SAP: Student Attrition Predictor
نویسندگان
چکیده
Increasing rates of student drop-outs with increase in popularity of Massive Open Online Courses (MOOCs) makes predicting student attrition an important problem to solve. Recently, we developed an algorithm based on artificial neural network for predicting student attrition in MOOCs using student sentiments. In this paper, we present a web-based tool based on our algorithm which can be used by educators to predict and reduce attrition during a course and by researchers to design and train their own system to predict
منابع مشابه
Predicting 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 کاملAn Examination of Criticisms made of Tinto ’ s 1975 Student Integration Model of Attrition
Vincent Tinto’s Student integration Model (SIM) (Tinto, 1975) remains the most influential model of dropout from tertiary education. This paper outlines the problems associated with student attrition and examines how the SIM models the factors that drive attrition behaviour. Three criticisms that have been made of the SIM are evaluated; 1: The SIM is not an adequate model of student attrition, ...
متن کاملDropout Prediction in MOOCs using Learner Activity Features
While MOOCs offer educational data at a new scale, many educators have been alarmed by their high dropout rates. Learners join a course with some motivation to persist for some or all of the course, but various factors, such as attrition or lack of satisfaction, can lead them to disengage or totally drop out. Educational interventions targeting such risk factors can help reduce dropout rates. H...
متن کاملStemming the flow: improving retention for distance learning students
Though concern about student attrition and failure is not a new phenomenon, higher education institutions (HEIs) have struggled to significantly reduce the revolving door syndrome. Open distance learning higher education is particularly susceptible to high student attrition. Despite a great deal of research into the student journey and factors impacting on likely success, we are not necessarily...
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 کامل