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1. Long Term Retention of Programming Concepts Learned Using Tracing Versus Debugging Tutors

   https://doi.org/10.1007/978-3-030-78270-2_39  

   Kumar, Amruth N. (June 2021, Artificial Intelligence in Education)

   null (Ed.)

   We studied long-term retention of the concepts that introductory programming students learned using two software tutors on tracing the behavior of functions and debugging functions. Whereas the concepts covered by the tutor on the behavior of functions were interdependent, the concepts covered by debugging tutor were independent. We analyzed the data of the students who had used the tutors more than once, hours to weeks apart. Our objective was to find whether students retained what they had learned during the first session till the second session. We found that the more the problems students solved during the first session, the greater the retention. Knowledge and retention varied between debugging and behavior tutors, even though they both dealt with functions, possibly because de-bugging tutor covered independent concepts whereas behavior tutor covered interdependent concepts. 

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2. 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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3. Does choosing the concept on which to solve each practice problem in an adaptive tutor affect learning?

   Kumar, Amruth N. (July 2019, Proceedings of AI-ED 2019)

   We conducted a controlled study to investigate whether having students choose the concept on which to solve each practice problem in an adaptive tutor helped improve learning. We analyzed data from an adaptive tutor used by introductory programming students over three semesters. The tutor presented code-tracing problems, used pretest-practice-post-test protocol, and presented line-by-line explanation of the correct solution as feedback. We found that choice did not in-crease the amount of learning or pace of learning. But, it resulted in greater improvement in score on learned concepts, and the effect size was medium. 

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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. An Epistemic Model-Based Tutor for Imperative Programming

   https://doi.org/10.1007/978-3-030-78270-2_38  

   Kumar, Amruth N. (June 2021, Artificial Intelligence in Educaiton)

   null (Ed.)

   We developed a tutor for imperative programming in C++. It covers algorithm formulation, program design and coding – all three stages involved in writing a program to solve a problem. The design of the tutor is epistemic, i.e., true to real-life programming practice. The student works through all the three stages of programming in interleaved fashion, and within the context of a single code canvas. The student has the sole agency to compose the program and write the code. The tutor uses goals and plans as prompts to scaffold the student through the programming process designed by an expert. It provides drill-down immediate feed-back at the abstract, concrete and bottom-out levels at each step. So, by the end of the session, the student is guaranteed to write the complete and correct program for a given problem. We used model-based architecture to implement the tutor be-cause of the ease with which it facilitates adding problems to the tutor. In a preliminary study, we found that practicing with the tutor helped students solve problems with fewer erroneous actions and less time. 

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