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<i title="International Association of Online Engineering (IAOE)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2mtfrrh4zrcn5opulbqvn7baxm" style="color: black;">International Journal of Emerging Technologies in Learning (iJET)</a>
Massive Open Online Courses (MOOCs) are increasingly used by learn-ers to acquire knowledge and develop new skills. MOOCs provide a trove of data that can be leveraged to better assist learners, including behavioral data from built-in collaborative tools such as discussion boards and course wikis. Data tracing social interactions among learners are especially inter-esting as their analyses help improve MOOCs' effectiveness. We particular-ly perform sentiment analysis on such data to predict<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3991/ijet.v16i23.24457">doi:10.3991/ijet.v16i23.24457</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2rgghpj6avdbbnwsn6qb3yjezq">fatcat:2rgghpj6avdbbnwsn6qb3yjezq</a> </span>
more »... ners at risk of dropping out, measure the success of the MOOC, and personalize the MOOC according to a learner's behavior and detected emotions. In this pa-per, we propose a novel approach to sentiment analysis that combines the advantages of the deep learning architectures CNN and LSTM. To avoid highly uncertain predictions, we utilize a Bayesian neural network (BNN) model to quantify uncertainty within the sentiment analysis task. Our em-pirical results indicate that: 1) The Bayesian CNN-LSTM model provides interesting performance compared to other models (CNN-LSTM, CNN, LSTM) in terms of accuracy, precision, recall, and F1-Score; and 2) there is a high correlation between the sentiment in forum posts and the dropout rate in MOOCs.
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