Large language models (LLMs) have emerged as powerful tools for developing educational systems.While previous studies have explored modeling student mistakes, a critical gap remains in understanding whether LLMs can generate correct solutions that represent student responses to free-response problems.We compare the distribution of solutions from four LLMs (one proprietary, two open-sourced general, and one open-sourced math models) with various sampling and prompting techniques and those from students teaching math problems to a conversational robot.Our study reveals discrepancies between the correct solutions produced by LLMs and by students.We discuss the practical implications of these findings for the design and evaluation of LLMsupported educational systems.

Yuya Asano, Diane Litman, Mingzhi Yu, Nikki Lobczowski, Timothy Nokes-Malach, Adriana Kovashka, Erin Walker. Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2022.

In online asynchronous learning environments, students are assigned exercises, but it is not clear how to incorporate the kinds of actions an in-person tutor might take such as explaining, providing more practice, prompting for reflection, and motivating. We explore approaches to adding "Drop-Downs'' that appear after a student submits an answer and that contain additional information to support learning. We conducted randomized A/B experiments exploring the impact of these Drop-Downs on student learning in the online portion of a flipped CS1 course. The deployed Drop-Downs in this course provided explanations, reflective prompts, additional problems, and motivational messages. The results suggest that students benefit from various Drop-Downs in different contexts, indicating the possibility of personalizing content based on the student's state. We discuss the resulting design implications of Drop-Downs in online learning systems.

Engaged preparation and study in combination with lectures are important for all courses but are particularly critical for online, hybrid, and inverted classrooms. Many instructors use online systems to deliver new course content and exercises, but students often delay assignments or game these systems (e.g., guessing on multiple-choice questions), often to the detriment of their learning. In an inverted CS1 course, many students self-reported high rates of gaming-the-system behavior, so we examine survey data to identify factors that contribute to engagement in these maladaptive behaviours. We supplement that analysis with interview data to gain a deeper understanding of the situation. We also implemented and evaluated a previously reported online intervention aimed at reducing gaming behavior. Unlike prior work, our intervention did not have a significant effect on guessing behavior. We discuss why the factors we identified might explain this result, as well as suggest future work to improve our understanding of gaming behaviours and inform the design of systems that encourage effective learning.

In the context of online programming homework for a university course, we explore the extent to which learners engage with optional prompts to self -explain answers they choose for problems. Such prompts are known to benefit learning in laboratory and classroom settings [4], but there are less data about the extent to which students engage with them when they are optional additions to online homework. We report data from a deployment of self-explanation prompts in online programming homework, providing insight into how the frequency of writing explanations is correlated with different variables, such as how early students start homework, whether they got a problem correct, and how proficient they are in the language of instruction. We also report suggestive results from a randomized experiment comparing several methods for increasing the rate at which people write explanations, such as including more than one kind of prompt. These findings provide insight into promising dimensions to explore in understanding how real students may engage with prompts to explain answers.

"Gaming the system" is the phenomenon where students attempt to perform well by systematically exploiting properties of the learning system, rather than learning the material. Frequent gaming tends to cause bad learning outcomes. Though existing studies tackle the problem by redesigning the system workflow to change students' behaviors automatically, gaming students discover new ways to game. We instead propose a novel way, reflective nudge, to reflectively influence students' attitudes by conveying reasons not to game via information visualizations. Particularly, we identify three common gaming contexts and involve students and instructors in co-designing three context-specific persuasive visualizations. We deploy our information visualizations in a real online learning platform. Through embedded surveys and in-person interviews, we find some evidence that the designs can promote students' reflection on gaming, and suggestive data that two of them can reduce gaming compared with control groups. Furthermore, we present insights into reflective nudge designs and practical issues concerning deployment.

A differential geometry based modeling of a belt object to represent its deformation path is proposed. Adequate deformation path of a belt object such as film circuit boards or flexible circuit boards must be generated for automatic manipulation and assembly. First, deformation of a belt object is described using the curvature of its central axis, torsion around the central axis and the curvature in the transverse direction. Second, a method to derive an adequate transition of the object shape from the initial state to the final state is proposed. It can be derived by minimizing the maximum of the local potential energy in the belt object during its manipulation. This is because locally excessive potential energy often leads to the excessive stress that makes fractures in the belt object. Finally, the validity of our proposed deformation path is verified by estimating the maximum local potential energy in a belt object.