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.
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Inside the Mind of a CS Undergraduate TA: A Firsthand Account of Undergraduate Peer Tutoring in Computer Labs
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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- Award ID(s):
- 1845900
- PAR ID:
- 10210724
- Date Published:
- Journal Name:
- ACM Technical Symposium on Computer Science Education
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Science as an enterprise has been and continues to be exclusionary, perpetuating inequities among whose voice is heard as well as what/whose knowledge is recognized as valid. Women, people of color, and persons with disabilities are still vastly outnumbered in science and engineering by their White, male counterparts. These types of imbalances create a gatekeeping culture of inequity and inaccessibility, particularly for traditionally underrepresented students. Science classrooms, especially at the undergraduate level, strive to mimic the broader practices of the scientific community and therefore have tremendous potential to perpetuate the exclusion of certain groups of people. They also have, however, the potential to be a catalyst for equitable participation in science. Utilizing pedagogies of empowerment such as culturally responsive science teaching (CRST) in undergraduate classrooms can mitigate the gatekeeping phenomenon seen in science. Teaching assistants (TAs) engage in more one‐on‐one time with students than most faculty in undergraduate biology education, yet minimal pedagogical training is offered to them. Therefore, training for improved pedagogical knowledge is important for TAs, but training for CRST is critical as TAs have broad and potentially lasting impact on students. This study explores the ways in which undergraduate biology TAs enact CRST. Using constructivist grounded theory methods, this study examined TAs' reflections, observation field notes, semistructured interviews, and focus groups to develop themes surrounding their enactment of CRST. Findings from this study showed that undergraduate biology TAs enact CRST in ways described by four themes:
Funds of Knowledge Connections ,Differentiating Instruction ,Intentional Scaffolding , andReducing Student Anxiety . These findings provide new insights into the ways undergraduate science education might be reimagined to create equitable science learning opportunities for all students. -
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Abstract This descriptive study focuses on using voice activity detection (VAD) algorithms to extract student speech data in order to better understand the collaboration of small group work and the impact of teaching assistant (TA) interventions in undergraduate engineering discussion sections. Audio data were recorded from individual students wearing head‐mounted noise‐cancelling microphones. Video data of each student group were manually coded for collaborative behaviours (eg, group task relatedness, group verbal interaction and group talk content) of students and TA–student interactions. The analysis includes information about the turn taking, overall speech duration patterns and amounts of overlapping speech observed both when TAs were intervening with groups and when they were not. We found that TAs very rarely provided explicit support regarding collaboration. Key speech metrics, such as amount of turn overlap and maximum turn duration, revealed important information about the nature of student small group discussions and TA interventions. TA interactions during small group collaboration are complex and require nuanced treatments when considering the design of supportive tools.
Practitioner notes What is already known about this topic
Student turn taking can provide information about the nature of student discussions and collaboration.
Real classroom audio data of small groups typically have lots of background noise and present challenges for audio analysis.
TAs have little training in how to productively intervene with students about collaborative skills.
What this paper adds
TA interaction with groups primarily focused on task progress and understanding of concepts with negligible explicit support on building collaborative skills.
TAs intervened with the groups often which gave the students little time for uptake of their suggestions or deeper discussion.
Student turn overlap was higher without the presence of TAs.
Maximum turn duration can be an important real‐time turn metric to identify the least verbally active student participant in a group.
Implications for practice and/or policy
TA training should include information about how to monitor groups for collaborative behaviours and when and how they should intervene to provide feedback and support.
TA feedback systems should keep track of previous interventions by TAs (especially in contexts where there are multiple TAs facilitating) and the duration since previous intervention to ensure that TAs do not intervene with a group too frequently with little time for student uptake.
Maximum turn duration could be used as a real‐time metric to identify the least verbally active student in a group so that support could be provided to them by the TAs.
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Many institutions use undergraduate teaching assistants (tutors) in their computing courses to help provide more resources to students. Because of the role tutors play in students' learning experiences, recent work in computing education has begun to explore student-tutor interactions through the tutor's perspective and through direct observation of the interactions. The results suggest that these interactions are cognitively challenging for tutors and may not be as beneficial for students' learning as one might hope. Given that many of these interactions may be unproductive, this work seeks to understand how student expectations of these sessions might be impacting the interactions' effectiveness. We interviewed 15 students in a CS2 course to learn about the expectations and desires that students have when they attend tutoring sessions. Our findings indicate that there is variation in what students consider a desired result from the interaction, that assignment deadlines affect students' expectations and desires for interactions, and that students do not always want what they believe is beneficial for their learning. We discuss implications for instructors and potential guidance for students and tutors to make tutoring sessions more effective.more » « less
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As enrollments in computing courses have surged, the ratio of students to faculty has risen at many institutions. Along with many other large undergraduate programs, our institution has adapted to this challenge by hiring increasing numbers of undergraduate tutors to help students. In early computing courses, their role at our institution is primarily to help students with their programming assignments. Despite our institution offering a training course for tutors, we are concerned about the quality and nature of these student-tutor interactions. As instruction moved online due to COVID-19, this provided the unique opportunity to record all student-tutor interactions (among consenting participants) for research. In order to gain an understanding of the behaviors common in these interactions, we conducted an initial qualitative analysis using open coding followed by a quantitative analysis on those codes. Overall, we found that students are not generally receiving the instruction we might hope or expect from these sessions. Notably, tutors often simply give students the solution to the problem in their code without teaching them about the process of finding and correcting their own errors. These findings highlight the importance of tutoring sessions for learning in introductory courses and motivate remediation to make these sessions more productive.more » « less
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