A Systematic Review of Empirical Studies on Learning Analytics Dashboards: A Self-Regulated Learning Perspective

This paper presents a systematic literature review of learning analytics dashboards (LADs) research that reports empirical findings to assess the impact on learning and teaching. Several previous literature reviews identified self-regulated learning as a primary focus of LADs. However, there has been much less understanding how learning analytics are grounded in the literature on self-regulated learning and how self-regulated learning is supported. To address this limitation, this review analyzed the existing empirical studies on LADs based on the wellknown model of self-regulated learning proposed by Winne and Hadwin. The results show that existing LADs are rarely grounded in learning theory, cannot be suggested to support metacognition, do not offer any information about effective learning tactics and strategies, and have significant limitations in how their evaluation is conducted and reported. Based on the findings of the study and through the synthesis of the literature, the paper proposes that future research and development should not make any a priori design decisions about representation of data and analytic results in learning analytics systems such as LADs. To formalize this proposal, the paper defines the model for user-centered learning analytics systems (MULAS). The MULAS consists of the four dimensions that are cyclically and recursively interconnected including: theory, design, feedback, and evaluation.Wannisa Matcha, Nora’ayu Ahmad Uzir, Dragan Ga sevic, and Abelardo Pard

Discovering Time Management Strategies in Learning Processes Using Process Mining Techniques

This paper reports the findings of a study that proposed a novel learning analytic methodology that combines process mining with cluster analysis to study time management in the context of blended and online learning. The study was conducted with first-year students (N = 241) who were enrolled in blended learning of a health science course. The study identified four distinct time management tactics and three strategies. The tactics and strategies were interpreted according to the established theoretical framework of self-regulated learning in terms of student decisions about what to study, how long to study, and how to study. The study also identified significant differences in academic performance among students who followed different time management strategies

Three-tier stratification for CNS COVID-19 to help decide which patients should undergo lumbar puncture with CSF analysis: a case report and literature review

The COVID-19 pandemic continues to overwhelm global healthcare systems. While the disease primarily causes pulmonary complications, reports of central nervous system (CNS) involvement have recently emerged ranging from encephalopathy to stroke. This raises a practical dilemma for clinicians as to when to pursue neuroimaging and lumbar tap with cerebrospinal fluid (CSF) analysis in COVID-19 patients with neurological symptoms. We present a case of an encephalopathic patient infected with SARS-CoV-2 with no pulmonary symptoms. We propose a three-tier risk stratification for CNS COVID-19 aiming to help clinicians to decide which patients should undergo CSF analysis. The neurological examination remains an integral component of screening and evaluating patients for COVID-19 considering the range of emerging CNS complications

Analytics of learning strategies: Role of course design and delivery modality

Generalizability of the value of methods based on learning analytics remains one of the big challenges in the field of learning analytics. One approach to testing generalizability of a method is to apply it consistently in different learning contexts. This study extends a previously published work by examining the generalizability of a learning analytics method proposed for detecting learning tactics and strategies from trace data. The method was applied to the datasets collected in three different course designs and delivery modalities, including flipped classroom, blended learning, and massive open online course. The proposed method combines process mining and sequence analysis. The detected learning strategies are explored in terms of their association with academic performance. The results indicate the applicability of the proposed method across different learning contexts. Moreover, the findings contribute to the understanding of the learning tactics and strategies identified in the trace data: learning tactics proved to be responsive to the course design, whereas learning strategies were found to be more sensitive to the delivery modalities than to the course design. These findings, well aligned with self-regulated learning theory, highlight the association of learning contexts with the choice of learning tactics and strategies