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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. 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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3. The Effectiveness of Visualization for Learning Expression Evaluation: A Reproducibility Study

   https://doi.org/10.1145/2899415.2899427  

   Kumar, Amruth N. (July 2016, Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education)

   A study was conducted to reproduce the results of an earlier study on the effectiveness of visualization for learning expression evaluation in a problem-solving software tutor on arithmetic expressions. In the current reproducibility study, data was collected from a software tutor on assignment expressions over six semesters. ANOVA analysis of the amount and speed of learning was conducted with treatment, sex and racial groups as fixed factors. Results include that visualization helped the students learn significantly more concepts, whether the students needed to use the tutor or benefited from using the tutor. However, it only benefited the less-prepared students. It did not help the students learn faster. It benefited both the sexes and traditionally represented as well as underrepresented groups. The current study confirmed almost all the results from the previous study, albeit for a harder topic. One reason why visualization was found to be effective in both these studies may be that the same visualization scheme was used by the students to both view feedback and construct their answers. 

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4. Allowing Revisions While Providing Error-Flagging Support: Is More Better?

   Kumar, Amruth N. (July 2020, Artificial Intelligence in Education 2020)

   In this study, we studied whether the number of revisions allowed per problem when error-flagging feedback is provided has a significant effect on learning. We used a partial cross-over study and analyzed the data collected by two adaptive tutors on while loops and for loops over six semesters. We found that when students were unfamiliar with the concepts, they solved fewer problems and therefore, learned significantly less when they were provided more opportunities for revision with error-flagging feedback. But, once they became more familiar with the concepts, allowing for more revisions had no deleterious effect on learning. 

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5. Inside the Mind of a CS Undergraduate TA: A Firsthand Account of Undergraduate Peer Tutoring in Computer Labs

   Markel, Julia M.; Guo, Philip J. (March 2021, ACM Technical Symposium on Computer Science Education)

   null (Ed.)

   As CS enrollments continue to grow, introductory courses are employing more undergraduate TAs. One of their main roles is performing one-on-one tutoring in the computer lab to help students understand and debug their programming assignments. What goes on in the mind of an undergraduate TA when they are helping students with programming? In this experience report, we present firsthand accounts from an undergraduate TA documenting her 36 hours of in-lab tutoring for a CS2 course, where she engaged in 69 one-on-one help sessions. This report provides a unique perspective from an undergraduate's point-of-view rather than a faculty member's. We summarize her experiences by constructing a four-part model of tutoring interactions: a) The tutor begins the session with an initial state of mind (e.g., their energy/focus level, perceived time pressure). b) They observe the student's outward state upon arrival (e.g., how much they seem to care about learning). c) Using that observation, the tutor infers what might be going on inside the student's mind. d) The combination of what goes on inside the tutor's and student's minds affects tutoring interactions, which progress from diagnosis to planning to an explain-code-react loop to post-resolution activities. We conclude by discussing ways that this model can be used to design scaffolding for training novice TAs and software tools to help TAs scale their efforts to larger classes. 

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