More Like this

1. Impact of a Step Based Tutoring System on Student Learning at The University of Texas at El Paso

   Chavez, Ana; Erives, Hector; Velez-Reyes, Miguel; Skromme, Brian J. (August 2022, Proceeding of the 2022 ASEE Annual Conference & Exposition)

   In this paper we describe the historical background of the introductory course in Electric Circuits I, how it has been taught, and the different modifications this course has undergone for the past few years. We describe preliminary results of a new step-based method on student learning which has been applied at the University of Texas at El Paso (UTEP) to improve students’ understanding of the topics covered in this course, and describe the step-based tutoring System, dubbed Circuit Tutor, developed by researchers at the UTEP. The results indicate Circuit Tutor platform can be used as a self-learning tool according to survey answers from students and the increasing passing rate in the Circuits I course. 

   more » « less
2. Interactive Editing of Circuits in a Step-Based Tutoring System

   Skromme, B; Redshaw, C.; Gupta, A; Gupta, S; Andrei, P; Erives, H; Bailey, A; Thompson, W; Bansal, S. (July 2020, American Society for Engineering Education Annual Conference)

   Step-based tutoring systems are known to be more effective than traditional answer-based systems. They however require that each step in a student’s work be accepted and evaluated automatically to provide effective feedback. In the domain of linear circuit analysis, it is frequently necessary to allow students to draw or edit circuits on their screen to simplify or otherwise transform them. Here, the interface developed to accept such input and provide immediate feedback in the Circuit Tutor system is described, along with systematic assessment data. Advanced simplification methods such as removing circuit sections that are removably hinged, voltage-splittable, or current-splittable are taught to students in an interactive tutorial and then supported in the circuit editor itself. To address the learning curve associated with such an interface, ~70 video tutorials were created to demonstrate exactly how to work the randomly generated problems at each level of each of the tutorials in the system. A complete written record or “transcript” of student’s work in the system is being made available, showing both incorrect and correct steps. Introductory interactive (multiple choice) tutorials are now included on most topics. Assessment of exercises using the interactive editor was carried out by professional evaluators for several institutions, including three that heavily serve underrepresented minorities. Both quantitative and qualitative methods were used, including focus groups, surveys, and interviews. Controlled, randomized, blind evaluations were carried out in three different course sections in Spring and Fall 2019 to evaluate three tutorials using the interactive editor, comparing use of Circuit Tutor to both a commercial answer-based system and to conventional textbook-based paper homework. In Fall 2019, students rated the software a mean of 4.14/5 for being helpful to learn the material vs. 3.05/5 for paper homework (HW), p < 0.001 and effect size d = 1.11σ. On relevant exam questions that semester, students scored significantly (p = 0.014) higher with an effect size of d = 0.64σ when using Circuit Tutor compared to paper HW in one class section, with no significant difference in the other section. 

   more » « less
3. “We Make a Great Team!”: Adults with Low Prior Domain Knowledge Learn more from a Peer Robot than a Tutor Robot

   https://doi.org/10.1109/HRI53351.2022.9889441  

   Salomons, Nicole; Pineda, Kaitlynn Taylor; Adejare, Aderonke; Scassellati, Brian (January 2022, ACM/IEEE International Conference on Human-Robot Interaction (HRI 2022))

   In peer tutoring, the learner is taught by a colleague rather than by a traditional tutor. This strategy has been shown to be effective in human tutoring, where students have higher learning gains when taught by a peer instead of a traditional tutor. Similar results have been shown in child-robot interactions studies, where a peer robot was more effective than a tutor robot at teaching children. In this work, we compare skill increase and perception of a peer robot to a tutor robot when teaching adults. We designed a system in which a robot provides personalized help to adults in electronic circuit construction. We compare the number of learned skills and preferences of a peer robot to a tutor robot. Participants in both conditions improved their circuit skills after interacting with the robot. There were no significant differences in number of skills learned between conditions. However, participants with low prior domain knowledge learned significantly more with a peer robot than a tutor robot. Furthermore, the peer robot was perceived as friendlier, more social, smarter, and more respectful than the tutor robot, regardless of initial skill level. 

   more » « less
4. Step Tutor: Supporting Students through Step-by-Step Example-Based Feedback

   https://doi.org/10.1145/3341525.3387411  

   Wang, Wengran; Rao, Yudong; Zhi, Rui; Marwan, Samiha; Gao, Ge; Price, Thomas W. (June 2020, International Conference on Innovation and Technology in Computer Science Education)

   Students often get stuck when programming independently, and need help to progress. Existing, automated feedback can help students progress, but it is unclear whether it ultimately leads to learning. We present Step Tutor, which helps struggling students during programming by presenting them with relevant, step-by-step examples. The goal of Step Tutor is to help students progress, and engage them in comparison, reflection, and learning. When a student requests help, Step Tutor adaptively selects an example to demonstrate the next meaningful step in the solution. It engages the student in comparing "before" and "after" code snapshots, and their corresponding visual output, and guides them to reflect on the changes. Step Tutor is a novel form of help that combines effective aspects of existing support features, such as hints and Worked Examples, to help students both progress and learn. To understand how students use Step Tutor, we asked nine undergraduate students to complete two programming tasks, with its help, and interviewed them about their experience. We present our qualitative analysis of students' experience, which shows us why and how they seek help from Step Tutor, and Step Tutor's affordances. These initial results suggest that students perceived that Step Tutor accomplished its goals of helping them to progress and learn. 

   more » « less
5. Leveraging LLM Tutoring Systems for Non-Native English Speakers in Introductory CS Courses

   Villegas_Molina, Ismael; Nikitza_Montalvo, Audria; Ochoa, Benjamin; Denny, Paul; Porter, Leo (June 2025, 2025 ASEE Annual Conference & Exposition)

   Computer science has historically presented barriers for non-native English speaking (NNES) students, often due to language and terminology challenges. With the rise of large language models (LLMs), there is potential to leverage this technology to support NNES students more effectively. Recent implementations of LLMs as tutors in classrooms have shown promising results. In this study, we deployed an LLM tutor in an accelerated introductory computing course to evaluate its effectiveness specifically for NNES students. Key insights for LLM tutor use are as follows: NNES students signed up for the LLM tutor at a similar rate to native English speakers (NES); NNES students used the system at a lower rate than NES students---to a small effect; NNES students asked significantly more questions in languages other than English compared to NES students, with many of the questions being multilingual by incorporating English programming keywords. Results for views of the LLM tutor are as follows: both NNES and NES students appreciated the LLM tutor for its accessibility, conversational style, and the guardrails put in place to guide users to answers rather than directly providing solutions; NNES students highlighted its approachability as they did not need to communicate in perfect English; NNES students rated help-seeking preferences of online resources higher than NES students; Many NNES students were unfamiliar with computing terminology in their native languages. These results suggest that LLM tutors can be a valuable resource for NNES students in computing, providing tailored support that enhances their learning experience and overcomes language barriers. 

   more » « less