Predict or describe? How learning analytics dashboard design influences motivation and statistics anxiety in an online statistics course.
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ABSTRACT: Supplementary information
The online version contains supplementary material available at 10.1007/s11423-021-09998-z.
SUBMITTER: Valle N
PROVIDER: S-EPMC8153529 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundStudents in the Faculty of Dentistry at the University of British Columbia have articulated challenges in understanding learning objectives in their oral epidemiology and statistics course. This study aimed to measure the impact of a course renewal intended to enhance student learning. Examples of educational interventions included providing more time for activities, increasing student interactivity, and integrating more hands-on applicable exercises using statistical software.MethodsAn online mixed-methods survey using a 5-point Likert scale and open-ended questions was distributed to 43 dental hygiene students before the course renewal and again to a second cohort of 43 students after course revisions. The survey asked students to rank their levels of challenge and self-confidence in learning 23 of the course objectives throughout each academic year. Four semi-structured interviews were also conducted with faculty and staff members involved in teaching or coordinating this course to understand their experiences after the course revisions.ResultsResponse rates were 32% to 57%. After the course renewal, the extent to which students in the entry-to-practice cohort felt extremely challenged to learn each objective was significantly reduced (25% vs. 3%, p < 0.001), and students' self-confidence scores significantly increased (12% vs. 30%, p < 0.001).The changes on the challenge and confidence scores in the degree-completion cohort were not statistically significant (23% vs. 24% and 31% vs. 36%, respectively). Student satisfaction levels increased in all 6 categories measured.ConclusionProviding students with more time to absorb their learning, increasing interactivity, offering timely feedback, and integrating applicable exercises using statistical software resulted in an enhanced learning environment.
| S-EPMC7906124 | biostudies-literature
Project description:BackgroundPrimary care management of hypertension under new guidelines incorporates assessment of cardiovascular disease risk and commonly requires review of electronic health record (EHR) data. Visual analytics can streamline the review of complex data and may lessen the burden clinicians face using the EHR. This study sought to assess the utility of a visual analytics dashboard in addition to EHR in managing hypertension in a primary care setting.MethodsPrimary care physicians within an urban, academic internal medicine clinic were tasked with performing 2 simulated patient encounters for hypertension management: the first using standard EHR, and the second using EHR paired with a visual dashboard. The dashboard included graphical blood pressure trends with guideline-directed targets, calculated atherosclerotic cardiovascular disease risk score, and relevant medications. Guideline-appropriate antihypertensive prescribing, correct target blood pressure goal, and total encounter time were assessed.ResultsWe evaluated 70 case simulations. Use of the dashboard with the EHR compared with use of the EHR alone was associated with greater adherence to prescribing guidelines (95% vs. 62%, P < 0.001) and more correct identification of blood pressure target (95% vs. 57%, P < 0.01). Total encounter time fell an average of 121 seconds (95% confidence interval 69-157 seconds, P < 0.001) in encounters that used the dashboard combined with the EHR.ConclusionsThe integration of a hypertension-specific visual analytics dashboard with EHR demonstrates the potential to reduce time and improve hypertension guideline implementation. Further widespread testing in clinical practice is warranted.
| S-EPMC8557440 | biostudies-literature
Project description:BACKGROUND:Standard methods for obtaining data may delay quality improvement (QI) interventions including for bronchiolitis, a common cause of childhood hospitalization. OBJECTIVE:To describe the use of a dashboard in the context of a multifaceted QI intervention aimed at reducing the use of chest radiographs, bronchodilators, antibiotics, steroids, and viral testing in patients with bronchiolitis. METHODS:This QI initiative took place at Children's Minnesota, a large, not-for-profit children's health care organization. A multidisciplinary bronchiolitis workgroup developed a local clinical guideline and order-set. Delays in obtaining baseline data prompted a pediatric hospitalist and information technology specialist to modify a vendor's dashboard to display data related to bronchiolitis guideline metrics. Patients 2 months to 2 years old with a bronchiolitis emergency department (ED)/inpatient encounter in the period October 1, 2014 to April 30, 2018 were included. The primary outcome was a functioning dashboard; a process measure was the percentage of ED clinician logins. Outcome measures included the percent use of guideline metrics (e.g., bronchodilators) displayed on statistical process control charts (ED vs. inpatient). Balancing measures included length of stay, charge ratios, and hospital revisits. RESULTS:A workgroup (formed October 2015) implemented a bronchiolitis order-set and guideline (February 2016) followed by a bronchiolitis dashboard (August 2016) consolidating disparate data sources loaded within 2 to 4 days of discharge. In total, 35% of ED clinicians logged in. Leaders used the dashboard to target and track interventions such as a bronchodilator order alert. There were improvements in most outcome metrics; however, timing did not suggest direct dashboard impact. ED balancing measures were lower after implementation. CONCLUSION:We described use of a dashboard to support a multifaceted QI initiative for bronchiolitis. Leaders used the dashboard for targeted interventions but the dashboard did not directly impact the observed improvements. Future studies should assess reasons for low individual dashboard use.
| S-EPMC6402943 | biostudies-literature
Project description:A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other types of longitudinal data. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal bulk cell population (bulk time course) data. We demonstrate that the explicit inclusion of the phenotypic composition estimate, derived from single cell RNA-sequencing data (scRNA-seq), improves accuracy in the prediction of new treatments with a concordance correlation coefficient (CCC) of 0.92 compared to a prediction accuracy of CCC = 0.64 when fitting on longitudinal bulk cell population data alone. To our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with bulk time-course data to jointly calibrate a mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multiple data types into mathematical models to develop optimized treatment regimens from data.
| S-EPMC8156495 | biostudies-literature
Project description:In many social science majors, e.g., psychology, students report high levels of statistics anxiety. However, these majors are often chosen by students who are less prone to mathematics and who might have experienced difficulties and unpleasant feelings in their mathematics courses at school. The present study investigates whether statistics anxiety is a genuine form of anxiety that impairs students' achievements or whether learners mainly transfer previous experiences in mathematics and their anxiety in mathematics to statistics. The relationship between mathematics anxiety and statistics anxiety, their relationship to learning behaviors and to performance in a statistics examination were investigated in a sample of 225 undergraduate psychology students (164 women, 61 men). Data were recorded at three points in time: At the beginning of term students' mathematics anxiety, general proneness to anxiety, school grades, and demographic data were assessed; 2 weeks before the end of term, they completed questionnaires on statistics anxiety and their learning behaviors. At the end of term, examination scores were recorded. Mathematics anxiety and statistics anxiety correlated highly but the comparison of different structural equation models showed that they had genuine and even antagonistic contributions to learning behaviors and performance in the examination. Surprisingly, mathematics anxiety was positively related to performance. It might be that students realized over the course of their first term that knowledge and skills in higher secondary education mathematics are not sufficient to be successful in statistics. Part of mathematics anxiety may then have strengthened positive extrinsic effort motivation by the intention to avoid failure and may have led to higher effort for the exam preparation. However, via statistics anxiety mathematics anxiety also had a negative contribution to performance. Statistics anxiety led to higher procrastination in the structural equation model and, therefore, contributed indirectly and negatively to performance. Furthermore, it had a direct negative impact on performance (probably via increased tension and worry in the exam). The results of the study speak for shared but also unique components of statistics anxiety and mathematics anxiety. They are also important for instruction and give recommendations to learners as well as to instructors.
| S-EPMC5522867 | biostudies-other
Project description:ObjectivesFace-to-face mindfulness interventions have been shown to significantly decrease perceived stress, anxiety and depression and research is beginning to show similar benefits for such courses delivered via the internet. We investigated the feasibility and effectiveness of an online mindfulness course for perceived stress, anxiety and depression.DesignA follow-up investigation of an online mindfulness course. Previous research examining the change in perceived stress showed promising results. Measures of anxiety and depression were added to the online mindfulness course and these were investigated as well as perceived stress using a new, larger sample.ParticipantsParticipants (N=273) were self-referrals to the online course who completed the outcome measure immediately before the course, upon course completion and at 1 month follow-up.InterventionThe programme consists of 10 sessions, guided meditation videos and automated emails, with elements of Mindfulness-Based Stress Reduction and Mindfulness-Based Cognitive Therapy, completed at a pace to suit the individual (minimum length 4 weeks).Primary and secondary outcome measuresThe Perceived Stress Scale, the Generalised Anxiety Disorder Assessment-7 and the Patient Health Questionnaire-9 (for depression). Mindfulness practice was self-reported at automated time points upon login, once the exercises and sessions for each week were completed.ResultsPerceived stress, anxiety and depression significantly decreased at course completion and further decreased at 1 month follow-up, with effect sizes comparable to those found with face-to-face and other online mindfulness courses and to other types of intervention, such as cognitive behavioural therapy for stress. The amount of meditation practice reported did affect outcome when controlling for baseline severity.ConclusionsThe online mindfulness course appears to be an acceptable, accessible intervention which reduces stress, anxiety and depression. However, there is no control comparison and future research is required to assess the effects of the course for different samples.
| S-EPMC3845392 | biostudies-literature
Project description:BackgroundOnline pharmacies have grown significantly in recent years, from US $29.35 billion in 2014 to an expected US $128 billion in 2023 worldwide. Although legitimate online pharmacies (LOPs) provide a channel of convenience and potentially lower costs for patients, illicit online pharmacies (IOPs) open the doors to unfettered access to prescription drugs, controlled substances (eg, opioids), and potentially counterfeits, posing a dramatic risk to the drug supply chain and the health of the patient. Unfortunately, we know little about IOPs, and even identifying and monitoring IOPs is challenging because of the large number of online pharmacies (at least 30,000-35,000) and the dynamic nature of the online channel (online pharmacies open and shut down easily).ObjectiveThis study aims to increase our understanding of IOPs through web data traffic analysis and propose a novel framework using referral links to predict and identify IOPs, the first step in fighting IOPs.MethodsWe first collected web traffic and engagement data to study and compare how consumers access and engage with LOPs and IOPs. We then proposed a simple but novel framework for predicting the status of online pharmacies (legitimate or illicit) through the referral links between websites. Under this framework, we developed 2 prediction models, the reference rating prediction method (RRPM) and the reference-based K-nearest neighbor.ResultsWe found that direct (typing URL), search, and referral are the 3 major traffic sources, representing more than 95% traffic to both LOPs and IOPs. It is alarming to see that direct represents the second-highest traffic source (34.32%) to IOPs. When tested on a data set with 763 online pharmacies, both RRPM and R2NN performed well, achieving an accuracy above 95% in their predictions of the status for the online pharmacies. R2NN outperformed RRPM in full performance metrics (accuracy, kappa, specificity, and sensitivity). On implementing the 2 models on Google search results for popular drugs (Xanax [alprazolam], OxyContin, and opioids), they produced an error rate of only 7.96% (R2NN) and 6.20% (RRPM).ConclusionsOur prediction models use what we know (referral links) to tackle the many unknown aspects of IOPs. They have many potential applications for patients, search engines, social media, payment companies, policy makers or government agencies, and drug manufacturers to help fight IOPs. With scarce work in this area, we hope to help address the current opioid crisis from this perspective and inspire future research in the critical area of drug safety.
| S-EPMC7479587 | biostudies-literature
Project description:The 2019-2020 global pandemic has been caused by a disease called coronavirus disease 2019 (COVID-19). This disease has been caused by the Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2). By April 30 2020, the World Health Organization reported 3,096,626 cases and 217,896 deaths, which implies an exponential growth for infection and deaths worldwide. Currently, there are various computer-based approaches that present COVID-19 data through different types of charts, which is very useful to recognise its behavior and trends. Nevertheless, such approaches do not allow for observation of any projection regarding confirmed cases and deaths, which would be useful to understand the trends of COVID-19. In this work, we have designed and developed an online dashboard that presents actual information about COVID-19. Furthermore, based on this information, we have designed a mathematical model in order to make projections about the evolution of cases and deaths worldwide and by country.
| S-EPMC7450472 | biostudies-literature
Project description:The immune system consists of many specialized cell populations that communicate with each other to achieve systemic immune responses. Our analyses of various measured immune cell population frequencies in healthy humans and their responses to diverse stimuli show that human immune variation is continuous in nature, rather than characterized by discrete groups of similar individuals. We show that the same three key combinations of immune cell population frequencies can define an individual's immunotype and predict a diverse set of functional responses to cytokine stimulation. We find that, even though interindividual variations in specific cell population frequencies can be large, unrelated individuals of younger age have more homogeneous immunotypes than older individuals. Across age groups, cytomegalovirus seropositive individuals displayed immunotypes characteristic of older individuals. The conceptual framework for defining immunotypes suggested by our results could guide the development of better therapies that appropriately modulate collective immunotypes, rather than individual immune components.
| S-EPMC5544312 | biostudies-literature
Project description:Massive Open Online Course (MOOC) platforms incorporate large course catalogs from which individual students may register multiple courses. We performed a network-based analysis of student achievement, considering how course-course interactions may positively or negatively affect student success. Our data set included 378,000 users and 1,000,000 unique registration events in France Université Numérique (FUN), a national MOOC platform. We adapt reliability theory to model certificate completion rates with a Weibull survival function, following the intuition that students "survive" in a course for a certain time before stochastically dropping out. Course-course interactions are found to be well described by a single parameter for user engagement that can be estimated from a user's registration profile. User engagement, in turn, correlates with certificate rates in all courses regardless of specific content. The reliability approach is shown to capture several certificate rate patterns that are overlooked by conventional regression models. User engagement emerges as a natural metric for tracking student progress across demographics and over time.
| S-EPMC7822273 | biostudies-literature