Tools to understand how students learn
Abstract/Contents
- Abstract
- As classroom sizes grow, instructor workload also increases. Despite innovations in technology to scale education, little has been done to improve upon the most critical component of student learning: unsupervised work on assignments. In computer science education, learning process---the way in which students design, debug, and explore programming assignments---is instrumental to performance and mastery. Yet few studies have defined assignment-centric metrics to measure learning process, much less design systems that transform the way we think about unsupervised work today. My work explores how to improve student assignment work so that both the teacher and learner benefit. While many tools analyze only a student's final submission, I focus on a paradigm to collect in-depth snapshots of in-progress student work. I first discuss the complexity of characterizing progress on a programming assignment with an abstraction called milestones, and I show how we can use machine learning methods to visualize how students work through an open-ended graphics-based assignment. Next, I present a tool, Pensieve, which organizes snapshots of student work so that teachers see a student's problem-solving approach. This tool facilitates sit-down student-teacher conversations, where teachers can give more in-depth feedback to each individual student. Thirdly, I present TMOSS, a tool to detect excessive collaboration---that is, when a student heavily relies on peer or online resources---at any point during unsupervised work on an assignment. For both Pensieve and TMOSS, I discuss pedagogical and cultural impacts on students as well as the classroom at large. This work points to a new paradigm for supporting learners and a path forward for designing new types of assignments that enhance the student experience. I close by discussing a graduating networking classroom project to reproduce existing research, which prepares students for research and industry careers in networking.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Yan, Lisa |
|---|---|
| Degree supervisor | McKeown, Nick |
| Degree supervisor | Piech, Chris (Christopher) |
| Thesis advisor | McKeown, Nick |
| Thesis advisor | Prabhakar, Balaji, 1967- |
| Thesis advisor | Sahami, Mehran |
| Degree committee member | Prabhakar, Balaji, 1967- |
| Degree committee member | Sahami, Mehran |
| Associated with | Stanford University, Department of Electrical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Lisa Yan. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Lisa Yan
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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