a MOOC: massive open online course.
In all, 12 studies used 1 data source [ 20 , 24 , 25 , 27 , 28 , 31 , 32 , 38 , 43 , 47 ], 11 studies used 2 data sources [ 21 , 26 , 29 , 30 , 33 , 34 , 36 , 40 , 42 , 45 , 51 ], 7 studies used 3 data sources [ 22 , 35 , 39 , 41 , 44 , 46 , 48 ], 2 studies used 4 data sources [ 23 , 37 ], and 1 study used 5 data sources [ 52 ]. The most used data sources were surveys followed by learning management system (LMS), quizzes, and interviews ( Table 2 ). “Other” data sources that are referred to in Table 2 include data collected from social media posts [ 37 ], registration forms [ 30 , 44 ], online focus groups [ 37 ], and homework performance data [ 21 ]. These data sources were used to collect data on different aspects of the evaluation.
Studies using different data sources (N=33).
Data source | Value, n (%) |
Surveys | 20 (30.8) |
Interviews | 8 (12.3) |
Learning Management System | 18 (27.7) |
Discussions | 5 (7.7) |
Quizzes | 9 (13.8) |
Other | 5 (7.7) |
In total, 8 studies collected data through interviews and had a population size ranging from 2 to 44 [ 23 , 37 , 39 , 42 , 43 , 49 , 51 , 52 ]. In total, 20 studies that collected data through surveys had a population size ranging from 25 to 10,392 [ 22 - 41 , 44 - 46 , 51 , 52 ]. In all, 18 studies that collected data through the LMS [ 22 , 24 , 26 , 29 - 31 , 33 - 35 , 39 - 42 , 44 - 46 , 48 , 52 ] had a population size made of participants or data points (eg, discussion posts) ranging from 59 to 209,871. Nine studies used quiz data [ 20 - 22 , 26 , 33 , 35 , 41 , 47 , 52 ]. Studies that used quiz data had a population size of 48 [ 20 ], 53 [ 47 ], 136 [ 41 ], 1080 [ 21 ], and 5255 [ 22 ]. Other data sources used did not have a clearly reported sample size for a particular source.
Table 3 shows the various data collection methods and their uses. Pre-MOOC surveys or pretests could be used for baseline data such as learner expectations [ 22 , 36 , 50 ] or learner baseline test scores [ 20 - 22 , 26 , 33 ], which, then, allows tests scores to be compared with post-MOOC survey and quiz data [ 20 - 22 , 27 , 33 ]. Table 3 explains how studies collected data to meet the aims of their evaluation. In general, surveys were used to collect demographic data, learner experience, and learner perceptions and reactions, whereas LMS data were used for tracking learner completion of the MOOCs.
Data collection methods and their uses in massive open online course evaluations.
Data | Uses |
Registration form | To collect demographic information [ , ] |
Pre-MOOC survey | To collect data on the following: demographic information [ , , , , , , ]; learners’ background [ , , , ] and expectations; perceptions [ , , ]; learners’ experience [ ]; learners’ past MOOC experience [ ]; learners’ self-efficacy [ ], motivation [ ], and goals [ , ]; assess learners’ knowledge [ ] and course efficacy [ ] |
Pretest | To collect baseline test scores for comparison with posttest scores [ - , , ] |
Learning management system data | To collect data on the following: demographic information [ ]; attendance rates [ , , ]; completion of the different components of the MOOC [ , , , , ]; quiz or assignment scores [ , , ]; learner activity [ ] |
Discussion posts | Feedback about the course [ ] and learner interactions [ ] |
Quiz, homework, or test (not specified as pre- or postquiz or test) | Grades to assess learning [ , , ] and a weekly quiz to record learners’ reaction to the tools called of the course [ ] |
Post-MOOC survey | To collect demographic information [ , - , , ]; to record the learning experience [ , , , , ]; to record course influence [ ]; to guide MOOC design [ ], course feedback [ , , , , , ], perceptions [ , ], excitement [ ], learners’ motivation [ , , ], learners’ satisfaction [ , , ], enjoyment of the course [ ] and “Patterns and levels of participation” in the course [ ], learning strategies [ ]; to assess learner knowledge [ ], course usefulness [ , ], course degree of perseverance [ ], reasons for dropping out of the MOOC [ ]; to recruit participants for research [ ]; to collect course feedback [ ] |
Posttest | To assess learning [ , ]; to assess confidence in applying learning [ ]; to assess satisfaction [ ]; to calculate the difference in scores compared with pretest [ , , ]; to assess knowledge retention 5 months post-MOOC [ ] |
End of MOOC quiz | To record learners’ feedback in relation to the course material (whether the course helped them become ) [ ] |
Postcourse interview | Course participation and evaluation [ , ]; course effectiveness [ , ]; sustainability of the course [ ]; reason for taking the course [ ]; learners’ motivation [ , ]; to understand learning behavior [ ]; or learners’ behavior [ ] |
Email interview | To understand learners’ behavior and learning in MOOCs [ ]; specify MOOC positives [ ]; motivation in MOOC; challenges in MOOC [ ] |
Online focus group | Assessment of the course: organization, assessment, and [ ] |
In terms of the data analysis methods, quantitative methods were the only type of method used in 16 studies with descriptive and inferential statistics, the top 2 preferred methods. Qualitative analysis methods such as thematic analyses, which can include grounded theory [ 49 ], focused coding [ 38 , 39 ], and content analysis [ 25 , 50 ], were mainly used in qualitative studies.
A summary of the parameters, indicators, and data analysis used for the MOOC evaluation can be found in Table 4 . Most notably, inferential statistics were used to analyze learning outcomes ( Table 4 ) such as the comparison of means or the use of regression methods to analyze quiz or test grades. These outcomes were also used as a measure to evaluate the overall effectiveness of a MOOC by the studies. Table 4 shows how the data collection method uses mentioned in Table 3 were measured and analyzed. In general, studies focused on measuring learner engagement and learners’ behavior–related indicators. Studies referred to learning in different ways such as learning , learning performance, learning outcome, or gain in comprehensibility depending on the learning material of the course. Other studies considered learning outcomes such as knowledge retention or what students took away from the course . There was a consensus that learner engagement can be measured by measuring the various learner activities in the course, whereas learner behavior was a more general term used by studies to describe the different MOOC evaluation measures. For teaching-focused evaluation, both Mackness et al [ 37 ] and Singh et al [ 36 ] used learner parameters to reflect and analyze pedagogical practices.
Data collection method uses mentioned earlier and how they were analyzed in massive open online course evaluations.
Data collection method uses, parameters or themes reported | Data analysis methods | |
| , ] ] ] ] ] ] ] [ ] ] ] | , ] test [ ] ] ] ]A matched-pairs test to measure the “gains in comprehensibility between the pretest and posttest” and an unpaired test to compare the pretest and posttest means [ ] ] ] test to examine student knowledge retention as measured by the postcourse and follow-up tests” [ ] tests to compare the quiz and assignment scores [ ] |
| , ] ] ] ] ], total number of reads in forums, the number of forum and post comments [ ] ] ] and frequency of attempting quizzes [ ] ] and frequency of interaction on online forums ] | , , , , , ] and frequency analysis [ ] ] ] ] and structural equation modeling [ ] |
| ] ] ] ] ] | ], regression analysis [ ], factor analysis of factors related to [ ], frequency analysis [ ], sentiment analysis of interview data [ ], and descriptive statistics [ ] |
| ] ] | ] ] |
| ] ] ] ] | ] ] ] ] |
| ] ] | ] |
| ] ] | ] ] |
| ] ] ] | ] ] ] |
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This study aimed to review current MOOC evaluation methods to understand the methods that have been used in published MOOC studies and subsequently to inform future designs of MOOC evaluation methods. Owing to the diversity of MOOC topics and learners, it is not possible to propose a single evaluation method for all MOOCs. Researchers aiming to evaluate a MOOC should choose a method based on the aims of their evaluation or the parameters they would like to measure. In general, data collection methods were similar in most evaluations, such as the use of interviews or survey data, and the analysis methods were highly heterogeneous among studies.
The cross-sectional study design was used in 31 of 33 of the included studies. The cross-sectional study design was used when the aim was to investigate the factors affecting outcomes for a population at a given time point [ 53 ]. For the MOOC evaluation, this is particularly useful for observing the population of learners and for understanding the factors affecting the success and impact of a MOOC. They are relatively inexpensive to conduct and can assess many outcomes and factors at the same time. However, cross-sectional study designs are subject to nonresponse bias , which means that studies are only representative of those who participated, who incidentally may happen to be different from the rest of the population [ 53 ].
One of the most effective methods of evaluation used in MOOCs was the use of baseline data to compare outcomes. Studies that did pretests and posttests had a less likelihood of bias in their outcomes owing to the measurement of exposure before the measurement of outcome [ 18 ]. Even when studies used pre- and postcourse surveys or tests, they were not longitudinal in design, as such a design requires a follow-up of the same individuals and requires observing them at multiple time points [ 53 ]. Therefore, the use of pre- and postsurveys or tests without linking the individuals may simply represent a difference in the groups studied rather than changes in learning or learner outcomes. The advantages of this method are that it can reduce bias, and quasi-experimental studies are known as strong methods. However, the disadvantage is that although this method may work with assessing learning, such as memorizing information, it may not work to assess skill development or the application of skills.
Understanding the aim behind the evaluation of MOOCs is critically important in designing MOOC evaluation methods as it influences the performance indicators and parameters to be evaluated. More importantly, motivation for the evaluation determines the data methods that will be used. One reason for the inability to conclude a standardized evaluation method from this review is that studies differ in the aspects and purposes of why they are conducting the evaluation. For example, not all studies perform evaluations of MOOCs to evaluate overall effectiveness, which is an important aspect to consider if MOOCs are to be adopted more formally in higher education [ 54 ]. The variability in the motivation of MOOC evaluations may also explain the high variability in the outcomes measured and reported.
In all, 12 studies used 1 data source and 11 studies used 2 data sources ( Table 3 ), which is not different from previous findings [ 10 ]. The results of this study also show that there is high flexibility in data collection methods for MOOC evaluations from survey data to LMS data to more distinct methods such as online focus groups [ 37 ]. The number of participants in the studies was exceedingly varied. This is due to the difference in the data collection methods used. For example, studies with data captured through the LMS, which is capable of capturing data from all of the learners who joined the course, had the highest number of learners. On the contrary, studies that used more time-consuming methods, such as surveys or interviews, generally had a lower number of participants. It is important to note that the MOOC evaluation is not necessarily improved by increasing the number of data sources but rather by conducting a meaningful analysis of the available data. Some studies preferred multiple methods of evaluation and assessment of learning. One paper argued that this allows to evaluate learning of the diverse MOOC population in a more effective way [ 22 ]. Studies should use the best data collection methods to answer their research aims and questions.
In total, 16 of 33 studies used only quantitative methods for analysis ( Table 4 ), which is in line with the general MOOC research, which has been predominated by quantitative methods [ 10 , 55 ]. Studies used statistical methods such as descriptive and inferential statistics for data analysis and interpretation of results. The availability of data from sources such as the LMS may have encouraged the use of descriptive statistical methods [ 10 ]. However, 17 of the 33 included studies used some form of qualitative data analysis methods either by using a qualitative study design or by using a mixed methods study design ( Table 4 ). This may be explained by the recent (2016-2017) rise in the use of qualitative methods in MOOC research [ 10 ].
Although inferential statistics can help create better outcomes from studies, this is not always possible. For example, one study [ 36 ] mentioned a high variation between pre- and postcourse survey participant numbers and another [ 29 ] mentioned a small sample size as reasons for not using inferential statistical methods. It should be noted that using data from multiple sources and having a large sample size does not guarantee the quality of the evaluation methods.
In MOOC research, qualitative data can be useful to understand the meaning of different behaviors as quantitative data, oftentimes, cannot answer why things happened [ 56 ].
Thematic and sentiment data analysis methods seek to represent qualitative data in a systematic way. The thematic analysis seeks to organize information into themes to find patterns [ 57 ]. This is especially useful for generalizing data for a subsequent analysis. For instance, Singh et al [ 36 ], Draffan et al [ 34 ], and Shapiro et al [ 23 ] all used a thematic analysis to simplify heterogeneous responses from interviewees and participants to understand what students enjoy about the MOOCs. Focused coding and grounded theory use similar approaches to grouping qualitative data into themes based on conceptual similarity and to developing analytic narratives. Liu et al [ 38 ] used focused coding to group data from course surveys into positive and negative aspects of MOOCs for future MOOC improvement [ 7 ]. Sentiment analysis and social network analysis are both qualitative analysis strategies with a greater focus on opinion-rich data [ 58 ]. These are important strategies used in understanding the opinions of learners and converting subjective feelings of learners into data that can be analyzed and interpreted.
The outcome measures reported greatly varied among studies, which is expected, as identifying the right outcome measures is an inherent challenge in educational research, including more traditional classroom-based studies [ 7 ].
The choice of evaluation methods is highly dependent on the aim of the evaluation and the size of the MOOCs. For quantitative measures, such as completion and participation rates, metrics can be easily collected through the MOOC platform. However, these metrics alone may be insufficient to provide insights into why students fail to complete the course for future improvement. Although it may be difficult to represent the problem holistically using qualitative methods, it can be useful in providing insights from individuals who participated in the MOOCs. Mixed methods studies combine the 2 modalities to better understand metrics generated and produce greater insights for future improvement of the MOOCs.
Learning outcomes were mostly analyzed by inferential statistical methods owing to the use of pretest and posttest methods and the calculation of gains in learning. This method may be most suited for MOOCs that require knowledge retention. Learning parameters also involved a lot of comparisons, either a comparison with pre-MOOC measures or a comparison with other learners or both. Social interactions were studied in 2 of the MOOC evaluations using social network analysis methods. Although the MOOC completion rate has been often cited as a parameter for MOOC success, it can be noticed that studies started to move away from only using completion rates. For example, studies looked at completion of different steps of the MOOCs or looked at overall completion. The learning outcomes reported in this review should be used with caution as not all of them have been validated or assessed for their reliability except for a few.
In total, 26 studies with a cross-sectional design had a low-quality assessment, whereas RCTs and quasi-experimental studies received a high-quality assessment. Having a high level of bias affects the generalizability of studies, which is a common problem in most research using data from MOOCs [ 30 , 59 ]. The availability of high risks of bias in current MOOC evaluations requires a closer look at what were the sources of bias and what methods can be used to reduce them. The use of not validated, self-reported data sources and the lack of longitudinal data also increases the risk of bias in these studies [ 56 ]. However, although most MOOCs struggle with learner retention and MOOC completion rates [ 54 ], it is understandable that studies are not able to collect longitudinal data.
The scarcity of studies focusing on the evaluation of the effectiveness of particular MOOCs relative to the number of available studies on MOOCs raises some questions. For example, many studies that were excluded from this review studied MOOC learners or aspects of the MOOCs without conducting an evaluation of course success or effectiveness. As shown in this review, there is a diverse range of evaluation methods, and the quality of these evaluation studies can be as diverse. The motivation of the evaluation exercise should be the basis of the evaluation study design to design effective quantitative or qualitative data collection strategies. The development of general guidance, standardized performance indicators, and an evaluation framework using a design thinking approach can allow these MOOC evaluation exercises to yield data of better quality and precision and allow improved evaluation outcomes. To provide a comprehensive evaluation of MOOCs, studies should try to use a framework to be able to systematically review all of the aspects of the course.
In general, the adoption of a mixed methods analysis considering both quantitative and qualitative data can be more useful for evaluating the overall quality of MOOCs. Although it is useful to have quantitative data such as learner participation and dropout rates, qualitative data gathered through interviews and opinion mining provide valuable insights into the reasons behind the success or failure of a MOOC. Studies of MOOC evaluations should aim to use data collection and analysis methods that can minimize the risk of bias and provide objective results. Whenever possible, studies should use comparison methods, such as the use of pretest or posttest or a comparison with other types of learners, as a control measure. In addition, learner persistence is an important indicator for MOOC evaluation that needs to be addressed in future research.
To our knowledge, this is the first study to systematically review the evaluation methods of MOOCs. The findings of this review can serve future MOOC evaluators with recommendations on their evaluation methods to facilitate better study designs and maximize the impact of these Web-based platforms. However, as a lot of MOOCs are not necessarily provided by universities and systematically evaluated and published, the scope of this review can only reflect a small part of MOOC evaluation studies.
There is no one way of completing a MOOC evaluation, but there are considerations that should be taken into account in every evaluation. First, because MOOCs are very large, there is a tendency to use quantitative methods using aggregate-level data. However, aggregate-level data do not always tell why things are happening. Qualitative data could further help interpret the results by exploring why things are happening. Evaluations lacked longitudinal data and very few accounted for confounding variables owing to data collection challenges associated with MOOCs such as not having longitudinal data or not having enough data sources. Future studies could help identify how these challenges could be overcome or minimized.
LMS may not report useful findings on an individual level, but they should still be considered and used in MOOC evaluations. Big data in the form of learning analytics can help with decision making, predicting learner behavior, and providing a more comprehensive picture of the phenomena studied [ 60 ]. Studies should still consider using LMS as it can provide a valuable addition to the research, but researchers need to be careful about the depth of the findings that can be concluded from LMS-only datasets.
The use of qualitative data could help enhance the findings from the studies by explaining the phenomena. Both quantitative and qualitative methods could play a key role in MOOC evaluations.
Current MOOC evaluations are subject to many sources of bias owing to the nature of the courses being open and available to a very large and diverse number of participants. However, methods are available to reduce the sources of bias. Studies could use a comparator, such as pretest scores, or other types of learners to be able to calculate relative changes in learning. In addition, studies could control for confounding variables to reduce bias.
This review has provided an in-depth view of how MOOCs can be evaluated and explored the methodological approaches used. Exploring MOOC methodological approaches has been stated as an area for future research [ 10 ]. The review also provided recommendations for future MOOC evaluations and for future research in this area to help improve the quality and reliability of the studies. MOOC evaluations could contribute to the development and improvement of these courses.
The authors would like to thank the medical librarian, Rebecca Jones, for her guidance in the search methods and for reviewing the search strategy used in this protocol. This work was funded by the European Institute of Technology and Innovation Health (Grant No. 18654).
IEEE | Institute of Electrical and Electronic Engineers |
LMS | learning management system |
MOOC | massive open online course |
PICO | population, intervention, comparator, outcome |
RCT | randomized controlled trial |
Multimedia appendix 2, multimedia appendix 3, multimedia appendix 4, multimedia appendix 5, multimedia appendix 6.
Authors' Contributions: AA and CL completed the screening of articles and data analysis. AA and CL completed the first draft of the manuscript. All authors reviewed and edited the manuscript for content and clarity. EM was the guarantor.
Conflicts of Interest: None declared.
by Christina Sabo | Nov 23, 2015 | Featured , Glossary
A Massive Open Online Course, or MOOC, is an online course that offers open access via the Internet for free or at a low cost. Many MOOCs are modeled after existing college or university courses, but unlike these courses, most MOOCs do not count for college credit. There are some exceptions, however, with a few providers offering certificates of completion if students take proficiency exams, or partnering with more traditional online programs to offer for-credit courses.
What else defines a MOOC? As their name suggests, many MOOCs have huge class sizes, with the largest on record topping out at over 300,000 students . Another important element of a MOOC is open licensing. This can apply not only to the course itself, which offers open enrollment, but also to much of the content, structure, and resources employed by the course, allowing anyone, anywhere to sign up and take the course.
Anyone who wants to sign up for a MOOC can and the majority of courses take place asynchronously, allowing students to study when and where it’s most convenient for them and also expanding accessibility to students anywhere in the world. Because they are free, it’s not uncommon for students to sign up for but not to complete a MOOC—there are no penalties. Concurrent enrollment is also common, as students can sign up for as many MOOCs as they want, from as many different providers as they want.
The content of MOOCs differs very little from traditional online courses but each MOOC is unique and the look and feel of the MOOC will often depend on the topic and instructor. Unlike online courses, however, the sheer size of MOOCs makes it impossible for instructors to respond to students individually so students must rely on their own motivation to stay on track and on other classmates in a MOOC for help with homework, studying, and even evaluation.
Depending on who you ask, MOOCs will be praised as the next big transformative movement in education or brushed off as over-hyped and already on the decline. Where does the truth lie? As it generally does, somewhere in between. Here’s a look at some of the pros and cons of today’s MOOCs.
The Pros: MOOCs make it possible for a large number of students to access educational opportunities at no or very low cost, democratizing access to education in an age when high college tuition makes attending school difficult for those without financial means. For those who are driven and can self-motivate, they can be an excellent way to refresh knowledge, learn new skills, or to simply engage in a topic of interest on a nearly limitless array of topics.
The Cons: One of the biggest problems with MOOCs is failure of students to actually complete them. A study by MIT and Harvard found that more than 95 percent of students dropped out of their MOOC before the session was over, a rate that’s almost unheard of in traditional online education. Why the mass exodus from the courses? The most commonly cited reason is a lack of live teacher engagement, whether through class discussion or through feedback on course assignments. While the end goal of many MOOCs is knowledge, not a degree, stats show that without teacher support, curiosity alone isn’t enough to sustain most students.
The first MOOC was launched in 2008 as an experiment by two Canadian educators. Since then, MOOCs have grown enormously, both in the number of students who take them and in variety. Take Coursera for example, one of the better known MOOC providers. In less than two years, it enrolled more than 1.7 million students and garnered partnerships with more than 30 top names in higher education, like Princeton, Columbia, and Duke. To date, it has enrolled almost 12 million students.
It’s not alone in the mad dash to embrace MOOCs. Over the past seven years, Google has released a MOOC-building tool allowing anyone to build a MOOC and numerous other large-scale providers have emerged including Udacity and and edX , offering highly innovative and incredibly popular courses taught by educators at top universities from around the world.
Despite the rapid rise in MOOCs, the format is still evolving and looking for ways to solve many of the problems that critics have pointed to as major stumbling blocks. While many predicted that the MOOC would fizzle out, even as far back as 2012, the format is still going strong, though perhaps not being the darling of higher education that it once was.
Over the next decade, MOOCs are likely to look for ways to improve teacher engagement and to offer greater opportunities for credit and certification, though this may also mean that many MOOCs move from being free to requiring students to pay tuition. While their demise has been predicted almost since their inception by the mode’s critics, it’s likely that MOOCs will be around for some time to come. While it’s unlikely that MOOCs will ever be the game-changer they were initially touted to be, MOOCs will still have an important role to play in the future of online education.
There are a few major ways that these free, open courses can be of use to you.
Be a student. Curious about what a MOOC is really like? Sign up for one. Not only will you will learn more about the format (critical if you want to build your own MOOC someday), but you’ll get a chance to learn about a topic of interest at no cost to you. It can be a great way to do some professional development and engage with the online learning environment with no penalty, monetary or otherwise, should you get too busy to complete it.
Use MOOCs to inform your own teaching. MOOCs have their pros and cons, both of which can be used to help you fine tune your own approach to online teaching and learning.
Develop your own MOOC. Want to share your expertise and research with the world? MOOCs are one way to do it. Launching a MOOC can be a lot of work, but because MOOCs are not for-credit, they are significantly easier to create than a traditional online course. Some MOOCs have been launched in conjunction with the publication of research, or because faculty members have knowledge in fields that aren’t always easy to find traditional courses in. Even better, MOOCs don’t have to span an entire semester—some MOOCs are only comprised of a few shorter seminars than can span a few days or a few weeks. If you want to learn more about crafting a MOOC, visit Learning Technologies. We can help you to develop your course content, better understand the intellectual property issues that may arise, and find the best forum for launching your course.
There’s a lot of information out there on MOOCs. These resources can help you to learn more and find out where to take some exciting new free, open online courses.
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Advantages and Disadvantages of MOOCs. The massive open online course model has several advantages that refer to all participants. First and foremost, it is the great diversity of ideas, opinions, and perspectives that resembles the openness exhibited by individuals in everyday life (Alraimi et al., 2015). Since persons from all over the globe ...
Abstract. Massive Open Online Courses (MOOCs) are designed for limitless participation via the web. MOOCs provide free open access courses for anyone who is interested in learning. MOOCs are offered through various platforms such as Coursera, edX, Udacity, SWAYAM, FutureLearn, Canvas Network, Khan Academy, and Open Yale Courses, offering online ...
The COVID-19 outbreak has raised enrollments in massive open online courses (MOOCs) in recent years [].MOOCs are a collection of ego web-based learning materials [2,3].According to Cope and Kalantzis [], MOOCs are one of the numerous instruments afforded by ICTs applied to education.MOOCs are a relatively new learning phenomenon, blending eLearning and open education [].
Massive Open Online Courses Essay. Exclusively available on IvyPanda®. Video-based learning (VBL) has a long history of development. First attempts were made at the time of the World War II. VBL were used to train soldiers. This method was very effective and led to the significant improvement of their skills.
Nature and Characteristics of Massive Open Online Courses. Massive Open Online Courses are online courses that are conducted and accessible through the Web (Wikipedia). They are with open access and an unlimited number of users can subscribe and attend. The term MOOC was introduced in 2008 by Dave Cormier.
Science, engineering and technology courses have been in the vanguard of the massive open online course movement. These classes also are providing fodder for scientific research on learning. When ...
The purpose of this paper is to provide an insight to the term Massive Online Open Courses (MOOCs), the practices, trends and challenges for the higher education institutions.
The Massive Open Online Course (MOOC) comprises four words with their own meanings. The first is Massive, which means infinite scalability, which means that as long as the capacity of the server ...
"A massive open online course (MOOC) is a free Web-based distance-learning program that is designed for the participation of large numbers of geographically dispersed students. ... Let us write you an essay from scratch. 450+ experts on 30 subjects ready to help; Custom essay delivered in as few as 3 hours; Write my essay. Get high-quality ...
Abstract. The proliferation of massive open online courses (MOOCs) has stirred a fervent debate about global access to higher education. While some commentators praise MOOCs for expanding educational opportunities in a more open and accessible fashion, others criticize this trend as a threat to current models of higher education and a low-quality substitute for traditional learning.
Introduction. When the first massive open online course (MOOC) was offered in 2008, the MOOC format—free, online, and open to anyone with an internet connection—was touted as revolutionary for its potential to democratize access to educational opportunities due to its theoretically universal availability [1-3].The earliest MOOCs used a connectivist paradigm in which the course was built ...
During the widespread development of open access online course materials in the last two decades, advances have been made in understanding the impact of instructional design on quantitative outcomes. Much less is known about the experiences of learners that affect their engagement with the course content.
An example of a short descriptive essay, written in response to the prompt "Describe a place you love to spend time in," is shown below. Hover over different parts of the text to see how a descriptive essay works. On Sunday afternoons I like to spend my time in the garden behind my house. The garden is narrow but long, a corridor of green ...
In contrast, there has been almost no discussion on the impact of massive open online courses (MOOCs) to higher education. Internationally this emphasis is reversing. MOOCs have the potential to upend the currently established model of higher education through the emergence of global university brands, while touchscreen mobile devices will have ...
The Impact of Massive Open Online Course in Education. Category: Education. Topic: Education System, Online Courses, Study. Pages: 5 (2337 words) Views: 947. Grade: 5. Download. The term MOOC was coined in 2008 by Dave Cornier of the University of Prince Edward Island in response to a course called Connectivism and Connective Knowledge (also ...
To enhance the development of massive open online course (MOOC) teaching and learning, prevalent methods and characteristics employed by college students in MOOC learning strategies must be investigated. This study employed the grounded theory approach to systematically encode and construct a learning strategy model for the use of MOOCs among college students in China. This study used data ...
How to write a descriptive essay using a structured outline. Introduction: Hook: An opening statement that captures attention while introducing the subject. Background: Includes a brief overview of the topic the descriptive essay is based on. Thesis statement: Clearly states the main point or purpose of the descriptive essay. Body paragraphs: Each paragraph should have
During the 2020 COVID-19 lockdown, edX launched an initiative, called the Remote Access Program, to give access to free certificates for its Massive Open Online Courses (MOOCs) to the communities of its partners. This paper describes the experience of a Higher Education (HE) institution in Europe participating in this initiative as a strategic action to spread MOOCs, not just as a resource to ...
A lecture or other content from an online course or Mooc is cited similarly to a podcast episode, with the instructor for the lecture listed in the author element of the reference and the names of all course faculty in the editor position within the source element.; Provide a link that will resolve for readers. Because users must enroll in the course and then log in to access the lecture, the ...
Massive Open Online Course Evaluation Research Design. In total, 16 studies used a quantitative study design, 11 studies used a qualitative study design, and 6 studies used a mixed methods study design. There was 1 RCT [ 20] and 1 quasi-experimental study [ 26 ]. In total, 4 studies evaluated more than 1 MOOC [ 27 - 30 ].
Abstract. Massive open online courses (MOOCs) are a novel and emerging mode of online learning. They offer the advantages of online learning and provide content including short video lectures, digital readings, interactive assignments, discussion fora, and quizzes. Besides stand-alone use, universities are also trying to integrate MOOC content ...
A Massive Open Online Course, or MOOC, is an online course that offers open access via the Internet for free or at a low cost. Many MOOCs are modeled after existing college or university courses, but unlike these courses, most MOOCs do not count for college credit. There are some exceptions, however, with a few providers offering certificates ...
Write a descriptive essay with no less than 500 words, citing the impact of massive open online course. - 10612062. answered ... to no settled judgment. (Not that these are the only options, of course, but the topic does tend to elicit strong feelings.) Wherever you fall in the spectrum of opinion, at least one of the two articles flagged here ...