46,401 research outputs found
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
Exploring The Value Of Folksonomies For Creating Semantic Metadata
Finding good keywords to describe resources is an on-going problem: typically we select such words manually from a thesaurus of terms, or they are created using automatic keyword extraction techniques. Folksonomies are an increasingly well populated source of unstructured tags describing web resources. This paper explores the value of the folksonomy tags as potential source of keyword metadata by examining the relationship between folksonomies, community produced annotations, and keywords extracted by machines. The experiment has been carried-out in two ways: subjectively, by asking two human indexers to evaluate the quality of the generated keywords from both systems; and automatically, by measuring the percentage of overlap between the folksonomy set and machine generated keywords set. The results of this experiment show that the folksonomy tags agree more closely with the human generated keywords than those automatically generated. The results also showed that the trained indexers preferred the semantics of folksonomy tags compared to keywords extracted automatically. These results can be considered as evidence for the strong relationship of folksonomies to the human indexer’s mindset, demonstrating that folksonomies used in the del.icio.us bookmarking service are a potential source for generating semantic metadata to annotate web resources
Forecasting of electricity prices in the Spanish electricity market using machine learning tools
The objective of this research assignment was to forecast electricity prices in the Spanish electricity market using three different machine learning techniques: k-nearest neighbours, support vector regression and artificial neural networks. The achieved results were compared and the quality of developed models was evaluated. The project was implemented in Python3.Incomin
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