ABSTRACT
Objective: To assess the role of learning portfolio in structuring daily routine of medical students in lockdown period by employing Gibbs model of reflection. Methodology: This cross sectional study was conducted in CMH Kharian Medical College from July to August, 2020. Data were collected from 2nd year MBBS students. Paper version of the Questionnaire was distributed based on Gibbs model of reflection. Students responses were recorded based on Likert scale (agree, disagree and neutral. Results: Out of 100 students, 96 responded. Out of these, 71% students felt motivated by mentor's feedback, 62% were of opinion that reflective writing provided them with a summary that can be used as a rapid revision tool later, 60% felt that learning portfolio helped to structure their working days better. Learning portfolio helped 58.56% students to track learning progress against defined learning objectives and 55.68% were of opinion that this learning assessment modality can be continued for other sessions. We found that 51.84% students agreed that keeping the learning portfolio updated fostered reflective thinking process. Conclusion: Incorporation of learning portfolios in early years of medical education can work as efficient reflective tools that facilitate the learner to structure daily working routine systematically. With feedback, this learning modality motivates the learner in a better way by identifying their shortcomings at an earlier stage.
ABSTRACT
In December 2019, a highly contagious disease, Coronavirus disease 2019 (COVID-19) was first detected in Wuhan, China. The disease has spread to 212 countries and territories worldwide. While this epidemic has continued to infect millions of people, several nations have resorted to complete lockdowns. People took social networks during this shutdown to share their opinions, feelings, and find a way to calm down. This study proposed a US-based sentiment analysis of the tweets using machine learning and the lexicon analysis approach. This US-based tweets dataset was collected by RStudio software from 30 January 2020 to 10th May 2020, contains 11858 tweets. We find the label corresponding to each tweet using TextBlob, that is to say, positive, negative, or neutral. To clean up the facts we pre-process the tweets. In a later step, different feature techniques such as bag-of-words (BoW) and term frequency-inverse document frequency (TF-IDF) are used to preserve expressive information. Finally, the random forest, gradient boosting machine, extra tree classifier, logistic regression, and support vector machine models are used to categorize beliefs as being positive, negative, or neutral. Our suggested pipeline output is assessed using accuracy, precision, recall F1 score. This research study shows how TF-IDF features can increase the performance of the supervised machine learning models and in this work, the gradient boosting machine outperforms the others and achieves high accuracy of 96% when paired with TF-IDF features. This analysis was done to analyze how the situation is being handled by citizens of the United States. The results of the experiments validate the approach's effectiveness. © 2021 IEEE.