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Knowledge Tracing models have been used to predict and understand student learning processes for over two decades, spanning multiple generations of student learners who have different relationships with the technologies used to provide them instruction and practice. Given that student experiences of education have changed dramatically in that time span, can we assume that the student learning process modeled by KT is stable over time? We investigate the robustness of four different KT models over five school years and find evidence of significant model decline that is more pronounced in the more sophisticated models. We then propose multiple avenues of future work to better predict and understand this phenomenon. In addition, to foster more longitudinal testing of novel KT architectures, we will be releasing student interaction data spanning those five years. 

To improve student learning outcomes within online learning platforms, struggling students are often provided with on-demand supplemental instructional content. Recently, services like Yup (yup.com) and UPcheive (upchieve.org) have begun to offer on-demand live tutoring sessions with qualified educators, but the availability of tutors and the cost associated with hiring them prevents many students from having access to live support. To help struggling students and offset the inequities intrinsic to high-cost services, we are attempting to develop a process that uses large language representation models to algorithmically identify relevant support messages from these chat logs, and distribute them to all students struggling with the same content. In an empirical evaluation of our methodology we were able to identify messages from tutors to students struggling with middle school mathematics problems that qualified as explanations of the content. However, when we distributed these explanations to students outside of the tutoring sessions, they had an overall negative effect on the students’ learning. Moving forward, we want to be able to identify messages that will promote equity and have a positive impact on students. 

Advancements in online learning platforms have revolutionized education in multiple different ways, transforming the learning experiences and instructional practices. The development of natural language processing and machine learning methods have helped understand and process student languages, comprehend their learning state, and build automated supports for teachers. With this, there has been a growing body of research in developing automated methods to assess students’ work both in mathematical and nonmathematical domains. These automated methods address questions of two categories; closed-ended (with limited correct answers) and open-ended (are often subjective and have multiple correct answers), where open-ended questions are mostly used by teachers to learn about their student’s understanding of a particular concept. Manually assessing and providing feedback to these open-ended questions is often arduous and time-consuming for teachers. For this reason, there have been several works to understand student responses to these open-ended questions to automate the assessment and provide constructive feedback to students. In this research, we seek to improve such a prior method for assessment and feedback suggestions for student open-ended works in mathematics. For this, we present an error analysis of the prior method ”SBERT-Canberra” for auto-scoring, explore various factors that contribute to the error of the method, and propose solutions to improve upon the method by addressing these error factors. We further intend to expand this approach by improving feedback suggestions for teachers to give to their students’ open-ended work. 

Studies have shown that on-demand assistance, additional instruction given on a problem per student request, improves student learning in online learning environments. Students may have opinions on whether an assistance was effective at improving student learning. As students are the driving force behind the effectiveness of assistance, there could exist a correlation between students’ perceptions of effectiveness and the computed effectiveness of the assistance. This work conducts a survey asking secondary education students on whether a given assistance is effective in solving a problem in an online learning platform. It then provides a cursory glance at the data to view whether a correlation exists between student perception and the measured effectiveness of an assistance. Over a three year period, approximately twenty-two thousand responses were collected across nearly four thousand, four hundred students. Initial analyses of the survey suggest no significance in the relationship between student perception and computed effectiveness of an assistance, regardless of if the student participated in the survey. All data and analysis conducted can be found on the Open Science Foundation website.