Write a 2- to 3-page critique of the article

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Write a 2- to 3-page critique of the article you found. In your critique, include responses to the following:

  • Why did the authors use correlation or bivariate regression?
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Initial Trends in Enrolment and Completion of Massive Open Online Courses Katy Jordan The Open University, UK Abstract The past two years have seen rapid development of massive open online courses (MOOCs) with the rise of a number of MOOC platforms. The scale of enrolment and participation in the earliest mainstream MOOC courses has garnered a good deal of media attention. However, data about how the enrolment and completion figures have changed since the early courses is not consistently released. This paper seeks to draw together the data that has found its way into the public domain in order to explore factors affecting enrolment and completion. The average MOOC course is found to enroll around 43,000 students, 6.5% of whom complete the course. Enrolment numbers are decreasing over time and are positively correlated with course length. Completion rates are consistent across time, university rank, and total enrolment, but negatively correlated with course length. This study provides a more detailed view of trends in enrolment and completion than was available previously, and a more accurate view of how the MOOC field is developing. Keywords: MOOCs; higher education; massive open online courses; online education; distance learning Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan Introduction In the past two years, massive open online courses (MOOCs) have entered the mainstream via the establishment of several high-profile MOOC platforms (primarily Coursera, EdX, and Udacity), offering free courses from a range of elite universities and receiving a great deal of media attention (Daniel, 2012). 2012 has been referred to as ‘the year of the MOOC’ (Pappano, 2012; Siemens, 2012), and some herald this as a significant event in shaping the future of higher education, envisioning a future where MOOCs offer full degrees and ‘bricks and mortar’ institutions decline (Thrun, cited in Leckart, 2012). There are clearly great potential individual and societal benefits to providing universitylevel education free of some of the traditional barriers to participation in elite education, such as cost and academic background. However, it is not clear the extent to which MOOCs provide these benefits in practice. MOOCs may favour those who are already educationally privileged; Daphne Koller of Coursera has stated that the majority of their students are already educated to at least undergraduate degree level, with 42.8% holding a bachelor’s degree, and a further 36.7% and 5.4% holding master’s and doctoral degrees (Koller & Ng, 2013). A further study of Coursera students enrolled in courses provided by the University of Pennsylvania indicates a greater dominance of highly educated students, 83.0% of respondents being graduates and 44.2% being educated at the postgraduate level (Emanuel, 2012). The author concludes that MOOCs are failing in their goal to reach disadvantaged students who would not ordinarily have access to educational opportunities (Emanuel, 2013). In order to succeed in a MOOC environment, higher digital literacy may be required of students (Yuan & Powell, 2013), potentially exacerbating pre-existing digital divides. In theory MOOCs remove geographical location as a boundary to access, although a lack of internet access may prevent this from being realized in practice (Guzdial, 2013). Although smallerscale, connectivist MOOCs have existed for several years, the development of largerscale MOOCs offered by elite institutions has propelled MOOCs into the mainstream. The earliest and perhaps most highly cited example is the Stanford AI class, which attracted 160,000 students (20,000 of whom completed the course) when it ran in autumn 2011 (Rodriguez, 2012). However, while this example is often used, it is unlikely to be representative of how the field is developing. A survey undertaken by The Chronicle of Higher Education in February 2013 suggested that the average MOOC enrolment is 33,000 students, with an average of 7.5% completing the course (Kolowich, 2013). Detailed studies of particular courses have emphasized that those who enroll upon courses have a wide variety of motivations for doing so (Breslow et al., 2013; Koller, Ng, Do, & Chen, 2013); however motivation does not predict whether a student will complete a course (Breslow et al., 2013). In examining completion and engagement with courses, studies have focused upon characterizing types of learners (Kizilcec, Piech, & Schneider, 2013; Koller et al., 2013). Limitations of these studies are that they focus upon a small number of early MOOCs, and ascribe Vol 15 | No 1 Feb/14 134 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan course completion primarily to student choice and motivation. There is a gap in the research literature here about what could be learnt about characteristics of courses themselves and their effect upon enrolment and completion, which this study sought to explore. Six-figure enrolment statistics have generated a good deal of interest in MOOCs in the higher education sector, and are frequently conflated with active participation or completion. However, the earliest courses are the most frequently cited examples and may not be representative of how the phenomenon is developing, and the extent to which enrolment numbers are indicative of completion has not been explored comprehensively. These issues are obscured to an extent by a lack of consistent data being made open to those outside of the MOOC platforms. For example, the Coursera data export policy gives individual institutions control over the data that is released about courses (Coursera, 2012), and in practice the extent of data sharing is highly variable and ad hoc. Now, over 18 months on from the advent of the large MOOC platforms, this paper seeks to synthesise the data that has found its way into the public domain in order to address some of the very basic questions associated with MOOCs. How massive is ‘massive’ in this context? Completion rates are reputedly low, but how low? From the available data, can we learn anything about factors which might affect enrolment numbers and completion rates? Methods The approach taken here drew together a variety of different publicly available sources of data online to aggregate information about enrolment and completion for as many MOOCs as possible. Information about enrolment numbers and completion rates were gathered from publicly available sources on the Internet. Given the media attention which MOOCs have garnered, and their ‘massive’ nature, there is a good deal of publicly available information to be found online, including news stories, university reports, conference presentations, and MOOC student bloggers. Issues of reliability associated with using this data are addressed below. 1 The list of completed MOOCs maintained at Class Central was used as a starting point for the inquiry. Completed courses from Coursera, EdX, and Udacity were identified for inclusion in the study, while other individual MOOCs and platforms were excluded. This criteria was used because (i) Coursera, EdX, and Udacity are the platforms which have received the greatest media focus and have fuelled the global interest in MOOCs, (ii) the platforms account for the vast majority of MOOCs to date, and (iii) the platforms reflect the higher education sector more broadly, offering courses presented from ‘bricks and 1 http://www.class-central.com/#pastlist Vol 15 | No 1 Feb/14 135 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan mortar’ institutions through the platforms. At the time of writing (22nd July 2013), this list comprised 279 courses (including courses which have run multiple times). Enrolment and completion figures were selected as the data to be collected for the courses, as these are the metrics which are most commonly available. Completion in this sense was defined as the percentages of students who had satisfied the courses’ criteria in order to gain a certificate. The exact activities required to achieve this vary according to course. Where possible, data was also recorded about the number of ‘active users’ in courses. Information about the number of active users was available for 33 courses, although some did not provide any definition of the term. Those courses who did define active users characterized them as students who actively engaged with the course material to some extent (as opposed to those who enrolled but did not use the course at all). For example, this includes having logged in to a course, attempted a quiz, or viewed at least one video. Data was also collected about the date a course began, the course length in weeks, and university ranking (using the Times Higher Education World Rankings; THE, 2013) in order to explore whether these factors affect enrolment and completion. The enrolment and completion data was collected in two ways: via internet searches and crowdsourcing information from students who participated in courses, by appealing via social media. Students contributed data which had been shared with them by the course instructor to the author’s blog (Jordan, 2013). This yielded information about enrolment numbers for a total of 91 courses (32.6% of total potential sample), and completion for 42 courses (15.1% of total). For transparency, the sources used for all data items are included here. Details of courses for which only enrolment data was available are shown in Table 1; details of courses for which completion data was found are shown in Table 2. Vol 15 | No 1 Feb/14 136 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan Course Institution Enrolled Start date Length (weeks) Platform Source Table 1: Data Drawn from Online Sources for Courses for which Enrolment Numbers Only were Available Introduction to Databases Stanford University 60000 2011-10-01 9 Coursera Widom, 2012 Human-Computer Interaction Stanford University 29105 2012-05-28 5 Coursera Lugton, 2012 Introduction to Sociology Princeton University 40000 2012-06-11 7 Coursera Lewin, 2012a Introduction to Finance University of Michigan 125000 2012-07-23 15 Coursera Masolova, 2013 Algorithms, Part I Princeton University 65000 2012-08-12 6 Coursera Princeton University, 2012 Introduction to Sustainability University of Illinois at UrbanaChampaign 32000 2012-08-27 8 Coursera Rushakoff, 2012 Securing Digital Democracy University of Michigan 14000 2012-09-03 5 Coursera University of Michigan, 2012 Statistics One Princeton University 96000 2012-09-03 12 Coursera Bialik, 2013 Modern & Contemporary American Poetry University of Pennsylvania 36000 2012-09-10 10 Coursera Unger, 2013 Introduction to Mathematical Thinking Stanford University 57592 2012-09-17 10 Coursera Devlin, 2012 A History of the World since 1300 Princeton University 83000 2012-09-17 12 Coursera Cervini, 2012 Organizational Analysis Stanford University 81000 2012-09-24 10 Coursera Hawkins, 2013 An Introduction to Interactive Programming in Python Rice University 54000 2012-10-15 Coursera Weinzimmer, 2012 The Modern World: Global History since 1760 University of Virginia 40000 2013-01-14 15 Coursera Kapsidelis, 2013 Microeconomics for Managers University of California, Irvine 37000 2013-01-21 10 Coursera Heussner, 2013 Fundamentals of Human Nutrition University of Florida 45000 2013-01-22 Coursera Nelson, 2013 Data Analysis Johns Hopkins University 102000 2013-01-22 Coursera Jordan, 2013 Vol 15 | No 1 8 Feb/14 137 Initial Trends in Enrolment and Completion of Massive Open Online Courses Length (weeks) Platform University of California, Irvine 15000 2013-01-28 5 Coursera Florida Public Health Training Center, 2013 Introduction to Digital Sound Design Emory University 45000 2013-01-28 4 Coursera Williams, 2013 Nutrition for Health Promotion and Disease Prevention University of California, San Francisco 50000 2013-01-28 6 Coursera Ferraro, 2013 Grow to Greatness: Smart Growth for Private Businesses, PartI University of Virginia 71000 2013-01-28 5 Coursera University of Virginia, 2013 Developing Innovative Ideas for New Companies University of Maryland, College Park 85000 2013-01-28 6 Coursera Welsh & Dragusin, 2013 The Modern and the Postmodern Wesleyan University 30000 2013-02-04 14 Coursera Roth, 2013 Clinical Problem Solving University of California, San Francisco 28000 2013-02-11 6 Coursera Harder, 2013 Aboriginal Worldviews and Education University of Toronto 23000 2013-02-25 4 Coursera Stauffer, 2013 Introduction to Music Production Berklee College of Music 50000 2013-03-01 6 Coursera Clark, 2013 Songwriting Berklee College of Music 65590 2013-03-01 6 Coursera Pattison, 2013 Sustainable Agricultural Land Management University of Florida 13000 2013-03-04 9 Coursera Nelson, 2013 How Things Work 1 University of Virginia 20000 2013-03-04 Coursera Burnette, 2012 Leading Strategic Innovation in Organizations Vanderbilt University 33000 2013-03-05 8 Coursera Furman University, 2013 Economic issues, Food & You University of Florida 16000 2013-03-18 10 Coursera Nelson, 2013 Global sustainable energy: past, present and future University of Florida 18000 2013-03-24 15 Coursera Nelson, 2013 Vol 15 | No 1 Source Start date Principles of Public Health Course Enrolled Institution Jordan Feb/14 138 Initial Trends in Enrolment and Completion of Massive Open Online Courses Start date Length (weeks) Platform Source Science, Technology, and Society in China I: Basic Concepts The Hong Kong University of Science and Technology 17000 2013-04-04 3 Coursera Sharma, 2013 Introduction to Improvisation Berklee College of Music 39000 2013-04-29 5 Coursera Burton, 2013 Grow to Greatness: Smart Growth for Private Businesses, Part II University of Virginia 71000 2013-04-29 4 Coursera University of Virginia, 2013 TechniCity Ohio State University 16000 2013-05-04 4 Coursera Campbell, 2013 Nutrition, Health, and Lifestyle: Issues and Insights Vanderbilt University 66000 2013-05-06 6 Coursera Moran, 2013 History of Rock, Part One University of Rochester 30000 2013-05-13 7 Coursera Rivard, 2013 First-Year Composition 2.0 Georgia Institute of Technology 17000 2013-05-27 8 Coursera Head, 2013 Creative Programming for Digital Media & Mobile Apps University of London International Programmes 70000 2013-06-03 6 Coursera Gillies, 2013 Growing Old Around the Globe University of Pennsylvania 4500 2013-06-10 6 Coursera Posey, 2013 Course Enrolled Institution Jordan Vol 15 | No 1 Feb/14 139 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan Completed Start date Length Platform 104000 41600 13000 2011-10-01 10 Coursera McKenna, 2012 Introduction to Artificial Intelligence Stanford University 160000 80000 20000 2011-10-01 10 Udacity Schmoller, 2012 6.002x Circuits and Electronics Massachusetts Institute of Technology 154763 7157 2012-03-05 14 MITx Lewin, 2012b Software Engineering for SaaS University of California, Berkeley 50000 3500 2012-05-18 5 Coursera Meyer, 2012 Listening to World Music University of Pennsylvania 36295 22018 2191 2012-07-23 7 Coursera Jordan, 2013 Internet History, Technology, and Security University of Michigan 46000 11640 4595 2012-07-23 13 Coursera Severance, 2012 Gamification University of Pennsylvania 81600 49776 8280 2012-08-27 6 Coursera Werbach, 2012 6.002x: Circuits and Electronics Massachusetts Institute of Technology 46000 6000 3008 2012-09-05 14 EdX Chu, 2013 Functional Programming Principles in Scala École Polytechnique Fédérale de Lausanne 50000 9593 2012-09-18 7 Coursera Miller & Odersky, 2012 Social Network Analysis University of Michigan 61285 25151 1410 2012-09-24 8 Coursera Jordan, 2012 Bioelectricity: A Quantitative Approach Duke University 12000 7761 313 2012-09-24 9 Coursera Belanger & Thornton, 2013 Greek and Roman Mythology University of Pennsylvania 55000 2500 2012-09-24 10 Coursera Jordan, 2013 An Introduction to Operations Management University of Pennsylvania 87000 58000 4000 2012-09-24 8 Coursera Barber, 2013 Mathematical Biostatistics Bootcamp Johns Hopkins University 15930 8380 740 2012-09-24 7 Coursera Anderson, 2012 Computing for Data Analysis Johns Hopkins University 50899 27900 2012-09-24 4 Coursera Simply Statistics, 2012 Vol 15 | No 1 Source Active Stanford University Institution Introduction to Machine Learning Course Enrolled Table 2: Data Drawn from Online Sources in Relation to MOOC Enrolment, Number of Active Users, and Completion Rates Feb/14 140 Initial Trends in Enrolment and Completion of Massive Open Online Courses Start date Length Platform 7 Coursera St. Petersburg College, 2013 33000 14000 1705 2012-10-10 12 Coursera Duke Today, 2012 Harvard University 150349 100953 1388 2012-10-15 24 EdX Malan, 2013 3.091x: Introduction to Solid State Chemistry Massachusetts Institute of Technology 28512 6000 2082 2012-10-15 12 EdX Chu, 2013 Computational Investing, Part I Georgia Institute of Technology 53205 28199 2554 2012-10-22 9 Coursera Balch, 2013a Think Again: How to Reason and Argue Duke University 226652 132000 5322 2012-11-26 12 Coursera Riddle, 2013a Introduction to Astronomy Duke University 60000 40000 2141 2012-11-27 8 Coursera Belanger, 2013 Drugs and the Brain California Institute of Technology 66800 10426 4400 2012-12-01 5 Coursera Lesiewicz, 2013 Calculus: Single Variable University of Pennsylvania 47000 7000 2013-01-07 13 Coursera Unger, 2013 Calculus One Ohio State University 35579 24385 2013-01-07 15 Coursera Evans, 2013 Image and video processing: From Mars to Hollywood with a stop at the hospital Duke University 40000 23000 4069 2013-01-14 9 Coursera Riddle, 2013b Artificial Intelligence Planning University of Edinburgh 29894 15546 654 2013-01-28 5 Coursera University of Edinburgh, 2013 E-learning and Digital Cultures University of Edinburgh 42844 21862 1719 2013-01-28 5 Coursera University of Edinburgh, 2013 Critical Thinking in Global Challenges University of Edinburgh 75844 35084 6909 2013-01-28 5 Coursera University of Edinburgh, 2013 University of Toronto Introduction to Genetics and Evolution Duke University CS50x: Introduction to Computer Science I Vol 15 | No 1 Source Completed 2012-09-24 Learn to Program: The Fundamentals Enrolled 8243 Institution 38502 Course Active Jordan Feb/14 141 Initial Trends in Enrolment and Completion of Massive Open Online Courses Institution Enrolled Active Completed Start date Length Platform Introduction to Philosophy University of Edinburgh 98128 53255 9445 2013-01-28 7 Coursera University of Edinburgh, 2013 Astrobiology and the Search for Extraterrestria l Life University of Edinburgh 39556 20413 7707 2013-01-28 5 Coursera University of Edinburgh, 2013 Equine Nutrition University of Edinburgh 23322 18998 8416 2013-01-28 5 Coursera University of Edinburgh, 2013 Introductory Organic Chemistry Part 1 University of Illinois at UrbanaChampaign 17400 9000 2013-01-28 8 Coursera Arnaud, 2013 Stat2.1x: Introduction to Statistics: Descriptive Statistics University of California, Berkeley 52661 8181 2013-02-20 5 EdX Adhikari, 2013 Computational Investing, Part I Georgia Institute of Technology 25589 15688 1165 2013-02-23 8 Coursera Balch, 2013b AIDS Emory University 18600 10601 2013-02-25 9 Coursera Williams, 2013 Introductory Human Physiology Duke University PatternOriented Software Architectures for Concurrent and Networked Software Vanderbilt University 30979 Introduction to Mathematical Thinking Stanford University 27930 A Beginner's Guide to Irrational Behavior Duke University 142839 Gamification University of Pennsylvania Medical Neuroscience Duke University Vol 15 | No 1 Source Course Jordan 33675 1036 2013-02-25 12 Coursera Zhou, 2013 20180 1643 2013-03-04 8 Coursera Jordan, 2013 1950 2013-03-04 10 Coursera Schmoller, 2013 82008 3892 2013-03-25 8 Coursera Jordan, 2013 66438 34548 5592 2013-04-01 6 Coursera Werbach, 2013 44980 18433 756 2013-04-08 12 Coursera Novicki, 2013 Feb/14 142 Initial Trends in Enrolment and Completion of Massive Open Online Courses Completed Start date Length Platform 1520 2013-04-15 6 Coursera Kenyon, 2013 2087 2013-04-16 7 Coursera Jordan, 2013 12197 500 2013-04-29 10 Coursera Signsofchao s blog, 2013 6918 1626 2013-04-30 7 Coursera Anderson, 2013 1432 2013-05-01 8 Coursera Farkas, 2013 58000 2013-05-01 8 Coursera Farkas, 2013 10000 5000 2013-05-20 8 Coursera Friedrich, 2013 26,915 15392 2013-06-03 6 Coursera Course site at Coursera Healthcare Innovation and Entrepreneurs hip Duke University Mathematical Biostatistics Bootcamp Johns Hopkins University 21916 Generating the Wealth of Nations University of Melbourne 28922 Sports and Society Duke University 19281 Introduction to International Criminal Law Case Western Reserve University 21000 Inspiring Leadership through Emotional Intelligence Case Western Reserve University 90000 Statistical Molecular Thermodynam ics University of Minnesota Introduction to Systems Biology Icahn School of Medicine at Mount Sinai Source Active 15596 Enrolled Institution Course Jordan Data analysis was conducted using linear regression carried out with Minitab statistical software. Linear regression was chosen as the approach to analysis because at this stage the aim of the research was exploratory, to identify potential trends rather than being explanatory and seeking to fit a model. This would be a valuable goal for follow-up research particularly if more consistent data became available for MOOCs more broadly. Linear regression analyses were carried out individually according to different factors of interest rather than as a single multiple regression due to issues of data consistency and availability; that is, data is not available for every field in Tables 1 and 2 for every course, so n varies according to different tests (see Results and Analysis section). Rather than discarding courses for which the full spectrum of data was not available and in order to gain the greatest insight possible into the different factors, a series of individual regression analyses were carried out. Vol 15 | No 1 Feb/14 143 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan Limitations There are a number of limitations which must be borne in mind with the approach taken by this study, including issues of validity of data and reliability of the research instruments used. In terms of validity, it should be noted that the accuracy of figures varies according to sources, with some institutions releasing highly accurate figures and others (particularly when releasing enrolment data through the press) are rounded figures. This reflects the fact that MOOC courses do not consistently release this information into the public domain, and most of the courses that would have been eligible for inclusion (67.4%) have not released any data. Of the institutions or instructors choosing to make data available, bias may be introduced according to their motivations for publicizing this information, which are unknown. There is also a degree of trust involved in the information provided by student informants via the blog. It should be emphasized that the study sought to be exploratory in nature, identifying trends of interest in the data as a starting point for further research but not seeking to explain or model the phenomenon. Reliability of the approach is less contentious as the data have been collected via several rounds of internet searches during the data collection period (February 13th to July 22nd 2013) and shown in full in Tables 1 and 2 should others wish to reproduce the tests or carry out alternative analyses. By collating data ‘in the open’ at the author’s blog (Jordan, 2013), this offered a platform for others (including course leaders) to scrutinize the data and provide more accurate figures in some cases. Results and Analysis Trends in Total Enrolment Figures Total enrolment numbers draws upon the data in both Tables 1 and 2, which comprises a total of 91 courses (excluding three courses which are missing total enrolment figures). Total enrolment figures range from 4,500 to 226,652 students, with a median value of 42,844. The data does not exhibit a normal distribution (Figure 1); six-figure enrolments are not representative of the ‘typical’ MOOC. Total enrolments are shown plotted against the date each course began in Figure 2. This demonstrates a negative correlation, with enrolment numbers decreasing over time. Vol 15 | No 1 Feb/14 144 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan 30 25 Frequency 20 15 10 5 0 0 40000 80000 120000 160000 Total number of students enrolled 200000 Figure 1. Histogram of total enrolment numbers for the sampled courses (n = 91). Total number of students enrolled 250000 200000 150000 100000 50000 0 1 -0 10 11 20 1 1 1 1 1 -0 -0 -0 -0 -0 01 04 07 10 01 12 12 12 13 12 20 20 20 20 20 Date course began 1 1 -0 -0 04 07 13 13 20 20 Figure 2. Scatterplot of total enrolment numbers plotted against course start date for the sampled courses (n = 91). Vol 15 | No 1 Feb/14 145 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan A regression analysis was carried out, prior to which the data was subject to a Box-Cox transformation as the residuals do not follow a normal distribution. Regression analysis showed that date significantly predicted total enrolment figures at the 95% significance level by the following formula: ln(Enrolled) = 104.249 - 0.00226915*StartDate (R2 = 0.1719, p < 0.001). The relationship is a negative correlation, indicating that as time has progressed, enrolment figures have decreased. The relationship is relatively weak (time as a factor accounts for 17.2% of the variance observed, as R2 is a measure of the fraction of variance explained by the model; Grafen & Hails, 2002), although the sample is sufficiently large that this is statistically significant (critical R2 values decrease according to sample size, with an n of 91 being relatively large; Siegel, 2011). This highlights that a focus upon figures from early courses is misleading and not representative of how the field is developing. The relationship between course length and total enrolments was also considered, and found to demonstrate a positive correlation between course length and total enrolment (Figure 3). Total number of students enrolled 250000 200000 150000 100000 50000 0 0 5 10 15 Course length (weeks) 20 25 Figure 3. Scatterplot of total enrolment numbers plotted against course length for the sampled courses (n = 87). Following a Box-Cox transformation, regression analysis showed that course length significantly predicted (at the 95% significance level) total enrolment figures by the following formula: ln(Enrolled) = 10.2248 + 0.0491206*Length (R2 = 0.0545, p = 0.029). The correlation between the variables is positive, indicating courses that are Vol 15 | No 1 Feb/14 146 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan longer attract a greater number of enrolments. The relationship is relatively weak, accounting for 5.5% of the variance observed, although the sample size is sufficiently large for this to be a statistically significant relationship. This positive correlation may suggest that prospective MOOC students prefer more substantial courses (however, see also the relationship between course length and completion rates). In addition, the relationship between university ranking and enrolment figures was considered, although it was not found to be significant at the 95% level. Trends in Completion Rates Completion rates were calculated as the percentage of students (out of the total enrolment for each course) who satisfied the criteria to gain a certificate for the course. This information was available for 39 courses in the sample. Completion rates range from 0.9% to 36.1%, with a median value of 6.5% (Figure 4). The data is skewed, so the higher completion rates are not representative, with completion rates of 5% being typical. 20 Frequency 15 10 5 0 0 5 10 15 20 25 30 Percentage of total enrollment to complete course 35 Figure 4. Histogram of completion rates for the sampled courses (n = 39). As the residuals were not normally distributed, a Box-Cox transformation was again carried out before conducting regression analysis. No significant relationships were found between completion rate and date, university ranking, or the total number of students enrolled. Completion rates remained consistent across these factors. A significant negative correlation was found however between completion rate and course Vol 15 | No 1 Feb/14 147 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan Percentage of total enrollment to complete course length, shown in Figure 5. Regression analysis showed that course length significantly predicted completion rate by the following formula: ln(PercentTotalCompleted) = 2.64802 - 0.100461*CourseLength (R2 = 0.2373, p = 0.002). The correlation in this case is negative, indicating that a lower proportion of students complete longer courses. Course length accounts for 23.4% of the variance observed, and the correlation is significant at the 95% significance level. 40 30 20 10 0 5 10 15 Course length (weeks) 20 25 Figure 5. Scatterplot of completion rate plotted against course length for the sampled courses (n = 39). While considering completion rate as the percentage of the total enrolment that complete the course is the type of data that is most readily available, a criticism of this characterization is that many students may enroll without even starting the course, and that completion rates would be better characterized as the proportion of active students who complete. This level of information is available for a subset of the sampled courses (39 courses with a number of active students and total enrolment; 33 courses with data about the proportion of active students who complete). The number of active students is remarkably consistent as a proportion of the total enrolment of the course (with approximately 50% of the total enrolment becoming active students). This is shown graphically in Figure 6. Regression analysis showed that total enrolment significantly predicted the number of active students by the following formula: Active = 0.543336*Enrolled (R2 = 0.9556, p < 0.001). The correlation is strong (accounting for 95.6% of the variance) and positive, showing a consistent relationship Vol 15 | No 1 Feb/14 148 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan between total enrolment and the percentage who become active students (being approximately 54% of those who enroll). 140000 Number of active students 120000 100000 80000 60000 40000 20000 0 0 50000 100000 150000 200000 Total number of students enrolled 250000 Figure 6. Scatterplot of number of active students plotted against total enrolment for the sampled courses (n = 39). When calculating completion rate as the percentage of active students who complete the course, completion rates range from 1.4% to 50.1%, with a median value of 9.8% (Figure 7). While completion rates as a percentage of active students span a wider range than completion rates as a percentage of total enrolments, there remains a strong skew towards lower values. The differences here would be worthwhile to explore in further detail to explore features of course design that may account for the wider variation observed. Vol 15 | No 1 Feb/14 149 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan 14 12 Frequency 10 8 6 4 2 0 0 12 24 36 Percentage of active students who complete course 48 Figure 7. Histogram of completion rates as a proportion of active students for the sampled courses (n = 39). No significant relationships were found between completion rate as a proportion of active users and date, university ranking, total enrolment, or (in contrast to completion rate as a percentage of total enrolment) course length. This may suggest that enrolled students may be put off starting longer courses, but this is less of an issue for those who do become actively engaged in the course. Conclusions The findings here demonstrate changes in the field since the concept of MOOCs entered the mainstream and the inception of the major MOOC platforms. It is misleading to invoke early enrolment and completion figures as representative of the phenomenon; six-figure enrolments are atypical, with the median average enrolment being 42,844 students, and decreasing over time as the number of courses available continues to increase. Although this is lower than the earliest examples, it emphasizes that it is inappropriate to compare completion rates of MOOCs to those in traditional bricks-andmortar institution-based courses. The majority of courses have been found to have completion rates of less than 10% of those who enroll, with a median average of 6.5%. The definition of completion rate used here is the percentage of enrolled students who satisfied the courses’ criteria in order to Vol 15 | No 1 Feb/14 150 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan earn a certificate, and this definition was used because it is the type of information that is most frequently available. There are potentially many ways in which MOOC students may participate in and benefit from courses without completing the assessments. The wider range of completion rates (while still remaining quite low overall, with a median of 10%) observed when defining completion as a percentage of active learners in courses is interesting and warrants further work to better understand the reasons why those who become engaged initially do or do not complete courses. This is not to say, however, that completion rates should be ignored entirely. Looking at completion rates is a starting point for better understanding the reasons behind them, and how courses could be improved for both students and course leaders. For example, the relationship between enrolments, completion, and course length is an interesting issue for MOOC course design, balancing the higher enrolments with the lower completion rates of longer courses. Figures about how many students achieved certificates obscure how many students attempted to gain a certificate but did not meet the criteria. Given that MOOCs are offered free of educational prerequisites, striving to improve teaching on courses so that students who wish to complete are assisted in doing so is an important pedagogical issue. The extent of understanding that can be gained outside of running a MOOC will continue to be constrained however as long as the release of detailed data about courses is limited. This study has only considered relationships between enrolment and completion and a small number of general factors for which data is available publicly; various other factors would be worthwhile to explore. For example, it would be useful to look at in terms of the underlying pedagogy, whether differences emerged based on how transmissive (so-called ‘xMOOCs’) or connectivist (‘cMOOCs’) courses are. The impact of different assessment types, being necessarily linked to the criteria for achieving a certificate of completion, would also be a worthwhile area to consider in further detail. Along with the studies discussed in the introduction which focus upon links between student demographics or behaviours and completion (Breslow et al., 2013; Kizilcec et al., 2013; Koller et al., 2013), a limitation of the approach used here is that the data neglects the student voice. While these approaches can identify trends and patterns, they are unable to explore in detail the reasons behind the trends observed. Acknowledgments The author would like to thank Professor Martin Weller and the two anonymous peer reviewers for their comments on drafts of this paper. Special thanks to all of the MOOC students, instructors, and other commentators who contributed data and thoughtful comments about MOOC completion rates to the authors’ blog. Vol 15 | No 1 Feb/14 151 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan References Adhikari, A. (2013). Completion. Stat2x, Spring 2013 blog. Retrieved from http://stat2x.blogspot.co.uk/2013/04/completion.html Anderson, N. (2012). Grades are in for a pioneering free Johns Hopkins online class. The Washington Post. Retrieved from http://www.washingtonpost.com/blogs/college-inc/post/grades-are-in-for-apioneering-free-johns-hopkins-online-class/2012/11/14/1bd60194-2e6b-11e289d4-040c9330702a_blog.html Anderson, S. (2013). Duke Sports and Society MOOC wraps up. Duke Center for Instructional Technology blog: http://cit.duke.edu/blog/2013/07/duke-sportsand-society-mooc-wraps-up/ Arnaud, C. H. (2013). Flipping chemistry classrooms. Chemical & Engineering News. Retrieved from http://cen.acs.org/articles/91/i12/Flipping-ChemistryClassrooms.html Balch, T. (2013a). About MOOC completion rates: The importance of student investment. The Augmented Trader blog: http://augmentedtrader.wordpress.com/2013/01/06/about-mooc-completionrates-the-importance-of-investment/ Balch, T. (2013b). MOOC student demographics. The Augmented Trader blog: http://augmentedtrader.wordpress.com/2013/01/27/mooc-studentdemographics/ Barber, M. (2013). Comment posted on the Introduction to Operations Management page. Coursetalk.org: http://coursetalk.org/coursera/an-introduction-tooperations-management Belanger, Y. (2013). IntroAstro: An intense experience. Retrieved from http://hdl.handle.net/10161/6679 Belanger, Y., & Thornton, J. (2013). Bioelectricity: A quantitative approach. Duke University’s First MOOC: http://dukespace.lib.duke.edu/dspace/bitstream/handle/10161/6216/Duke_Bi oelectricity_MOOC_Fall2012.pdf Breslow, L., Pritchard, D. E., DeBoer, J., Stump, G. S., Ho, A. D., & Seaton, D. T. (2013). Studying learning in the worldwide classroom: Research into edX’s first MOOC. Research and Practice in Assessment, 8, 13-25. Burnette, D. (2012). The way of the future. The University of Virginia Magazine. Retrieved from Vol 15 | No 1 Feb/14 152 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan http://uvamagazine.org/features/article/the_way_of_the_future#.UdrX_1Pp6 ic Burton, G. (2013). Did they just say, “39,000 students enrolled in my Improvisation course?” OMG!. Garyburton.com news/opinion: http://www.garyburton.com/opinion/did-they-just-say-30000-studentsenrolled-in-my-improvisation-course-omg/ Campbell, G. (2013). The technicity story, part 2. The Technicity Story blog: http://blogs.lt.vt.edu/technicitystory/2013/04/24/the-technicity-story-part-2/ Cervini, E. (2012) Mass revolution or mass con? Universities and open courses. Crikey. At http://www.crikey.com.au/2012/12/18/mass-revolution-or-mass-conuniversities-and-open-courses/?wpmp_switcher=mobile Chu, J. (2013). Duflo, Lander, Lewin to lead spring-semester MITx courses. MIT News: http://web.mit.edu/newsoffice/2013/mitx-spring-offerings-0131.html Clark, S. (2013). Coursera – Introduction to music production by Loundon Stearns. Bytes and Banter blog: http://bytesandbanter.blogspot.co.uk/2013/06/coursera-introduction-tomusic.html Coursera. (2012). (DRAFT) Data export procedures. Retrieved from https://docs.google.com/viewer?a=v&pid=forums&srcid=MDMyNTg5NzM4O TAxMTY2NDg5NzEBMDEwNDAzNzI4ODgxODU0NTkwODQBLTkwOXZQa2h uODRKATQBAXYy Daniel, J. S. (2012). Making sense of MOOCs: Musings in a maze of myth, paradox and possibility. Journal of Interactive Media in Education. Retrieved from http://www-jime.open.ac.uk/jime/article/view/2012-18 Devlin, K. (2012a). Liftoff: MOOC planning – part 7. Devlin’s Angle blog: http://mooctalk.org/2012/09/21/mooc-planning-part-7/ Duke Today. (2012). Introduction to genetics and evolution, a preliminary report. Duke Today: http://today.duke.edu/node/93914 Emanuel, E. J. (2013). Online education: MOOCs taken by educated few. Nature, 503(342). Retrieved from http://dx.doi.org/10.1038/503342a Evans, T. (2013). Here’s the scoop on Ohio State MOOCs. Digital Union, Ohio State University: http://digitalunion.osu.edu/2013/04/01/osu-coursera-moocs/ Farkas, K. (2013). Case Western Reserve University’s free online courses exceeded expectations. Cleveland.com: Vol 15 | No 1 Feb/14 153 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan http://www.cleveland.com/metro/index.ssf/2013/07/case_western_reserve_u niversit_9.html Ferraro, K. (2013). Nutrition consulting. Ingrain Health: http://www.ingrainhealth.com/nutrition-consulting.html Florida Public Health Training Center. (2013). A public health refresher course. Florida Public Health Training Center Online Mentor Program blog: http://phmentorships.wordpress.com/2013/02/01/a-public-health-refreshercourse/ Friedrich, A. (2013). UMN faculty: MOOCs have made us rethink learning. On Campus: http://blogs.mprnews.org/oncampus/2013/07/umn-faculty-moocs-havemade-us-rethink-learning/ Furman University. (2013). TEDx FurmanU 2013 Redesigning Education Cast. Tedxfurmanu.com website: http://www.tedxfurmanu.com/#!2013/c1g5h Gillies, M. (2013). Creative programming for digital media & mobile apps. Marco Gillies webpage at Goldsmiths, University of London: http://www.doc.gold.ac.uk/~mas02mg/MarcoGillies/creative-programmingfor-digital-media-mobile-apps/ Grafen, A., & Hails, R. (2002). Modern statistics for the life sciences. Oxford: Oxford University Press. Guzdial, M. (2013). Slides from “The revolution will be televised” MOOCopalpse panel. Computing Education blog: http://computinged.wordpress.com/2013/03/09/slides-from-the-revolutionwill-be-televised-moocopalypse-panel/ Harder, B. (2013). Are MOOCs the future of medical education? BMJ Careers: http://careers.bmj.com/careers/advice/view-article.html?id=20012502 Hawkins, D. (2013). Massive open online courses (MOOCs): The Thursday plenary session. Against the Grain Blog: http://www.against-thegrain.com/2013/06/massive-open-online-courses-moocs-the-thursdayplenary-session/ Head, K. (2013). Inside a MOOC in progress. The Chronicle of Higher Education. Retrieved from http://chronicle.com/blogs/wiredcampus/inside-a-mooc-inprogress/44397 Heussner, K. M. (2013). More growing pains for Coursera: In another slip-up, professor departs mid-course. Gigaom: http://gigaom.com/2013/02/19/more-growingpains-for-coursera-in-another-slip-up-professor-drops-out-mid-course/ Vol 15 | No 1 Feb/14 154 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan Jordan, K. (2012). Networked life, social network analysis, & a new appreciation for feedback. MoocMoocher blog: http://moocmoocher.wordpress.com/2012/12/21/networked-life-socialnetwork-analysis-a-new-appreciation-for-feedback/ Jordan, K. (2013). Synthesising MOOC completion rates. MoocMoocher blog: http://moocmoocher.wordpress.com/2013/02/13/synthesising-mooccompletion-rates? Kapsidelis, K. (2013). U. Va. set to launch global classrooms. Times Dispatch. Retrieved from http://www.timesdispatch.com/news/local/education/college/u-va-setto-launch-global-classrooms/article_53fbd2b8-8bb1-58ff-89281eaca612a103.html Kenyon, A. (2013). Healthcare Innovation and Entrepreneurship final comments. Duke Center for Instructional Technology blog: http://cit.duke.edu/blog/2013/07/healthcare-innovation-andentrepreneurship-final-comments/ Kizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. Third International Conference on Learning Analytics and Knowledge, LAK ’13 Leuven, Belgium. Koller, D., & Ng, A. (2013). The online revolution: Education for everyone. Seminar presentation at the Said Business School, Oxford University, 28th January 2013. Retrieved from http://www.youtube.com/watch?v=mQ-KsOW4fU&feature=youtu.be Koller, D., Ng, A., Do, C., & Chen, Z. (2013). Retention and intention in massive open online courses: In depth. Educause Review. Retrieved from http://www.educause.edu/ero/article/retention-and-intention-massive-openonline-courses-depth-0 Kolowich, S. (2013, March 21). The professors who make the MOOCs. The Chronicle of Higher Education. Retrieved from http://chronicle.com/article/TheProfessors-Behind-the-MOOC/137905/#id=overview Leckart, S. (2012). The Stanford education experiment could change higher education forever. Wired Magazine. Retrieved from http://www.wired.com/wiredscience/2012/03/ff_aiclass/3/ Lesiewicz, A. (2013). Drugs and the brain. ATA Science & Technology Division blog: http://ata-sci-tech.blogspot.co.uk/2013/02/drugs-and-brain.html Vol 15 | No 1 Feb/14 155 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan Lewin, T. (2012a). College of future could be come one, come all. The New York Times. Retrieved from http://www.nytimes.com/2012/11/20/education/colleges-turnto-crowd-sourcing-courses.html Lewin, T. (2012b). One course, 150,000 students. The New York Times. Retrieved from http://www.nytimes.com/2012/07/20/education/edlife/anant-agarwaldiscusses-free-online-courses-offered-by-a-harvard-mitpartnership.html?ref=education Lugton, M. (2012). Review of the Coursera Human Computer Interaction Course blog: http://reflectionsandcontemplations.wordpress.com/2012/07/14/review-ofthe-coursera-human-computer-interaction-course/ Malan, D. J. (2013). This was CS50x. CS50 blog: https://blog.cs50.net/2013/05/01/0/ Masolova, E. (2013). Interview with Daphne Koller, CEO of COURSERA. Eduson blog: https://www.eduson.tv/blog/coursera McKenna, L. (2012). The big idea that can revolutionize higher education: ‘MOOC’. The Atlantic. Retrieved from http://www.theatlantic.com/business/archive/2012/05/the-big-idea-that-canrevolutionize-higher-education-mooc/256926/ Meyer, R. (2012). What it’s like to teach a MOOC (and what the heck’s a MOOC?). The Atlantic. Retrieved from http://www.theatlantic.com/technology/archive/2012/07/what-its-like-toteach-a-mooc-and-what-the-hecks-a-mooc/260000/ Miller, H., & Odersky, M. (2012). Functional programming principles in Scala: Impressions and statistics. Scala Documentation website: http://docs.scalalang.org/news/functional-programming-principles-in-scala-impressions-andstatistics.html Moran, M. (2013). Free online nutrition course kicks off May 6th. Vanderbilt News. Retrieved from http://news.vanderbilt.edu/2013/05/coursera-nutrition/ Nelson, B. (2013). UF offers massive online learning for free. 1565today.com: http://1565today.com/uf-offers-massive-learning-online-for-free/ Novicki, A. (2013). Medical Neuroscience in Coursera has just finished. Duke Center for Instructional Technology blog: http://cit.duke.edu/blog/2013/07/courseramedical-neuroscience-week-3/ Pappano, L. (2012). The year of the MOOC. The New York Times. http://www.nytimes.com/2012/11/04/education/edlife/massive-open-onlinecourses-are-multiplying-at-a-rapid-pace.html?pagewanted=1 Vol 15 | No 1 Feb/14 156 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan Pattison, P. (2013). Coursera songwriting course starts July 19th. Patpattison.com: http://www.patpattison.com/news/entry?id=16 Posey, J. (2013). Free Penn online course offers lessons on growing old. Penn News. Retrieved from http://www.upenn.edu/pennnews/news/free-penn-onlinecourse-offers-lessons-growing-old Princeton University. (2012). Office of Information Technology administrative report, September 07, 2012. Retrieved from http://www.princeton.edu/oit/about/oitadministrative-report/PDFs/Admin_09-12.pdf Riddle, R. (2013a). Preliminary results on Duke’s third Coursera effort, “Think Again”. Duke Center for Instructional Technology blog: http://cit.duke.edu/blog/2013/06/preliminary-results-on-dukes-thirdcoursera-effort-think-again/ Riddle, R. (2013b). Duke MOOCs: Looking back on “Image and Video Processing”. Duke Center for Instructional Technology blog: http://cit.duke.edu/blog/2013/06/looking-back-on-image-and-videoprocessing/ Rivard, R. (2013). Three out of 2U. Inside Higher Ed. Retrieved from http://www.insidehighered.com/news/2013/05/17/three-universities-backaway-plan-pool-courses-online Rodriguez, C. O. (2012). MOOCs and the AI-Stanford like Courses: Two successful and distinct course formats for massive open online courses. European Journal of Open, Distance, and E-Learning. Retrieved from http://www.eurodl.org/index.php?article=516 Roth, M. S. (2013). My modern experience teaching a MOOC. The Chronicle of Higher Education. Retrieved from http://chronicle.com/article/My-Modern-MOOCExperience/138781 Rushakoff, H. (2012). Free to learn: Geology, chemistry, and microeconomics are among U of I’s first free online courses on Coursera. University of Illinois at Urbana-Champaign College of Liberal Arts & Sciences News. Retrieved from http://www.las.illinois.edu/news/2012/coursera/ Schmoller, S. (2012). Peter Norvig’s TED talk reflecting on creating and running the online AI course. Schmoller.net: http://fm.schmoller.net/2012/07/peternorvigs-ted-talk-about-the-ai-course.html#more Schmoller, S. (2013). Second report from Keith Devlin’s and Coursera’s Introduction to Mathematical Thinking MOOC. Schmoller.net: Vol 15 | No 1 Feb/14 157 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan http://fm.schmoller.net/2013/06/second-report-from-keith-devlins-itmtcourse.html Severance, C. (2012). Internet history, technology and security (IHTS) grand finale lecture slides. Retrieved from http://www.slideshare.net/fullscreen/csev/internet-history-technology-andsecurity-grand-finale-lecture-20121001/7 Sharma, Y. (2013). Hong Kong MOOC draws students from around the world. The Chronicle of Higher Education. Retrieved from http://chronicle.com/article/Hong-Kong-MOOC-DrawsStudents/138723/?cid=wc&utm_source=wc&utm_medium=en Siegel, A. F. (2011). Practical business statistics (6th ed.). Oxford: Academic Press. Siemens, G. (2012). MOOCs are really a platform. Elearnspace blog: http://www.elearnspace.org/blog/2012/07/25/moocs-are-really-a-platform/ Signsofchaos blog. (2013). An assessment of a MOOC. Signsofchaos blog: http://signsofchaos.blogspot.co.uk/2013/07/an-assessment-of-mooc.html Simply Statistics. (2012). Computing for data analysis (Simply statistics edition). Simply Statistics blog: http://simplystatistics.org/2012/10/29/computing-for-dataanalysis-simply-statistics-edition/ St. Petersburg College. (2013). Alex Sharpe successfully completes University of Toronto online course via Coursera. The CCIT Bulletin, St. Petersburg College. Retrieved from http://www.spcollege.edu/ccit-bulletin/?p=1012 Stauffer, J. (2013). Connected Arctic educators discussion thread. https://plus.google.com/114587962656605254648/posts/fmLmhDE9cSk Times Higher Education. (2013). World university rankings 2012-2013. Retrieved from http://www.timeshighereducation.co.uk/world-university-rankings/201213/world-ranking Unger, M. (2013). Eye on the future: Coursera. Penn Current. Retrieved from http://www.upenn.edu/pennnews/current/2013-02-21/eye-future/eye-futurecoursera University of Edinburgh. (2013). MOOCs @ Edinburgh 2013 – Report #1. University of Edinburgh: http://www.era.lib.ed.ac.uk/bitstream/1842/6683/1/Edinburgh%20MOOCs% 20Report%202013%20%231.pdf University of Michigan. (2012). Halderman’s “Securing Digital Democracy” opens on Coursera. Department of Electrical Engineering and Computer Science: Vol 15 | No 1 Feb/14 158 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan http://www.eecs.umich.edu/eecs/about/articles/2012/Halderman_Coursera_l aunch.html University of Virginia. (2013). U. Va. Darden School’s first Coursera class reaches 71,000 registrants. University of Virginia Darden School of Business news. Retrieved from http://www.darden.virginia.edu/web/Media/Darden-NewsArticles/2013/Dardens-First-Coursera-Class-Reaches-71000-Registrants/ Weinzimmer, S. (2012). Rice’s first Coursera class enrolls 54,00. The Rice Thresher. Retrieved from http://www.ricethresher.org/rice-s-first-coursera-class-enrolls54-000-1.2932146#.UcsMlJw1DTo Welsh, D. H. B., & Dragusin, M. (2013). The new generation of massive open online courses (MOOCs) and entrepreneurship education. Small Business Institute Journal, 9(1), 51-65. Wesleyan University. (2013). Passion driven statistics. Wesleyan University Quantitative Analysis Center: http://www.wesleyan.edu/qac/studentprofile/homepage_slideshow_coursera_information.html Werbach, K. (2012). Gamification course wrap-up. PennOpenLearning YouTube channel: http://www.youtube.com/watch?v=NrFmiqhBep4 Werbach, K. (2013). Gamification Spring 2013 statistics. Coursera Gamification YouTube channel: http://www.youtube.com/watch?v=E8_3dNEMukQ&feature=youtu.be Williams, K. (2013). Emory and Coursera: Benefits beyond the numbers. Emory news center: http://news.emory.edu/stories/2013/05/er_coursera_update/campus.html Widom, J. (2012). From 100 students to 100,000. ACM SigMod Blog: http://wp.sigmod.org/?p=165 Yuan, L., & Powell, S. (2013). MOOCs and open education: Implications for higher education (JISC CETIS white paper). Retrieved from http://publications.cetis.ac.uk/2013/667 Zhou, H. (2013). Duke University completes its first “Introductory Human Physiology” MOOC! Duke Center for Instructional Technology blog: http://cit.duke.edu/blog/2013/06/reflection-physio/ Vol 15 | No 1 Feb/14 159 Initial Trends in Enrolment and Completion of Massive Open Online Courses Jordan Vol 15 | No 1 Feb/14 160 Deakin Research Online This is the authors’ final peered reviewed (post print) version of the item published as: Palmer, Stuart, Holt, Dale and Bray, Sharyn 2008, Does the discussion help? The impact of a formally assessed online discussion on final student results, British journal of educational technology, vol. 39, no. 5, pp. 847-858. Available from Deakin Research Online: http://hdl.handle.net/10536/DRO/DU:30017812 Reproduced with the kind permission of the copyright owner. Copyright : 2008, Wiley-Blackwell Does the discussion help? The impact of a formally assessed online discussion on final student results Stuart Palmer, Dale Holt and Sharyn Bray Drs Stuart Palmer and Dale Holt are Senior Lecturers in the Institute of Teaching and Learning at Deakin University in Australia. Ms Sharyn Bray is a Research Assistant in the School of Engineering and Information Technology at Deakin University in Australia. Address for correspondence: Dr Stuart Palmer, Institute of Teaching and Learning, Deakin University, Geelong, Victoria, 3217, Australia. Tel: +613 5227 8143; fax: +613 5227 8129; email: spalm@deakin.edu.au Abstract While there is agreement that participation in online asynchronous discussions can enhance student learning, it has also been identified that there is a need to investigate the impact on student course performance of participation in online discussions. This paper presents a case study based on an undergraduate engineering management unit employing a formally assessed online discussion area. It was observed that while many students read a significant number of discussion postings, generally, the posting of new and reply messages occurred at the minimum level required to qualify for the assignment marks. Based on correlation and multiple regression analysis, it was observed that two variables were significantly related to a student’s final unit mark – prior academic ability and the number of new postings made to the online discussion. Each new posting contributed three times as much to the final unit mark as its nominal assessment value, suggesting that the work in preparing their new discussion postings assisted students in the completion of a range of assessable tasks for the unit. The number of postings read was not significantly correlated with the final unit mark, suggesting that passive lurking in this online discussion did not significantly contribute to student learning outcomes. Introduction Dialogue is considered to be an essential element of human learning, particularly for distance education (Gorsky & Caspi, 2005). It includes interactions between students and teachers, exchanges between students, interactions between students and others not directly involved in their learning processes and dialogue with oneself in the form of reflective thought (Webb, Jones, Barker & van Schaik, 2004). With the advent of online technologies in teaching and learning, particularly in distance education, the use of online discussion forums is now a widespread medium for learning dialogue. Online discussion can be synchronous through the use of real-time chat tools, but many examples of online discussions documented in the literature present the use of asynchronous discussion. That is, where students post new and follow-up messages to an electronic bulletin-board at the times that suit them, and not necessarily at the same time that other students are accessing the discussion system. The claimed benefits of online asynchronous discussion forums include:  the time between postings for reflective thought that might lead to more considered responses than those possible in face-to-face situations (Garrison, Anderson & Archer, 1999);  for off-campus students, two-way communication can be enhanced, reducing student isolation and making possible dialogue with other students (Kirkwood & Price, 2005);  the convenience of choice of place and time to learners (Cotton & Yorke, 2006);  the creation of a sense of community (Davies & Graff, 2005);  the development of skills for working in virtual teams (Conaway, Easton & Schmidt, 2005);  increased student completion rates from increased peer interaction and support (Wozniak, 2005); and  increased student control, ability for students to express their own ideas without interruption, the possibility to learn from the collectively created content, the creation of a permanent record of one’s thoughts, the creation of a reusable instructional tool that models expected answers and discussion use, and they create a valuable archive of material for investigation and research (Hara, Bonk & Angeli, 2000). While there is wide agreement that participation in online asynchronous discussions can enhance student learning, and significant work has been done characterising, and theorising on the nature of student communications in online discussions, it has also been identified that there is a need to investigate the impact on student course performance of participation in online discussions (Hara et al., 2000). In a combined quantitative and qualitative analysis of the online discussion postings of education students studying by distance education in Australia it was found that those students achieving the highest final unit grade also had the highest frequency of posting, and that lower achieving students were less active online; though the authors do not claim these findings as conclusive evidence of the effect of online participation on learning outcomes (as measured by marked assessment activities) (Stacey & Rice, 2002). In a quantitative analysis of two online discussions in the UK involving 543 computing students, it was found that both the number of student accesses of the system and the number of student postings to the system were significant predictors of variance in final mark (in one case) and variance in final grade (in the other case) (Webb et al., 2004). In a quantitative analysis of online discussion usage involving 122 UK business students based on what percentage of all online system accesses related to usage of the online communication system, it was found that students achieving high or medium passing grades were significantly more active in the discussion area than students achieving a low passing grade, and in turn, students achieving a low passing grade were significantly more active than students who failed (Davies & Graff, 2005). It is noted that while the literature suggests a correlation between increased interaction and increased learning, there is limited research to understand the impact of different types of postings on learning outcomes (as measured by unit final grade) (Conaway et al., 2005). Simply encouraging students to get more involved in online discussions may not necessarily lead to better learning outcomes – there is a need to understand what are the ‘salient factors’ in online interaction that might enhance learning (Davies & Graff, 2005). One debated factor is whether student participation in online discussions should be optional or mandatory. It has been noted that some learning theories suggest that user motives largely determine how students engage with learning activities; intrinsically motivated learners will invest high levels of cognitive effort regardless of any associated rewards, whereas extrinsically motivated learners may be enticed to participate by gaining unit marks, but their engagement may be instrumental and shallow (Kuk, 2003). While there is evidence that online discussion interaction carried out on a voluntary basis may lead to better learning outcomes (as measured by unit final grade) (Weisskirch & Milburn, 2003), a pragmatic approach suggests that discussion contribution is likely to be low unless there is some compulsion to participate (Graham & Scarborough, 2001). Students have many competing demands on their time, and if their use of online learning tools is optional, the perceived benefits of participation will need to outweigh the perceived efforts of using the system. In this case, for some students, there may be benefits in providing extrinsic motivators for students to learn and use the system (Garland & Noyes, 2004). Another form of optional engagement with online discussion forums is ‘lurking’, where students enrolled in a discussion do not make postings, rather they simply read the postings of others. These lurkers may not be detected by some online systems, and the question remains, are these lurkers learning or not? (Hara et al., 2000) There is some evidence that both active participation (posting) and passive participation (lurking) may be beneficial to online discussion users (Webb et al., 2004). A final, but important question about student learning and participation in online discussions relates to the presumption that the often observed correlation between student participation (number of postings, assessed quality of posting, etc) and learning outcomes (student final unit mark/grade, etc) is causative, and not simply the result of more able and/or motivated students engaging more deeply with the online discussion than less able students (Cotton & Yorke, 2006). Is it possible that the students with the best results in a unit would have done well in the unit, regardless of whether an online discussion was employed or not? Context The School of Engineering and Information Technology at Deakin University in Australia offers a three year Bachelor of Technology (BTech) and a four year Bachelor of Engineering (BE) at undergraduate level. These programs are delivered in both on-campus and offcampus modes. These programs include the second-year engineering management / professional practice study unit SEB221 Managing Industrial Organisations. consists of four modules: This unit 1. Systems Concepts for Engineers and Technologists; 2. Managing People in Organisations; 3. Manufacturing and the Environment; and 4. Occupational Health and Safety. Prior to 2005, this unit was delivered in both on-campus (weekly classroom lectures) and offcampus (printed study guides) modes, with on-campus students generally purchasing the printed study guides as well, and all students having access to an online area providing basic resources, including an optional asynchronous discussion forum and the capacity for academic staff to post ‘announcements’ to all class members. The unit assessment regime consisted of two assignments each worth 25 % of the unit marks and an end-of-semester examination worth 50 % of the unit marks. In 2005, this unit was converted to ‘wholly online’ delivery mode, where all teaching of the unit occurred online (Holt & Challis, 2007). The printed study guides were replaced by a CD-ROM version of the study materials, enhanced with interactive/animated diagrams and video material. Up to this time, the first author had academic responsibility for the Managing People in Organisations module, and was not responsible for unit overall. The assessment regime was not changed for wholly online delivery. At the end of 2005, due to staffing changes, the first author assumed full responsibility for the entirety of SEB221, and a review of the wholly online delivery strategy for the unit was undertaken. Deakin University’s policy and procedure for ‘Online Technologies in Courses and Units’ requires that wholly online units be, “… designed to help students to develop their skills in communicating and collaborating in an online environment…” (Holt & Challis, 2007). While the inclusion of an optional general online discussion area may have met the ‘letter of the law’ for the wholly online unit policy, it was considered inadequate as a means for genuinely developing student online communication and collaboration skills. For 2006, ten % of the unit marks were taken from the final examination and dedicated to formally assessed assignment activity based around the online discussion area. The other unit assessment items were retained. A summary of the assignment instructions given to students is provided below. ‘DSO’ refers to Deakin Studies Online, the local name of the Blackboard course management system (CMS) used at Deakin University. This assignment requires you to both reflect on your studies and to constructively engage with the wholly online environment used in this unit. You are required to post reflections on the course material and to comment on the postings made by other students during the semester. You have two types of task in this assignment. Task 1 – Reflect on the course material you have studied in the current week. Identify what you think is the most important topic, access the DSO system for this unit, open the Assignment 1 forum area for the appropriate week, select ‘Compose Message’ and post a few paragraphs on your selected topic that explain why you think it is important. Task 2 – Review some of the Assignment 1 posts made by other students and select one to comment on. With that message open select ‘Reply’ and post a follow-up to the original message. You may add your own additional thoughts/reasons for why that topic is important, you may wish to contribute an example related to that topic from your own experience, or something else. You need to make at least five postings for each type of task given above, ie, at least ten postings in total, five of type one and five of type two. You should make only one of each type of posting in a given week. Only the best posting for either task type in a given week will be marked. If your postings demonstrate constructive and thoughtful reflection, you will be awarded up to 1 mark per posting, up to a maximum of 10 marks in total for the assignment. You can make more than five postings for each type of task to maximise your mark for Assignment 1. Please use your own thought/words, do not simply reproduce the course notes. Please note that the forum areas will not remain open for posting all semester, ie, it will not be possible to complete all your postings late in the semester. In summary, students were asked to make at least five ‘new’ postings reflecting on the course material, with up to one mark awarded for each of the five ‘best’ new posts, and, to make at least five ‘follow-up’ postings reflecting on the prior posts of their peers, with up to one mark awarded for each of the five ‘best’ follow-up posts. Student participation in the online discussion was made ‘mandatory’ in the sense that marks were assigned to participation. As noted previously, the literature suggests that some form of extrinsic motivation is required to ensure a high level of student discussion participation. A weighting of ten % was chosen for discussion participation – this figure is noted in case studies elsewhere in the literature (Graham & Scarborough, 2001; Hara et al., 2000). It was felt that this weighting would provide incentive for most students to participate, while at the same time not compromising the unit assessment regime should there be unforeseen implementation issues with this initial trial of the asynchronous discussion assignment. Strategies to promote a high level of participation in online discussions include requiring a specific number of postings per assignment and/or per week (Conaway et al., 2005). In this case, both these strategies were combined. It has been found that a key element in the effective use of computer conferencing is ‘intentional design’ of the online environment (Harasim, 1991). Intentional design includes designating conferences (online discussion areas) according the nature of the task (formal or informal), the duration of the task (one week, whole semester, etc), size of the group (plenary, small group etc), etc. Separate weekly discussion spaces were created to structure the formal student assignment postings. This permitted newer discussion areas to be progressively revealed, and older areas to be progressively set as read-only as the semester progressed. A separate informal area was maintained for general unit discussion and questions. As noted, the assignment-related discussion areas did not remain open all semester, to encourage students to engage with the unit material in a timely manner across the semester. Due to the nature of the assignment task, all of the discussion areas were open to all students – there was no separate small-group discussions employed. As this was the first time a formally assessed online discussion task was employed in this unit, it was decided to undertake a quantitative investigation to explore the forms of student engagement with the online discussion, the impact of participation on the students’ final unit result, whether passive participation/lurking had any benefit, and whether any impact/benefit was separable from the students’ prior general academic performance in their studies. Method Student participation in online discussions can be analysed in quantitative terms (number of postings, length of postings, number of messages read, etc), qualitative terms (does the posting exhibit cognitive/social/teaching presence?, does the posting exhibit knowledge/comprehension/application/analysis?, is the posting on task/off task?, etc) or some combination of quantitative and qualitative. Quantitative analysis can be performed quickly using system data, but may not yield a complete picture of student engagement in the discussion (Hara et al., 2000). However, qualitative analysis requires the examination of every student posting to classify the content, consuming significant time and open to the variation in message content classification by different assessors (Cotton & Yorke, 2006). At the commencement of the semester, an initial model posting of the type expected was made to seed the discussion and provide an exemplar to students. During the semester, student postings were assessed on an on-going basis according to the published criteria. Both in initial and follow-up postings, students were asked to discuss unit content, hence the postings were primarily assessed based on the quality of cognitive presence. Following the completion of the semester, data on the student usage of the online discussion area was compiled from the following sources:  student age (whole years at the end of semester);  student gender (male or female);  student normal mode of study (on-campus or off-campus);  student course of study (BTech, BE or other);  student prior general academic performance (measured at Deakin by the Weighted Average Mark (WAM));  the total number of discussion messages read (or at least opened) by the student;  the total number of new/initial discussion postings made by the student;  the total number of follow-up/reply discussion postings made by the student; and  the final unit mark obtained by the student for SEB221. The collected data were analysed and the following information was compiled:  descriptive statistics on the use of the discussion areas;  visualisation of the patterns of usage of the discussion areas;  investigation of correlation (Pearson’s linear correlation coefficient) between data variable pairs; and  multivariate linear regression to find the significant independent variables contributing to the dependent variable ‘final unit mark’. Results and discussion Descriptive statistics The number of student completing the unit (still officially enrolled at the end of the semester) was 86. The total number of assessable messages posted was 645. The average number of words per posting was 290. Figure 1 shows the distribution of assessable student postings across the semester. 120 No. of messages 100 80 60 40 20 0 1 2 3 4 5 6 7 8 9 10 11 12 Week Figure 1: Distribution of assessable student postings across the semester There was a general downward trend in discussion posting until week 8, after which the number of remaining weeks in the semester equalled the number of posts required from a student to maximise their possible mark, and after which the general trend picked up again slightly, perhaps indicating a belated effort by those students who hadn’t actively engaged with the discussion assignment task previously. Figure 2 shows the ranked distribution of total new/initial postings made by students. 10 9 8 No. new posts 7 6 5 4 3 2 1 0 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 Student Figure 2: Ranked distribution of total new/initial postings made by students The mean number of new postings was 3.8, with a standard deviation of 2.8. The median and modal number was 5, and the range was 0 to 9. Figure 3 shows the ranked distribution of total follow-up/reply postings made by students. 50 45 No. replies posted 40 35 30 25 20 15 10 5 0 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 Student Figure 3: Ranked distribution of total follow-up/reply postings made by students The mean number of follow-up postings was 3.7, with a standard deviation of 5.4. The median number was 3.5, the modal number was 0, and the range was 0 to 47. It is well known that students take a strategic approach to study, and the learning activities they engage most fully with are those most clearly associated with what will be assessed (James, McInnis & Devlin, 2002). Even though marks were attached to students’ contribution to the online discussion as an overt indicator that participation was considered important, and disregarding students with a final mark of zero for the unit, 16.7 % of students made no new/initial postings and 11.9 % of students made no follow-up/reply postings. A similar rate of students foregoing assessment worth ten % based on participation in an online asynchronous discussion task is noted in the literature (Graham & Scarborough, 2001). Figures 2 and 3 suggest that even those students who did engage with the assignment task only tended to do the minimum required (one new post and one reply post per week, up to a maximum of ten combined) to qualify for the assignment marks on offer. This type of minimum student engagement in an assessable online discussion activity is reported elsewhere (Hara et al., 2000), and reinforces the idea that students are busy, and extrinsic motivation is likely to be necessary to encourage even a basic level of participation in online discussion activities. Figure 4 shows the ranked distribution of total number of messages read by students – technically, the CMS records the number of messages ‘opened’ by students, but this was taken as a proxy measure of number of messages ‘read’ by students. 800 No. messages read 700 600 500 400 300 200 100 0 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 Student Figure 4: Ranked distribution of total number of messages read by students The mean number of messages read was 149.6, with a standard deviation of 201.7. The median number was 63.5, the modal number was 669, and the range was 0 to 669. Note that the figure of 669 is higher than the figure of 645 assessable messages given above, as it includes some messages posted by students who did not complete the unit, but that were never-the-less read by the completing students. Interestingly, the modal number of messages read was also the maximum number, indicating that a significant proportion of students read every single discussion posting. Visualisation of patterns of usage A method for visualising the message posting profile of all students together as a group was devised. A ranking factor was computed for each student, based on weighting postings early in the semester higher, and postings later in the semester lower. This factor was used to rank order all students from highest to lowest. Figure 5 shows the rank ordered profile of new/initial postings made by students across the semester. Figure 5: Rank ordered profile of new postings by students across the semester Four relatively distinct discussion new posting profiles, with approximately equal proportions of students in each can be observed. Students 1-21 (21 students, 24.4 %) made their required five (or so) posts, commencing at week one, and then generally left the discussion space. Students 22-44 (23 students, 26.7 %) commenced their posts in week one and then had a range of posting profiles, typically not continuous, re-entering the discussion space at various points over the twelve weeks. Students 45-69 (25 students, 29.1 %) commenced their posts some time after week one and then had a range of posting profiles, typically not continuous, with students who commenced their posting late in the twelve week period exhibiting more intense posting in an attempt to meet the assignment criteria of making five new posts in total. Students 70-86 (17 students, 19.8 %) made no postings at all during the twelve week period. Data variable paired value correlations Two significant correlations were observed; final unit mark and WAM (r = +0.43, p < 4105 ), and final unit mark and total number of new postings (r = +0.49, p < 210-6). Inspection of variable pair scatter plots revealed that the relationship between final unit mark and number of new postings plateaued after five new postings. After the data range for the number of new postings was limited to five or less the correlation was (r = +0.59, p < 410-9). As might be expected, a correlation was observed between previous academic performance (as measured by the student’s WAM), and final unit result in SEB221. The observed correlation between total number of new postings and final unit mark was strongest for number of new posts between zero and five. This is not surprising as, while students were allowed to make multiple new postings per week, only the single ‘best’ new posting result was taken as the mark for that week. While both WAM and number of new posts appear to have a positive correlation with final unit mark, they do not have a significant correlation with each other (r = +0.23, p > 0.033), suggesting that they are not significantly multicollinear with the final unit result, and that both contribute independently and positively to the final unit mark. Multivariate regression Following removal of three data items with an unknown (not BE or BTech) course of study and four data items for students with a final unit mark of zero (did not complete unit but did not official withdraw their enrolment), multivariate linear regression analysis was conducted with final unit mark as the dependent variable. All other known variables were initially introduced as independent variables, and step-wise regression was performed until all remaining variables were significant. Table 1 shows the coefficients of the regression model and their significance. Table 1: Multivariate linear regression model for dependant variable ‘final unit mark’ Variable Coefficient Standard error Beta Significance No. new posts (≤ 5) 3.05 0.47 0.50 p < 110-8 WAM 0.51 0.08 0.48 p < 310-8 Constant 28.17 5.50 - p < 310-6 An Analysis of Variance (ANOVA) test suggests that the regression model is significant (F78 = 47.29, p < 510-14), though the model predicts only 55.4 % of the variation on final unit mark (R2 = 0.554). The regression residuals were approximately normally distributed. The model explains only just over half of the variation observed in the final unit mark, hence there exist other factors with a significant influence on final unit mark that were not available in the data collected for this analysis. The results of the regression analysis support the results of the data pair correlation analysis that both the number of new postings and WAM contribute significantly and independently to final unit mark. Based on the marking scheme of ‘up to 1 mark per posting’, it would be expected, all other things being equal, that posting one new message would add approximately one mark to the final unit result. Instead, the regression analysis indicates that there was a significant benefit (up to three marks per new posting) beyond the notionally allocated marks for new postings. This suggests that the work that students completed in preparing their new discussion postings engaged them with the unit material and assisted them in the completion of other assessable tasks for the unit. None of the student demographic characteristics (age, gender, mode of study and course of study) were found to be significantly correlated with levels of participation in the discussion (messages read, new postings and reply postings), suggesting that all students were able to participate in the online discussion exercise on a generally equal basis. It has been proposed that the ways in which students engage with online asynchronous discussions will influence the learning outcomes achieved (Cotton & Yorke, 2006). The four types of student engagement with the discussion space identified in Figure 5 were used as a grouping variable and entered into the multiple regression analysis, but it was not found to be a significant contributor to final units result. Conclusion A formally assessed online discussion task was introduced into an engineering management unit delivered in wholly online mode, as a response to a perceived need to better-develop student online communication skills. While it was qualitatively observed that student participation in unit online discussions increased significantly compared to previous unit offerings, following the introduction of a formally assessed online discussion task, a quantitative examination was undertaken to investigate the impact of the students’ participation in the online discussion on their final unit results. It was observed that while many students read a significant number of discussion postings, generally, the posting of new and reply messages occurred at the minimum level required to qualify for the assignment marks. Based on new postings to the online discussion, four distinct patterns of posting were observed. Based on correlation and multiple regression analysis, it was observed that two measured variables were significantly related to a student’s final unit mark – their weighted average mark (used as a proxy measure for general prior academic ability) and the number of new postings that they made to the online discussion. In addition, these two variables were not significantly correlated with each other, and were both significant in the regression model obtained, suggesting that both contribute independently to the final unit mark. The regression model explained more than half of the observed variation in final unit mark, and while it shouldn’t be interpreted literally as the ‘formula’ that determines a student’s final unit mark, it does suggest that the influence of active participation in the online discussion assignment through the posting of reflective contributions based on the course material made about the same contribution to a student’s final unit mark as their general prior academic ability. Further, the regression model indicated that each new posting contributed three times as much to the final unit mark as its nominal assessment value of ‘up to 1 mark per posting’ would otherwise indicate, suggesting that the work in preparing their new discussion postings engaged students with the unit material and assisted them in the completion of a range of assessable tasks for the unit. However, while active contribution to the online discussion in the form of new posts was a significant factor in the final unit mark, simply reading the posts of other students was not. The number of postings read was not significantly correlated with the final unit mark, suggesting that passive ‘lurking’ in this online discussion did not significantly contribute to student learning outcomes (as measured by final unit mark). References Conaway, R. N., Easton, S. S. & Schmidt, W. V. (2005). Strategies for Enhancing Student Interaction and Immediacy in Online Courses. Business Communication Quarterly, 68(1), 23-35. Cotton, D. & Yorke, J. (2006, 3-6 December). Analysing online discussions: What are students learning? Paper presented at the 23rd annual ascilite conference: Who’s learning? Whose technology?, Sydney University Press, Sydney. Davies, J. & Graff, M. (2005). Performance in e-learning: online participation and student grades. British Journal of Educational Technology, 36(4), 657-663. Garland, K. & Noyes, J. (2004). The effects of mandatory and optional use on students’ ratings of a computer-based learning package. British Journal of Educational Technology, 35(3), 263-273. Garrison, D. R., Anderson, T. & Archer, W. (1999). Critical Inquiry in a Text-Based Environment: Computer Conferencing in Higher Education. The Internet and Higher Education, 2(2/3), 87-105. Gorsky, P. & Caspi, A. (2005). Dialogue: a theoretical framework for distance education instructional systems. British Journal of Educational Technology, 36(2), 137-144. Graham, M. & Scarborough, H. (2001). Enhancing the learning environment for distance education students. Distance Education, 22(2), 232-244. Hara, N., Bonk, C. J. & Angeli, C. (2000). Content analysis of online discussion in an applied educational psychology course. Instructional Science, 28(2), 115-152. Harasim, L. (1991, 8-11 January). Designs & tools to augment collaborative learning in computerized conferencing systems. Paper presented at the Twenty-Fourth Annual Hawaii International Conference on System Sciences, Institute of Electrical and Electronics Engineers, Kauai, Hawaii. Holt, D. & Challis, D. (2007). From policy to practice: one university's experience of implementing strategic change through wholly online teaching and learning. Australasian Journal of Educational Technology, 23(1), 110-131. James, R., McInnis, C. & Devlin, M. (2002). Assessing Learning in Australian Universities. Melbourne, Australia: Centre for the Study of Higher Education and The Australian Universities Teaching Committee. Kirkwood, A. & Price, L. (2005). Learners and learning in the twenty-first century: what do we know about students’ attitudes towards and experiences of information and communication technologies that will help us design courses? Studies in Higher Education, 30(3), 257-274. Kuk, G. (2003). E-Learning Hubs: Affordance, Motivation and Learning Outcomes [WWW document]. Retrieved 10 February, 2007, URL: http://www.business.heacademy.ac.uk/resources/reflect/conf/2003/kuk/kuk.pdf Stacey, E. & Rice, M. (2002). Evaluating an online learning environment. Australian Journal of Educational Technology, 18(3), 323-340. Webb, E., Jones, A., Barker, P. & van Schaik, P. (2004). Using e-learning dialogues in higher education. Innovations in Education and Teaching International, 41(1), 93-103. Weisskirch, R. S. & Milburn, S. S. (2003). Virtual discussion: Understanding college students’ electronic bulletin board use. Internet and Higher Education, 6(3), 215-225. Wozniak, H. (2005). Online discussions: Improving the quality of the student experience [WWW document]. Retrieved 10 February, 2007, URL: http://www.odlaa.org/events/2005conf/ref/ODLAA2005Wozniak.pdf
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Article Critique: Correlation and Bivariate Regression in Practice
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Article Critique: Correlation and Bivariate Regression in Practice
Jordan, K. (2014). Initial trends in enrolment and completion of massive open online
courses. The International Review of Research in Open and Distributed Learning, 15(1).
Introduction
Massive open online courses are such courses as designed for huge numbers of students,
which can be accessed by anyone from different locations provided they are connected to the
Internet. These courses are open to anyone and have no much entry qualifications. They offer a
complete course experience on specific online platforms for free. There has been an increase in
such course across different higher learning institutions. Significant aspects involved in the
running of these courses include enrollment and completi...


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