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1. 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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2. What Time is It? Student Modeling Needs to Know.

   Mao, Y. ; Marwan, S. ; Price, T.W. ; Barnes, T. ; & Chi, M. ( January 2020 , In Proceedings of the 13th International Conference on Educational Data Mining (EDM) 2020)

   null (Ed.)

   Abstract: Modeling student learning processes is highly complex since it is influenced by many factors such as motivation and learning habits. The high volume of features and tools provided by computer-based learning environments confounds the task of tracking student knowledge even further. Deep Learning models such as Long-Short Term Memory (LSTMs) and classic Markovian models such as Bayesian Knowledge Tracing (BKT) have been successfully applied for student modeling. However, much of this prior work is designed to handle sequences of events with discrete timesteps, rather than considering the continuous aspect of time. Given that time elapsed between successive elements in a student’s trajectory can vary from seconds to days, we applied a Timeaware LSTM (T-LSTM) to model the dynamics of student knowledge state in continuous time. We investigate the effectiveness of T-LSTM on two domains with very different characteristics. One involves an open-ended programming environment where students can self-pace their progress and T-LSTM is compared against LSTM, Recent Temporal Pattern Mining, and the classic Logistic Regression (LR) on the early prediction of student success; the other involves a classic tutor-driven intelligent tutoring system where the tutor scaffolds the student learning step by step and T-LSTM is compared with LSTM, LR, and BKT on the early prediction of student learning gains. Our results show that TLSTM significantly outperforms the other methods on the self-paced, open-ended programming environment; while on the tutor-driven ITS, it ties with LSTM and outperforms both LR and BKT. In other words, while time-irregularity exists in both datasets, T-LSTM works significantly better than other student models when the pace is driven by students. On the other hand, when such irregularity results from the tutor, T-LSTM was not superior to other models but its performance was not hurt either. 

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3. 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. 

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4. Lived Experiences of African American Engineering Students at a PWI Through the Lens of Navigational Capital

   Damas, S. ; Benson, L. ( February 2022 , 2022 CoNECD (Collaborative Network for Engineering & Computing Diversity))

   There are significant disparities between the conferring of science, technology, engineering, and mathematics (STEM) bachelor’s degrees to minoritized groups and the number of STEM faculty that represent minoritized groups at four-year predominantly White institutions (PWIs). Studies show that as of 2019, African American faculty at PWIs have increased by only 2.3% in the last 20 years. This study explores the ways in which this imbalance affects minoritized students in engineering majors. Our research objective is to describe the ways in which African American students navigate their way to success in an engineering program at a PWI where the minoritized faculty representation is less than 10%. In this study, we define success as completion of an undergraduate degree and matriculation into a Ph.D. program. Research shows that African American students struggle with feeling like the “outsider within” in graduate programs and that the engineering culture can permeate from undergraduate to graduate programs. We address our research objective by conducting interviews using navigational capital as our theoretical framework, which can be defined as resilience, academic invulnerability, and skills. These three concepts come together to denote the journey of an individual as they achieve success in an environment not created with them in mind. Navigational capital has been applied in education contexts to study minoritized groups, and specifically in engineering education to study the persistence of students of color. Research on navigational capital often focuses on how participants acquire resources from others. There is a limited focus on the experience of the student as the individual agent exercising their own navigational capital. Drawing from and adapting the framework of navigational capital, this study provides rich descriptions of the lived experiences of African American students in an engineering program at a PWI as they navigated their way to academic success in a system that was not designed with them in mind. This pilot study took place at a research-intensive, land grant PWI in the southeastern United States. We recruited two students who identify as African American and are in the first year of their Ph.D. program in an engineering major. Our interview protocol was adapted from a related study about student motivation, identity, and sense of belonging in engineering. After transcribing interviews with these participants, we began our qualitative analysis with a priori coding, drawing from the framework of navigational capital, to identify the experiences, connections, involvement, and resources the participants tapped into as they maneuvered their way to success in an undergraduate engineering program at a PWI. To identify other aspects of the participants’ experiences that were not reflected in that framework, we also used open coding. The results showed that the participants tapped into their navigational capital when they used experiences, connections, involvement, and resources to be resilient, academically invulnerable, and skillful. They learned from experiences (theirs or others’), capitalized on their connections, positioned themselves through involvement, and used their resources to achieve success in their engineering program. The participants identified their experiences, connections, and involvement. For example, one participant who came from a blended family (African American and White) drew from the experiences she had with her blended family. Her experiences helped her to understand the cultures of Black and White people. She was able to turn that into a skill to connect with others at her PWI. The point at which she took her familial experiences to use as a skill to maneuver her way to success at a PWI was an example of her navigational capital. Another participant capitalized on his connections to develop academic invulnerability. He was able to build his connections by making meaningful relationships with his classmates. He knew the importance of having reliable people to be there for him when he encountered a topic he did not understand. He cultivated an environment through relationships with classmates that set him up to achieve academic invulnerability in his classes. The participants spoke least about how they used their resources. The few mentions of resources were not distinct enough to make any substantial connection to the factors that denote navigational capital. The participants spoke explicitly about the PWI culture in their engineering department. From open coding, we identified the theme that participants did not expect to have role models in their major that looked like them and went into their undergraduate experience with the understanding that they will be the distinct minority in their classes. They did not make notable mention of how a lack of minority faculty affected their success. Upon acceptance, they took on the challenge of being a racial minority in exchange for a well-recognized degree they felt would have more value compared to engineering programs at other universities. They identified ways they maneuvered around their expectation that they would not have representative role models through their use of navigational capital. Integrating knowledge from the framework of navigational capital and its existing applications in engineering and education allows us the opportunity to learn from African American students that have succeeded in engineering programs with low minority faculty representation. The future directions of this work are to outline strategies that could enhance the path of minoritized engineering students towards success and to lay a foundation for understanding the use of navigational capital by minoritized students in engineering at PWIs. Students at PWIs can benefit from understanding their own navigational capital to help them identify ways to successfully navigate educational institutions. Students’ awareness of their capacity to maintain high levels of achievement, their connections to networks that facilitate navigation, and their ability to draw from experiences to enhance resilience provide them with the agency to unleash the invisible factors of their potential to be innovators in their collegiate and work environments. 

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5. Investigating the Impact of Backward Strategy Learning in a Logic Tutor: Aiding Subgoal Learning Towards Improved Problem Solving

   https://doi.org/10.1007/s40593-023-00338-1  

   Shabrina, Preya ; Mostafavi, Behrooz ; Abdelshiheed, Mark ; Chi, Min ; Barnes, Tiffany ( August 2023 , International Journal of Artificial Intelligence in Education)

   Learning to derive subgoals reduces the gap between experts and students and makes students prepared for future problem solving. Researchers have explored subgoal-labeled instructional materials in traditional problem solving and within tutoring systems to help novices learn to subgoal. However, only a little research is found on problem-solving strategies in relationship with subgoal learning. Also, these strategies are under-explored within computer-based tutors and learning environments. The backward problem-solving strategy is closely related to the process of subgoaling, where problem solving iteratively refines the goal into a new subgoal to reduce difficulty. In this paper, we explore a training strategy for backward strategy learning within an intelligent logic tutor that teaches logic-proof construction. The training session involved backward worked examples (BWE) and problem solving (BPS) to help students learn backward strategy towards improving their subgoaling and problem-solving skills. To evaluate the training strategy, we analyzed students’ 1) experience with and engagement in learning backward strategy, 2) performance and 3) proof construction approaches in new problems that they solved independently without tutor help after each level of training and in posttest. Our results showed that, when new problems were given to solve without any tutor help, students who were trained with both BWE and BPS outperformed students who received none of the treatment or only BWE during training. Additionally, students trained with both BWE and BPS derived subgoals during proof construction with significantly higher efficiency than the other two groups.

    

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