Creating practice questions for programming learning is not easy. It requires the instructor to diligently organize heterogeneous learning resources, i.e., conceptual programming concepts and procedural programming rules. Today’s programming question generation (PQG) is still largely replying on the demanding creation task performed by the instructors without advanced technological support. In this work, we propose a semantic PQG model that aims to help the instructor generate new programming questions and expand the assessment items. The PQG model is designed to transform conceptual and procedural programming knowledge from textbooks into a semantic network by the Local Knowledge Graph (LKG) and Abstract Syntax Tree (AST). For any given question, the model queries the established network to find related code examples and generates a set of questions by the associated LKG/AST semantic structures. We conduct analysis to compare instructor-made questions from 9 undergraduate introductory programming courses and textbook questions. The results show that the instructor-made questions had much simpler complexity than the textbook ones. The disparity of topic distribution intrigued us to further research the breadth and depth of question quality and also to investigate the complexity of the questions in relations to the student performances. Finally, we report an user study results on the proposed AI-infused semantic PQG model in examining the machine-generated questions quality.

/pdf/ai-infused-semantic-model-to-enrich-and-expand-programming-1o64zrpv.pdf

AI-Infused Semantic Model to Enrich and Expand Programming Question Generation

Commercial voice assistants are largely feminized and associated with stereotypically feminine traits such as warmth and submissiveness. As these assistants continue to be adopted for everyday uses, it is imperative to understand how the portrayed gender shapes the voice assistant's ability to mitigate errors, which are still common in voice interactions. We report a study (N=40) that examined the effects of voice gender (feminine, ambiguous, masculine), error mitigation strategies (apology, compensation) and participant's gender on people's interaction behavior and perceptions of the assistant. Our results show that AI assistants that apologized appeared warmer than those offered compensation. Moreover, male participants preferred apologetic feminine assistants over apologetic masculine ones. Furthermore, male participants interrupted AI assistants regardless of perceived gender more frequently than female participants when errors occurred. Our results suggest that the perceived gender of a voice assistant biases user behavior, especially for male users, and that an ambiguous voice has the potential to reduce biases associated with gender-specific traits. 

Gender Biases in Error Mitigation by Voice Assistants

VisOJ: real-time visual learning analytics dashboard for online programming judge

In order to solve the problem that the traditional query based on secondary retrieval is too rigid so as to automatically filter out valuable target documents, and repeated queries consume a lot of time, an intelligent interactive information retrieval process and processing flow for fund project document queries is proposed. Based on the feedback information of users evaluating project documents, ID3 algorithm, CLCC algorithm and SVM classification function are used to learn the potential intention and target of users' query respectively, and the learned rule knowledge or classification function is applied to support the project document query. The experimental computations and analysis are conducted for the query of project documents in a fund review management as an example. The results show that the number of project documents read and evaluated by the user in each interactive query loop is no more than 5% of the total number of documents returned from the previous query or 20 items, and together with the project documents already read and evaluated, they constitute the set of machine learning samples. The maximum number of interactive query cycle is set to 5. Among the three machine learning methods, ID3 also shows good prediction performance when post-processing algorithm is used. ID3 generates a decision tree with large width and small height. CLCC algorithm is better than ID3 algorithm, mainly because the rule post-processing of CLCC is more flexible, and the generated concept rules contain more merge rules and each merge rule is shorter. The SVM method has the best prediction performance, mainly because the project document keyword vectors are all continuous real values. It is concluded that the fund project intelligent interactive information retrieval process and processing flow accurately describes the potential query intention and target of user evaluation project documents, and establishes a user query project document classification learning knowledge base system, thus realizing the knowledge-based project document query support. 

Application of Machine Learning Algorithms in Project Economics Review

In order to solve the problem that the traditional query based on secondary retrieval is too rigid so as to automatically filter out valuable target documents, and repeated queries consume a lot of time, an intelligent interactive information retrieval process and processing flow for fund project document queries is proposed. Based on the feedback information of users evaluating project documents, ID3 algorithm, CLCC algorithm and SVM classification function are used to learn the potential intention and target of users' query respectively, and the learned rule knowledge or classification function is applied to support the project document query. The experimental computations and analysis are conducted for the query of project documents in a fund review management as an example. The results show that the number of project documents read and evaluated by the user in each interactive query loop is no more than 5% of the total number of documents returned from the previous query or 20 items, and together with the project documents already read and evaluated, they constitute the set of machine learning samples. The maximum number of interactive query cycle is set to 5. Among the three machine learning methods, ID3 also shows good prediction performance when post-processing algorithm is used. ID3 generates a decision tree with large width and small height. CLCC algorithm is better than ID3 algorithm, mainly because the rule post-processing of CLCC is more flexible, and the generated concept rules contain more merge rules and each merge rule is shorter. The SVM method has the best prediction performance, mainly because the project document keyword vectors are all continuous real values. It is concluded that the fund project intelligent interactive information retrieval process and processing flow accurately describes the potential query intention and target of user evaluation project documents, and establishes a user query project document classification learning knowledge base system, thus realizing the knowledge-based project document query support.

College classes are becoming increasingly large. A critical component in scaling class size is the collaboration and interactions among instructors, teaching assistants, and students. We develop a prototype of anIntelligent Voice Instructor-AssistantSystem (IVIAS)for supportinglargeClasses,in whichAmazon Web Services, Alexa Voice Services and self-developed services are used. It uses ascraping service for reading the questions and answers from the past and current course discussion boards, organizes the questions in JSON format and stored them in thedatabase, which can be accessed by AWS Alexa skills. When a voice question from a student comes, Alexais used fortranslating the voice sentence into texts. Then Siamesedeep LSTM (Long Short-Term Memory)model is introducedto calculate the similarity between the question asked and the questions in the database to find the best-matched answer. Questions with no match will be sent to the instructor, and instructor’s answer will be added into the database. Experiments show that the implemented model achieve promising results that can lead to a practical system. IVIAS starts with a small set of questions. It can grow through learning and improving when more and more questions are asked and answered.

/pdf/intelligent-voice-instructor-assistant-system-for-hu8ehazl2j.pdf

Intelligent Voice Instructor-Assistant System for Collaborative and Interactive Classes

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Intelligent Voice Instructor-Assistant System for Collaborative and Interactive Classes