Analytics Dashboard Research Articles

CorpusVis: Visual Analysis of Digital Sheet Music Collections

AbstractManually investigating sheet music collections is challenging for music analysts due to the magnitude and complexity of underlying features, structures, and contextual information. However, applying sophisticated algorithmic methods would require advanced technical expertise that analysts do not necessarily have. Bridging this gap, we contribute CorpusVis, an interactive visual workspace, enabling scalable and multi‐faceted analysis. Our proposed visual analytics dashboard provides access to computational methods, generating varying perspectives on the same data. The proposed application uses metadata including composers, type, epoch, and low‐level features, such as pitch, melody, and rhythm. To evaluate our approach, we conducted a pair‐analytics study with nine participants. The qualitative results show that CorpusVis supports users in performing exploratory and confirmatory analysis, leading them to new insights and findings. In addition, based on three exemplary workflows, we demonstrate how to apply our approach to different tasks, such as exploring musical features or comparing composers.

Learning Loss Recovery Dashboard: A Proposed Design to Mitigate Learning Loss Post Schools Closure

Research has shown the effectiveness of designing a Learning Analytics Dashboard (LAD) for learners and instructors, including everyone’s levels of progress and performance. An intertwined relationship exists between learning analytics (LA) and the learning process. Understanding information or data about learners and their learning journey can contribute to a deeper understanding of learners and the learning process. The design of an effective learning dashboard relies heavily on LA, including assessment of the learning process, i.e., gains and losses. A Learning Loss Recovery Dashboard (LLRD) can be designed as an instructional tool, to support the learning process as well as learners’ performance and their academic achievement. The current project proposes a LLRD prototype model to deal with potential learning loss; increase the achievement of learning outcomes; and provide a single, comprehensive learning process, where schools can evaluate and remedy any potential learning loss resulting from the distance-learning period that was caused by the COVID-19 pandemic. This systematic dashboard prototype functions to determine learning gains by K–12 learners. It is expected that the implementation of the proposed dashboard would provide students, teachers, and educational administrators with an integrated portal, for a holistic and unified remedial experience for addressing learning loss.

Team interactions with learning analytics dashboards

Student-facing visualisations have attracted increased attention with recent developments in data-driven tools to support individual and group work. Learning analytics dashboards (LADs), a data-enhanced feedback tool that allows students to make sense of their learning by providing insights into their learning behaviours represents one of the prominent examples of this trend. While these visualisation tools are increasingly used to study and enhance students' learning in academic contexts, current research is limited regarding the effects of the LADs in K-12 environments. There is a missed opportunity to empower teams and allow instructors and researchers to understand how teams use the LAD to regulate their learning. In this study, we developed a K-12 LAD for supporting students' collaborative work and evaluated with respect to students' perceived usefulness of the proposed LAD and the association between its use and course performance. The study followed a mixed-methods approach, combining quantitative analysis of log data from the dashboard and qualitative analysis of students’ perceptions using surveys and focus groups. Our results show that different roles within teams have distinguished engagement patterns with the LAD and that the tool improves the collaborative learning experience. We postulate that the implications of this study will aid future research work when investigating the behaviours of teams and optimising their learning using LADs.

CADA: a teacher-facing learning analytics dashboard to foster teachers’ awareness of students’ participation and discourse patterns in online discussions

Despite the potential of learning analytics (LA) to support teachers’ everyday practice, its adoption has not been fully embraced due to the limited involvement of teachers as co-designers of LA systems and interventions. This is the focus of the study described in this paper. Following a design-based research (DBR) approach and guided by concepts from the socio-cultural perspective and human-computer interaction (HCI), we design, test, and evaluate a teacher-facing LA dashboard, the Canvas Discussion Analytics Dashboard (CADA), in real educational settings. The goal of this dashboard is to support teachers’ roles in online environments through insights into students’ participation and discourse patterns. We evaluate CADA through 10 in-depth interviews with university teachers to examine their experiences using CADA in seven blended undergraduate and graduate courses over a one-year period. The findings suggest that engaging teachers throughout the analytics tool design process and giving them control/agency over LA tools can favour their adoption in practice. Additionally, the alignment of dashboard metrics with relevant theoretical constructs allows teachers to monitor the learning designs and make course design changes on the fly. The teachers in this study emphasise the need for LA dashboards to provide actionable insights by moving beyond what things are towards how things should be. This study has several contributions. First, we make an artefact contribution (e.g. CADA), an LA dashboard to support teachers with insights into students’ online discussions. Second, by leveraging theory, and working with the teachers to develop and implement a dashboard in authentic teaching environments, we make an empirical, theoretical and methodological contribution to the field of learning analytics and technology enhanced learning. We synthesise these through practical design and implementation considerations for researchers, dashboard developers, and higher education institutions.

471 Empowering the Participant Voice (EPV): Participant Feedback to Improve the Clinical Research Enterprise

OBJECTIVES/GOALS: Six CTSA sites formed a collaboration to DEVELOP, DEMONSTRATE, AND DISSEMINATE new infrastructure to streamline collection and analysis of research participant feedback, using the Research Participant Perception Survey (RPPS), common standards, and customized REDCap-based tools, to improve the clinical research enterprise. METHODS/STUDY POPULATION: DEVELOP charter, consensus approach, core survey, deployment standards, data-use agreements; define meta-data, system requirements for the infrastructure, use-cases. Engage stakeholders for broad institutional and community input. Build RPPS/REDCap project, visual analytics Dashboard External Module, and Program Dashboard module for evaluation. Configure to use with Multilingual Module. DEMONSTRATE by implementing site-based use cases that reflect local priorities and span diverse populations, testing different methods of survey deployment (REDCap, patient portal, SMS) to showcase utility and flexibility. Generate data for local and inter-institutional benchmarking. Refine, then DISSEMINATE new infrastructure across the Consortium and REDCap community for broader testing and uptake. RESULTS/ANTICIPATED RESULTS: The project team refined the RPPS survey for inclusivity and mode of informed consent; defined standards for survey timing, sampling, and study metadata; configured the data dictionary in English and Spanishfor use with the multi-lingual module ; developed tools for project evaluation. Stakeholder engagement identified themes of anticipated value and fears about feedback. We designed an At-a-Glance Dashboard to display survey results with detailed analytics and filters. A REDCap application programming interface will send de-identified site data to the EPV Consortium Database to support benchmarking. Full implementation began November 2021 and will scale in 2022. Dissemination to Consortium and REDCap users is ongoing through presentations and a project website (www.Rockefeller.edu/research/epv). DISCUSSION/SIGNIFICANCE: Direct feedback from representative populations about their experiences in research is essential to understand and resolve barriers to broad participation in research. Streamlined RPPS/REDCap infrastructure provides a platform for local and national benchmarking, and collection of actionable data to improve clinical research.

Visualizing Opioid-Use Variation in a Pediatric Perioperative Dashboard.

Anesthesiologists integrate numerous variables to determine an opioid dose that manages patient nociception and pain while minimizing adverse effects. Clinical dashboards that enable physicians to compare themselves to their peers can reduce unnecessary variation in patient care and improve outcomes. However, due to the complexity of anesthetic dosing decisions, comparative visualizations of opioid-use patterns are complicated by case-mix differences between providers. This single-institution case study describes the development of a pediatric anesthesia dashboard and demonstrates how advanced computational techniques can facilitate nuanced normalization techniques, enabling meaningful comparisons of complex clinical data. We engaged perioperative-care stakeholders at a tertiary care pediatric hospital to determine patient and surgical variables relevant to anesthesia decision-making and to identify end-user requirements for an opioid-use visualization tool. Case data were extracted, aggregated, and standardized. We performed multivariable machine learning to identify and understand key variables. We integrated interview findings and computational algorithms into an interactive dashboard with normalized comparisons, followed by an iterative process of improvement and implementation. The dashboard design process identified two mechanisms-interactive data filtration and machine-learning-based normalization-that enable rigorous monitoring of opioid utilization with meaningful case-mix adjustment. When deployed with real data encompassing 24,332 surgical cases, our dashboard identified both high and low opioid-use outliers with associated clinical outcomes data. A tool that gives anesthesiologists timely data on their practice patterns while adjusting for case-mix differences empowers physicians to track changes and variation in opioid administration over time. Such a tool can successfully trigger conversation amongst stakeholders in support of continuous improvement efforts. Clinical analytics dashboards can enable physicians to better understand their practice and provide motivation to change behavior, ultimately addressing unnecessary variation in high impact medication use and minimizing adverse effects.

Technology Enhanced Learning Analytics Dashboard in Higher Education

During the COVID-19 pandemic period, all the Sri Lankan universities delivered lectures in fully online mode using Virtual Learning Environments. In fully online mode, students cannot track their performance level, their progress in the course, and their performances compared to the rest of the class. This paper presents research work conducted at the University of Colombo School of Computing (UCSC), Sri Lanka, to solve the above problems and facilitate students learning in fully online and blended learning environments using Learning Analytics. The research objective is to design and create a Technology Enhanced Learning Analytics (TELA) dashboard for improving students’ motivation, engagement, and grades. The Design Science research strategy was followed to achieve the objectives of the research. Initially, a literature survey was conducted analyzing features and limitations in current Learning Analytic dashboards. Then, current Learning Analytic plugins for Moodle were studied to identify their drawbacks. Two surveys with 136 undergraduate students and interviews with 12 lecturers were conducted to determine required features of the TELA system. The system was designed as a Moodle Plugin. Finally, an evaluation of the system was done with third-year undergraduate students of the UCSC. The results showed that the TELA dashboard can improve students' motivation, engagement, and grades. As a result of the system, students could track their current progress and performance compared to the peers, which helps to improve their motivation to engage more in the course. Also, the increased engagement in the course enhances the student’s self-confidence since the student can see continuous improvement of his/her progress and performance which in turn improves the student’s grades.

Learning analytics dashboard: a tool for providing actionable insights to learners

This study investigates current approaches to learning analytics (LA) dashboarding while highlighting challenges faced by education providers in their operationalization. We analyze recent dashboards for their ability to provide actionable insights which promote informed responses by learners in making adjustments to their learning habits. Our study finds that most LA dashboards merely employ surface-level descriptive analytics, while only few go beyond and use predictive analytics. In response to the identified gaps in recently published dashboards, we propose a state-of-the-art dashboard that not only leverages descriptive analytics components, but also integrates machine learning in a way that enables both predictive and prescriptive analytics. We demonstrate how emerging analytics tools can be used in order to enable learners to adequately interpret the predictive model behavior, and more specifically to understand how a predictive model arrives at a given prediction. We highlight how these capabilities build trust and satisfy emerging regulatory requirements surrounding predictive analytics. Additionally, we show how data-driven prescriptive analytics can be deployed within dashboards in order to provide concrete advice to the learners, and thereby increase the likelihood of triggering behavioral changes. Our proposed dashboard is the first of its kind in terms of breadth of analytics that it integrates, and is currently deployed for trials at a higher education institution.

Creating surveillance data infrastructure using laboratory analytics: Leveraging visiun and epic systems to support COVID-19 pandemic response

BackgroundPandemics are unpredictable and can rapidly spread. Proper planning and preparation for managing the impact of outbreaks is only achievable through continuous and systematic collection and analysis of health-related data. We describe our experience on how to comply with required reporting and develop a robust platform for surveillance data during an outbreak. Materials and methodsAt Mount Sinai Health System, New York City, we applied Visiun, a laboratory analytics dashboard, to support main response activities. Epic System Inc.’s SlicerDicer application was used to develop clinical and research reports. We followed World Health Organization (WHO); federal and state guidelines; departmental policies; and expert consultation to create the framework. ResultsThe developed dashboard integrated data from scattered sources are used to seamlessly distribute reports to key stakeholders. The main report categories included federal, state, laboratory, clinical, and research. The first two groups were created to meet government and state reporting requirements. The laboratory group was the most comprehensive category and included operational reports such as performance metrics, technician performance assessment, and analyzer metrics. The close monitoring of testing volumes and lab operational efficiency was essential to manage increasing demands and provide timely and accurate results. The clinical data reports were valuable for proper managing of medical surge requirements, such as healthcare workforce and medical supplies. The reports included in the research category were highly variable and depended on healthcare setting, research priorities, and available funding. We share a few examples of queries that were included in the designed framework for research projects. ConclusionWe reviewed here the key components of a conceptual surveillance framework required for a robust response to COVID-19 pandemics. We demonstrated leveraging a lab analytics dashboard, Visiun, combined with Epic reporting tools to function as a surveillance system. The framework could be used as a generic template for possible future outbreak events.

The virtual peripheral nerve academy: education for the identification and treatment of peripheral nerve disorders

Millions of people around the globe suffer peripheral nerve injuries caused by trauma and medical disorders. However, medical school curricula are profoundly deficient in peripheral nerve education. This lack of knowledge within the healthcare profession may cause inadequate patient care. We developed the Virtual Peripheral Nerve Academy (VPNA) as a reusable virtual learning environment to provide medical students with detailed education on the peripheral nervous system (PNS). Students are introduced to the PNS through virtual 3D rendering of the human body, wherein they visualize individual nerves through dissection and observe normal motor and sensory function associated with each nerve. PNS structures that are absent from traditional texts are included in this visualization, ranging from the innervation of joints to the normal anatomic variation required for differential diagnosis of pain after an injury. Detailed modules on peripheral nerve disorders allow students to observe pathophysiological mechanisms, associated symptomatology, and appropriate treatments. Students are briefed on a patient clinical case, then interact with a patient avatar to learn the appropriate diagnostics, including physical exam maneuvers and electrodiagnostic testing. Interactive modules on peripheral nerve surgeries detail surgical techniques. The VPNA data and analytics dashboards allow medical students and course instructors to assess skill improvement and identify specific learning needs. The built-in learner management system and availability on both computer-based and virtual reality platforms facilitate integration into any existing medical school curricula. Ultimately, this immersive technology enables every medical student to learn about the peripheral nervous system and gain competency in treating real-life nerve pathologies.

DashBot: Insight-Driven Dashboard Generation Based on Deep Reinforcement Learning.

Analytical dashboards are popular in business intelligence to facilitate insight discovery with multiple charts. However, creating an effective dashboard is highly demanding, which requires users to have adequate data analysis background and be familiar with professional tools, such as Power BI. To create a dashboard, users have to configure charts by selecting data columns and exploring different chart combinations to optimize the communication of insights, which is trial-and-error. Recent research has started to use deep learning methods for dashboard generation to lower the burden of visualization creation. However, such efforts are greatly hindered by the lack of large-scale and high-quality datasets of dashboards. In this work, we propose using deep reinforcement learning to generate analytical dashboards that can use well-established visualization knowledge and the estimation capacity of reinforcement learning. Specifically, we use visualization knowledge to construct a training environment and rewards for agents to explore and imitate human exploration behavior with a well-designed agent network. The usefulness of the deep reinforcement learning model is demonstrated through ablation studies and user studies. In conclusion, our work opens up new opportunities to develop effective ML-based visualization recommenders without beforehand training datasets.

Influential factors in decision support capabilities of a mobile-enabled interactive analytical dashboard system

Influential factors in decision support capabilities of a mobile-enabled interactive analytical dashboard system

Influential factors in decision support capabilities of a mobile-enabled interactive analytical dashboard system

Influential factors in decision support capabilities of a mobile-enabled interactive analytical dashboard system

Discovering the effects of learning analytics dashboard on students’ behavioral patterns using differential sequence mining

Interventions based on learning analytics have a very important place in closing the learning analytics loop. However, data-driven studies that test the effects of learning analytics-based interventions on students' online learning behaviors are very limited. In this study, the effect of the student-facing learning analytics dashboard (LAD) on the learning behavior of students in the online learning environment was investigated by using the differential pattern mining method. In a completely remote course, the learning behaviors of the participants before the introduction of the dashboard were compared with the learning behaviors they exhibited after the dashboard was introduced. In this way, it has become possible to analyze the behavior changes after the dashboard intervention. Wilcoxon signed-rank test was used to test whether these behavioral changes were statistically significant or not. According to the Wilcoxon signed-rank test results, while there is no significant difference in terms of students’ assignment and quiz interactions, it is seen that there is a statistically significant increase in terms of students’ forum-related activities such as reading other students’ posts, starting a new discussion, and replying others’ posts. Students’ SCORM interactions (e.g, launch, complete) were also increased after engaging with the LAD. In addition, it was found that the overall interaction of students in the online learning environment increased by 57% when the LAD was used.

MultiVision: Designing Analytical Dashboards with Deep Learning Based Recommendation.

We contribute a deep-learning-based method that assists in designing analytical dashboards for analyzing a data table. Given a data table, data workers usually need to experience a tedious and time-consuming process to select meaningful combinations of data columns for creating charts. This process is further complicated by the needs of creating dashboards composed of multiple views that unveil different perspectives of data. Existing automated approaches for recommending multiple-view visualizations mainly build on manually crafted design rules, producing sub-optimal or irrelevant suggestions. To address this gap, we present a deep learning approach for selecting data columns and recommending multiple charts. More importantly, we integrate the deep learning models into a mixed-initiative system. Our model could make recommendations given optional user-input selections of data columns. The model, in turn, learns from provenance data of authoring logs in an offline manner. We compare our deep learning model with existing methods for visualization recommendation and conduct a user study to evaluate the usefulness of the system.

An AI-enabled predictive analytics dashboard for acute neurosurgical referrals

An AI-enabled predictive analytics dashboard for acute neurosurgical referrals

An End-to-End Named Entity Recognition Platform for Vietnamese Real Estate Advertisement Posts and Analytical Applications

The volume and complexity of publicly available real estate data have been snowballing. As a result, information extraction and processing have become increasingly challenging and essential for many PropTech (Property Technology) companies worldwide. The challenges are even more pronounced with languages other than English, such as Vietnamese, where few studies in this field have taken place. This paper presents an end-to-end framework for automatically collecting real estate advertisement posts from different data sources, extracting useful information, and storing computed data into proper data warehouses and data marts for the Vietnamese advertisement posts in real estate. After that, one can serve aggregated data for other descriptive and predictive analytics. We combine two models for constructing the most appropriate extraction step: Noise Filtering and Named Entity Recognition (NER). These models can help process initial input data and extract all helpful information. The experiment results show that using PhoBERT_<i>large</i> can achieve the best performance compared to other approaches. Furthermore, we can obtain the corresponding F1 scores of the Noise filtering module and the NER module as 0.8697 and 0.8996, respectively. Finally, we utilize Superset for implementing analytic dashboards to visualize the predicted results and serve for further analysis and management processes.

A model to improve recommendation systems based on Spark platform

With the popularity of using the internet, the evolution of technology, the rapid growth of data volume, and applying recommendation systems in diverse life domains, adopting recommendation systems became indispensable. Most of the traditional recommendation systems are built un-oriented of real-time data challenges (such as item cold-start, scalability, heterogeneous data, items change, and users' opinions change), resulting in ineffective, unfulfilling recommendations. Therefore, what is needed is a real-time-based recommendation model that can make the user find what s/he needs without even searching for it; it's like building a model aware of the users' nature and behaviour. This paper aims at building a recommendation model that fulfils the user requirements and overcomes the shortcomings of the traditional recommendation systems. The proposed recommendation model is built on Apache Spark, Apache Kafka and alternative least square (ALS) algorithm, and the model is divided into two parts: recommendation system and real-time analytics dashboard. An e-commerce website is chosen as a case study to apply our model and the results are presented.

Dynamic Force-directed Graph with Weighted Nodes for Scholar Network Visualization

Through the growth of portals and venues to publish academic publications, the number of academic publications is growing exponentially in recent years. An effective exploration and fast navigation in the collection of academic publications become an urgent need to help academic researchers find publications related to their research and the surrounding community. A scholar network visualization approach is proposed to help users to explore a large number of academic publications concerning the strength of the relationship between each publication. The approach is realized by creating a web-based interface using D3 JavaScript algorithm that allows the visualization to focus on how data are connected to each other more accurately than the conventional lines of data seen in traditional data representation. The proposed approach visualizes data by incorporating a force-directed graph with weighted nodes and vertices to give more descriptive information of millions of raw data such as author names, publication title, publication year, publication venue and number of citations from the scholar network dataset. By introducing a weighted relationship in the network visualization, the proposed approach can give a more insightful detail of each publication such as a highly cited publication by looking at and exploring the generated interactive graph. The proposal is targeted to be incorporated into a larger-scale scholar network analytical dashboard that can offer various visualization approaches under one flagship application.

The Promise of Small Data for Telemedicine in Chronic Condition Management: A Real-World Case Series

Connected care is defined as the “real-time, electronic communication between a patient and a provider, including telehealth, remote patient monitoring, and secure email communication between clinicians and their patients” (Alliance of Connected Care). Connected care can create a high-value interaction strategy with patients when it makes thoughtful use of commercially available digital health technologies with demonstrated both clinical and economic effectiveness. Karantis360™, is a home sensor technology that enables real-time tracking, data analytics and predictive care for personal (at home) care powered by IBM Watson Health. IndividuALLyticsTM is a telemedicine platform driven by a patent-pending an N-of-1 analytical engine and related digital dashboards that provides individual, patient-level evaluation of treatment response. The underlying technology combines disparate digital health technology data with the best evidence-base guidelines with N-of-1 methodology. The output allows for creation of personalized treatments empirically tested at the patient-level over time (aka over the course of care). When aggregated both within and across persons, the time-ordered data can build predictive pathways of behavior and ensure the relevant care and medical treatments are in place to support effective medical and self-management of chronic illness. This case-series report describes the implementation of a joint home sensor technology (big data) and an N-of-1 analytic engine (small data) with three elderly consented volunteer customers-patients of Karantis360™. Each person underwent successive, 2-week behavioral change treatment phases to determine usability, utility regarding medical and self-management and any proximal effects on health risks.