A Systematic Literature Review on the Quality of MOOCs

MOOC-based blended learning for knowledge translation capacity-building: A qualitative evaluative study

This qualitative study investigated the effectiveness of blended learning using MOOCs (massive open online courses) for capacity-building in knowledge translation (KT). The evaluation followed Kirkpatrick's updated model. A total of 23 semi-directed interviews were conducted with participants working at a research centre in Côte d'Ivoire, with a first wave of interviews immediately post-training and a second wave after five months. Results showed that the training met learners' needs, with both the content and teaching format being deemed appropriate. Learners reacted positively to face-to-face activities and affirmed the importance of coaching for putting learning into practice. Specific KT skills and principles appeared to have been acquired, such as a procedure for structuring the KT process and improved skills for communicating and presenting scientific knowledge. Five months after the training, encouraging changes were reported, but the sustainability of the new KT practices remained uncertain. KT capacity-building initiatives in low- and middle-income countries struggle to meet demand. Little is known about effective KT training in that context, and even less in non-anglophone countries. The study presented here contributes to the understanding of success factors from the learners' standpoint.

MOOCs Design: A Conceptual Framework for Continuous Teacher Training in Portugal

This paper aims to present a conceptual framework for Massive Open Online Courses (MOOCs) design with regard to continuous teacher training, where a mixed methodology research approach was used. This methodology was structured in two consecutive phases: The first phase adopted a sequential exploratory strategy, where a scoping literature review approach was applied, and analysis content techniques were used to map and analyze the key dimensions in the design of MOOCs. The second phase was based on the concurrent triangulation strategy, where the quantitative data were extracted from 103 questionnaires and the qualitative data were obtained from two mini focus group interviews, which contributed to the development of the framework. Based on the data collected in phase 2, we proposed a framework which is structured in three main dimensions and ten subdimensions: (i) Resources—Human and Technological infrastructure; (ii) Design—Course overview, Target learners, Pedagogical approaches, Goals, Learning materials, content and activities and Assessment activities; and (iii) Organization and monitoring—Accreditation and Data monitoring and evaluation. This paper contributes to the actual state of the art in MOOCs design given the inexistence of frameworks for such courses in the specific case of continuous teacher training, and it shows the importance of accreditation recognition by the Portuguese entities.

Learning from each other in the COVID-19 pandemic

The increase in cases of coronavirus disease 2019 (COVID-19) worldwide has been paralleled by increasing information, and misinformation. Accurate public health messaging is essential to counter this, but education may also have a role. Early in the outbreak, The London School of Hygiene & Tropical Medicine partnered with FutureLearn to develop a massive open online course (MOOC) on COVID-19. Our approach was grounded in social constructivism, supporting participation, sharing uncertainties, and encouraging discussion. The first run of the course included over 200,000 participants from 184 countries, with over 88,000 comments at the end of the three-week run. Many participants supported each other's learning in their responses and further questions. Our experience suggests that open education can complement traditional messaging, potentially providing a sustainable approach to countering the spread of misinformation in public health.

Study on the university students' satisfaction of the wisdom tree massive open online course platform based on parameter optimization intelligent algorithm

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.