A Study on Decision-Making Factors of University Students to Intention to Accept Massive Open Online Course (MOOC) - Focused on Intention to Accept MOOC in Extracurricular and Curricular (Flipped Learning) Domains -

2017 ◽  
Vol 13 ◽  
pp. 7-31
Author(s):  
Lee Jeong Ki ◽  
Kim Hyo Eun ◽  
Ji Hyuk Joo
2021 ◽  
Vol 104 (3_suppl) ◽  
pp. 003685042110542
Author(s):  
Chou-Yuan Lee ◽  
Ling-Ming Ruan ◽  
Zne-Jung Lee ◽  
Jian-Qiong Huang ◽  
Jie Yao ◽  
...  

Introduction: Curriculum learning through the wisdom tree massive open online course platform not only gets rid of the limitations of specialty, school and region, eliminates the limitations of time and space in traditional teaching, but also effectively solves the problem of educational equity. Objectives: This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. Methods: This study takes the university students in Fuzhou city information management department as the survey object, and adopts the electronic questionnaire survey method. A total of 1136 formal questionnaires were responded, and 1028 valid questionnaires were obtained after data cleaning and deleting invalid questionnaires (the effective rate was 90.49%). In this paper, the reliability and validity of the questionnaire were tested by IBM SPSS-20.0 software, and six explanatory variables including function, achievement, exercise, quality, richness, and interaction were obtained by principal component analysis. Then, the questionnaire data is converted to CSV (comma separated values) format for analysis. This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. In this paper, the proposed algorithm is compared with decision tree, random forest, k-nearest neighbor, and support vector machine to verify its performance. Results: The experimental results show that training set classification accuracy of decision tree, random forest, k-nearest neighbor, only support vector machine and the proposed algorithm (simulated annealing + support vector machine) are 92.21%, 96.10%, 95.67%, 97.29%, and 99.58%, respectively. Conclusion: The proposed algorithm simulated annealing + support vector machine does increase the classification accuracy. At the same time, the 11 decision rules generated by simulated annealing + decision tree can provide useful information for decision makers.


Author(s):  
Hermano Carmo ◽  
Teresa Maia e Carmo

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


2020 ◽  
Vol 41 (S1) ◽  
pp. s192-s193
Author(s):  
Florian Salm ◽  
Tobias Kramer ◽  
Cornelius Remschmidt ◽  
Petra Gastmeier ◽  
Sandra Schneider

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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