“Turn on, Tune in, Drop out”: Anticipating student dropouts in Massive Open Online Courses
نویسندگان
چکیده
In this paper, we explore student dropout behavior in Massive Open Online Courses(MOOC). We use as a case study a recent Coursera class from which we develop a survival model that allows us to measure the influence of factors extracted from that data on student dropout rate. Specifically we explore factors related to student behavior and social positioning within discussion forums using standard social network analytic techniques. The analysis reveals several significant predictors of dropout.
منابع مشابه
The Impact of Small Learning Group Composition on Drop-Out Rate and Learning Performance in a MOOC
A commonly known and widely studied problem of massive open online courses (MOOCs) is the high drop-out rate of students. In this paper we propose and analyze the composition of small learning groups as a solution to this problem. In an experiment, we composed such small learning groups in a MOOC context using two methods: Random grouping and grouping by an algorithm that considers selected stu...
متن کاملThe Impact of Small Learning Group Composition on Student Engagement and Success in a MOOC
A commonly known and widely studied problem of massive open online courses (MOOCs) is the high drop-out rate of students. In this paper we propose and analyze the composition of small learning groups as a solution to this problem. In an experiment, we composed such small learning groups in a MOOC context using two methods: Random grouping and grouping by an algorithm that considers selected stu...
متن کاملThe Potentiality of Dynamic Assessment in Massive Open Online Courses (MOOCs): The Case of Listening Comprehension MOOCs
Massive Open Online Courses (MOOCs) as a new shaking educational development provide the scene for achieving social inclusion and dissemination of knowledge. Anyhow, facilitating network learning experiences through creating an adaptive learning environment can pave the way for this open and energetic way to learning. The present study aimed to explore the possible role of Dynamic Assessment (D...
متن کاملPredicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks
While there is increase in popularity of massive open online courses in recent years, high rates of drop-out in these courses makes predicting student attrition an important problem to solve. In this paper, we propose an algorithm based on artificial neural network for predicting student attrition in MOOCs using sentiment analysis and show the significance of student sentiments in this task. To...
متن کاملPredicting Student Retention in Massive Open Online Courses using Hidden Markov Models
Massive Open Online Courses (MOOCs) have a high attrition rate: most students who register for a course do not complete it. By examining a student's history of actions during a course, we can predict whether or not they will drop out in the next week, facilitating interventions to improve retention. We compare predictions resulting from several modeling techniques and several features based on ...
متن کامل