Applying XP Ideas Formally: The Story Card and Extreme X-Machines

By gathering requirements on story cards extreme programming (XP) makes requirements collection easy. However it is less clear how the story cards are translated into a �finished product. We propose that a formal specification method based on X-Machines can be used to direct this transition. Extreme X-Machines �t in to the XP method well, without large overheads in design and maintenance. We also investigate how such machines adapt to change in the story cards and propose how this could be further enhanced

Dropout Model Evaluation in MOOCs

The field of learning analytics needs to adopt a more rigorous approach for predictive model evaluation that matches the complex practice of model-building. In this work, we present a procedure to statistically test hypotheses about model performance which goes beyond the state-of-the-practice in the community to analyze both algorithms and feature extraction methods from raw data. We apply this method to a series of algorithms and feature sets derived from a large sample of Massive Open Online Courses (MOOCs). While a complete comparison of all potential modeling approaches is beyond the scope of this paper, we show that this approach reveals a large gap in dropout prediction performance between forum-, assignment-, and clickstream-based feature extraction methods, where the latter is significantly better than the former two, which are in turn indistinguishable from one another. This work has methodological implications for evaluating predictive or AI-based models of student success, and practical implications for the design and targeting of at-risk student models and interventions