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1. Long Term Retention of Programming Concepts Learned Using Software Tutors

   Kumar, Amruth N. (June 2020, Intelligent Tutoring Systems 2020)

   Do students retain the programming concepts they have learned using software tutors over the long term? In order to answer this question, we analyzed the data collected by a software tutor on selection statements. We used the data of the students who used the tutor more than once to see whether they had retained for the second session what they had learned during the first session. We found that students retained over 71% of selection concepts that they had learned during the first session. The more problems students solved during the first session, the greater the percentage of retention. Even when students already knew a concept and did not benefit from using the tutor, a small percentage of concepts were for-gotten from the first session to the next, corresponding to transience of learning. Transience of learning varied with concepts. We list confounding factors of the study. 

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2. Comparing Bayesian Knowledge Tracing Model Against Naïve Mastery Model

   https://doi.org/10.1007/978-3-030-80421-3_9  

   Getseva, Vanesa; Kumar, Amruth N. (June 2021, Intelligent Tutoring Systems)

   null (Ed.)

   We conducted a study to see if using Bayesian Knowledge Tracing (BKT) models would save time and problems in programming tutors. We used legacy data collected by two programming tutors to compute BKT models for every concept covered by each tutor. The novelty of our model was that slip and guess parameters were computed for every problem presented by each tutor. Next, we used cross-validation to evaluate whether the resulting BKT model would have reduced the number of practice problems solved and time spent by the students represented in the legacy data. We found that in 64.23% of the concepts, students would have saved time with the BKT model. The savings varied among concepts. Overall, students would have saved a mean of 1.28 minutes and 1.23 problems per concept. We also found that BKT models were more effective at saving time and problems on harder concepts. 

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3. Providing the Option to Skip Feedback – A Reproducibility Study

   Kumar, Amruth N. (May 2019, Intelligent Tutoring Systems)

   Would providing choice lead to improved learning with a tutor? We had conducted and reported a controlled study earlier, wherein, introductory programing students were given the choice of skipping the line-by-line feedback provided after each incorrect answer in a tutor on if/if-else statements. Contrary to expectations, the study found that the choice to skip feedback did not lead to greater learning. We tried to reproduce these results using two tutors on if/if-else and switch statements, and with a larger subject pool. We found that whereas choice did not lead to greater learning on if/if-else tutor in this reproducibility study either, it resulted in decreased learning on switch tutor. We hypothesize that skipping feedback is indeed detrimental to learning. But, inter-relationships among the concepts covered by a tutor and the transfer of learning facilitated by these relationships compensate for the negative effect of skipping line-by-line feedback. We also found contradictory results between the two studies which highlight the need for reproducibility studies in empirical research. 

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4. Undergraduate Computing Tutors' Perceptions of their Roles, Stressors, and Barriers to Effectiveness

   https://doi.org/10.1145/3641554.3701784  

   Villegas_Molina, Ismael; Kim, Jeannie; Montalvo, Audria; Larragoitia, Apollo; Lim, Rachel S; Guo, Philip J; Krause-Levy, Sophia; Porter, Leo (February 2025, ACM)

   Undergraduate teaching assistants (tutors) are commonly employed in computing courses to help students with programming assignments. Prior research in computing education has reported the benefits of tutoring both for students and for the tutors' own learning. In contrast, recent research that examined actual tutoring sessions has reported that these sessions may be less productive than one might hope, with tutors often just giving students the answers to their problems without trying to teach the underlying concepts. To better understand why tutors may be employing these suboptimal practices, we interviewed ten tutors across early computing courses in higher education to identify their perceived role in these sessions, what stressors and factors influence their ability to perform their job effectively, and what kinds of best practices they learned in their tutor training course. Tutors reported their roles around student learning, gauging student understanding, identifying or providing solutions to students, and providing socioemotional support. They reported their stressors around environmental factors (e.g., number of students waiting to be helped, preparation time, peer-tutor frustrations), internal influences, student behavior, student skill levels, and feeling the need to ''read a student's mind.'' Regarding their tutor training course, Tutors reported learning about interaction guidelines and procedures and question-based problem solving. We conclude by discussing how these results may contribute to the less-effective behaviors seen in prior research and potential ways to improve tutoring in computing courses. 

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5. 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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