Online education systems such as massive open online courses (MOOCs) with an open environment have grown around the globe and have been broadcasted widely. Nonetheless, many participants who registered for these courses are not completing and thus it led to the high dropout rates publicised in papers and the media. The low accomplishment rates of less than 15% completion rates have been recognised as one of the main difficulties within MOOCs (Jordan, 2013). MOOC participants represent large online learning community with distinct motivational interest. Research shows that one of the causes of the low completion rate in MOOC could be due to the lack of enthusiasm and procastination of learners to self-regulate and engage consistently with the course (Barnard et al., 2009). Learners who exhibit the ability to self-regulate their learning perform better academically as compared to learners with non or minima self-regulated learning skills. This research described the self-regulated learning ability identified amongst different learners’ modes of study. The two main modes are: self-directed and Instrutor-led modes. The study focus on examining and investigating whether there exists better performance of self-regulated learning strategies amongst the learners from related study mode.
In order to investigate the self-regulated learning dimensions, a novel ‘eLDa’ tool was developed to deliver a course in Python programming, computing concepts and how to teach computing in schools. This research introduces this novel tool approach of learning which aims to allow learners to actively study in their own chosen path, and also providing the framework of an instructional direction to support participants in order to set-goals and to gain access to materials suitable for their own needs.
1.1 THE eLDa PLATFORM
A novel platform, known as ‘eLDa’, was created to explore the approach and analyse the effects of novel features in order to encourage motivation, support and to foster self-regulation in learning. eLDa is implemented in WordPress content management system (CMS) with plugins to support the novel features which allows the learners to chose their route to follow in the course in order to attain their own learning objectives or follow the directed path led by the instructor to achieve the course goals. This platform supports two basic modes of learning: self-directed and instructor-led in which a recommended prerequisites order of lessons helps to cover the full course curriculum.
Participants: A total of 107 participants registered initially to participate in the course. However, only 27 active participants engaged with the course. This study conducted a purposive sample size of 11 learners who completed the self-regulated learning survey used in this research.
Data collection process: The data collection process was obtained using an existing instrument know as ‘online self-regulated learning questionnaire’ (OSLQ), which was used for measuring self-regulated learneing dimensions (Barnard et al., 2009). A 19-item scale with 5-point likert scale response format, with 5-point Likert-type response format which constituted values ranging from 5-strongly agree to 1-strongly disagree, was applied to collect learners’ responses in order to evaluate and answer the research questions. The OSLQ was conducted using existing strategies such as: goal settings, environment structuring, help seeking, time management, task strategies, and self-evaluation.
Analysis: Analysis was performed using Statistical Package for the Social Sciences (SPSS) tool to evaluate the learners’ responses in order to investigate individual self-regulated learning strategies and also identify the level of self-regulated learning amongst the participants reveals areas that needs improving.
Results: The results indicate better high self-regulated learning skills amongst learners that chose the path of a self-directed learning as compare to those that followed instructor-led mode of study.
Our results indicate two distinct representation of the individual profile of self-regulated learning from the analyse sample: high self-regulators and low self-regulators. The results reveal that the competent self-regulators show high level of self-regulated strategies in their responses with few strategies to improve. But for the low self-regulators, these learners need to improve in their self-regulated learning strategies, as most of their responses fell into the negative scales. The results also indicate the individuality of the SRL dimensions observed from the learners, which reveals the paths that most of the learners wish to study.
In summary, we define success as not the level of completers, but the learners meeting their expectations. Some issues of low completion rates in MOOC might not be because the learners are not motivated to participate, but as some of the learners are engaging with the course at their own pace. In this new innovative learning platform (known as ‘eLDa’), completion rate was measured in relation to the learners achieving their learning goals.
Keywords: eLDa, Self-Regulated Learning, MOOC, Learning patterns
 Jordan, K. (2013). MOOC Completion Rates: The Data, Available at: http://www.katyjordan.com/MOOCproject.html [Accessed: 8/08/2015].
 Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S. L. (2009). Measuring self-regulation in online and blended learning environments. The Internet and Higher Education, 12(1), 1-6.
I’d love to see the full paper associated with this presentation once it’s published. please keep me posted.