HarvardX and MITx: The First Year of Open Online Courses, Fall 2012-Summer 2013

Ho, Andrew Dean; Reich, Justin; Nesterko, Sergiy O.; Seaton, Daniel T.; Mullaney, Tommy; Waldo, Jim; Chuang, Isaac L.

doi:10.2139/ssrn.2381263

Cited by 232 publications

(252 citation statements)

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“…It also has positive effect on learners" educational self-efficiency and their learning achievement, it provides wide insight to plan whole process of learning [10] [12]. Cognition factor is related to learners; using to various strategies to perform their learning assignment [13], and affective factor is connected to mental process composed of concentration, interest, investment and effort.…”

Section: Issues Of Mooc Participationmentioning

confidence: 99%

“…Followings <Table 1> are hypothetical constructs per the fundamental definitions. [4], [8], [15], [19] Emphasizing learning environment focused on interaction away from traditional online education a method of MOOC planning and realizing learning environment [6], [8], [14], [16], [19] Changing and Evolving Education System focused on learners factors needed for learning continuity [2], [3], [6], [8], [11], [14] A role of destructive innovation for existing education system a role of MOOC improving existing education system [7], [18], [19] Providing high quality courses to anyone in the dimension of lifelong education a direction of practice in the national and personal dimension [2], [8], [10], [12], [19] 3. Method…”

Section: Issues Of Mooc Participationmentioning

confidence: 99%

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A Study on Learners’ Needs to Increase MOOC Participation

Kim¹,

Lee²,

Chang³

2017

AJEMR

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Section: Issues Of Mooc Participationmentioning

confidence: 99%

Section: Issues Of Mooc Participationmentioning

confidence: 99%

A Study on Learners’ Needs to Increase MOOC Participation

Kim¹,

Lee²,

Chang³

2017

AJEMR

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“…The comparison in Table 1 shows that the categorisation used in this article is less granular in differentiating active learners. Both Nelson (2014) and Ho et al (2014 differentiate based on repeated activity, while Huin et al (2016) differentiate based on learner intention. Neither of those aspects are the focus of this article.…”

Section: Definitions Of Learner Participationmentioning

confidence: 99%

“…These are categories that seem, superficially at least, to be evident in our data, and warrants further investigation. Bali (2014) and Ho et al (2014) suggest placing less emphasis on course completion and completion rates. Given this, an area for further research is the exploration of minimal criteria to ''complete'' a course, but with more engaging and challenging optional stretch activities.…”

Section: Further Researchmentioning

confidence: 99%

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Supporting, categorising and visualising diverse learner behaviour on MOOCs with modular design and micro-learning

Leach¹,

Hadi²

2016

J Comput High Educ

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Much is made of the diversity for MOOCs learners-with their varied motivations and interests; yet MOOCs are often run and judged on the assumption that learners would progress through the course in its entirety, to completion. This paper presents an analysis of three recently delivered MOOCs that were designed to support a broader set of learner goals. A modular design was used, where each part included well defined learning outcomes and assessment criteria, and where completion was rewarded with digital badges. The paper proposes a new categorisation of learner achievement and methods of visualising learner behaviour that compliment this more open design. Results show that this approach recognises microlearning that is missed if only completion rates are considered.

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Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach

Onan

2020

Comp Applic In Engineering

220

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Massive open online courses (MOOCs) are recent innovative approaches in distance education, which provide learning content to participants without age-, gender-, race-, or geography-related barriers. The purpose of our research is to present an efficient sentiment classification scheme with high predictive performance in MOOC reviews, by pursuing the paradigms of ensemble learning and deep learning. In this contribution, we seek to answer several research questions on sentiment analysis on educational data. First, the predictive performance of conventional supervised learning methods, ensemble learning methods and deep learning methods has been evaluated. Besides, the efficiency of text representation schemes and word-embedding schemes has been evaluated for sentiment analysis on MOOC evaluations. For the evaluation task, we have analyzed a corpus containing 66,000 MOOC reviews, with the use of machine learning, ensemble learning, and deep learning methods.The empirical analysis indicate that deep learning-based architectures outperform ensemble learning methods and supervised learning methods for the task of sentiment analysis on educational data mining. For all the compared configurations, the highest predictive performance has been achieved by long short-term memory networks in conjunction with GloVe word-embedding scheme-based representation, with a classification accuracy of 95.80%.

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HarvardX and MITx: The First Year of Open Online Courses, Fall 2012-Summer 2013

Cited by 232 publications

References 0 publications

A Study on Learners’ Needs to Increase MOOC Participation

A Study on Learners’ Needs to Increase MOOC Participation

Supporting, categorising and visualising diverse learner behaviour on MOOCs with modular design and micro-learning

Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach

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