Identifying learning-inductive content in programming discussion forums

Online programming discussion forums are popular trouble-shooting and problem-solving sites for programmers and learners to reach out for help. The massive volumes of forum threads harbor tremendous amounts of information, but at the same time increase the complexity of search and navigation. In this work, we make use of programming discussions´ syntactic, semantic and social features to model content associated with learning activities based on the ICAP learning framework. Our main goal is to detect useful content for learning programming in a large scale of questions and answers, while at the same time experiment with an artificial intelligence approach to detect learning-inductive content. We build regression models based on the defined constructive learning activities. Results reveal a passive-proactive learning behavior in an online programming discussion forum. The findings also reconfirm the value of programming discussion content, disregarding the crowds´ approval. The automatic detection of constructive learning activities from programming discussions can be a helpful classifier in identifying relevant educational resources. Overall, this project contributes to our understanding on analyzing and utilizing mass programming discussion content for online programming language learning.