What Makes Learning Analytics Research Matter

The ongoing changes and challenges brought on by the COVID-19 pandemic have exacerbated long-standing inequities in education, leading many to question basic assumptions about how learning can best benefit all students. Thirst for data about learning is at an all-time high, sometimes without commensurate attention to ensuring principles this community has long valued: privacy, transparency, openness, accountability, and fairness. How we navigate this dynamic context is critical for the future of learning analytics. Thinking about the issue through the lens of JLA publications over the last eight years, we highlight the important contributions of “problem-centric” rather than “tool-centric” research. We also value attention (proximal or distal) to the eventual goal of closing the loop, connecting the results of our analyses back to improve the learning from which they were drawn. Finally, we recognize the power of cycles of maturation: using information generated about real-world uses and impacts of a learning analytics tool to guide new iterations of data, analysis, and intervention design. A critical element of context for such work is that the learning problems we identify and choose to work on are never blank slates; they embed societal structures, reflect the influence of past technologies; and have previous enablers, barriers and social mediation acting on them. In that context, we must ask the hard questions: What parts of existing systems is our work challenging? What parts is it reinforcing? Do these effects, intentional or not, align with our values and beliefs? In the end what makes learning analytics matter is our ability to contribute to progress on both immediate and long-standing challenges in learning, not only improving current systems, but also considering alternatives for what is and what could be. This requires including stakeholder voices in tackling important problems of learning with rigorous analytic approaches to promote equitable learning across contexts. This journal provides a central space for the discussion of such issues, acting as a venue for the whole community to share research, practice, data and tools across the learning analytics cycle in pursuit of these goals.</jats:p

Time for Change: Why Learning Analytics Needs Temporal Analysis

Learning is a process that occurs over time: We build understanding, change perspectives, and develop skills over the course of extended experiences. As a field, learning analytics aims to generate understanding of, and support for, such processes of learning. Indeed, a core characteristic of learning analytics is the generation of high-resolution temporal data about various types of actions. Thus, we might expect study of the temporal nature of learning to be central in learning analytics research and applications. However, temporality has typically been underexplored in both basic and applied learning research. As Reimann (2009) notes, although “researchers have privileged access to process data, the theoretical constructs and methods employed in research practice frequently neglect to make full use of information relating to time and order” (p. 239). Typical approaches to analysis often aggregate across data due to a collection of conceptual, methodological, and operational challenges. As described below, insightful temporal analysis requires (1) conceptualising the temporal nature of learning constructs, (2) translating these theoretical propositions into specific methodological approaches for the capture and analysis of temporal data, and (3) practical methods for capturing temporal data features and using analyses to impact learning contexts. There is a pressing need to address these challenges if we are to realize the exciting possibilities for temporal learning analytics

Learning Analytics Impact: Critical Conversations on Relevance and Social Responsibility

Our 2019 editorial opened a dialogue about what is needed to foster an impactful field of learning analytics (Knight, Wise, &amp; Ochoa, 2019). As we head toward the close of a tumultuous year that has raised profound questions about the structure and processes of formal education and its role in society, this conversation is more relevant than ever. That editorial, and a recent online community event, focused on one component of the impact: standards for scientific rigour and the criteria by which knowledge claims in an interdisciplinary, multi-methodology field should be judged. These initial conversations revealed important commonalities across statistical, computational, and qualitative approaches in terms of a need for greater explanation and justification of choices in using appropriate data, models, or other methodological approaches, as well as the many micro-decisions made in applying specific methodologies to specific studies. The conversations also emphasize the need to perform different checks (for overfitting, for bias, for replicability, for the contextual bounds of applicability, for disconfirming cases) and the importance of learning analytics research being relevant by situating itself within a set of educational values, making tighter connections to theory, and considering its practical mobilization to affect learning. These ideas will serve as the starting point for a series of detailed follow-up conversations across the community, with the goal of generating updated standards and guidance for JLA articles.</jats:p

A Multidimensional Deep Learner Model of Urgent Instructor Intervention Need in MOOC Forum Posts

In recent years, massive open online courses (MOOCs) have become one of the most exciting innovations in e-learning environments. Thousands of learners around the world enroll on these online platforms to satisfy their learning needs (mostly) free of charge. However, despite the advantages MOOCs offer learners, dropout rates are high. Struggling learners often describe their feelings of confusion and need for help via forum posts. However, the often-huge numbers of posts on forums make it unlikely that instructors can respond to all learners and many of these urgent posts are overlooked or discarded. To overcome this, mining raw data for learners’ posts may provide a helpful way of classifying posts where learners require urgent intervention from instructors, to help learners and reduce the current high dropout rates. In this paper we propose, a method based on correlations of different dimensions of learners’ posts to determine the need for urgent intervention. Our initial statistical analysis found some interesting significant correlations between posts expressing sentiment, confusion, opinion, questions, and answers and the need for urgent intervention. Thus, we have developed a multidimensional deep learner model combining these features with natural language processing (NLP). To illustrate our method, we used a benchmark dataset of 29598 posts, from three different academic subject areas. The findings highlight that the combined, multi-dimensional features model is more effective than the text-only (NLP) analysis, showing that future models need to be optimised based on all these dimensions, when classifying urgent posts

Learning Analytics: Looking to the Future

This second issue of the Journal of Learning Analytics in 2017 is the first edited by the full new journal editorial team. As the baton is passed on, we would like to thank the founding editors for their work initiating the journal and nurturing its development over the past several years. We look forward to continuing that tradition of excellence. This issue includes four research paper contributions, and a special section on the ‘Shape of Educational Data’. This editorial is also an opportunity for us to reflect on the development of the journal so far, and describe some changes we are making to continue the expansion and maturation of a growing community of learning analytics researchers and practitioners.</jats:p

Risk factors for wound infection in surgery for spinal metastasis

Wound infection rates are generally higher in patients undergoing surgery for spinal metastasis. Risk factors of wound infection in these patients are poorly understood. Purpose To identify demographic and clinical variables that may be associated with patients experiencing a higher wound infection rate. Study design Retrospective study with prospectively collected data of spinal metastasis patients operated consecutively at a University Teaching Hospital, adult spine division which is a tertiary referral centre for complex spinal surgery. Patient sample Ninety-eight patients were all surgically treated, consecutively from January 2009 to September 2011. Three patients had to be excluded due to inadequate data. Outcome measures Physiological measures, with presence or absence of microbiologically proven infection. Methods Various demographic and clinical data were recorded, including age, serum albumin level, blood total lymphocyte count, corticosteroid intake, Malnutrition Universal Screening Tool (MUST) score, neurological disability, skin closure material used, levels of surgery and administration of peri-operative corticosteroids. No funding was received from any sources for this study and as far as we are aware, there are no potential conflict of interest-associated biases in this study. Results Higher probabilities of infection were associated with low albumin level, seven or more levels of surgery, use of delayed/non-absorbable skin closure material and presence of neurological disability. Of these factors, levels of surgery were found to be statistically significant at the 5 % significance level. Conclusion Risk of infection is high (17.9 %) in patients undergoing surgery for spinal metastasis. Seven or more vertebral levels of surgery increase the risk of infection significantly (p < 0.05). Low albumin level and presence of neurological disability appear to show a trend towards increased risk of infection. Use of absorbable skin closure material, age, low lymphocyte count, peri-operative administration of corticosteroids and MUST score do not appear to influence the risk of infection

Homeostatic competition drives tumor growth and metastasis nucleation

We propose a mechanism for tumor growth emphasizing the role of homeostatic regulation and tissue stability. We show that competition between surface and bulk effects leads to the existence of a critical size that must be overcome by metastases to reach macroscopic sizes. This property can qualitatively explain the observed size distributions of metastases, while size-independent growth rates cannot account for clinical and experimental data. In addition, it potentially explains the observed preferential growth of metastases on tissue surfaces and membranes such as the pleural and peritoneal layers, suggests a mechanism underlying the seed and soil hypothesis introduced by Stephen Paget in 1889 and yields realistic values for metastatic inefficiency. We propose a number of key experiments to test these concepts. The homeostatic pressure as introduced in this work could constitute a quantitative, experimentally accessible measure for the metastatic potential of early malignant growths.Comment: 13 pages, 11 figures, to be published in the HFSP Journa

Renal epithelial cells retain primary cilia during human acute renal allograft rejection injury

OBJECTIVES: Primary cilia are sensory organelles which co-ordinate several developmental/repair pathways including hedgehog signalling. Studies of human renal allografts suffering acute tubular necrosis have shown that length of primary cilia borne by epithelial cells doubles throughout the nephron and collecting duct, and then normalises as renal function returns. Conversely the loss of primary cilia has been reported in chronic allograft rejection and linked to defective hedgehog signalling. We investigated the fate of primary cilia in renal allografts suffering acute rejection. RESULTS: Here we observed that in renal allografts undergoing acute rejection, primary cilia were retained, with their length increasing 1 week after transplantation and remaining elevated. We used a mouse model of acute renal injury to demonstrate that elongated renal primary cilia in the injured renal tubule show evidence of smoothened accumulation, a biomarker for activation of hedgehog signalling. We conclude that primary cilium-mediated activation of hedgehog signalling is still possible during the acute phase of renal allograft rejection

Negative life events and suicide risk in college students: Conditional indirect effects of hopelessness and self-compassion

Objective: Suicide risk is a significant public health concern for college students and may be exacerbated by hopelessness resulting from negative life events (NLE), yet may be ameliorated by self-compassion. We examined the mediating role of hopelessness in the relation between NLE and suicidal behavior, and the moderating influence of self-compassion on all model paths. Participants: Participants were 338 undergraduates (89% white; 67% female). Data were collected from December 2014 to December 2015. Methods: Participants completed the Life Events Checklist for College Students, Beck Hopelessness Inventory, Self-Compassion Scale, and Suicidal Behaviors Questionnaire – Revised. Results: Negative life events were related to greater hopelessness and, in turn, to more suicidal behavior, yet self-compassion attenuated this effect. Conclusions: Self-compassion may buffer the NLE–hopelessness linkage, thereby reducing suicide risk among college students. Therapeutic promotion of self-compassion, and reduction of hopelessness, may be important suicide prevention strategies on college campuses