scholarly journals Analyzing the Sentiment of MOOC Discussion Posts

2019 ◽  
Vol 2 (2) ◽  
pp. 1-2
Author(s):  
Haniya Ahmed ◽  
Kenny Wong
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Online Course ◽  
Massive Open Online ◽  
Massive Open Online Course ◽  
Positive Sentiment ◽  
Negative Sentiment

The purpose of the project is to identify common difficulties that learners may face and to understand their emotions as they progress through MOOCs. MOOC is an abbreviation for the Massive Open Online Course and the research deals with the data from ten different courses from Coursera. The data is used to extract pieces of text that students have made. Then, those certain texts are required to be sent to Google Cloud Natural Language API. This app allows users to get a sentiment analysis of a text. The main goal is to assist instructors with monitoring MOOC to make it more efficient and easier for students to progress since it assists to improve the courses.  To achieve this, the first step is to gather all the data from each of the courses. Then use programming to dump all that data into one big database. The program that is used here is called Pycharm and user is required to use python and sql to aid him in dumping the data in the database. Once the database is created, coding is done to only select out the pieces of information that are needed. These texts should be where students make comments or ask questions. Next, the data is queried to send these texts to Google Cloud Natural Language API. Here, the program breaks down all the sentences to only be just words. Then the program is going to categorize each word according to whether its connotation is positive, negative or neutral. Next, all the words are sorted according to their connotations. The overall sentiment depends on the emotion that has the highest number. If positives and negatives are all balanced out then the sentiment is neutral. Sentiment scores range from -1 to 1, where -1 is the most negative, 1 is the most positive and anywhere near 0 is neutral.  Positive sentiment scores indicate instructors that students are doing well on their course and neutral sentiment scores indicate that the course is balanced out with difficulties and easy tasks. However, negative sentiment is the most important to instructors since it indicates them that students are struggling and they need to improve the course.

scholarly journals Teaching a Massive Open Online Course on Natural Language Processing

2021 ◽  
Author(s):  
Ekaterina Artemova ◽  
Murat Apishev ◽  
Denis Kirianov ◽  
Veronica Sarkisyan ◽  
Sergey Aksenov ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Online Course ◽  
Massive Open Online ◽  
Massive Open Online Course

sentiment analysis on massive open online course evaluation

2021 ◽  
Author(s):  
Xiaowei Yan ◽  
Guangmin Li ◽  
Qian Li ◽  
Jiejie Chen ◽  
Wenjing Chen ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Course Evaluation ◽  
Online Course ◽  
Massive Open Online ◽  
Massive Open Online Course

Valor dos massive open online course (MOOC) na educação para a cidadania

2018 ◽  
Vol 13 (2) ◽  
Author(s):  
Hermano Carmo ◽  
Teresa Maia e Carmo
Keyword(s):  
Online Courses ◽  
Massive Open Online Courses ◽  
Online Course ◽  
Massive Open Online ◽  
Massive Open Online Course ◽  
Tecnología Educativa

A sociedade contemporânea é marcada por três macrotendências que a identificam como uma sociedade singular na história humana: processo de mudança acelerada, desigualdade crescente e fibrilhação dos sistemas de poder. Tais tendências têm tido como efeitos um quadro de ameaças e oportunidades que tanto têm constituído gigantesco desafio aos sistemas educativos quanto configuram a urgência de ressocialização de todas as gerações vivas no sentido da construção de uma cidadania global. Nesse contexto, propõe-se um modelo que configura uma estratégia de educação para a cidadania, com dois eixos, quatro vertentes e dez áreas-chave. Seguidamente, descreve-se e discute-se a emergência quase explosiva dos Massive Open Online Courses (MOOC) a partir de instituições de ensino superior internacionalmente reconhecidas, no quadro do novo paradigma digital, sua diversidade e seu potencial ainda em aberto. Confrontando a nova abordagem educativa com o modelo de educação para a cidadania proposto, conclui-se constituir um meio robusto para o potenciar.Palavras-chave:Conjuntura. Macrotendências. Educação para a cidadania. MOOC. Tecnologia educativa. Paradigma digital.Link: http://revista.ibict.br/inclusao/article/view/4171/3642

scholarly journals Digital Education on Prudent Antibiotic Use—Evaluation of a Massive Open Online Course for General Practitioners in Germany

2020 ◽  
Vol 41 (S1) ◽  
pp. s192-s193
Author(s):  
Florian Salm ◽  
Tobias Kramer ◽  
Cornelius Remschmidt ◽  
Petra Gastmeier ◽  
Sandra Schneider
Keyword(s):  
Antibiotic Therapy ◽  
General Practitioners ◽  
Antibiotic Use ◽  
Background Information ◽  
Online Course ◽  
Massive Open Online ◽  
Questionnaire Response ◽  
Face To Face ◽  
Massive Open Online Course ◽  
Assessment Questionnaire

Background: Antimicrobial resistance is a growing global health problem predominantly driven by overuse of antibiotics. In humans, most antibiotics are used outside the hospital. Overprescribing for acute respiratory infections (ARIs) is common despite clear guidelines. The need for further training of general practitioners is well known. Objective: To develop and evaluate a massive open online course (MOOC) on antibiotic therapy of common infectious diseases in general practice. Methods: A 4-week MOOC was developed on the basis of previous face-to-face trainings (platform, Hasso Plattner Institute for Digital Engineering) and was conducted 3 times between July 10, 2017, and May 31, 2019. The course was promoted through various general practitioner (GP) networks, local multipliers, and conferences and in the local trade press. In addition to epidemiological background information, the focus was on guideline-based diagnostics and treatment of ARI, side effects of antibiotics, correct drug selection, dosage and duration of indicated antibiotic therapy, as well as aspects of doctor–patient communication. Content included videos, self-tests, additional written material, and an optional exam. At the end of the course, participants were asked to complete a voluntary, anonymous online assessment questionnaire (LimeSurveyPro software). Usage data from the MOOC platform and data from the questionnaire were analyzed using IBM SPSS statistical software. Results: In total, 2,177 registered persons retrieved content (= learners). The proportion of learners dropped from 99.6% in week 1 to 40.7% in week 4. However, among those attending week 4, the average proportion of content used was still high (74.5%). Furthermore, 27.5% of learners completed the course, 23.8% took the exam, and 19.7% passed the exam. Moreover, 284 learners answered the assessment questionnaire (response rate, 13.0%); 62.3% were women, and the mean age was 45.9 years. Also, 225 participants (79.2%) stated that they were physicians; 122 of these worked as general practitioners (54.2% of physicians). Among the other physicians, 23% stated were in specialist training and 15.6% had a different specialist designation. The average overall rating of the course was 1.31 (1 = very good to 6 = not sufficient). General practitioners rated it slightly better than other physicians (1.23 vs 1.41). The clinical relevance was rated at 1.27 (GPs vs other physicians, 1.18 vs 1.35). For all scores, see Table 1. Conclusions: A massive open online course appears to be an appropriate format in which to deliver clinical relevant content concerning prudent antibiotic use in the outpatient setting. It is a good complement to existing face-to-face formats and helps to cover needs related to antibiotic training.Funding: NoneDisclosures: None

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