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Digital higher education continues its evolution towards a wider use of technology to support learning experiences. The global pandemic has elevated new sets of educational challenges that highlight the importance of technology as a critical component of contemporary education systems. However, reliance on technology also raises concerns about sustaining support in these new modes of delivery while effectively promoting the attainment of learning objectives. Learning analytics offers the possibility of supporting designers and educators to gain a deeper understanding of how learners are engaging and progressing in their learning process. Increased understanding opens the door to a higher level of personalization to meet the diverse range of learners. However, achieving this connection between comprehensive data sets and personalization requires the combination of design, delivery and analytics and the participation of various stakeholder groups. This chapter presents the conceptual Learning Analytics Model for Personalization (LAMP) to represent the relationships between the elements in a digital learning experience with comprehensive data capture and personalized learner support actions. The model is showcased in the context of the provision of personalized learner feedback.

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 (N2013 = 290, N2014 = 316 and N2015 = 415).

Prior education studies have consistently emphasized the importance of sustained and active student engagement to aid academic performance and achievement of learning outcomes (e.g., Hockings, Cooke,Yamashita, McGinty, & Bowl, 2008; Michael, 2006). The positive impact of such active learning models on academic outcomes has been well established, particularly, in the STEM (Science, Technology, Engineering and Mathematics) disciplines. Refereed/Peer-reviewed

This paper presents the results of a natural experiment investigating the effects of instructional conditions and experience on the adoption and sustained use of a learning tool. The experiment was conducted with undergraduate students, enrolled into four performing art courses (N = 77) at a research intensive university in Canada. The students used the video annotation software CLAS for course-based self-assessment on their performances. Although existing research offers insights into the factors predicting students’ intentions of accepting a learning tool, much less is known about factors that affect actual adoption and sustained tool use. The study explored the use of CLAS amongst undergraduate students in four courses across two consecutive semesters. Trace data of students’ tool use, graph-based measures of metacognitive monitoring, and text cohesion of video annotations were used to estimate the volume of tool use and the quality of the learning strategy and learning products created. The results confirmed that scaffolding (e.g., graded activity with instructional feedback) is required to guide students’ initial tool use, although scaffolding did not have an independent significant effect on the quantity of tool use. The findings demonstrated that the use of the tool is strongly influenced by the experience an individual student gains from scaffolded conditions. That is, the students sustained their use of the learning tool in future courses even when the tool use was not graded nor was instructional feedback provided. An important implication is that students’ tool use is not solely driven by motivation – rather, it is shaped by instructional conditions and experience with the tool use. Refereed/Peer-reviewed

This issue is a call for researchers and practitioners to reflect on progress to date and understand the criticality of theory – how it facilitates interpretation of findings but also how it can also restrict and confine our thinking through the assumptions many theoretical models bring. As education paradigms further shift and juxtapose informal and formal learning settings there is a need to re-visit any underlying theoretical assumptions.

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 Refereed/Peer-reviewed

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. Refereed/Peer-reviewed

This editorial discusses events that marked the period since the publication of the previous issue – the 5th International Conference on Learning Analytics and Knowledge (LAK 2015), Learning Analytics Summer Institutes (LASIs 2015), and Learning Analytics Policy Briefing in the European Parliament. This period saw releases of two important publications for system-wide implementation of learning analytics in higher education published by Jisc and the Australian Government’s Office for Learning and Teaching. An important recognition of the maturation of the field of learning analytics is the recent publication of the 2015 Google Scholar Metrics identifying the LAK proceedings as the only conference proceedings among the 20 most cited publication venues in educational technology. Building bridges for enhancing impact is another important activity for the field maturation through developing linkages of learning analytics with educational data mining, user modeling, the learning sciences, technology enhanced learning, cyber-learning, and learning at scale. This editorial also introduces a special section published in this issue dedicated to the exploration of connections between self-regulated learning and learning analytics, introduces two regular research papers featured in this issue and describes several special sections that will be published in future issues of the journal.

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. Refereed/Peer-reviewed

This chapter focuses on the design of a flipped learning experience and, in particular, focuses on the types of activities and their scheduling. The problem can be described as how to stratify the type of activities and how they are distributed in time such that students are provided a gradual and engaging approach to achieve the learning outcomes. The flipped learning design model described in this section has been deployed in a first-year engineering course on computer systems at a higher education institution. In the remainder of the chapter, we assume that students with respect to a topic need to traverse the levels of the revised Bloom’s taxonomy starting with acquiring basic knowledge about a concept and then making a transition to the point where they can evaluate or create artefacts within that area.

This final issue for 2015 includes a special section of invited papers from the recent Learning Analytics and Knowledge conference (LAK15). The collected papers connect with the conference theme of “Scaling up: Big data to Big Impact” and reflect the emerging trends and future directions of learning analytics research.

© 2016 Copyright held by the owner/author(s). 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.

The rapidly changing higher education (HE) environment has transformed the ways academics work to overcome the challenges of ever-evolving imperatives. Changes in public sector funding models, the increase in the number and range of student capabilities, and the raised importance of university status have contributed to increasing accountability measures, including the wide adoption of standards and quality metrics in all aspects of academic practice. Academics are now at the center of coordinated institutional teaching quality enhancement efforts, putting a strain on their ability to perform equally well in research and teaching. In this chapter, a teaching and course development framework is proposed integrating ideas around reflective practice with the systematic use of evidence generated using methodologies emerging from the field of learning analytics and educational data science. The proposed framework could help academics to develop their teaching practice leveraging on previously untapped sources of evidence readily available in contemporary HE institutions and utilizing the disciplinary research skillset, which is not typically applied to teaching development. A case study illustrating the application of the proposed framework to scholarly evaluation of an academic development program (adopting online and face-to-face activities) is presented and discussed.

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. Refereed/Peer-reviewed

Designing, validating and deploying learning analytics tools for instructors or students is a challenge that requires techniques and methods from different disciplines, such as software engineering, human-computer interaction, computer graphics, educational design and psychology. Whilst each of these disciplines has established its own design methodologies, there is a need for methodological frameworks that meet the specific demands of the cross-disciplinary space defined by learning analytics. In particular there is no systematic workflow for producing learning analytics tools that are both technologically feasible and truly underpin the learning experience. In this paper, we present a set of guiding principles and recommendations derived from the LATUX workflow. LATUX is a five-stage workflow to design, validate and deploy awareness interfaces in technology-enabled learning environments. LATUX is grounded on a well-established design process for creating, testing and re-designing user interfaces. We extend this process by integrating the pedagogical requirements, to guide the design of learning analytics visualisations that can inform instructors’ pedagogical decisions or intervention strategies. The workflow is illustrated with a case study in which collaborative activities were deployed in a real classroom. Finally, the paper proposes a research agenda to support designers and implementers of learning analytics interfaces.

Designing, deploying and validating learning analytics tools for instructors or students is a challenge requiring techniques and methods from different disciplines, such as software engineering, human-computer interaction, educational design and psychology. Whilst each of these disciplines has consolidated design methodologies, there is a need for more specific methodological frameworks within the cross-disciplinary space defined by learning analytics. In particular there is no systematic workflow for producing learning analytics tools that are both technologically feasible and truly underpin the learning experience. In this paper, we present the LATUX workflow, a five-stage workflow to design, deploy and validate awareness tools in technology-enabled learning environments. LATUX is grounded on a well-established design process for creating, testing and re-designing user interfaces. We extend this process by integrating the pedagogical requirements to generate visual analytics to inform instructors' pedagogical decisions or intervention strategies. The workflow is illustrated with a case study in which collaborative activities were deployed in a real classroom. Refereed/Peer-reviewed

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 selfreflections adopted more linguistic and psychological related processes in comparison to the course with non-graded selfreflections. 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. Refereed/Peer-reviewed

This issue of the Journal of Learning Analytics comprises two special issue sections. The first of which presents five papers from the 4th International Learning Analytics and Knowledge conference held in Indianapolis. The second showcases the current or recent work of doctoral students who attended the 2nd Learning Analytics Summer Institute at Harvard University, Boston. The issue also includes two articles in the Hot Spots section, discussing the application of learning analytics initiatives in higher education institutions from different perspectives – broad-scale initiatives to individual course design. The breadth and diversity of the articles covered in this issue demonstrate how the discipline has matured and moved towards understanding student learning to inform pedagogical practice and curricular redesign coupled with strategies for the application and adoption of LA strategies across institutions

This article introduces the special issue from SoLAR’s Learning Analytics and Knowledge conference. Learning analytics is an emerging field incorporating theory and practice from numerous disciplines to investigate how learner interactions with digital artefacts can provide actionable data to the learner herself, and educators about the learning process. As the field continues to expand there is a timely opportunity to evaluate its ongoing maturation. This evaluation could be in part informed by regular scientometric analyses from both the Journal and Conference publications. These analyses can collectively provide insight into the development of learning analytics more broadly and assist with the allocation of resources to under‐represented areas for example.