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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). This study examines students’ experiences in using technology to connect with peers and their instructors during the COVID-19 pandemic when learning remotely. The research inquiry focusses on the second-year cohort as prior research has revealed that this group of learners tend to struggle with their learning (Kyndt et al., 2017; Milsom, 2015; Milsom & Yorke, 2015; Southgate et al., 2014; Virtue et al., 2017; Webb & Cotton, 2019) and experience higher levels of anxiety and depression compared to students in other years of university study prior to the COVID 19 pandemic (Liu et al., 2019). To examine their experience in peer-to-peer networks and their interactions with instructors for help seeking, interviews were undertaken at a large metropolitan Australian University in 2021 with 26 second-year students across different disciplines who had experienced emergency remote teaching in their first and second year of study. The findings reveal that students resist using the discussion board in the Learning Management System because of perceptions of exposure and embarrassment in asking questions when they feel they are expected to know the answer. Students report that synchronous video classes using technology such as Zoom, increase feelings of isolation and they reach out to their peers via social media technology instead. Students are intentional in their choice of technology in connecting with peers, however in the absence of physical connections, there remains a gap in productive engagement with peers. The findings show that second-year students are reluctant to reach out to their instructor when technology is their only mode of interaction, and students report that they would have been more likely to ask for help during a face-to-face class.

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. Refereed/Peer-reviewed

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

Video-based self-reflection and annotation is receiving increasing attention within the education literature. The importance of such technologies in education relate, in part, to the interactive nature and functionality these tools bring to aid learning engagement. In particular, these tools are well aligned with the need to promote and develop student meta-cognitive skills through the use of self-reflection activities. However, in the context of video-based learning environments, the nature of a students’ self-reflective process is not well understood. We attempt to address this gap in the literature in two main ways. First, we developed a coding instrument to assess the depth of a students’ self-reflection captured through the use of a video annotation tool. We then explored the linguistic and discourse properties of the student self-reflections using Coh-Metrix, a theoretically grounded computational linguistics facility. The adopted approach applies comprehensive analysis of language and discourse features associated with the specificity of students’ internal self-feedback, which is externalized as self-reflections in video annotations. The results suggest that levels of self-reflection have characteristically different linguistic properties, and these differences align with the underlying cognitive mechanisms associated with distinct reflective activities. The paper provides a detailed discussion of the findings in the context of the theoretical, methodological, and practical implications associated with video-based self-reflection and video annotation usc Refereed/Peer-reviewed

The use of standards-based assessment (SBA) has gained prominence in higher education due to a wide range of research evidence that supports its effectiveness in improving learning and teaching. Although the concept is widely used in academic discourse and practice, it has multiple interpretations which have led to diverse assessment practices to the extent that some of these depart from the principles of SBA. To help build a common understanding of what constitutes SBA practices, we explored the construct of academic SBA practices using both theoretical and empirical approaches. We developed a tool, based on the principles of SBA emerging from practices noted in the literature and identified from focus groups. We used factor analysis to extract the dimensions of the construct and to establish empirical evidence for the utilisation of the tool using a data set of 410 academics who engaged in self-assessment. Results showed that a six-factor model is the most parsimonious among other models. We used these dimensions to develop a framework for describing academic SBA practices. The use of the framework and tool has critical implications for curriculum design, professional practice and development, and policy articulation 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

Videos are becoming a core component of many pedagogical approaches, particularly with the rise in interest in blended learning, flipped classrooms, and massive open and online courses (MOOCs). Although there are a variety of types of videos used for educational purposes, lecture videos are the most widely adopted. Furthermore, with recent advances in video streaming technologies, learners’ digital footprints when accessing videos can be mined and analyzed to better understand how they learn and engage with them. The collection, measurement, and analysis of such data for the purposes of understanding how learners use videos can be referred to as video analytics. Coupled with more traditional data collection methods, such as interviews or surveys, and performance data to obtain a holistic view of how and why learners engage and learn with videos, video analytics can help inform course design and teaching practice. In this chapter, we provide an overview of videos integrated in the curriculum including an introduction to multimedia learning and discuss data mining approaches for investigating learner use, engagement with, and learning with videos, and provide suggestions for future directions.

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.

The need for flexibility in learning and the affordances of technology provided the impetus for the rise of blended learning (BL) globally across higher education institutions. However, the adoption of BL practices continues at a low pace due to academics’ low digital fluency, various views and BL definitions, and limited standards-based tools to guide academic practice. To address these issues, this paper introduces a BL framework, based on one definition and with criteria and standards of practice to support the evaluation and advancement of BL in higher education. The framework is theoretically underpinned by the extant literature and supported by focus group discussions. The evidence supporting the criteria and standards are discussed with suggestions for how they can be used to guide course design, academic practice, and professional development.Keywords: digital literacy; criteria; standards; academic development; curriculum design(Published: 26 October 2015)Citation: Research in Learning Technology 2015, 23: 28451 - http://dx.doi.org/10.3402/rlt.v23.28451

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.