In 2015, 35 million learners participated online in 4,200 MOOCs organised by over 500 universities. Learning designers orchestrate MOOC content to engage learners at scale and retain interest by carefully mixing videos, lectures, readings, quizzes, and discussions. Universally, far fewer people actually participate in MOOCs than originally sign up with a steady attrition as courses progress. Studies have correlated social engagement to completion rates. The FutureLearn MOOC platform specifically provides opportunities to share opinions and to reflect by posting comments, replying, or following discussion threads. This paper investigates learners' social behaviours in MOOCs and the impact of engagement on course completion. A preliminary study suggested that dropout rates will be lower when learners engage in repeated and frequent social interactions. We subsequently reviewed the literature of prediction models and applied social network analysis techniques to characterise participants' online interactions examining implications for participant achievements. We analysed discussions in an eight week FutureLearn MOOC, with 9855 enrolled learners. Findings indicate that if learners starts following some , the probability of their finishing the course is increased; if learners also interact with those they follow, they are highly likely to complete, both important factors to add to the prediction of completion model.
Abstract. The advent and rise of Massive Open Online Courses (MOOCs) have brought many issues to the area of educational technology. Researchers in the field have been addressing these issues such as pedagogical quality of MOOCs, high attrition rates, and sustainability of MOOCs. However, MOOCs personalisation has not been subject of the wide discussions around MOOCs. This paper presents a critical literature survey and analysis of the available literature on personalisation in MOOCs to identify the needs, the current states and efforts to personalise learning in MOOCs. The findings illustrate that there is a growing attention to personalisation to improve learners' individual learning experiences in MOOCs. In order to implement personalised services, personalised learning path, personalised assessment and feedback, personalised forum thread and recommendation service for related learning materials or learning tasks are commonly applied.
Massive Open Online Courses (MOOCs) have a great potential for sustainable education. Millions of learners annually enrol on MOOCs designed to meet the needs of an increasingly diverse and international student population. Participants’ backgrounds vary by factors including age, education, location, and first language. MOOC authors address consequent needs by ensuring courses are well-organised. Learning is structured into discrete steps, prioritising clear communication; video components incorporate subtitles. Variability in participants’ language abilities inevitably create barriers to learning, a problem most extreme for those studying in a language which is not their first. This paper investigates how to identify ESL participants and how best to predict factors associated with their course completion. This study proposes a novel method for automatically categorising (English as Primary and Official Language; English as Official but not Primary Language; and English as a second Language groups) 25,598 participants studying FutureLearn “Understanding Language: Learning and Teaching” MOOC using natural language processing. We compared algorithms’ performance when extracting discernible features in participants’ engagement. Engagement in discussions at the end of the first week is one of the strongest predictive features, while overall, learner behaviours in the first two weeks were identified as the most strongly predictive feature.
Abstract:Researchers in the field of educational technology are paying huge attention to the widespread adoption of Massive Open Online Courses (MOOCs) in the study of learning online. MOOCs are discussed in many angles including pedagogy, learning sustainability, and business model. However, there are very few discussions around MOOCs personalisation. In this paper, it is aimed to examine and analyse the literature on personalisation of MOOCs to identify the needs, the current states and efforts to personalise learning in MOOCs. The findings denote that the pedagogical design of MOOCs is currently insufficient due to massive and geographically dispersed learners with diverse educational backgrounds, learning requirements and motivations. Many believe that personalisation could address this lacking in MOOCs. Among the most popular services being proposed or implemented in the literature are personalised learning path, personalised assessment and feedback, personalised forum thread and recommendation service for related learning materials or learning tasks.
Abstract. In this study, we aim to analyse English as a Second Language (ESL) and English as a First Language (EFL) MOOC participants' engagements in a MOOC. We aim to find out key points which directly effect learners' dropout and performance in MOOCs. We worked on a FutureLearn data which is provided by the University of Southampton. The course is Understanding Language: Learning and Teaching MOOC that was run between 2016-04-04 and 2016-05-02 is chosen for the analysis. According to the results, it is very challenging to identify who is a second language English speaker by using their location information. One of the important findings is that first language English speakers wrote longer comments. In order to identify strategies for ESL MOOC participants, which is one of the ultimate goal of our research, there is a need for much deeper analyses.
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