Unknown

Dataset Information

5

Massive Open Online Course Evaluation Methods: Systematic Review.


ABSTRACT: 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.

SUBMITTER: Alturkistani A 

PROVIDER: S-EPMC7215503 | BioStudies | 2020-01-01

SECONDARY ACCESSION(S): 10.18261/issn.1891-943x-2018-01-04

REPOSITORIES: biostudies

altmetric image

Publications

Massive Open Online Courses (MOOC) Evaluation Methods: Protocol for a Systematic Review.

Foley Kimberley K   Alturkistani Abrar A   Carter Alison A   Stenfors Terese T   Blum Elizabeth E   Car Josip J   Majeed Azeem A   Brindley David D   Meinert Edward E  

JMIR research protocols 20190307 3


<h4>Background</h4>Massive open online courses (MOOCs) have increased in popularity in recent years. They target a wide variety of learners and use novel teaching approaches, yet often exhibit low completion rates (10%). It is important to evaluate MOOCs to determine their impact and effectiveness, but little is known at this point about the methodologies that should be used for evaluation.<h4>Objective</h4>The purpose of this paper is to provide a protocol for a systematic review on MOOC evalua  ...[more]

Similar Datasets

1000-01-01 | S-EPMC6297852 | BioStudies
1000-01-01 | S-EPMC6091905 | BioStudies
1000-01-01 | S-EPMC6173253 | BioStudies
2019-01-01 | S-EPMC6427096 | BioStudies
2020-01-01 | S-EPMC7243831 | BioStudies
2019-01-01 | S-EPMC7008416 | BioStudies
2016-01-01 | S-EPMC4991885 | BioStudies
2020-01-01 | S-EPMC7592287 | BioStudies
2018-01-01 | S-EPMC6276217 | BioStudies
2019-01-01 | S-EPMC6762193 | BioStudies