1,445 research outputs found
Massive open online course completion rates revisited: Assessment, length and attrition
This analysis is based upon enrolment and completion data collected for a total of 221 Massive Open Online Courses (MOOCs). It extends previously reported work (Jordan, 2014) with an expanded dataset; the original work is extended to include a multiple regression analysis of factors that affect completion rates and analysis of attrition rates during courses. Completion rates (defined as the percentage of enrolled students who completed the course) vary from 0.7% to 52.1%, with a median value of 12.6%. Since their inception, enrolments on MOOCs have fallen while completion rates have increased. Completion rates vary significantly according to course length (longer courses having lower completion rates), start date (more recent courses having higher percentage completion) and assessment type (courses using auto grading only having higher completion rates). For a sub-sample of courses where rates of active use and assessment submission across the course are available, the first and second weeks appear to be critical in achieving student engagement, after which the proportion of active students and those submitting assessments levels out, with less than 3% difference between them
Capturing "attrition intensifying" structural traits from didactic interaction sequences of MOOC learners
This work is an attempt to discover hidden structural configurations in
learning activity sequences of students in Massive Open Online Courses (MOOCs).
Leveraging combined representations of video clickstream interactions and forum
activities, we seek to fundamentally understand traits that are predictive of
decreasing engagement over time. Grounded in the interdisciplinary field of
network science, we follow a graph based approach to successfully extract
indicators of active and passive MOOC participation that reflect persistence
and regularity in the overall interaction footprint. Using these rich
educational semantics, we focus on the problem of predicting student attrition,
one of the major highlights of MOOC literature in the recent years. Our results
indicate an improvement over a baseline ngram based approach in capturing
"attrition intensifying" features from the learning activities that MOOC
learners engage in. Implications for some compelling future research are
discussed.Comment: "Shared Task" submission for EMNLP 2014 Workshop on Modeling Large
Scale Social Interaction in Massively Open Online Course
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Learning Design for Student Retention
Student retention is an issue of increasing interest to higher education institutions, educators and students. Much of the work in this area focuses on identifying and improving interventions that occur during the presentation of a course. This paper suggests that these represent only one set of factors that can influence student withdrawal, and equally important are design based factors that can aid retention throughout the course. The main research question addressed by the paper is what design-related factors impact on student retention. An analysis of student withdrawal at the UK Open University conducted by the researchers produced a synthesis of seven key factors in the design phase that can influence retention. These factors have been given the ICEBERG acronym: Integrated, Collaborative, Engaging, Balanced, Economical, Reflective and Gradual. Examples of how these factors can be implemented are provided, and conclusions focus on how the model has been embedded in the module production process at the Open University
MOOC adaptation and translation to improve equity in participation
There is an urgent need to improve elementary and secondary school classroom practices across India and the scale of this challenge is argued to demand new approaches to teacher professional learning. Massive Open Online Courses (MOOCs) represent one such approach and which, in the context of this study, is considered to provide a means by which to transcend traditional training processes and disrupt conventional pedagogic practices. This paper offers a critical review of a large-scale MOOC deployed in English, and then in Hindi, to support targeted sustainable capacity building within an education development initiative (TESS-India) across seven states in India. The study draws on multiple sources of participant data to identify and examine features which stimulated a buzz around the MOOCs, leading to over 40,000 registrations and a completion rate of approximately 50% for each of the two MOOCs
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From crowdsourcing data to network building: Reflections on conducting research in the open
This commentary presents an account of a recent project as an example of engaged research. The project focused upon collecting and analysing the completion rates of Massive Open Online Courses (MOOCs). It began informally, through blogging, and developed into a funded research project and formal academic outputs. In addition to its formal outputs, the project is also cited as an example of the benefits of conducting an âopenâ research project. This reflective piece will tell the story of the project, and lessons learned about the value of openness and the interplay of different social media tools in the research process
How learnersâ interactions sustain engagement: a MOOC case study
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 learnersâ 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 participantsâ 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 someone, 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
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