Early Prediction of ‘At-Risk’ Learners on Virtual Platforms using ODFs
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Abstract
This Learning analytics are one of the most important assistance tools used by educators for early identification of at-risk learners. Researchers have used many AI based tools for monitoring learning and improving learner’s performances by using any early intervention strategies to reduce dropout rates on online platforms that lacks face-to-face acknowledgement and feedback. Online platforms have Online Discussion Forums (ODFs) where a learner can post his queries and interact with other learners or the instructor. It becomes one of the useful indicators of tracking participation of a learner in the teaching learning process. Learners who actively participate in interaction on these online discussion platforms and contribute to the learning content required by other users are believed to give better performance as compared to those who do not participate in forum discussion. This paper focuses on the aspects of forum discussion like frequency of posts, sentimental analysis of forum post, number of threads initiated or replied to, and also how recent the post to predict the learners who could be at-risk of dropping out. The prediction model uses a data set from secondary resource. Various metrics like Confusion Matrix and Loss curve are employed to measure the accuracy of the model. Results indicate that data captured using forum posts can help in early identification of At-risk Learners.
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