English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Educational datamining involves the application of datamining techniques tostudent activity. However, in the context of computer programming, manydatamining techniques can not be applied because they expect vector-shapedinput whereas computer programs have the form of syntax trees. In this paper,we present ast2vec, a neural network that maps Python syntax trees to vectorsand back, thereby facilitating datamining on computer programs as well as theinterpretation of datamining results. Ast2vec has been trained on almost half amillion programs of novice programmers and is designed to be applied acrosslearning tasks without re-training, meaning that users can apply it without anyneed for (additional) deep learning. We demonstrate the generality of ast2vecin three settings: First, we provide example analyses using ast2vec on aclassroom-sized dataset, involving visualization, student motion analysis,clustering, and outlier detection, including two novel analyses, namely aprogress-variance-projection and a dynamical systems analysis. Second, weconsider the ability of ast2vec to recover the original syntax tree from itsvector representation on the training data and two further large-scaleprogramming datasets. Finally, we evaluate the predictive capability of asimple linear regression on top of ast2vec, obtaining similar results totechniques that work directly on syntax trees. We hope ast2vec can augment theeducational datamining toolbelt by making analyses of computer programs easier,richer, and more efficient.

ast2vec: Utilizing Recursive Neural Encodings of Python Programs