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1. Asking great questions

   https://doi.org/10.1002/sta4.471  

   Vance, Eric A.; Trumble, Ilana M.; Alzen, Jessica L.; Smith, Heather S. (October 2022, Stat)

   The questions we ask and how we ask them will make a difference in how successful we are in meetings, in collaborations and in our careers as statisticians and data scientists. What makes a question good and what makes a good question great? Great questions elicit information useful for accomplishing the tasks of a project and strengthen the statistician–domain expert relationship. Great questions have three parts: the question, the answer and the paraphrasing of the answer to create shared understanding. We discuss three strategies for asking great questions: preface questions with statements about the intent behind asking the question; follow the question with behaviours and actions consistent with the prefaced words including actions such as listening, paraphrasing and summarizing; and model a collaborative relationship via the asking of a great question. We describe the methods and results of a study that shows how questions can be assessed, that statisticians can learn to ask great questions and that those who have learned this skill consider it to be valuable for their careers. We provide practical guidelines for learning how to ask great questions so that statisticians can improve their collaboration skills and thus increase their impact to help address societal challenges. 

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2. What Makes an Inquisitive Engineer? An Exploration of Question-Asking, Self-Efficacy, and Outcome Expectations among Engineering Students.

   Pink, S.; Rieken, B.; Bjorklund, T.; Sheppard, S. (January 2017, Proceedings of the American Society for Engineering Education Annual Conference, June 25-28. Columbus, OH.)

   In order be successful, engineers must ask their clients, coworkers, and bosses questions. Asking questions can improve work quality and make the asker appear smarter. However, people often hesitate to ask questions for fear of seeming incompetent or inferior. This study investigates: what characteristics and experiences are connected to engineering students’ perceptions of asking questions? We analyzed data from a survey of over a thousand engineering undergraduates across a nationally representative sample of 27 U.S. engineering schools. We focused on three dependent variables: question-asking self-efficacy (how confident students are in their ability to ask a lot of questions), social outcome expectations around asking questions (whether students believe if they ask a lot of questions, they will earn the respect of their colleagues), and career outcome expectations (whether they believe asking a lot of questions will hurt their chances for getting ahead at work). We were surprised to find that question-asking self-efficacy or outcome expectations did not significantly vary by gender, under-represented minority status, and school size. However, students with high question-asking self-efficacy and outcome expectations were more likely to have engaged in four extracurricular experiences: participating in an internship or co-op, conducting research with a faculty member, participating in a student group, and holding a leadership role in an organization or student group. The number of different types of these extracurricular activities a student engaged in correlated with question-asking self-efficacy and positive outcome expectations around asking questions. The results illustrate the relationship between extracurricular activities and students’ self-efficacy and behavior outcome expectations. The college experience is more than just formal academic classes. Students learn from experiences that occur after class or during the summer, and ideally these experiences complement class-derived skills and confidence in asking questions. 

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3. Asking Clarifying Questions in Open-Domain Information-Seeking Conversations

   https://doi.org/10.1145/3331184.3331265  

   Aliannejadi, Mohammad; Zamani, Hamed; Crestani, Fabio; Croft, W. Bruce (July 2019, Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR'19)

   Users often fail to formulate their complex information needs in a single query. As a consequence, they need to scan multiple result pages and/or reformulate their queries, which is a frustrating experience. Alternatively, systems can improve user satisfaction by proactively asking questions from the users to clarify their information needs. Asking clarifying questions is especially important in information-seeking conversational systems, since they can only return a limited number (often only one) of results. In this paper, we formulate the task of asking clarifying questions in open-domain information retrieval. We propose an offline evaluation methodology for the task. In this research, we create a dataset, called Qulac, through crowdsourcing. Our dataset is based on the TREC Web Track 2009-2012 data and consists of over 10K question-answer pairs for 198 TREC topics with 762 facets. Our experiments on an oracle model demonstrate that asking only one good question leads to over 100% retrieval performance improvement, which clearly demonstrates the potential impact of the task. We further propose a neural model for selecting clarifying question based on the original query and the previous question-answer interactions. Our model significantly outperforms competitive baselines. To foster research in this area, we have made Qulac publicly available. 

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4. Predicting and Analyzing Students’ Higher-Order Questions in Collaborative Problem-Solving

   https://doi.org/10.58459/icce.2024.4807  

   ZHANG, Shan; EARLE-RANDELL, Toni V; SHEN, Qian; BOTELHO, Anthony F; ISRAEL, Maya; BOYER, Kristy Elizabeth; LYNCH, Collin F; WIEBE, Eric (November 2024, International Conference on Computers in Education)

   Question-asking is a crucial learning and teaching approach. It reveals different levels of students' understanding, application, and potential misconceptions. Previous studies have categorized question types into higher and lower orders, finding positive and significant associations between higher-order questions and students' critical thinking ability and their learning outcomes in different learning contexts. However, the diversity of higher-order questions, especially in collaborative learning environments. has left open the question of how they may be different from other types of dialogue that emerge from students' conversations, To address these questions, our study utilized natural language processing techniques to build a model and investigate the characteristics of students' higher-order questions. We interpreted these questions using Bloom's taxonomy, and our results reveal three types of higher-order questions during collaborative problem-solving. Students often use Why, How and What If' questions to I) understand the reason and thought process behind their partners' actions: 2) explore and analyze the project by pinpointing the problem: and 3) propose and evaluate ideas or alternative solutions. In addition. we found dialogue labeled 'Social'. 'Question - other', 'Directed at Agent', and 'Confusion/Help Seeking' shows similar underlying patterns to higher-order questions, Our findings provide insight into the different scenarios driving students' higher-order questions and inform the design of adaptive systems to deliver personalized feedback based on students' questions. 

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5. Asking Clarifying Questions Based on Negative Feedback in Conversational Search

   https://doi.org/10.1145/3471158.3472232  

   Ai, Q.; Bi, K.; Croft, W. B. (July 2021, Proceedings of the 7th ACM International Conference on the Theory of Information Retrieval (ICTIR 2021))

   null (Ed.)

   Users often need to look through multiple search result pages or reformulate queries when they have complex information-seeking needs. Conversational search systems make it possible to improve user satisfaction by asking questions to clarify users’ search intents. This, however, can take significant effort to answer a series of questions starting with “what/why/how”. To quickly identify user intent and reduce effort during interactions, we propose an intent clarification task based on yes/no questions where the system needs to ask the correct question about intents within the fewest conversation turns. In this task, it is essential to use negative feedback about the previous questions in the conversation history. To this end, we propose a Maximum-Marginal-Relevance (MMR) based BERT model (MMR-BERT) to leverage negative feedback based on the MMR principle for the next clarifying question selection. Experiments on the Qulac dataset show that MMR-BERT outperforms state-of-the-art baselines significantly on the intent identification task and the selected questions also achieve significantly better performance in the associated document retrieval tasks. 

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