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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. A methodological approach for researching online teacher professional development

   Johnson, M. M. ( April 2021 , Middle Atlantic ASEE Section Spring 2021 Conference)

   null (Ed.)

   COVID-19 has altered the landscape of teaching and learning. For those in in-service teacher education, workshops have been suspended causing programs to adapt their professional development to a virtual space to avoid indefinite postponement or cancellation. This paradigm shift in the way we conduct learning experiences creates several logistical and pedagogical challenges but also presents an important opportunity to conduct research about how learning happens in these new environments. This paper describes the approach we took to conduct research in a series of virtual workshops aimed at teaching rural elementary teachers about engineering practices and how to teach a unit from an engineering curriculum. Our work explores how engineering concepts and practices are socially constructed through interactions with teachers, students, and artifacts. This approach, called interactional ethnography has been used by the authors and others to learn about engineering teaching and learning in precollege classrooms. The approach relies on collecting data during instruction, such as video and audio recordings, interviews, and artifacts such as journal entries and photos of physical designs. Findings are triangulated by analyzing these data sources. This methodology was going to be applied in an in-person engineering education workshop for rural elementary teachers, however the pandemic forced us to conduct the workshops remotely. Teachers, working in pairs, were sent workshop supplies, and worked together during the training series that took place over Zoom over four days for four hours each session. The paper describes how we collected video and audio of teachers and the facilitators both in whole group and in breakout rooms. Class materials and submissions of photos and evaluations were managed using Google Classroom. Teachers took photos of their work and scanned written materials and submitted them all by email. Slide decks were shared by the users and their group responses were collected in real time. Workshop evaluations were collected after each meeting using Google Forms. Evaluation data suggest that the teachers were engaged by the experience, learned significantly about engineering concepts and the knowledge-producing practices of engineers, and feel confident about applying engineering activities in their classrooms. This methodology should be of interest to the membership for three distinct reasons. First, remote instruction is a reality in the near-term but will likely persist in some form. Although many of us prefer to teach in person, remote learning allows us to reach many more participants, including those living in remote and rural areas who cannot easily attend in-person sessions with engineering educators, so it benefits the field to learn how to teach effectively in this way. Second, it describes an emerging approach to engineering education research. Interactional ethnography has been applied in precollege classrooms, but this paper demonstrates how it can also be used in teacher professional development contexts. Third, based on our application of interactional ethnography to an education setting, readers will learn specifically about how to use online collaborative software and how to collect and organize data sources for research purposes. 

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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. 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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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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