Abstract
Recent years witness the significant surge in awareness and exploitation of social media especially community Question and Answer (Q&A) websites by academicians and professionals. These sites are, large repositories of vast data, pawing ways to new avenues for research through applications of data mining and data analysis by investigation of trending topics and the topics of most attention of users. Educational Data Mining (EDM) techniques can be used to unveil potential of Community Q&A websites. Conventional Educational Data Mining approaches are concerned with generation of data through systematic ways and mined it for knowledge discovery to improve educational processes. This paper gives a novel idea to explore already generated data through millions of users having variety of expertise in their particular domains across a common platform like StackOverFlow (SO), a community Q&A website where users post questions and receive answers about particular problems. This study presents an EDM framework to classify community data into Software Engineering subjects. The framework classifies the SO posts according to the academic courses along with their best solutions to accommodate learners. Moreover, it gives teachers, instructors, educators and other EDM stakeholders an insight to pay more attention and focus on commonly occurring subject related problems and to design and manage of their courses delivery and teaching accordingly. The data mining framework performs preprocessing of data using NLP techniques and apply machine learning algorithms to classify data. Amongst all, SVM gives better performs with 72.06% accuracy. Evaluation measures like precision, recall and F-1 score also used to evaluate the best performing classifier.
Highlights
Mounting volume of data across social media and community websites has become a rich and highly potential knowledge source for a large group of users
How to utilize knowledge of crowdsourced Question and Answer (Q&A) websites by educators to improve their teaching, delivery and coverage of subjects? And how improve the learning process of learners and trainees? This paper presents a framework for text mining to discover the well-defined topics and categories which have been most frequently asked about in StackOverflow [11]
This study describes an early attempt to address the problem in relation with CS, IT and software development by proposing a Community Educational Data Mining (CEDM) framework to investigate potential and benefits of SO information to computer related educational stakeholder
Summary
Mounting volume of data across social media and community websites has become a rich and highly potential knowledge source for a large group of users. People very often tend to utilize pedagogical values of social media like web communities, online platforms, Community Questioning Answering (CQA), generally known as questioning answering websites, crawling large amount of data from a large array of geographically distributed users with variety of expertise. Pearsonās latest online annual report on social media for teaching and learning reveals that there has been an acute rise in social media in higher education institutions in recent years. It is clearly indicated from popularity community websites and social media technologies like Facebook, Twitter, StackExchange, Quora, etc [5]
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