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1. 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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2. Visualization and ecohydrologic models: Opening the box

   https://doi.org/10.1002/hyp.13991  

   Tague, Christina; Frew, James (December 2020, Hydrological Processes)

   Abstract Earth system models synthesize the science of interactions amongst multiple biophysical and, increasingly, human processes across a wide range of scales. Ecohydrologic models are a subset of earth system models that focus particularly on the complex interactions between ecosystem processes and the storage and flux of water. Ecohydrologic models often focus at scales where direct observations occur: plots, hillslopes, streams, and watersheds, as well as where land and resource management decisions are implemented. These models complement field‐based and data‐driven science by combining theory, empirical relationships derived from observation and new data to create virtual laboratories. Ecohydrologic models are tools that managers can use to ask “what if” questions and domain scientists can use to explore the implications of new theory or measurements. Recent decades have seen substantial advances in ecohydrologic models, building on both new domain science and advances in software engineering and data availability. The increasing sophistication of ecohydrologic models however, presents a barrier to their widespread use and credibility. Their complexity, often encoding 100s of relationships, means that they are effectively “black boxes,” at least for most users, sometimes even to the teams of researchers that contribute to their design. This opacity complicates the interpretation of model results. For models to effectively advance our understanding of how plants and water interact, we must improve how we visualize not only model outputs, but also the underlying theories that are encoded within the models. In this paper, we outline a framework for increasing the usefulness of ecohydrologic models through better visualization. We outline four complementary approaches, ranging from simple best practices that leverage existing technologies, to ideas that would engage novel software engineering and cutting edge human–computer interface design. Our goal is to open the ecohydrologic model black box in ways that will engage multiple audiences, from novices to model developers, and support learning, new discovery, and environmental problem solving. 

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3. 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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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. Optimal Time-Backlog Tradeoffs for the Variable-Processor Cup Game. Accepted to

   Kuszmaul, W.; Narayanan, S. (January 2022, International Colloquium on Automata, Languages and Programming (ICALP 2022))

   The \emph{p-processor cup game} is a classic and widely studied scheduling problem that captures the setting in which a p-processor machine must assign tasks to processors over time in order to ensure that no individual task ever falls too far behind. The problem is formalized as a multi-round game in which two players, a filler (who assigns work to tasks) and an emptier (who schedules tasks) compete. The emptier's goal is to minimize backlog, which is the maximum amount of outstanding work for any task. Recently, Kuszmaul and Westover (ITCS, 2021) proposed the \emph{variable-processor cup game}, which considers the same problem, except that the amount of resources available to the players (i.e., the number p of processors) fluctuates between rounds of the game. They showed that this seemingly small modification fundamentally changes the dynamics of the game: whereas the optimal backlog in the fixed p-processor game is Θ(logn), independent of p, the optimal backlog in the variable-processor game is Θ(n). The latter result was only known to apply to games with \emph{exponentially many} rounds, however, and it has remained an open question what the optimal tradeoff between time and backlog is for shorter games. This paper establishes a tight trade-off curve between time and backlog in the variable-processor cup game. Importantly, we prove that for a game consisting of t rounds, the optimal backlog is Θ(n) if and only if t≥Ω(n3). Our techniques also allow for us to resolve several other open questions concerning how the variable-processor cup game behaves in beyond-worst-case-analysis settings. 

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