Higher education faces increasing pressure from governments and employers to ensure graduates are equipped with the knowledge and capabilities required for the future workplace. While technical knowledge is assessed through grades, professional skills are complex and remain difficult to evaluate systematically. While curriculum mapping offers a potential solution, it is often applied in a simplistic, accreditation-driven manner that merely records the presence or absence of skills embedded in assessments. Such an approach overlooks the relative contribution of each skill to the assessments and, hence, cannot be used to estimate skill development. Other noted approaches have relied on the use of self-assessment reports or surveys, and as such are subjective and cannot provide longitudinal evidence of skill development. This study addresses these limitations by proposing a novel curriculum analytics method, weighted Performance Factor Analysis, to model skill scores using assessment grades and granular weighted skill-assessment mapping. Students’ development of seven professional skills were examined along with how they transition across an accounting degree program. The findings show distinct patterns and trajectories of skill development. Overall, the study makes a significant methodological contribution to measuring skills and offers insights into how graduates develop their professional skills across the curriculum.

Learning Analytics (LA) aims to provide university instructors with meaningful data and insights that can be used to improve courses. However, instructors are often met with challenges that arise when wanting to use LA to inform their educational design decisions. For instance, there may be a misalignment between instructors’ needs and the data and insights LA systems provide. Further research is required to understand instructors’ expectations of LA and how it can support the diversity of educational designs. This case study addresses this gap by investigating the role of LA in instructors’ educational decision-making processes. The study employs self-determination theory's constructs to examine instructors’ existing practices when using LA to support their decision-making. The study reveals that LA enables instructors to make data-informed iterative educational design decisions, supporting their need for competence and relatedness. The emotional aspect of LA is an important consideration that can easily lead to demotivation and avoidance of LA. Support is needed to address instructors’ psychological needs so instructors can fully utilise LA to make effective educational design decisions. The findings inform a framework for considering how instructors’ data-informed educational decision-making can be understood. The implications of our findings and opportunities for the future are discussed.

In response to the evolving demands of the contemporary workplace, higher education (HE) institutions are increasingly emphasising the development of transversal skills and graduate attributes (GAs). The development of GAs, such as effective communication, collaboration, and lifelong learning, are non-linear and follow distinct trajectories for individual learners. The ability to trace and measure the progression of GA remains a significant challenge. While previous studies have focused on empirical methods for measuring GAs in individual courses, a notable gap exists in understanding their longitudinal development within HE programs. To address this research gap, our study focuses on measuring and tracing the development of GAs in an Initial Teacher Education (ITE) undergraduate program at a large public university in Australia. By combining learning analytics (LA) with psychometric models, we analysed students’ assessment grades to measure learners’ GA development in each year of the ITE program. The resulting measurements enabled the identification of distinct profiles of GA attainment, as demonstrated by learners and their distinct pathways. The overall approach allows for a comprehensive representation of a learner's progress throughout the program of study. As such, the developed approach sets the grounds for more personalised learning support, program evaluation, and improvement of students’ GA attainment.

The term Learner Profile has proliferated over the years, and more recently, with the increased advocacy around personalising learning experiences. Learner profiles are at the center of personalised learning, and the characterisation of diversity in classrooms is made possible by profiling learners based on their strengths and weaknesses, backgrounds and other factors influencing learning. In this paper, we discuss three common approaches of profiling learners based on students’ cognitive knowledge, skills and competencies and behavioral patterns, all latter commonly used within Learning Analytics (LA). Although each approach has its strengths and merits, there are also several disadvantages that have impeded adoption at scale. We propose that the broader adoption of learner profiles can benefit from careful combination of the methods and practices of three primary approaches, allowing for scalable implementation of learner profiles across educational systems. In this regard, LA can leverage from other aligned domains to develop valid and rigorous measures of students' learning and propel learner profiles from education research to more mainstream educational practice. LA could provide the scope for monitoring and reporting beyond an individualised context and allow holistic evaluations of progress. There is promise in LA research to leverage the growing momentum surrounding learner profiles and make a substantial impact on the field's core aim - understanding and optimising learning as it occurs.

Abstract: Research in learning analytics (LA) has long held a strong interest in improving student self-regulated learning and measuring the impact of feedback on student outcomes. Despite more than a decade of work in this space very little is known around the contextual factors that influence the topics and diversity of feedback and assessment a student encounters during their full program of study. This paper presents research investigating the institutional adoption of a personalized feedback tool. The reported findings illustrate an association between the topics of feedback, student performance, year level of the course and discipline. The results highlight the need for LA research to capture feedback, assessment and learning outcomes over an entire program of study. Herein we propose a more integrated model drawing on contemporary understandings of feedback with current research findings. The goal is to push LA towards addressing more complex teaching and learning processes from a systems lens. The model posed in this paper begins to illustrate where and how LA can address noted deficits in education practice to better understand how feedback and assessment are enacted by instructors and interpreted by students.

Learning analytics (LA) has been presented as a viable solution for scaling timely and personalised feedback to support students’ self-regulated learning (SRL). Research is emerging that shows some positive associations between personalised feedback with students’ learning tactics and strategies as well as time management strategies, both important aspects of SRL. However, the definitive role of feedback on students’ SRL adaptations is under-researched; this requires an examination of students’ recalled experiences with their personalised feedback. Furthermore, an important consideration in feedback impact is the course context, comprised of the learning design and delivery modality. This mixed-methods study triangulates learner trace data from two different course contexts, with students’ qualitative data collected from focus group discussions, to more fully understand the impact of their personalised feedback and to explicate the role of this feedback on students’ SRL adaptations. The quantitative analysis showed the contextualised impact of the feedback on students’ learning and time management strategies in the different courses, while the qualitative analysis highlighted specific ways in which students used their feedback to adjust these and other SRL processes.

Models and processes developed in learning analytics research are increasing in sophistication and predictive power. However, the ability to translate analytic findings to practice remains problematic. This study aims to address this issue by establishing a model of learner behaviour that is both predictive of student course performance, and easily interpreted by instructors. To achieve this aim, we analysed fine grained trace data (from 3 offerings of an undergraduate online course, N=1068) to establish a comprehensive set of behaviour indicators aligned with the course design. The identified behaviour patterns, which we refer to as observed study strategies, proved to be associated with the student course performance. By examining the observed strategies of high and low performers throughout the course, we identified prototypical pathways associated with course success and failure. The proposed model and approach offers valuable insights for the provision of process-oriented feedback early in the course, and thus can aid learners in developing their capacity to succeed online.

Learning Analytics (LA) studies the learning process in order to optimize learning opportunities for students. Although LA has quickly risen to prominence, there remain questions regarding the impact LA has made to date. To evaluate the extent that LA has impacted our understanding of learning and produced insights that have been translated to mainstream practice or contributed to theory, we reviewed the research published in 2011-2018 LAK conferences and Journal of Learning Analytics. The reviewed studies were coded according to five dimensions: study focus, data types, purpose, institutional setting, and scale of research and implementation. The coding and subsequent epistemic network analysis indicates that while LA research has developed in the areas of focus and sophistication of analyses, the impact on practice, theory and frameworks have been limited. We hypothesize that this finding is due to a continuing predominance of small-scale techno-centric exploratory studies that to date have not fully accounted for the multi-disciplinarity that comprises education. For the field to reach its potential in understanding and optimizing learning and learning environments, there must be a purposeful shift to move from exploratory models to more holistic and integrative systems-level research. This necessitates greater effort applied to understanding the research cycles that emerge when multiple knowledge domains coalesce into new fields of research.

The use of learning trace data together with various analytical methods has proven successful in detecting patterns in learning behaviour, identifying student profiles, and clustering learning resources. However, interpretation of the findings is often difficult and uncertain due to a lack of contextual data (e.g., data on student motivation, emotion or curriculum design). In this study we explored the integration of student self-reports about cognitive load and self-efficacy into the learning process and collection of relevant students' perceptions as learning traces. Our objective was to examine the association of traced measures of relevant learning constructs (cognitive load and self-efficacy) with i) indicators of the students' learning behaviour derived from trace data, and ii) the students' academic performance. The results indicated the presence of association between some indicators of students' engagement with learning activities and traced measures of cognitive load and self-efficacy. Correlational analysis demonstrated significant positive correlation between the students' course performance and traced measures of cognitive load and self-efficacy.

Learning Analytics Dashboards (LAD) are becoming an increasingly popular way to provide students with personalised feedback. Despite the number of LADs being developed, significant research gaps exist around the student perspective, especially how students make sense of graphics provided in LADs, and how they intend to act on the feedback provided therein. This study employed a randomized-controlled trial to examine students' sense-making of LADs showing four different frames of reference, and to what extent the impact of LADs was mediated by baseline self-regulation. Using a mix of quantitative and qualitative data analysis, the results revealed rather distinct patterns in students' sense-making across the four LADs. These patterns involved the intersection of visual salience and planned learning actions. However, collectively, across all four LADs a consistent theme emerged around students planned learning actions. This theme was classified as time and study environment management. A key finding of the study is that the use of LADs as a primary feedback process should be personalized and include training and support to aid student sensemaking.

Despite strong interest in learning analytics (LA), adoption at a large-scale organizational level continues to be problematic. This may in part be due to the lack of acknowledgement of existing conceptual LA models to operationalize how key adoption dimensions interact to inform the realities of the implementation process. This paper proposes the framing of LA adoption in complexity leadership theory (CLT) to study the overarching system dynamics. The framing is empirically validated in a study analysing interviews with senior staff in Australian universities (n=32). The results were coded for several adoption dimensions including leadership, governance, staff development, and culture. The coded data were then analysed with latent class analysis. The results identified two classes of universities that either i) followed an instrumental approach to adoption - typically top-down leadership, large scale project with high technology focus yet demonstrating limited staff uptake; or ii) were characterized as emergent innovators - bottom up, strong consultation process, but with subsequent challenges in communicating and scaling up innovations. The results suggest there is a need to broaden the focus of research in LA adoption models to move on from small-scale course/program levels to a more holistic and complex organizational level.

Reflective writing has been widely recognized as one of the most effective activities for fostering students' reflective and critical thinking. The analysis of students' reflective writings has been the focus of many research studies. However, to date this has been typically a very labor-intensive manual process involving content analysis of student writings. With recent advancements in the field of learning analytics, there have been several attempts to use text analytics to examine student reflective writings. This paper presents the results of a study examining the use of theoretically-sound linguistic indicators of different psychological processes for the development of an analytics system for assessment of reflective writing. More precisely, we developed a random-forest classification system using linguistic indicators provided by the LIWC and Coh-Metrix tools. We also examined what particular indicators are representative of the different types of student reflective writings.

There has been a growing trend in higher education towards increased use and adoption of Massive Open Online Courses (MOOCs). Despite this interest in learning at scale, limited work has compared MOOC activity across subsequent course offerings. In this study, we explore forum activity in ten iterations of the same MOOC. Our results suggest that participation in MOOC forums has changed over the past four years of delivery. First, overall participation in MOOC forums have decreased. Second, in later iterations cohorts of more committed forum users start to resemble formal online courses in size (67>n>36). However, despite the smaller groups of learners that should find it easier to form connections with one another, our analysis did not reveal the expected increase in the quality of social activity. Instead, MOOC forums evolved into smaller on-task question and answer (Q&A) spaces, not capitalizing on the opportunities for social learning. We discuss practical and research implications of such changes.

Video materials have become an integral part of university learning and teaching practice. While empirical research concerning the use of videos for educational purposes has increased, the literature lacks an overview of the specific effects of videos on diverse learning outcomes. To address such a gap, this paper presents preliminary results of a large-scale systematic review of peer-reviewed empirical studies published from 2007-2017. The study synthesizes the trends observed through the analysis of 178 papers selected from the screening of 2531 abstracts. The findings summarize the effects of manipulating video presentation, content and tasks on learning outcomes, such as recall, transfer, academic achievement, among others. The study points out the gap between large-scale analysis of fine-grained data on video interaction and experimental findings reliant on established psychological instruments. Narrowing this gap is suggested as the future direction for the research on video-based learning.

Learning analytics research has often been touted as a means to address concerns regarding student retention outcomes. However, few research studies to date, have examined the impact of the implemented intervention strategies designed to address such retention challenges. Moreover, the methodological rigor of some of the existing studies has been challenged. This study evaluates the impact of a pilot retention program. The study contrasts the findings obtained by the use of different methods for analysis of the effect of the intervention. The pilot study was undertaken between 2012 and 2014 resulting in a combined enrolment of 11,160 students. A model to predict attrition was developed, drawing on data from student information system, learning management system interactions, and assessment. The predictive model identified some 1868 students as academically at-risk. Early interventions were implemented involving learning and remediation support. Common statistical methods demonstrated a positive association between the intervention and student retention. However, the effect size was low. The use of more advanced statistical methods, specifically mixed-effect methods explained higher variability in the data (over 99%), yet found the intervention had no effect on the retention outcomes. The study demonstrates that more data about individual differences is required to not only explain retention but to also develop more effective intervention approaches.

In this poster, we present the results of the study which examined the relationship between student differences in their use of the available technology and their perceived levels of cognitive presence within the MOOC context. The cognitive presence is a construct used to measure the level of practical inquiry in the Communities of Inquiry model. Our results revealed the existence of three clusters based on student technology use. The clusters significantly differed in terms of their levels of cognitive presence, most notably they differed on the levels of problem resolution.

The facilitation of interpersonal relationships within a respectful learning climate is an important aspect of teaching practice. However, in large-scale online contexts, such as MOOCs, the number of learners and highly asynchronous nature militates against the development of a sense of belonging and dyadic trust. Given these challenges, instead of conventional instruments that reflect learners' affective perceptions, we suggest a set of indicators that can be used to evaluate social activity in relation to the participation structure. These group-level indicators can then help teachers to gain insights into the evolution of social activity shaped by their facilitation choices. For this study, group-level indicators were derived from measuring information exchange activity between the returning MOOC posters. By conceptualizing this group as an identity-based community, we can apply exponential random graph modelling to explain the network's structure through the configurations of direct reciprocity, triadic-level exchange, and the effect of participants demonstrating super-posting behavior. The findings provide novel insights into network amplification, and highlight the differences between the courses with different facilitation strategies. Direct reciprocation was characteristic of non-facilitated groups. Exchange at the level of triads was more prominent in highly facilitated online communities with instructor's involvement. Super-posting activity was less pronounced in networks with higher triadic exchange, and more pronounced in networks with higher direct reciprocity.

This workshop aims to promote strategic planning for learning analytics in higher education through developing institutional policies. While adoption of learning analytics is predominantly seen in small-scale and bottom-up patterns, it is believed that a systemic implementation can bring the widest impact to the education system and lasting benefits to learners. However, the success of it highly depends on the adopted strategy that meets the needs of various stakeholders and systematically pushes the institution towards achieving its targets. It is imperative to develop a learning analytics policy that ensures a practice that is valid, effective and ethical.

In this paper we present a learning analytics conceptual framework that supports enquiry-based evaluation of learning designs. The dimensions of the proposed framework emerged from a review of existing analytics tools, the analysis of interviews with teachers, and user scenarios to understand what types of analytics would be useful in evaluating a learning activity in relation to pedagogical intent. The proposed framework incorporates various types of analytics, with the teacher playing a key role in bringing context to the analysis and making decisions on the feedback provided to students as well as the scaffolding and adaptation of the learning design. The framework consists of five dimensions: temporal analytics, tool-specific analytics, cohort dynamics, comparative analytics and contingency. Specific metrics and visualisations are defined for each dimension of the conceptual framework. Finally the development of a tool that partially implements the conceptual framework is discussed.

An ongoing challenge for Learning Analytics research has been the scalable derivation of user interaction data from multiple technologies. The complexities associated with this challenge are increasing as educators embrace an ever growing number of social and content-related technologies. The Experience API (xAPI) alongside the development of user specific record stores has been touted as a means to address this challenge, but a number of subtle considerations must be made when using xAPI in Learning Analytics. This paper provides a general overview to the complexities and challenges of using xAPI in a general systemic analytics solution - called the Connected Learning Analytics (CLA) toolkit. The importance of design is emphasised, as is the notion of common vocabularies and xAPI Recipes. Early decisions about vocabularies and structural relationships between statements can serve to either facilitate or handicap later analytics solutions. The CLA toolkit case study provides us with a way of examining both the strengths and the weaknesses of the current xAPI specification, and we conclude with a proposal for how xAPI might be improved by using JSON-LD to formalise Recipes in a machine readable form.

As the field of learning analytics continues to mature, there is a corresponding evolution and sophistication of the associated analytical methods and techniques. In this regard social network analysis (SNA) has emerged as one of the cornerstones of learning analytics methodologies. However, despite the noted importance of social networks for facilitating the learning process, it remains unclear how and to what extent such network measures are associated with specific learning outcomes. Motivated by Simmel's theory of social interactions and building on the argument that social centrality does not always imply benefits, this study aimed to further contribute to the understanding of the association between students' social centrality and their academic performance. The study reveals that learning analytics research drawing on SNA should incorporate both - descriptive and statistical methods to provide a more comprehensive and holistic understanding of a students' network position. In so doing researchers can undertake more nuanced and contextually salient inferences about learning in network settings. Specifically, we show how differences in the factors framing students' interactions within two instances of a MOOC affect the association between the three social network centrality measures (i.e., degree, closeness, and betweenness) and the final course outcome.

This demonstration introduces the Connected Learning Analytics (CLA) Toolkit. The CLA toolkit harvests data about student participation in specified learning activities across standard social media environments, and presents information about the nature and quality of the learning interactions.

The pervasive collection of data has opened the possibility for educational institutions to use analytics methods to improve the quality of the student experience. However, the adoption of these methods faces multiple challenges particularly at the course level where instructors and students would derive the most benefit from the use of analytics and predictive models. The challenge lies in the knowledge gap between how the data is captured, processed and used to derive models of student behavior, and the subsequent interpretation and the decision to deploy pedagogical actions and interventions by instructors. Simply put, the provision of learning analytics alone has not necessarily led to changing teaching practices. In order to support pedagogical change and aid interpretation, this paper proposes a model that can enable instructors to readily identify subpopulations of students to provide specific support actions. The approach was applied to a first year course with a large number of students. The resulting model classifies students according to their predicted exam scores, based on indicators directly derived from the learning design.

Research in formal education has repeatedly offered evidence of the importance of social interactions for student learning. However, it remains unclear whether the development of such interpersonal relationships has the same influence on learning in the context of large-scale open online learning. For instance, in MOOCs group members frequently change and the volume of interactions can quickly amass to chaos, therefore impeding an individual's propensity to foster meaningful relationships. This paper examined a MOOC for its potential to develop social processes. As it is exceedingly difficult to establish a relationship with somebody who seldom accesses a MOOC discussion, we singled out a cohort defined by its participants' regularity of forum presence. The study, analysed this 'cohort' and its development, in comparison to the entire MOOC learner network. Mixed methods of social network analysis (SNA), content analysis and statistical network modelling, revealed the potential for unfolding social processes among a more persistent group of learners in the MOOC setting.

Connections established between learners via interactions are seen as fundamental for connectivist pedagogy. Connections can also be viewed as learning outcomes, i.e. learners' social capital accumulated through distributed learning environments. We applied linear mixed effects modeling to investigate whether the social capital accumulation interpreted through learners' centrality to course interaction networks, is influenced by the language learners use to express and communicate in two connectivist MOOCs. Interactions were distributed across the three social media, namely Twitter, blog and Facebook. Results showed that learners in a cMOOC connect easier with the individuals who use a more informal, narrative style, but still maintain a deeper cohesive structure to their communication.

All forms of learning take time. There is a large body of research suggesting that the amount of time spent on learning can improve the quality of learning, as represented by academic performance. The wide-spread adoption of learning technologies such as learning management systems (LMSs), has resulted in large amounts of data about student learning being readily accessible to educational researchers. One common use of this data is to measure time that students have spent on different learning tasks (i.e., time-on-task). Given that LMS systems typically only capture times when students executed various actions, time-on-task measures are estimated based on the recorded trace data. LMS trace data has been extensively used in many studies in the field of learning analytics, yet the problem of time-on-task estimation is rarely described in detail and the consequences that it entails are not fully examined.

The higher education sector has seen a shift in teaching approaches over the past decade with an increase in the use of video for delivering lecture content as part of a flipped classroom or blended learning model. Advances in video technologies have provided opportunities for students to now annotate videos as a strategy to support their achievement of the intended learning outcomes. However, there are few studies exploring the relationship between video annotations, student approaches to learning, and academic performance. This study seeks to narrow this gap by investigating the impact of students' use of video annotation software coupled with their approaches to learning and academic performance in the context of a flipped learning environment. Preliminary findings reveal a significant positive relationship between annotating videos and exam results. However, negative effects of surface approaches to learning, cognitive strategy use and test anxiety on midterm grades were also noted. This indicates a need to better promote and scaffold higher order cognitive strategies and deeper learning with the use of video annotation software.

This poster outlines the process and purpose of two related Australian Office for Learning and Teaching (OLT) commissioned grants to investigate the current usage and future potential of learning analytics in Australian Higher Education, with a view to developing resources to guide Australian universities in their adoption of learning analytics. The commissioned grants run from February 2014 to June 2015. Preliminary results will be available for LAK 15.

This paper provides an evaluation of the current state of the field of learning analytics through analysis of articles and citations occurring in the LAK conferences and identified special issue journals. The emerging field of learning analytics is at the intersection of numerous academic disciplines, and therefore draws on a diversity of methodologies, theories and underpinning scientific assumptions. Through citation analysis and structured mapping we aimed to identify the emergence of trends and disciplinary hierarchies that are influencing the development of the field to date. The results suggest that there is some fragmentation in the major disciplines (computer science and education) regarding conference and journal representation. The analyses also indicate that the commonly cited papers are of a more conceptual nature than empirical research reflecting the need for authors to define the learning analytics space. An evaluation of the current state of learning analytics provides numerous benefits for the development of the field, such as a guide for under-represented areas of research and to identify the disciplines that may require more strategic and targeted support and funding opportunities.

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.

Although video annotation software is no longer considered as a new innovation, its application in promoting student self-regulated learning and reflection skills has only begun to emerge in the research literature. Advances in text and video analytics provide the capability of investigating students' use of the tool and the psychometrics and linguistic processes evident in their written annotations. This paper reports on a study exploring students' use of a video annotation tool when two different instructional approaches were deployed -- graded and non-graded self-reflection annotations within two courses in the performing arts. In addition to counts and temporal locations of self-reflections, the Linguistic Inquiry and Word Counts (LIWC) framework was used for the extraction of variables indicative of the linguistic and psychological processes associated with self-reflection annotations of videos. The results indicate that students in the course with graded self-reflections adopted more linguistic and psychological related processes in comparison to the course with non-graded self-reflections. In general, the effect size of the graded reflections was lower for students who took both courses in parallel. Consistent with prior research, the study identified that students tend to make the majority of their self-reflection annotations early in the video time line. The paper also provides several suggestions for future research to better understand the application of video annotations in facilitating student learning.

Web technologies, such as lecture recordings, have the capacity to capture and store massive amounts of data from individuals' online behavior. Such data can provide insight into student learning processes and the relationship between online trace data and academic performance alerting educators to when intervention may be required or if their learning activities may need to be adjusted. This paper discusses how data captured from students' use of lecture recordings accessed through a Collaborative Lecture Annotation System (CLAS) when aggregated and correlated with assessment data can help educators evaluate the impact of the recordings on their students' learning. Such information can help inform and alert educators to when adjustments may be required to their pedagogical approach.

The wealth of data available through student management systems and eLearning systems has the potential to provide faculty with important, just-in-time information that may allow them to positively intervene with struggling students and/or enhance the learning experience during the delivery of a course. This information might also facilitate post-delivery review and reflection for faculty who wish to revise course design and content. But to be effective, this data needs to be appropriate to the context or pedagogical intent of the course -- this is where learning analytics meets learning design.

The Social Networks Adapting Pedagogical Practice (SNAPP) tool was developed to provide instructors with the capacity to visualise the evolution of participant relationships within discussions forums. Providing forum facilitators with access to these forms of data visualisations and social network metrics in 'real-time', allows emergent interaction patterns to be analysed and interventions to be undertaken as required. SNAPP essentially serves as an interaction diagnostic tool that assists in bringing the affordances of 'real-time' social network analysis to fruition. This paper details the functional features included in SNAPP 2.0 and how they relate to learning activity intent and participant monitoring. SNAPP 2.0 includes the ability to view the evolution of participant interaction over time and annotate key events that occur along this timeline. This feature is useful in terms of monitoring network evolution and evaluating the impact of intervention strategies on student engagement and connectivity. SNAPP currently supports discussion forums found in popular commercial and open source Learning Management Systems (LMS) such as Blackboard, Desire2Learn and Moodle and works in both Internet Explorer and Firefox.

Government and institutionally-driven reforms focused on quality teaching and learning in universities emphasize the importance of developing replicable, scalable teaching approaches that can be evaluated. In this context, learning design and learning analytics are two fields of research that may help university teachers design quality learning experiences for their students, evaluate how students are learning within that intended learning context and support personalized learning experiences for students. Learning Designs are ways of describing an educational experience such that it can be applied across a range of disciplinary contexts. Learning analytics offers new approaches to investigating the data associated with a learner's experience. This paper explores the relationship between learning designs and learning analytics.