This paper examines whether natural language processing technologies can provide teachers with high-quality formative feedback about questioning practices that promote rich inclusive mathematical discourse within classrooms. This paper describes how a training dataset was collected and labeled using teacher questioning classifications that are grounded in the mathematics education literature, and it compares the performance of four classifier models fine-tuned using that dataset. Of the models tested, we find that RoBERTa, an open-source LLM, had a 76% accuracy in classifying questions. These modern transfer-learning based approaches require significantly fewer data points than traditional machine-learning methods and are ideal in low-resource scenarios like question classification. The paper concludes by discussing potential use cases within the field of mathematics teacher education and describes how the classifier models created can be publicly accessed.

The nature and quality of classroom instruction is highly correlated to teachers' ability to rehearse effective teaching strategies. Utilizing research-based teaching practices increases teacher effectiveness, confidence, and retention along with improving student achievements. High-fidelity, AI-based simulated classroom systems enable teachers to rehearse and get feedback on specific pedagogical skills. One primary challenge is that current conversational agents (CA) can have task-oriented conversations, however more varied dialogue-oriented conversations such as that between a teacher and student for a domain-specific task (like a mathematical scenario) can be difficult to model. This paper presents a high-fidelity, AI-based classroom simulator to help teachers rehearse research-based mathematical questioning skills. The system relies on advances in deep-learning uncertainty quantification and natural language processing while acknowledging the limitations of CAs for specific pedagogical needs.

A large body of research demonstrates how teachers' questioning strategies can improve student learning outcomes. However, developing new scenarios is challenging because of the lack of training data for a specific scenario and the costs associated with labeling. This paper presents a high-fidelity, AI-based classroom simulator to help teachers rehearse research-based mathematical questioning skills. Using a human-in-the-loop approach, we collected a high-quality training dataset for a mathematical questioning scenario. Using recent advances in uncertainty quantification, we evaluated our conversational agent for usability and analyzed the practicality of incorporating a human-in-the-loop approach for data collection and system evaluation for a mathematical questioning scenario.

High-fidelity, AI-based simulated classroom systems enable teachers to rehearse effective teaching strategies. However, dialogue-oriented open-ended conversations such as teaching a student about scale factors can be difficult to model. This paper builds a text-based interactive conversational agent to help teachers practice mathematical questioning skills based on the well-known Instructional Quality Assessment. We take a human-centered approach to designing our system, relying on advances in deep learning, uncertainty quantification, and natural language processing while acknowledging the limitations of conversational agents for specific pedagogical needs. Using experts' input directly during the simulation, we demonstrate how conversation success rate and high user satisfaction can be achieved.

We propose exploring alternative designs for a conversational agent developed as a tool to provide feedback within the education domain for pre-service teachers, students pursuing their teaching certificate, to practice their questioning skills in a given scenario. We utilize a component-based approach in the design of our conversational agent and this research focuses on proposing methods within the knowledge base component specifically leveraging unstructured text as the foundation of the knowledge base. Through leveraging unstructured text we intend to explore the possibilities of improving conversational agent response quality while minimizing resources required of domain experts in scenario development.

Conversational agents and assistants have been used for decades to facilitate learning. There are many examples of conversational agents used for educational and training purposes in K-12, higher education, healthcare, the military, and private industry settings. The most common forms of conversational agents in education are teaching agents that directly teach and support learning, peer agents that serve as knowledgeable learning companions to guide learners in the learning process, and teachable agents that function as a novice or less-knowledgeable student trained and taught by a learner who learns by teaching. The Instructional Quality Assessment (IQA) provides a robust framework to evaluate reading comprehension and mathematics instruction. We developed a system for pre-service teachers, individuals in a teacher preparation program, to evaluate teaching instruction quality based on a modified interpretation of IQA metrics. Our demonstration and approach take advantage of recent advances in Natural Language Processing (NLP) and deep learning for each dialogue system component. We built an open-source conversational agent system to engage pre-service teachers in a specific mathematical scenario focused on scale factor with the aim to provide feedback on pre-service teachers’ questioning strategies. We believe our system is not only practical for teacher education programs but can also enable other researchers to build new educational scenarios with minimal effort.

Campaign analysis is an integral part of American democracy and has many complexities in its dynamics. Experts have long sought to understand these dynamics and evaluate campaign performance using a variety of techniques. We explore campaign financing and standing in the polls as two components of campaign performance in the context of the 2020 Democratic primaries. We show where these measures exhibit represent similar dynamics and where they differ. We focus on identifying change points in the trend for all candidates using joinpoint regression models. We find how these change points identify major events such as failure or success in a debate. Joinpoint regression reveals who the voters support when they stop supporting a specific candidate. This study demonstrates the value of joinpoint regression in political campaign analysis and it represents a crossover of this technique into the political domain building a foundation for continued exploration and use of this method.

Machine learning techniques applied to the Natural Language Processing (NLP) component of conversational agent development show promising results for improved accuracy and quality of feedback that a conversational agent can provide. The effort required to develop an educational scenario specific conversational agent is time consuming as it requires domain experts to label and annotate noisy data sources such as classroom videos. Previous approaches to modeling annotations have relied on labeling thousands of examples and calculating inter-annotator agreement and majority votes in order to model the necessary scenarios. This method, while proven successful, ignores individual annotator strengths in labeling a data point and under-utilizes examples that do not have a majority vote for labeling. We propose using a multi-task weak supervision method combined with active learning to address these concerns. This approach requires less labeling than traditional methods and shows significant improvements in precision, efficiency, and time-requirements than the majority vote method (Ratner 2019). We demonstrate the validity of this method on the Google Jigsaw data set and then propose a scenario to apply this method using the Instructional Quality Assessment(IQA) to define the categories for labeling. We propose using probabilistic modeling of annotator labeling to generate active learning examples to further label the data. Active learning is able to iteratively improve the training performance and accuracy of the original classification model. This approach combines state-of-the art labeling techniques of weak supervision and active learning to optimize results in the educational domain and could be further used to lessen the data requirements for expanded scenarios within the education domain through transfer learning.