Interaction Patterns in Korea Massive Open Online Course (K-MOOC) through a Case Study

2017 ◽  
Vol 23 (04) ◽  
pp. 735-762 ◽  
Author(s):  
Sangeun Nam ◽  
KyooLak Cho
Keyword(s):  
Online Course ◽  
Interaction Patterns ◽  
Massive Open Online ◽  
Massive Open Online Course

scholarly journals Oral production skills and pronunciation improvement in MOOCs for migrants and refugees: an unavoidable challenge

2021 ◽  
Vol 13 (2) ◽  
Author(s):  
Victoria Marrero-Aguiar
Keyword(s):  
Online Course ◽  
Massive Open Online ◽  
Oral Production ◽  
Massive Open Online Course ◽  
Oral Skills

This article is focused on the challenges posed by the development of oral production skills (speaking, pronunciation) in a Massive Open Online Course (MOOC), a resource that is totally conditioned by the technologies and has very limited posibilities for individual adaptation. First of all, the difficulties that this goal poses are reviewed and confronted with some successful precedents that show how to deal with those challenges. Next, we present a case study in which some strategies and resources have been used to develop oral skills and improve pronunciation in technologically mediated environments, an Spanish L-MOOC for migrants and refugees, absolute beginners, developed at UNED (Spain).

scholarly journals Utilizing Text Mining and Feature-Sentiment-Pairs to Support Data-Driven Design Automation Massive Open Online Course

2021 ◽  
Vol 16 (01) ◽  
pp. 134
Author(s):  
Nasa Zata Dina ◽  
Riky Tri Yunardi ◽  
Aji Akbar Firdaus
Keyword(s):  
Text Mining ◽  
Design Automation ◽  
Online Reviews ◽  
User Preferences ◽  
User Preference ◽  
Online Course ◽  
Massive Open Online ◽  
Massive Open Online Course ◽  
Related Design

This study aimed to develop a case-based design framework to analyze online us-er reviews and understanding the user preferences in a Massive Open Online Course (MOOC) content-related design. Another purpose was to identify the fu-ture trends of MOOC content-related design. Thus, it was an effort to achieve da-ta-driven design automation. This research extracts pairs of keywords which are later called Feature-Sentiment-Pairs (FSPs) using text mining to identify user preferences. Then the user preferences were used as features of an MOOC content-related design. An MOOC case study is used to implement the proposed framework. The online reviews are collected from www.coursera.org as the MOOC case study. The framework aims to use these large scale online review data as qualitative data and converts them into quantitative meaningful infor-mation, especially on content-related design so that the MOOC designer can de-cide better content based on the data. The framework combines the online re-views, text mining, and data analytics to reveal new information about users’ preference of MOOC content-related design. This study has applied text mining and specifically utilizes FSPs to identify user preferences in the MOOC content-related design. This framework can avoid the unwanted features on the MOOC content-related design and also speed up the identification of user preference.

scholarly journals Data Collection Approaches to Enable Evaluation of a Massive Open Online Course About Data Science for Continuing Education in Health Care: Case Study

10.2196/10982 ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. e10982 ◽  
Author(s):  
Abrar Alturkistani ◽  
Azeem Majeed ◽  
Josip Car ◽  
David Brindley ◽  
Glenn Wells ◽  
...  
Keyword(s):  
Health Care ◽  
Data Collection ◽  
Continuing Education ◽  
Data Science ◽  
Online Course ◽  
Massive Open Online ◽  
Massive Open Online Course

scholarly journals Towards Adaptive Gamification: A Method Using Dynamic Player Profile and a Case Study

2022 ◽  
Vol 12 (1) ◽  
pp. 486
Author(s):  
Inmaculada Rodríguez ◽  
Anna Puig ◽  
Àlex Rodríguez
Keyword(s):  
Adaptive Method ◽  
Online Course ◽  
Preliminary Evaluation ◽  
Massive Open Online ◽  
Initial Information ◽  
Massive Open Online Course ◽  
Different Types ◽  
The One ◽  
Game Elements

The design of gamified experiences following the one-fits-all approach uses the same game elements for all users participating in the experience. The alternative is adaptive gamification, which considers that users have different playing motivations. Some adaptive approaches use a (static) player profile gathered at the beginning of the experience; thus, the user experience fits this player profile uncovered through the use of a player type questionnaire. This paper presents a dynamic adaptive method which takes players’ profiles as initial information and also considers how these profiles change over time based on users’ interactions and opinions. Then, the users are provided with a personalized experience through the use of game elements that correspond to their dynamic playing profile. We describe a case study in the educational context, a course integrated on Nanomoocs, a massive open online course (MOOC) platform. We also present a preliminary evaluation of the approach by means of a simulator with bots that yields promising results when compared to baseline methods. The bots simulate different types of users, not so much to evaluate the effects of gamification (i.e., the completion rate), but to validate the convergence and validity of our method. The results show that our method achieves a low error considering both situations: when the user accurately (Err = 0.0070) and inaccurately (Err = 0.0243) answers the player type questionnaire.

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