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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. Aligning tutor discourse rigorous thinking with tutee content mastery for predicting math achievements

   Abdelshiheed, M; Jacobs, J; D’Mello, S (July 2024, Artificial Intelligence in Education. AIED 2024. Lecture Notes in Computer Science)

   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 (N = 1080) 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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3. Aligning Tutor Discourse Supporting Rigorous Thinking with Tutee Content Mastery for Predicting Math Achievement

   https://doi.org/10.1007/978-3-031-64299-9_11  

   Abdelshiheed, Mark; Jacobs, Jennifer K; D’Mello, Sidney K (July 2024, Springer Cham)

   Olney, A M; Chounta, I A; Liu, Z; Santos, O C; Bittencourt, I I (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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4. Using Trained Tutors to Improve Mechanical Engineering Technology Student Writing

   Clippinger, D.; Pflueger, R. C.; Nozaki, S.; Bodenhamer, J. (June 2023, American Society for Engineering Education)

   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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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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