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

2018 ◽  
Author(s):  
Abrar Alturkistani ◽  
Azeem Majeed ◽  
Josip Car ◽  
David Brindley ◽  
Glenn Wells ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Science ◽  
Evaluation Model ◽  
Evaluation Methodology ◽  
Online Course ◽  
Massive Open Online ◽  
Massive Open Online Course ◽  
Depth Analysis ◽  
Semistructured Interviews ◽  
Learner Perceptions

BACKGROUND This study presents learner perceptions of a pilot massive open online course (MOOC). OBJECTIVE The objective of this study was to explore data collection approaches to help inform future MOOC evaluations on the use of semistructured interviews and the Kirkpatrick evaluation model. METHODS A total of 191 learners joined 2 course runs of a limited trial of the MOOC. Moreover, 7 learners volunteered to be interviewed for the study. The study design drew on semistructured interviews of 2 learners transcribed and analyzed using Braun and Clark’s method for thematic coding. This limited participant set was used to identify how the Kirkpatrick evaluation model could be used to evaluate further implementations of the course at scale. RESULTS The study identified several themes that could be used for further analysis. The themes and subthemes include learner background (educational, professional, and topic significance), MOOC learning (learning achievement and MOOC application), and MOOC features (MOOC positives, MOOC negatives, and networking). There were insufficient data points to perform a Kirkpatrick evaluation. CONCLUSIONS Semistructured interviews for MOOC evaluation can provide a valuable in-depth analysis of learners’ experience of the course. However, there must be sufficient data sources to complete a Kirkpatrick evaluation to provide for data triangulation. For example, data from precourse and postcourse surveys, quizzes, and test results could be used to improve the evaluation methodology.

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 Nursing assessment: the elaboration and development of a massive open online course

2020 ◽  
Vol 41 (spe) ◽  
Author(s):  
Cibele Duarte Parulla ◽  
Daniel Magno Galdino ◽  
Daiane Dal Pai ◽  
Karina de Oliveira Azzolin ◽  
Ana Luísa Petersen Cogo
Keyword(s):  
Distance Learning ◽  
Continuing Education ◽  
Health Professionals ◽  
Assessment Method ◽  
Online Course ◽  
Preliminary Assessment ◽  
Massive Open Online ◽  
Nursing Assessment ◽  
School Of Nursing ◽  
Massive Open Online Course

ABSTRACT Objective: Describing the stages of elaboration and development of a massive open online course on "Nursing Assessment". Method: Experience report of the construction of a free course, developed between 2015 and 2016 with the partnership of the School of Nursing and the Nucleus of Support to Distance Learning. The course was hosted on the Lúmina platform. Results: The construction of the course began in 2015 and the first edition was made available in September 2016, with 693 participants. The aim of the choice and elaboration of the material was to design an attractive and quality course for the community. The stages observed in its elaboration were choice of theme, course production, preliminary assessment and launching the first edition. Conclusions: The course has been shown to be a support for in-class teaching, as well as for the continuing education of health professionals.

scholarly journals Ein Data Engineering Kurs für 10.000 Teilnehmer

2020 ◽  
Author(s):  
Nicolas Alder ◽  
Tobias Bleifuß ◽  
Leon Bornemann ◽  
Felix Naumann ◽  
Tim Repke
Keyword(s):  
Data Science ◽  
Online Course ◽  
Massive Open Online ◽  
Massive Open Online Course ◽  
Data Engineering

Zusammenfassung Im Januar und Februar 2020 boten wir auf der openHPI Plattform einen Massive Open Online Course (MOOC) mit dem Ziel an, Nicht-Fachleute in die Begriffe, Ideen, und Herausforderungen von Data Science einzuführen. In über hundert kleinen Kurseinheiten erläuterten wir über sechs Wochen hinweg ebenso viele Schlagworte. Wir berichten über den Aufbau des Kurses, unsere Ziele, die Interaktion mit den Teilnehmerinnen und Teilnehmern und die Ergebnisse des Kurses.

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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