learning in higher education institutions means an increased opportunity for instructors and administrators to monitor student activity and interaction with the course content and peers. This paper demonstrates how the analysis of data captured from various IT systems could be used to inform decision making process for university management and administration. It does so by providing details of a large research project designed to identify the range of applications for LMS derived data for informing strategic decision makers and teaching staff. The visualisation of online student engagement/effort is shown to afford instructors with early opportunities for providing additional student learning assistance and intervention – when and where it is required. The capacity to establish early indicators of ‘at-risk’ students provides timely opportunities for instructors to re-direct or add resources to facilitate progression towards optimal patterns of learning behaviour. The project findings provide new insights into student learning that complement the existing array of evaluative methodologies, including formal evaluations of teaching. Thus the project provides a platform for further investigation into new suites of diagnostic tools that can, in turn, provide new opportunities to inform continuous, sustained improvement of pedagogical practice

Setting learning analytics in context: overcoming the barriers to large-scale adoption

Once learning analytics have been successfully developed and tested, the next step is to implement them at a larger scale – across a faculty, an institution or an educational system. This introduces a new set of challenges, because education is a stable system, resistant to change. Implementing learning analytics at scale involves working with the entire technological complex that exists around technology-enhanced learning (TEL). This includes the different groups of people involved – learners, educators, administrators and support staff – the practices of those groups, their understandings of how teaching and learning take place, the technologies they use and the specific environments within which they operate. Each element of the TEL Complex requires explicit and careful consideration during the process of implementation, in order to avoid failure and maximise the chances of success. In order for learning analytics to be implemented successfully at scale, it is crucial to provide not only the analytics and their associated tools but also appropriate forms of support, training and community building