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Effectiveness of Using Voice Assistants in Learning: A Study at the Time of COVID-19.

ABSTRACT: The use of advanced learning technologies in a learning management system (LMS) can greatly assist learning processes, especially when used in university environments, as they promote the development of Self-Regulated learning, which increases academic performance and student satisfaction towards personal learning. One of the most innovative resources that an LMS may have is an Intelligent Personal Assistant (IPA). We worked with a sample of 109 third-grade students following Health Sciences degrees. The aims were: (1) to verify whether there will be significant differences in student access to the LMS, depending on use versus non-use of an IPA. (2) To verify whether there will be significant differences in student learning outcomes depending on use versus non-use of an IPA. (3) To verify whether there will be significant differences for student satisfaction with teaching during the COVID-19 pandemic, depending on use versus non-use of an IPA. (4) To analyze student perceptions of the usefulness of an IPA in the LMS. We found greater functionality in access to the LMS and satisfaction with teaching, especially during the health crisis, in the group of students who had used an IPA. However, both the expansion of available information and the usability of the features embedded in an IPA are still challenging issues.

SUBMITTER: Saiz-Manzanares MC

PROVIDER: S-EPMC7432376 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Effectiveness of Using Voice Assistants in Learning: A Study at the Time of COVID-19.

Sáiz-Manzanares María Consuelo MC Marticorena-Sánchez Raúl R Ochoa-Orihuel Javier J

International journal of environmental research and public health 20200804 15

The use of advanced learning technologies in a learning management system (LMS) can greatly assist learning processes, especially when used in university environments, as they promote the development of Self-Regulated learning, which increases academic performance and student satisfaction towards personal learning. One of the most innovative resources that an LMS may have is an Intelligent Personal Assistant (IPA). We worked with a sample of 109 third-grade students following Health Sciences deg ...[more]

PMID: 32759832

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Project description:BackgroundVoice assistants (VAs) are devices that respond to human voices and can be commanded to do a variety of tasks. Nowadays, VAs are being used to obtain health information, which has become a critical point of analysis for researchers in terms of question understanding and quality of response. Particularly, the COVID-19 pandemic has and still is severely affecting people worldwide, which demands studies on how VAs can be used as a tool to provide useful information.ObjectiveThis work aimed to perform a quality analysis of different VAs' responses regarding the actual and important subject of COVID-19 vaccines. We focused on this important subject since vaccines are now available and society has urged for the population to be rapidly immunized.MethodsThe proposed study was based on questions that were collected from the ofﬁcial World Health Organization website. These questions were submitted to the 5 dominant VAs (Alexa, Bixby, Cortana, Google Assistant, and Siri), and responses were evaluated according to a rubric based on the literature. We focused this study on the Portuguese language as an additional contribution, since previous works are mainly focused on the English language, and we believe that VAs cannot be optimized to foreign languages.ResultsResults showed that Google Assistant has a better overall performance, and only this VA and Samsung Bixby achieved high scores on question understanding in the Portuguese language. Regarding the obtained answers, the study also showed the best Google Assistant overall performance.ConclusionsUnder the urgent context of COVID-19 vaccination, this work can help to understand how VAs must be improved to be more useful to the society and how careful people must be when considering VAs as a source of health information. VAs have been demonstrated to perform well regarding comprehension and user-friendliness. However, this work has found that they must be better integrated to their information sources to be useful as health information tools.

Project description:BackgroundIn Japan, individuals with mild COVID-19 illness previously required to be monitored in designated areas and were hospitalized only if their condition worsened to moderate illness or worse. Daily monitoring using a pulse oximeter was a crucial indicator for hospitalization. However, a drastic increase in the number of patients resulted in a shortage of pulse oximeters for monitoring. Therefore, an alternative and cost-effective method for monitoring patients with mild illness was required. Previous studies have shown that voice biomarkers for Parkinson disease or Alzheimer disease are useful for classifying or monitoring symptoms; thus, we tried to adapt voice biomarkers for classifying the severity of COVID-19 using a dynamic time warping (DTW) algorithm where voice wavelets can be treated as 2D features; the differences between wavelet features are calculated as scores.ObjectiveThis feasibility study aimed to test whether DTW-based indices can generate voice biomarkers for a binary classification model using COVID-19 patients' voices to distinguish moderate illness from mild illness at a significant level.MethodsWe conducted a cross-sectional study using voice samples of COVID-19 patients. Three kinds of long vowels were processed into 10-cycle waveforms with standardized power and time axes. The DTW-based indices were generated by all pairs of waveforms and tested with the Mann-Whitney U test (α<.01) and verified with a linear discrimination analysis and confusion matrix to determine which indices were better for binary classification of disease severity. A binary classification model was generated based on a generalized linear model (GLM) using the most promising indices as predictors. The receiver operating characteristic curve/area under the curve (ROC/AUC) validated the model performance, and the confusion matrix calculated the model accuracy.ResultsParticipants in this study (n=295) were infected with COVID-19 between June 2021 and March 2022, were aged 20 years or older, and recuperated in Kanagawa prefecture. Voice samples (n=110) were selected from the participants' attribution matrix based on age group, sex, time of infection, and whether they had mild illness (n=61) or moderate illness (n=49). The DTW-based variance indices were found to be significant (P<.001, except for 1 of 6 indices), with a balanced accuracy in the range between 79% and 88.6% for the /a/, /e/, and /u/ vowel sounds. The GLM achieved a high balance accuracy of 86.3% (for /a/), 80.2% (for /e/), and 88% (for /u/) and ROC/AUC of 94.8% (95% CI 90.6%-94.8%) for /a/, 86.5% (95% CI 79.8%-86.5%) for /e/, and 95.6% (95% CI 92.1%-95.6%) for /u/.ConclusionsThe proposed model can be a voice biomarker for an alternative and cost-effective method of monitoring the progress of COVID-19 patients in care.

Project description:BackgroundVoice assistants allow users to control appliances and functions of a smart home by simply uttering a few words. Such systems hold the potential to significantly help users with motor and cognitive disabilities who currently depend on their caregiver even for basic needs (eg, opening a door). The research on voice assistants is mainly dedicated to able-bodied users, and studies evaluating the accessibility of such systems are still sparse and fail to account for the participants' actual motor, linguistic, and cognitive abilities.ObjectiveThe aim of this work is to investigate whether cognitive and/or linguistic functions could predict user performance in operating an off-the-shelf voice assistant (Google Home).MethodsA group of users with disabilities (n=16) was invited to a living laboratory and asked to interact with the system. Besides collecting data on their performance and experience with the system, their cognitive and linguistic skills were assessed using standardized inventories. The identification of predictors (cognitive and/or linguistic) capable of accounting for an efficient interaction with the voice assistant was investigated by performing multiple linear regression models. The best model was identified by adopting a selection strategy based on the Akaike information criterion (AIC).ResultsFor users with disabilities, the effectiveness of interacting with a voice assistant is predicted by the Mini-Mental State Examination (MMSE) and the Robertson Dysarthria Profile (specifically, the ability to repeat sentences), as the best model shows (AIC=130.11).ConclusionsUsers with motor, linguistic, and cognitive impairments can effectively interact with voice assistants, given specific levels of residual cognitive and linguistic skills. More specifically, our paper advances practical indicators to predict the level of accessibility of speech-based interactive systems. Finally, accessibility design guidelines are introduced based on the performance results observed in users with disabilities.

Project description:Abstract Background Comparative effectiveness of coronavirus disease 2019 (COVID-19) vaccines across patient subgroups is poorly understood and essential to precisely targeting vaccination strategies. Methods We used the US Department of Veterans Affairs COVID-19 Shared Data Resource to identify veterans who utilize VA health care and had no documented severe acute respiratory syndrome coronavirus 2 infection before December 11, 2020. Using a test-negative case–control design (TND), we used conditional logistic regression with adjustment for covariates to estimate vaccine effectiveness (VE) over time for veterans who received 2 doses of mRNA vaccines or 1 dose of Ad26.Cov2.S. Results We identified 4.8 million veterans with a mean age of 64 years, of whom 58% had ≥1 chronic disease. Vaccine effectiveness for symptomatic infections, hospitalizations, and ICU admission or death declined over time and varied by the type of vaccine (P < 0.01). VE estimates against symptomatic infection during months 1 and 7 for mRNA-1273 compared with BNT162b2 were 89.7% (95% CI, 84.4%–93.0%) and 57.3% (95% CI, 48.4%–64.7%) vs 81.6% (95% CI, 75.9%–85.9%) and 22.5% (95% CI, 7.2%–35.2%) for individuals age <65 years and 78.4% (95% CI, 71.1%–83.9%) and 36.2% (95% CI, 27.7%–43.6%) vs 66.3% (95% CI, 55.7%–74.4%) and −23.3% (95% CI, −40.5% to −8.2%) in subjects age ≥65 years; against hospitalization 92.0% (95% CI, 76.1%–97.3%) and 83.1% (95% CI, 66.8%–91.4%) vs 85.6% (95% CI, 72.6%–92.4%) and 57.0% (95% CI, 31.2%–73.2%) in subjects age <65 years and 66.1% (95% CI, 45.3%–79.0%) and 64.7% (95% CI, 55.2%–72.3%) vs 61.0% (95% CI, 41.3%–74.2%) and 1.7% (95% CI, −22.0% to 20.8%) in those age ≥65 years; against ICU admission or death 89.2% (95% CI, 49.5%–97.7%) and 84.4% (95% CI, 59.0%–94.1%) vs 87.6% (95% CI, 61.0%–96.1%) and 66.4% (95% CI, 7.7%–87.8%) in subjects age <65 years and 75.4% (95% CI, 51.7%–87.5%) and 73.8 (95% CI, 62.9%–81.5%) vs 67.4% (95% CI, 32.6%–84.3%) and 29.3% (95% CI, 2.3%–48.9%) in subjects age ≥65 years, respectively (Pinteraction < .01 for all comparisons). Similarly, mRNA-1273 was more effective than BNT162b2 in veterans with >1 chronic disease. Conclusions mRNA-1273 was more effective than BNT162b2 in older veterans and those with chronic diseases.

Project description:Many virological tests have been implemented during the Coronavirus Disease 2019 (COVID-19) pandemic for diagnostic purposes, but they appear unsuitable for screening purposes. Furthermore, current screening strategies are not accurate enough to effectively curb the spread of the disease. Therefore, the present study was conducted within a controlled clinical environment to determine eventual detectable variations in the voice of COVID-19 patients, recovered and healthy subjects, and also to determine whether machine learning-based voice assessment (MLVA) can accurately discriminate between them, thus potentially serving as a more effective mass-screening tool. Three different subpopulations were consecutively recruited: positive COVID-19 patients, recovered COVID-19 patients and healthy individuals as controls. Positive patients were recruited within 10 days from nasal swab positivity. Recovery from COVID-19 was established clinically, virologically and radiologically. Healthy individuals reported no COVID-19 symptoms and yielded negative results at serological testing. All study participants provided three trials for multiple vocal tasks (sustained vowel phonation, speech, cough). All recordings were initially divided into three different binary classifications with a feature selection, ranking and cross-validated RBF-SVM pipeline. This brough a mean accuracy of 90.24%, a mean sensitivity of 91.15%, a mean specificity of 89.13% and a mean AUC of 0.94 across all tasks and all comparisons, and outlined the sustained vowel as the most effective vocal task for COVID discrimination. Moreover, a three-way classification was carried out on an external test set comprised of 30 subjects, 10 per class, with a mean accuracy of 80% and an accuracy of 100% for the detection of positive subjects. Within this assessment, recovered individuals proved to be the most difficult class to identify, and all the misclassified subjects were declared positive; this might be related to mid and short-term vocal traces of COVID-19, even after the clinical resolution of the infection. In conclusion, MLVA may accurately discriminate between positive COVID-19 patients, recovered COVID-19 patients and healthy individuals. Further studies should test MLVA among larger populations and asymptomatic positive COVID-19 patients to validate this novel screening technology and test its potential application as a potentially more effective surveillance strategy for COVID-19.

Dataset Information

Effectiveness of Using Voice Assistants in Learning: A Study at the Time of COVID-19.

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Effectiveness of Using Voice Assistants in Learning: A Study at the Time of COVID-19.

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