HarvardX and MITx: Two Years of Open Online Courses Fall 2012-Summer 2014

Ho, Andrew Dean; Chuang, Isaac L.; Reich, Justin; Coleman, Cody; Whitehill, Jacob; Northcutt, Curtis G.; Williams, Joseph Jay; Hansen, John D.; Lopez, Glenn; Petersen, Rebecca P.

doi:10.2139/ssrn.2586847

Cited by 152 publications

(175 citation statements)

References 8 publications

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“…(70 % of women and 58 % of men). This is in line with the high educational levels reported in previous research on MOOCs (Ho et al 2015).…”

Section: Resultssupporting

confidence: 92%

“…These figures are higher than reported in other studies. For example, Ho et al (2015) reported a median age of less than 30 years in Harvardx and MITx courses and Zhenghao et al (2015) report a median age of 41 for Coursera MOOCs. Although longitudinal research suggested that the average age of MOOC learners is increasing, (Glass et al 2016), the high mean age in our data may be caused by the fact that we were selecting only learners who were currently working or looking for a job, and excluding younger people such as students.…”

Section: Resultsmentioning

confidence: 99%

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Does digital competence and occupational setting influence MOOC participation? Evidence from a cross-course survey

et al. 2016

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While MOOCs are recognized nowadays as a potential format for professional development and lifelong learning, little research has been conducted on the factors that influence MOOC participation of professionals and unemployed in MOOCs. Based on a framework developed earlier, we conducted a study, which focused on the influence of background variables such us digital competence, age, gender and educational level on MOOC participation. Occupational setting was considered as a moderator in the analysis of the impact of digital skills. Results of the study showed that MOOCs were an important tool for unemployed participants who were more likely to enroll in MOOCs than employed learners. MOOCs were also a way for workers who do not received employer support for other training activities to get professional development training. Results of the regression analysis The paper is based on a conference paper presented during the Fourth European MOOCs Stakeholder Summit (eMOOCs2016).The views expressed in this article are purely those of the authors and should not be regarded as the official position of the European Commission. showed that a person's level of digital competence was an important predictor for enrolment in MOOCs and that specifically interaction skills were more important than information skills for participating in the MOOC context.

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“…(70 % of women and 58 % of men). This is in line with the high educational levels reported in previous research on MOOCs (Ho et al 2015).…”

Section: Resultssupporting

confidence: 92%

Section: Resultsmentioning

confidence: 99%

Does digital competence and occupational setting influence MOOC participation? Evidence from a cross-course survey

et al. 2016

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“…Indeed, given the lack of personal investment needed to sign up for a course, researchers and evaluators are not able to infer the quality of a MOOC based on completion rate alone. The openness and accessibility to MOOCs has even led some to argue that completion rates are "misleading and counterproductive indicators" of the quality and potential of MOOCs (Ho et al, 2015). DeBoer et al (2014) elaborate on this point, asserting that completion rates must be re-conceptualized for a MOOC environment where learners may consider MOOCs less of a traditional course than a collection of learning resources.…”

Section: Learner Completionmentioning

confidence: 99%

Big Data Characterization of Learner Behaviour in a Highly Technical MOOC Engineering Course

et al. 2016

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ABSTRACT:MOOCs attract a large number of learners with largely unknown diversity in terms of motivation, ability, and goals. To understand more about learners in highly technical engineering MOOCs, this study investigates patterns of learners' (n = 337) behaviour and performance in the Nanophotonic Modelling MOOC, offered through nanoHUB-U. The authors explored clusters of learner clickstream patterns using the k-means++ algorithm and found five clusters of learner behaviour, labelled according to learners' use of materials: Fully Engaged, Consistent Viewers, One-Week Engaged, Two-Week Engaged, and Sporadic learners. The Kruskal-Wallis nonparametric statistical test yielded a significant difference (p< 0.01) between learners' access of course materials in each cluster. The researchers then examined the participation and mean scores on course quizzes and exams for each learner group. One-Week Engaged learners, on average, scored significantly lower on the first week's assessment. Two-Week Engaged learners, on average, scored significantly lower on the second week's assessments. Other differences found in learners' participation and performance on quizzes and tests based on the five clusters are discussed. These findings suggest that some of the high dropout numbers in advanced MOOCs may be related to learners' performance on course assessments. In addition, integration of learner access to course material with course assessment scores provides a much richer understanding of learners in a MOOC.

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“…Apparently, MOOCs seem to be no longer a threat for higher education -as Ho et al (2015) conclude in their survey that most of those course participants who work towards a certificate tend to be mature students either refreshing their knowledge or acquiring new skills rather than learners venturing for their first university education [3]. In other words, there is a clear preference for face-to-face university education at the undergraduate level which is supported by the data from the US government report.…”

Section: Introductionmentioning

confidence: 99%

“…In other words, there is a clear preference for face-to-face university education at the undergraduate level which is supported by the data from the US government report. There are twice as many distance learners among graduates compared to online undergraduate students (22% and 11% respectively) [7].…”

Section: Introductionmentioning

confidence: 99%

The Choice of Effective Methods and Approaches to the Design of an Online Course

Didenko¹,

Aksenova

Loginova³

2016

Int. J. Emerg. Technol. Learn.

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Abstract-The main purpose of this work is to show the importance of using process cards and Bloom' Taxonomy that allow to raise efficiency of on-line courses. It analyses existing on-line courses and demand of such courses in its dynamics. Application of process cards and Bloom's Taxonomy will encourage good design practice in projecting and implementing on-line courses thus improving educational results retention rate.

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HarvardX and MITx: Two Years of Open Online Courses Fall 2012-Summer 2014

Cited by 152 publications

References 8 publications

Does digital competence and occupational setting influence MOOC participation? Evidence from a cross-course survey

Does digital competence and occupational setting influence MOOC participation? Evidence from a cross-course survey

Big Data Characterization of Learner Behaviour in a Highly Technical MOOC Engineering Course

The Choice of Effective Methods and Approaches to the Design of an Online Course

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