Peers, teachers, and the mechanisms of schooling : using big data to understand education processes
Abstract/Contents
- Abstract
- My dissertation intends to contribute to the knowledge of the mechanisms of education production by specifying the micro behaviors such as absences and classroom interactions among students, teachers and peers. Through using novel empirical approaches and unusually detailed data that track classroom processes, I am able to measure student and teacher behaviors and examine how these behaviors affect student short-term learning and longer-run outcomes. The three papers span from online higher education to in-person K-12 classrooms, and have immediate implications for current policy debates about virtual learning and teacher policy. In paper one, with Eric Bettinger and Susanna Loeb, I examine how peer interaction affects student learning outcomes and persistence in online courses offered by a large for-profit university. We creatively measure peer interaction using detailed written communications between students in online discussion board. To my knowledge, this is the first paper that examines peers by "what they do" instead of "who they are". This paper was published in 2016 by the Journal of Policy Analysis and Management. The second paper uses "text-as-data" methods to create objective metrics of teacher practices in English language arts (ELA) at 4th to 5th grade. Different from the conventional approach of teacher observation, I quantify teacher behaviors through their language and interaction with students from word-to-word transcriptions of classroom videos. I then compare these new measures with ratings created from classroom observation protocols and value-added scores. With Susanna Loeb, the third paper measures teachers' contribution to student engagement in secondary school using students' unexcused class absences. After creating value-added to student attendance, we compare this measure with teachers' impact on student test scores, and how these different dimensions of teacher effectiveness influence student high school graduation, dropping out, and Advanced Placement (AP) courses taking. This paper expands our understanding about the multidimensionality of teacher effects. The three papers use new types of data that are not often seen in educational research, including written communication in virtual classrooms, verbal interactions in elementary school classrooms, and class attendance records in secondary school. They together exemplify how the use of detailed data from education processes can significantly deepen our understanding of the complex mechanisms of schooling and inform education policymaking.
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 | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Liu, Jing |
|---|---|
| Degree supervisor | Loeb, Susanna |
| Thesis advisor | Loeb, Susanna |
| Thesis advisor | Bettinger, Eric |
| Thesis advisor | Cohen, Julia Phillips |
| Thesis advisor | Domingue, Ben |
| Degree committee member | Bettinger, Eric |
| Degree committee member | Cohen, Julia Phillips |
| Degree committee member | Domingue, Ben |
| Associated with | Stanford University, Graduate School of Education. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Jing Liu. |
|---|---|
| Note | Submitted to the Graduate School of Education. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Jing Liu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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