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The capacity to foster interpersonal interactions in massive open online courses (MOOCs) has frequently been contested, particularly when learner interactions are limited to MOOC forums. The establishment of social presence--a perceived sense of somebody being present and "real"--is among the strategies to tackle the challenges of online learning and could be applied in MOOCs. Thus far, social presence in MOOCs has been under-researched. Studies that previously examined social presence in MOOCs did not account for the peculiar nature of open online learning. In contrast to the existing work, this study seeks to understand how learners perceive social presence, and the different nuances of social presence in diverse MOOC populations. In particular, we compare perceptions of social presence across the groups of learners with different patterns of forum participation in three edX MOOCs. The findings reveal substantial differences in how learners with varying forum activity perceive social presence. Perceptions of social presence also differed in courses with the varying volume of forum interaction and duration. Finally, learners with sustained forum activity generally reported higher social presence scores that included low affectivity and strong group cohesion perceptions. With this in mind, this study is significant because of the insights in brings to the current body of knowledge around social presence in MOOCs. The study's findings also raise questions about the effectiveness of transferring existing socio-constructivist constructs into the MOOC contexts.

The learning analytics community has matured significantly over the past few years as a middle space where technology and pedagogy combine to support learning experiences. To continue to grow and connect these perspectives, research needs to move beyond the level of basic support actions. This means exploring the use of data to prove richer forms of actions, such as personalized feedback, or hybrid approaches where instructors interpret the outputs of algorithms and select an appropriate course of action. This paper proposes the following three contributions to connect data extracted from the learning experience with such personalized student support actions: 1) a student--instructor centred conceptual model connecting a representation of the student information with a basic set of rules created by instructors to deploy Personalized Learning Support Actions (PLSAs); 2) a software architecture based on this model with six categories of functional blocks to deploy the PLSAs; and 3) a description of the implementation of this architecture as an open-source platform to promote the adoption and exploration of this area. Notes for practice include the following: (1) The report draws on research findings related to the effect of personalized feedback on student satisfaction and academic performance (Pardo, Jovanovic, Dawson, Gaševic, & Mirriahi, 2018); (2) The main contribution is the description of the design and implementation of an open source platform for researchers and practitioners to connect data with personalized learning support actions; (3) The area of learning analytics needs tools such as the one described in this document to serve as a vehicle to exchange insights among researchers and practitioners; and (4) This is an example of the note for practice and research.

The use of analytic methods for extracting learning strategies from trace data has attracted considerable attention in the literature. However, there is a paucity of research examining any association between learning strategies extracted from trace data and responses to well-established self-report instruments and performance scores. This paper focuses on the link between the learning strategies identified in the trace data and student reported approaches to learning. The paper reports on the findings of a study conducted in the scope of an undergraduate engineering course (N = 144) that followed a flipped classroom design. The study found that learning strategies extracted from trace data can be interpreted in terms of deep and surface approaches to learning. The detected significant links with self-report measures are with small effect sizes for both the overall deep approach to learning scale and the deep strategy scale. However, there was no observed significance linking the surface approach to learning and surface strategy nor were there significant associations with motivation scales of approaches to learning. The significant effects on academic performance were found, and consistent with the literature that used self-report instruments showing that students who followed a deep approach to learning had a significantly higher performance.

Research and development in learning analytics has established viable solutions for scaling personalised feedback to all students. However, questions remain regarding how such feedback is perceived, interpreted and acted upon by stakeholders. The present study reports on the analysis of focus group data from four courses to understand students' sensemaking of personalised, learning analytics-based feedback. Deductive analysis and epistemic network analysis were carried out to identify students' perceptions and associated emotional responses to the feedback. The results demonstrate that students mostly held positive perceptions of their feedback -- especially in regard to quality, task-focus and instructor care. While emotions to feedback were often negative, students also noted this increased their motivation to invest more effort in the learning tasks. The results of the study provide further evidence of the interpersonal communication variables influencing students' proactive recipience of feedback. Importantly, the findings show that students were mostly actively engaged with their personalised feedback, and that the instructor-mediated approach to feedback based on learning analytics is aligned to the principles of effective feedback.

Although technological advances have brought about new opportunities for scaling feedback to students, there remain challenges in how such feedback is presented and interpreted. There is a need to better understand how students make sense of such feedback to adapt self-regulated learning processes. This study examined students' sense-making of learning analytics--based personalised feedback across four courses. Results from a combination of thematic analysis and epistemic network analysis show an association between student perceptions of their personalised feedback and how these map to subsequent self-described self-regulated learning processes. Most notably, the results indicate that personalised feedback, elaborated by personal messages from course instructors, helps students refine or strengthen important forethought processes of goal-setting, as well as to reduce procrastination. The results highlight the need for instructors to increase the dialogic element in personalised feedback in order to reduce defensive reactions from students who hold to their own learning strategies. This approach may prompt reflection on the suitability of students' current learning strategies and achievement of associated learning goals. Implications for practice or policy: (1) Personalised feedback based on learning analytics should be informed by an understanding of students' self-regulated learning; (2) Instructors implementing personalised feedback should align this closely with the course curriculum; (3) Instructors implementing personalised feedback in their courses should consider the relational element of feedback by using a positive tone; and (4) Personalised feedback can be further enhanced by increasing the dialogic element and by including more information about learning strategies.

Distributed Massive Open Online Courses (MOOCs) are based on the premise that online learning occurs through a network of interconnected learners. The teachers' role in distributed courses extends to forming such a network by facilitating communication that connects learners and their separate personal learning environments scattered around the Internet. The study reported in this paper examined who fulfilled such an influential role in a particular distributed MOOC--a connectivist course (cMOOC) offered in 2011. Social network analysis was conducted over a socio-technical network of the Twitter-based course interactions, comprising both human course participants and hashtags; where the latter represented technological affordances for scaling course communication. The results of the week-by-week analysis of the network of interactions suggest that the teaching function becomes distributed among influential actors in the network. As the course progressed, both human and technological actors comprising the network subsumed the teaching functions, and exerted influence over the network formation. Regardless, the official course facilitators preserved a high level of influence over the flow of information in the investigated cMOOC.

With widespread adoption of Learning Management Systems (LMS) and other learning technology, large amounts of data--commonly known as trace data--are readily accessible to researchers. Trace data has been extensively used to calculate time that students spend on different learning activities--typically referred to as time-on-task. These measures are used to build predictive models of student learning in order to understand and improve learning processes. While time-on-task measures have been used in Learning Analytics research, the consequences of their use are not fully described or examined. This paper presents findings from two experiments regarding different time-on-task estimation methods and their influence on research findings. Based on modelling different student performance measures with popular statistical methods in two datasets (one online, one blended), our findings indicate that time-on-task estimation methods play an important role in shaping the final study results, particularly in online settings where the amount of interaction with LMS is typically higher. The primary goal of this paper is to raise awareness and initiate debate on the important issue of time-on-task estimation within the broader learning analytics community. Finally, the paper provides an overview of commonly adopted time-on-task estimation methods in educational and related research fields.

The rapid advances in information and communication technologies, coupled with increased access to information and the formation of global communities, have resulted in interest among researchers and academics to revise educational practice to move beyond traditional "literacy" skills towards an enhanced set of "multiliteracies" or "new media literacies". Measuring the literacy of a population, in the light of its linkage to individual and community wealth and wellbeing, is essential to determining the impact of compulsory education. The opportunity now is to develop tools to assess individual and societal attainment of these new literacies. Drawing on the work of Jenkins and colleagues (2006) and notions of a participatory culture, this paper proposes a conceptual framework for how learning analytics can assist in measuring individual achievement of multiliteracies and how this evaluative process can be scaled to provide an institutional perspective of the educational progress in fostering these fundamental skills.

In the new era of big educational data, learning analytics (LA) offer the possibility of implementing real-time assessment and feedback systems and processes at scale that are focused on improvement of learning, development of self-regulated learning skills, and student success. However, to realize this promise, the necessary shifts in the culture, technological infrastructure, and teaching practices of higher education, from assessment-for-accountability to assessment-for-learning, cannot be achieved through piecemeal implementation of new tools. We propose here that the challenge of successful institutional change for learning analytics implementation is a wicked problem that calls for new adaptive forms of leadership, collaboration, policy development and strategic planning. Higher education institutions are best viewed as complex systems underpinned by policy, and we introduce two policy and planning frameworks developed for complex systems that may offer institutional teams practical guidance in their project of optimizing their educational systems with learning analytics.

A core goal for most learning analytic projects is to move from small-scale research towards broader institutional implementation, but this introduces a new set of challenges because institutions are stable systems, resistant to change. To avoid failure and maximize success, implementation of learning analytics at scale requires explicit and careful consideration of the entire TEL [technology-enhanced learning] technology complex: the different groups of people involved, the educational beliefs and practices of those groups, the technologies they use, and the specific environments within which they operate. It is crucial not only to provide analytics and their associated tools, but also to begin with a clear strategic vision, assess institutional culture critically, identify potential barriers to adoption, develop approaches that can overcome these, and put in place appropriate forms of support, training, and community building. In this paper, we offer tools and case studies that will support educational institutions in deploying learning analytics at scale with the goal of achieving specified learning and teaching objectives. The ROMA [RAPID Outcome Mapping Approach] Framework offers a step-by-step approach to the institutional implementation of learning analytics and this approach is grounded here by case studies of practice from the UK and Australia.

Learning analytics (LA) has demonstrated great potential in improving teaching quality, learning experience and administrative efficiency. However, the adoption of LA in higher education is often beset by challenges in areas such as resources, stakeholder buy-in, ethics and privacy. Addressing these challenges in a complex system requires agile leadership that is responsive to pressures in the environment and capable of managing conflicts. This paper examines LA adoption processes among 21 UK higher education institutions using complexity leadership theory as a framework. The data were collected from 23 interviews with institutional leaders and subsequently analysed using a thematic coding scheme. The results showed a number of prominent challenges associated with LA deployment, which lie in the inherent tensions between innovation and operation. These challenges require a new form of leadership to create and nurture an adaptive space in which innovations are supported and ultimately transformed into the mainstream operation of an institution. This paper argues that a complexity leadership model enables higher education to shift towards more fluid and dynamic approaches for LA adoption, thus ensuring its scalability and sustainability.

The success of the flipped classroom (FC) is effectively reliant on the level of student engagement with the preparatory activities prior to attending face-to-face teaching sessions. Information about the nature and level of student engagement with these activities can help instructors make informed decisions regarding how to best support student learning. Despite the comprehensive data collection enabled by the increasing presence of education technologies, few studies have used these data to investigate the range of strategies students employ in FC models. This study addresses this deficit by proposing an analytical approach that allows for exploring, first, the strategies students use to interact with online preparation activities; and, second, evolution of those strategies over the duration of a course delivered with a FC pedagogy. The proposed approach identified eight learning strategies and six trajectories of strategy change over the duration of the course, with statistically significant effects of strategy change trajectories on academic performance.

There is little debate regarding the importance of student feedback for improving the learning process. However, there remain significant workload barriers for instructors that impede their capacity to provide timely and meaningful feedback. The increasing role technology is playing in the education space may provide novel solutions to this impediment. As students interact with the various learning technologies in their course of study, they create digital traces that can be captured and analysed. These digital traces form the new kind of data that are frequently used in learning analytics to develop actionable recommendations that can support student learning. This paper explores the use of such analytics to address the challenges impeding the capacity of instructors to provide personalised feedback at scale. The case study reported in the paper showed how the approach was associated with a positive impact on student perception of feedback quality and on academic achievement. The study was conducted with first year undergraduate engineering students enrolled in a computer systems course with a blended learning design across three consecutive years (N[subscript 2013] = 290, N[subscript 2014] = 316 and N[subscript 2015] = 415).

Purpose: The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach to advancing our understanding of the learning process. This promise motivated the emergence of the field of learning analytics and supported the education sector in moving toward data-informed strategic decision making. Yet, progress to date in embedding such data-informed processes has been limited. The purpose of this paper is to address a commonly posed question asked by educators, managers, administrators and researchers seeking to implement learning analytics -- how do we start institutional adoption of learning analytics? Design/methodology/approach: A narrative review is performed to synthesize the existing literature on learning analytics adoption in higher education. The synthesis is based on the established models for the adoption of business analytics and finding two projects performed in Australia and Europe to develop and evaluate approaches to adoption of learning analytics in higher education. Findings: The paper first defines learning analytics and touches on lessons learned from some well-known case studies. The paper then reviews the current state of institutional adoption of learning analytics by examining evidence produced in several studies conducted worldwide. The paper next outlines an approach to learning analytics adoption that could aid system-wide institutional transformation. The approach also highlights critical challenges that require close attention in order for learning analytics to make a long-term impact on research and practice of learning and teaching. Originality/value: The paper proposed approach that can be used by senior leaders, practitioners and researchers interested in adoption of learning analytics in higher education. The proposed approach highlights the importance of the socio-technical nature of learning analytics and complexities pertinent to innovation adoption in higher education institutions.

Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis of learning-related constructs used in the prediction and measurement of student engagement and learning outcome. Based on our literature review, we identify current gaps in the research, including a lack of solid frameworks to explain learning in open online setting. Finally, we put forward a novel framework suitable for open online contexts based on a well-established model of student engagement. Our model is intended to guide future work studying the association between contextual factors (i.e., demographic, classroom, and individual needs), student engagement (i.e., academic, behavioral, cognitive, and affective engagement metrics), and learning outcomes (i.e., academic, social, and affective). The proposed model affords further interstudy comparisons as well as comparative studies with more traditional education models.

This article reports on the findings of a study that investigated the effects of instructional conditions and prior experience on students' self-reflection. The study was conducted with the use of a video annotation tool that was used by undergraduate performing arts students to reflect on their video-recorded performances. The study shows a consistent positive effect of previous experience with the video annotation tool for engagement with reflection. Graded instructional conditions with feedback had a positive effect on increasing higher order reflections particularly for students with prior experience with the video annotation tool for reflective purposes. The finding suggests that when including reflection in the curriculum, it is important to consider introducing it at a program or degree level rather than individual courses in order to provide an opportunity for students to gain experience with reflection and any particular tool that is used (e.g., a video annotation tool). Furthermore, reflective tasks should be scaffolded into the curriculum with ample opportunity for formative feedback and summative assessment in order to encourage higher order thinking and foster students' metacognitive awareness and monitoring for increased goal-setting and acknowledgement of the motive or effect of their observed behavior.

The rapid growth of blended and online learning models in higher education has resulted in a parallel increase in the use of audio-visual resources among students and teachers. Despite the heavy adoption of video resources, there have been few studies investigating their effect on learning processes and even less so in the context of academic development. This paper uses learning analytic techniques to examine how academic teaching staff engage with a set of prescribed videos and video annotations in a professional development course. The data was collected from two offerings of the course at a large research-intensive university in Australia. The data was used to identify patterns of activity and transition states as users engaged with the course videos and video annotations. Latent class analysis and hidden Markov models were used to characterise the evolution of engagement throughout the course. The results provide a detailed description of the evolution of learner engagement that can be readily translated into action aimed at increasing the quality of the learning experience.

The pedagogical benefits of the social media may be most pronounced when they impact groups of learners who are at a disadvantage in conventional face-to-face contexts. Among such disadvantaged groups are the deaf or hard of hearing (DHH) students who may experience new opportunities with the help of the social media. This paper stems from the assumption that social media can play an important role in enhancing the learning experience for DHH students. The paper presents evidence around derived from a systematic review of the literature on the use of social media by DHH students in educational settings. A comprehensive search through multiple known databases identified a dataset of 172 papers from which 11 papers met the criteria for in-depth analysis. The analysis revealed that DHH students often reported a positive impact of social media on their learning in the form of increased interaction, learning motivation, as well as support and feedback. Students also reported challenges such as privacy, time management, inappropriate content, perceived isolation and parental resistance to adoption. Besides presenting the evidence found in literature, our analysis highlights that researching the effect of social media on DHH student learning remains an under-explored area of study.

This study explores the types of learning profiles that evolve from student use of video annotation software for reflective learning. The data traces from student use of the software were analysed across four undergraduate courses with differing instructional conditions. That is, the use of graded or non-graded self-reflective annotations. Using hierarchical cluster analysis, four profiles of students emerged: minimalists, task-oriented, disenchanted, and intensive users. Students enrolled in one of the courses where grading of the video annotation software was present, were exposed to either another graded course (annotations graded) or non-graded course (annotations not graded) in their following semester of study. Further analysis revealed that in the presence of external factors (i.e., grading), more students fell within the task-oriented and intensive clusters. However, when the external factor is removed, most students exhibited the disenchanted and minimalist learning behaviors. The findings provide insight into how students engage with the different features of a video annotation tool when there are graded or non-graded annotations and, most importantly, that having experience with one course where there are external factors influencing students' use of the tool is not sufficient to sustain their learning behaviour in subsequent courses where the external factor is removed.

Student evaluation of teaching (SET) is now common practice across higher education, with the results used for both course improvement and quality assurance purposes. While much research has examined the validity of SETs for measuring teaching quality, few studies have investigated the factors that influence student participation in the SET process. This study aimed to address this deficit through the analysis of an SET respondent pool at a large Canadian research-intensive university. The findings were largely consistent with available research (showing influence of student gender, age, specialisation area, and final grade on SET completion). However, the study also identified additional influential course-specific factors such as term of study, course year level, and course type as statistically significant. Collectively, such findings point to substantively significant patterns of bias in the characteristics of the respondent pool. Further research is needed to specify and quantify the impact (if any) on SET scores. We conclude, however, by recommending that such bias does not invalidate SET implementation, but instead should be embraced and reported within standard institutional practice, allowing better understanding of feedback received, and driving future efforts at recruiting student respondents.