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1. Aligning tutor discourse supporting rigorous thinking with tutee content mastery for predicting math achievement

   Abdelshiheed, M ; Jacobs, JK ; D’Mello, S ( July 2024 , Proceedings of the International Conference on Artificial Intelligence in Education)

   Olney, AM ; Chounta, IA ; Liu, Z ; Santos, OC ; Bittencourt, II (Ed.)

   This work investigates how tutoring discourse interacts with students’ proximal knowledge to explain and predict students’ learning outcomes. Our work is conducted in the context of high-dosage human tutoring where 9th-grade students attended small group tutorials and individually practiced problems on an Intelligent Tutoring System (ITS). We analyzed whether tutors’ talk moves and students’ performance on the ITS predicted scores on math learning assessments. We trained Random Forest Classifiers (RFCs) to distinguish high and low assessment scores based on tutor talk moves, student’s ITS performance metrics, and their combination. A decision tree was extracted from each RFC to yield an interpretable model. We found AUCs of 0.63 for talk moves, 0.66 for ITS, and 0.77 for their combination, suggesting interactivity among the two feature sources. Specifically, the best decision tree emerged from combining the tutor talk moves that encouraged rigorous thinking and students’ ITS mastery. In essence, tutor talk that encouraged mathematical reasoning predicted achievement for students who demonstrated high mastery on the ITS, whereas tutors’ revoicing of students’ mathematical ideas and contributions was predictive for students with low ITS mastery. Implications for practice are discussed. 

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2. Not a Team but Learning as One: The Impact of Consistent Attendance on Discourse Diversification in Math Group Modeling

   https://doi.org/10.1145/3627043.3659554  

   Abdelshiheed, Mark ; Jacobs, Jennifer ; D'Mello, Sidney ( June 2024 , ACM)

   This work investigates relationships between consistent attendance —attendance rates in a group that maintains the same tutor and students across the school year— and learning in small group tutoring sessions. We analyzed data from two large urban districts consisting of 206 9th-grade student groups (3 − 6 students per group) for a total of 803 students and 75 tutors. The students attended small group tutorials approximately every other day during the school year and completed a pre and post-assessment of math skills at the start and end of the year, respectively. First, we found that the attendance rates of the group predicted individual assessment scores better than the individual attendance rates of students comprising that group. Second, we found that groups with high consistent attendance had more frequent and diverse tutor and student talk centering around rich mathematical discussions. Whereas we emphasize that changing tutors or groups might be necessary, our findings suggest that consistently attending tutorial sessions as a group with the same tutor might lead the group to implicitly learn as a team despite not being one. 

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3. Personalized Robot Tutoring using the Assistive Tutor POMDP (AT-POMDP)

   Ramachandran, A. ; Sebo, S. ; Scassellati, B. ( January 2019 , AAAI)

   Selecting appropriate tutoring help actions that account for both a student’s content mastery and engagement level is essential for effective human tutors, indicating the critical need for these skills in autonomous tutors. In this work, we formulate the robot-student tutoring help action selection problem as the Assistive Tutor partially observable Markov decision process (AT-POMDP). We designed the AT-POMDP and derived its parameters based on data from a prior robot-student tutoring study. The policy that results from solving the ATPOMDP allows a robot tutor to decide upon the optimal tutoring help action to give a student, while maintaining a belief of the student’s mastery of the material and engagement with the task. This approach is validated through a between-subjects field study, which involved 4th grade students (n = 28) interacting with a social robot solving long division problems over five sessions. Students who received help from a robot using the AT-POMDP policy demonstrated significantly greater learning gains than students who received help from a robot with a fixed help action selection policy. Our results demonstrate that this robust computational framework can be used effectively to deliver diverse and personalized tutoring support over time for students. 

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4. THE BELIEFS AND PRACTICES OF GRADUATE STUDENT TUTORS ENGAGED IN ONLINE TUTORING DURING THE PANDEMIC

   Mule’, T ; Houston, S. ; Piontek, J. ; Webb, J. ; Harrell-Williams, L. ( November 2022 , Proceedings of the forty-fourth annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education)

   Lischka, A. E. ; Dyer, E. B. ; Jones, R. S. ; Lovett, J. N. ; Strayer, J. ; & Drown, S. (Ed.)

   Many higher education institutions in the United States provide mathematics tutoring services for undergraduate students. These informal learning experiences generally result in increased final course grades (Byerly & Rickard, 2018; Rickard & Mills, 2018; Xu et al., 2014) and improved student attitudes toward mathematics (Bressoud et al., 2015). In recent years, research has explored the beliefs and practices of undergraduate and, sometimes graduate, peer tutors, both prior to (Bjorkman, 2018; Johns, 2019; Pilgrim et al., 2020) and during the COVID19 pandemic (Gyampoh et al., 2020; Mullen et al., 2021; Van Maaren et al., 2021). Additionally, Burks and James (2019) proposed a framework for Mathematical Knowledge for Tutoring Undergraduate Mathematics adapted from Ball et al. (2008) Mathematical Knowledge for Teaching, highlighting the distinction between tutor and teacher. The current study builds on this body of work on tutors’ beliefs by focusing on mathematical sciences graduate teaching assistants (GTAs) who tutored in an online setting during the 2020-2021 academic year due to the COVID-19 pandemic. Specifically, this study addresses the following research question: What were the mathematical teaching beliefs and practices of graduate student tutors participating in online tutoring sessions through the mathematics learning center (MLC) during the COVID-19 pandemic? 

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5. Using Trained Tutors to Improve Mechanical Engineering Technology Student Writing

   Clippinger, D. ( June 2023 , 2023 ASEE Annual Conference, Baltimore, MD, USA)

   In an earlier work, the authors compared the writing style of Mechanical Engineering Technology (MET) students in an “untutored” state to the writing style of “tutored” students, where the tutoring was provided by “generic” writing center tutors. The results of this study showed that aside from changes in the diction of the students’ work, there was little measurable improvement in the quality of student writing as measured by both the AAC&U VALUE Rubric and by the authors’ voice-development-style-diction method. The current work builds on the results of the previous work by providing training on a just-in-time basis for the writing center tutors. As with previous years, the students participating in the study were MET students in a last-semester capstone industrial design course. This course is organized around a series of open-ended industry-sponsored projects for which the students are expected to develop a solution to a mechanical engineering problem. The students work on the projects in teams of three or four students and complete the work over a two-semester sequence offered annually on a fall-spring basis. The assignment in the study was identical to that of previous years: an “analysis” report in which students are expected to apply content from previous courses to one aspect of the industry-sponsored design project. The present study will compare the results from three iterations of the study: the work of “untutored” students, i.e. those who did not received any writing center assistance whatsoever, those who tutored by “generic” writing center tutors, and lastly, the works of those tutored by tutors specifically trained in support of the specific intervention. In the two cases where tutor interaction occurred, it was required as a component of the course to ensure participation by the entire student cohort. In general, the interactions with the specially-trained tutors produced works with a more mature writing style on the part of the student as compared to those works produced by students who had interacted with the untrained tutors or no tutors at all. The work will also discuss survey data collected on the “generic” and specially-trained tutoring sessions and discuss the differences in the results. Preliminary results show that the specially-trained tutors reported pronounced levels of engagement in the tutoring session, as measured by student note-taking, student questions, student receptiveness to suggestions, and student desire to understand the reasoning behind the tutors’ suggestions. Specially-trained tutors also reported significantly higher levels of student interest suggestions about grammar, style, content, format, and citations. Overall, it is concluded that specific training for the tutors was most associated with increased levels of interaction between tutor and student. As the students in the final group (“trained tutors”) were told prior to the tutoring session that the tutors were “specially trained,” it remains to be determined if the increased interaction was due to better tutor preparation or a higher estimation of the value of the tutoring session on the part of the students receiving the tutoring. This is proposed as an extension to the current work. 

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