Forecasting student achievement in MOOCs with natural language processing

@inproceedings{Robinson2016ForecastingSA,
  title={Forecasting student achievement in MOOCs with natural language processing},
  author={Carly Robinson and Michael Yeomans and Justin Reich and Chris Hulleman and Hunter Gehlbach},
  booktitle={LAK},
  year={2016}
}
Student intention and motivation are among the strongest predictors of persistence and completion in Massive Open Online Courses (MOOCs), but these factors are typically measured through fixed-response items that constrain student expression. We use natural language processing techniques to evaluate whether text analysis of open responses questions about motivation and utility value can offer additional capacity to predict persistence and completion over and above information obtained from… CONTINUE READING
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