Automatic Identification of Ineffective Online Student Questions in Computing Education

This Research Full Paper explores automatic identification of ineffective learning questions in the context of large-scale computer science classes. The immediate and accurate identification of ineffective learning questions opens the door to possible automated facilitation on a large scale, such as alerting learners to revise questions and providing adaptive question revision suggestions. To achieve this, 983 questions were collected from a question & answer platform implemented by an introductory programming course over three semesters in a large research university in the Southeastern United States. Questions were firstly manually classified into three hierarchical categories: 1) learning-irrelevant questions, 2) effective learning-relevant questions, 3) ineffective learningrelevant questions. The inter-rater reliability of the manual classification (Cohen's Kappa) was .88. Four different machine learning algorithms were then used to automatically classify the questions, including Naive Bayes Multinomial, Logistic Regression, Support Vector Machines, and Boosted Decision Tree. Both flat and single path strategies were explored, and the most effective algorithms under both strategies were identified and discussed. This study contributes to the automatic determination of learning question quality in computer science, and provides evidence for the feasibility of automated facilitation of online question & answer in large scale computer science classes

Classification of factoid questions intent using grammatical features

In question-answering systems, question classification is a fundamental task. Identifying the accurate question type enhances the retrieval of more accurate answers. Factoid questions are the most challenging type of question to classify in which many approaches have been proposed with the objective of enhancing the classification of this type of question. In this paper, a grammar-based framework is used. The framework makes use of three main features which are, grammatical features, domain specific features and patterns. Using machine learning algorithms for the classification process, experimental results show that our approach has a good level of accuracy. Keywords: Information retrieval, Question classification, Factoid questions, Grammatical features, Machine learnin

Questionnaire integration system based on question classification and short text semantic textual similarity, A

2018 Fall.Includes bibliographical references.Semantic integration from heterogeneous sources involves a series of NLP tasks. Existing re- search has focused mainly on measuring two paired sentences. However, to find possible identical texts between two datasets, the sentences are not paired. To avoid pair-wise comparison, this thesis proposed a semantic similarity measuring system equipped with a precategorization module. It applies a hybrid question classification module, which subdivides all texts to coarse categories. The sentences are then paired from these subcategories. The core task is to detect identical texts between two sentences, which relates to the semantic textual similarity task in the NLP field. We built a short text semantic textual similarity measuring module. It combined conventional NLP techniques, including both semantic and syntactic features, with a Recurrent Convolutional Neural Network to accomplish an ensemble model. We also conducted a set of empirical evaluations. The results show that our system possesses a degree of generalization ability, and it performs well on heterogeneous sources

Function-based question classification for general QA

In contrast with the booming increase of internet data, state-of-art QA (question answering) systems, otherwise, concerned data from specific domains or resources such as search engine snippets, online forums and Wikipedia in a somewhat isolated way. Users may welcome a more general QA system for its capability to answer questions of various sources, integrated from existed specialized sub-QA engines. In this framework, question classification is the primary task. However, the current paradigms of question classification were focused on some specified type of questions, i.e. factoid questions, which are inappropriate for the general QA. In this paper, we propose a new question classification paradigm, which includes a question taxonomy suitable to the general QA and a question classifier based on MLN (Markov logic network), where rule-based methods and statistical methods are unified into a single framework in a fuzzy discriminative learning approach. Experiments show that our method outperforms traditional question classification approaches.