Dataset Information

A survival model for course-course interactions in a Massive Open Online Course platform.

ABSTRACT: Massive Open Online Course (MOOC) platforms incorporate large course catalogs from which individual students may register multiple courses. We performed a network-based analysis of student achievement, considering how course-course interactions may positively or negatively affect student success. Our data set included 378,000 users and 1,000,000 unique registration events in France Université Numérique (FUN), a national MOOC platform. We adapt reliability theory to model certificate completion rates with a Weibull survival function, following the intuition that students "survive" in a course for a certain time before stochastically dropping out. Course-course interactions are found to be well described by a single parameter for user engagement that can be estimated from a user's registration profile. User engagement, in turn, correlates with certificate rates in all courses regardless of specific content. The reliability approach is shown to capture several certificate rate patterns that are overlooked by conventional regression models. User engagement emerges as a natural metric for tracking student progress across demographics and over time.

SUBMITTER: Wintermute EH

PROVIDER: S-EPMC7822273 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A survival model for course-course interactions in a Massive Open Online Course platform.

Wintermute Edwin H EH Cisel Matthieu M Lindner Ariel B AB

PloS one 20210122 1

Massive Open Online Course (MOOC) platforms incorporate large course catalogs from which individual students may register multiple courses. We performed a network-based analysis of student achievement, considering how course-course interactions may positively or negatively affect student success. Our data set included 378,000 users and 1,000,000 unique registration events in France Université Numérique (FUN), a national MOOC platform. We adapt reliability theory to model certificate completion r ...[more]

PMID: 33481871

Similar Datasets

Project description:BACKGROUND:Massive open online courses (MOOCs) have the potential to make a broader educational impact because many learners undertake these courses. Despite their reach, there is a lack of knowledge about which methods are used for evaluating these courses. OBJECTIVE:The aim of this review was to identify current MOOC evaluation methods to inform future study designs. METHODS:We systematically searched the following databases for studies published from January 2008 to October 2018: (1) Scopus, (2) Education Resources Information Center, (3) IEEE (Institute of Electrical and Electronic Engineers) Xplore, (4) PubMed, (5) Web of Science, (6) British Education Index, and (7) Google Scholar search engine. Two reviewers independently screened the abstracts and titles of the studies. Published studies in the English language that evaluated MOOCs were included. The study design of the evaluations, the underlying motivation for the evaluation studies, data collection, and data analysis methods were quantitatively and qualitatively analyzed. The quality of the included studies was appraised using the Cochrane Collaboration Risk of Bias Tool for randomized controlled trials (RCTs) and the National Institutes of Health-National Heart, Lung, and Blood Institute quality assessment tool for cohort observational studies and for before-after (pre-post) studies with no control group. RESULTS:The initial search resulted in 3275 studies, and 33 eligible studies were included in this review. In total, 16 studies used a quantitative study design, 11 used a qualitative design, and 6 used a mixed methods study design. In all, 16 studies evaluated learner characteristics and behavior, and 20 studies evaluated learning outcomes and experiences. A total of 12 studies used 1 data source, 11 used 2 data sources, 7 used 3 data sources, 4 used 2 data sources, and 1 used 5 data sources. Overall, 3 studies used more than 3 data sources in their evaluation. In terms of the data analysis methods, quantitative methods were most prominent with descriptive and inferential statistics, which were the top 2 preferred methods. In all, 26 studies with a cross-sectional design had a low-quality assessment, whereas RCTs and quasi-experimental studies received a high-quality assessment. CONCLUSIONS:The MOOC evaluation data collection and data analysis methods should be determined carefully on the basis of the aim of the evaluation. The MOOC evaluations are subject to bias, which could be reduced using pre-MOOC measures for comparison or by controlling for confounding variables. Future MOOC evaluations should consider using more diverse data sources and data analysis methods. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):RR2-10.2196/12087.

Project description:BackgroundClinical training during the COVID-19 pandemic is high risk for medical students. Medical schools in low- and middle-income countries (LMIC) have limited capacity to develop resources in the face of rapidly developing health emergencies. Here, a free Massive Open Online Course (MOOC) was developed as a COVID-19 resource for medical students working in these settings, and its effectiveness was evaluated.MethodsThe RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework was utilized to evaluate the effectiveness of MOOC in teaching medical students about COVID-19. The data sources included the student registration forms, metrics quantifying their interactions within the modules, students' course feedback, and free-text responses. The data were collected from the Moodle learning management system and Google analytics from May 9 to September 15, 2020. The research team analyzed the quantitative data descriptively and the qualitative data thematically.ResultsAmong the 16,237 unique visitors who accessed the course, only 6031 medical students from 71 medical schools registered, and about 4993 (83% of registrants) completed the course, indicating high levels of satisfaction (M = 8.17, SD = 1.49) on a 10-point scale. The mean scores of each assessment modules were > 90%. The free-text responses from 987 unique students revealed a total of 17 themes (e.g., knowing the general information on COVID-19, process management of the pandemic in public health, online platform use, and instructional design) across the elements of the RE-AIM framework. Mainly, the students characterized the MOOC as well-organized and effective.ConclusionsMedical students learned about COVID-19 using a self-paced and unmonitored MOOC. MOOCs could play a vital role in the dissemination of accurate information to medical students in LMIC in future public health emergencies. The students were interested in using similar MOOCs in the future.

Project description:INTRODUCTION:The educational technology of massive open online courses (MOOCs) has been successfully applied in a wide variety of disciplines and are an intense focus of educational research at this time. Educators are now looking to MOOC technology as a means to improve professional medical education, but very little is known about how medical MOOCs compare with traditional content delivery. METHODS:A retrospective analysis of the course evaluations for the Medicine as a Business elective by fourth-year medical students at Southern Illinois University School of Medicine (SIU-SOM) for the 2012-2015 academic years was conducted. This course was delivered by small group flipped classroom discussions for 2012-2014 and delivered via MOOC technology in 2015. Learner ratings were compared between the two course delivery methods using routinely collected course evaluations. RESULTS:Course enrollment has ranged from 6-19 students per year in the 2012-2015 academic years. Student evaluations of the course are favorable in the areas of effective teaching, accurate course objectives, meeting personal learning objectives, recommending the course to other students, and overall when rated on a 5-point Likert scale. The majority of all student ratings (76-95%) of this elective course are for the highest possible choice (Strongly agree or Excellent) for any criteria, regardless if the course was delivered via a traditional or MOOC format. Statistical analysis of these ratings suggests that the Effective Teacher and Overall Evaluations did not statistically differ between the two delivery formats. DISCUSSION:Student ratings of this elective course were highly similar when delivered in a flipped classroom format or by using MOOC technology. The primary advantage of this new course format is flexibility of time and place for learners, allowing them to complete the course objectives when convenient for them. The course evaluations suggest this is a change that is acceptable to the target audience. CONCLUSIONS:This study suggests that learner evaluations of a fourth-year medical school elective course do not significantly differ when delivered by flipped classroom group discussions or via MOOC technology in a very small single center observational study. Further investigation is required to determine if this delivery method is an acceptable and effective means of teaching in the medical school environment.

Project description:IntroductionIncreasing number of Massive Open Online Courses (MOOCs) are being used to train learners at scale in various healthcare-related skills. However, many challenges in course delivery require further understanding, for example, factors exploring the reasons for high MOOC dropout rates, recorded low social interaction between learners and the lack of understanding of the impact of a course facilitators' presence in course engagement. There is a need to generate further evidence to explore these detriments to MOOC course delivery to enable enhanced course learning design. The proposed mixed-methods evaluation of the MOOC was determined based on the MOOC's aims and objectives and the methodological approaches used to evaluate this type of a course. The MOOC evaluation will help appraise the effectiveness of the MOOC in delivering its intended objectives. This protocol aims to describe the design of a study evaluating learners knowledge, skills and attitudes in a MOOCs about data science for healthcare.Methods and analysisStudy participants will be recruited from learners who have registered for the MOOC. On registration, learners will be given an opportunity to opt into the study and complete informed consent. Following completion of the course, study participants will be contacted to complete semistructured interviews. Interviews will be transcribed and coded using thematic analysis, with data analysed using two evaluation models: (1) the reach, effectiveness, adoption, implementation, maintenance framework and the (2) Kirkpatrick model drawing data from pre and post-course surveys and post-MOOC semi-structured interviews. The primary goal of the evaluation is to appraise participants' knowledge, skills and attitude after taking the MOOC.Ethics and disseminationEthics approval for this study was obtained from Imperial College London through the Education Ethics Review Process (EERP) (EERP1617-030). A summary of the research findings will be reported through a peer-reviewed journal and will be presented at an international conference.

Dataset Information

A survival model for course-course interactions in a Massive Open Online Course platform.

Publications

A survival model for course-course interactions in a Massive Open Online Course platform.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets