Investigating Virtual Learning Environments
PIs:
Mark Warschauer, University of California, Irvine, School of Education
Di Xu, University of California, Irvine, School of Education
Padhraic Smyth, University of California, Irvine, UCI Donald Bren School of Information and Computer Sciences
Teomara Rutherford, University of Delaware, School of Education
Rachel Baker, University of California, Irvine, School of Education
Brian Sato, University of California, Irvine, School of Biological Sciences
Postdoctoral Scholar:
Fernando Rodriguez, University of California, Irvine, School of Education
Christian Fischer, University of California, Irvine, Teaching and Learning Research Center
Research Staff:
Kameryn Denaro, University of California, Irvine, School of Education
Tamara Tate, University of California, Irvine, Teaching and Learning Research Center
Graduate Students:
Dennis Dang (Education)
Joseph Aubele (Education)
Yiwen Lin (Education)
Mariela Rivas (Education)
Renzhe Yu (Education)
Affiliates:
Nia Dowell, Assistant Professor, University of California, Irvine
Qiujie Li, Post-Doc, New York University
Peter McPartlan, Post-Doc, San Diego State University
Funding Source: NSF Grant Number 1535300, 2015-2020
Online Learning Research Center: olrc.us
Summary: With 69% of higher education institutions stating that online learning is a critical part of their long-term strategy and 32% of higher education students taking at least one course online (Allen & Seaman, 2013), new forms of virtual learning are raising the specter of radically transformed undergraduate education. This is particularly true for lower-division courses in science, technology, engineering, and mathematics (STEM), which disproportionately rely on large lectures. The possibilities of providing lecture material online, as is done through the popular Khan Academy and new massive online open courses (MOOCs), suggest to many that virtual learning may have the potential to serve as a comparable or even advantageous alternative to traditional in-person lectures. Debates on this issue highlight both the high hopes and potential pitfalls. To date, few rigorous studies have convincingly demonstrated the effects of virtual learning. There have been a number of studies, including some meta-analyses, that have found benefits linked with virtual learning; however, only a handful have used rigorous experimental or quasi-experimental methods to identify causal effects (e.g., Figlio, Rush, & Yin, 2010; Xu & Jaggars, 2013).
In sum, despite their increasingly important role and fast growth in the national education landscape, the field has limited information about the impacts of different course delivery formats on student learning outcomes and on potential strategies to improve their effectiveness. To fill this research gap, the project systematically explored the effectiveness of virtual learning in the particular setting of STEM courses in large research universities. This setting offers the means to test moderation hypotheses regarding student populations, course content, and instructional practices within a context that plays a special role in the STEM pipeline to graduate study and advanced careers. In keeping with the theory of affordances (see Warschauer, 2003) in which we set our work, by conducting both experiments and observations across a broad swath of courses, we aimed to flesh out the heterogeneous effects of virtual learning environments and identify what kinds of environments and instructional techniques are most potent for aiding diverse learners.
This project had three major goals:
1) to explore how different types of virtual learning environments affect teaching and learning processes, outcomes, and attitudes toward STEM, both in general and for particular groups of underrepresented learners;
2) to examine how variation in instructional practices is associated with student learning gains for the purposes of distilling potential strategies to design high-quality virtual learning environments and improve student learning;
3) to understand the student experience of learning within virtual learning environments through investigation of learning behaviors and the ways in which these behaviors relate to student outcomes.
Training and Software Products
Intro to Python for Data Science
Python for Clickstream Analysis
Canvas API Crawler for Accessing Learning Analytic Data
Mark Warschauer, University of California, Irvine, School of Education
Di Xu, University of California, Irvine, School of Education
Padhraic Smyth, University of California, Irvine, UCI Donald Bren School of Information and Computer Sciences
Teomara Rutherford, University of Delaware, School of Education
Rachel Baker, University of California, Irvine, School of Education
Brian Sato, University of California, Irvine, School of Biological Sciences
Postdoctoral Scholar:
Fernando Rodriguez, University of California, Irvine, School of Education
Christian Fischer, University of California, Irvine, Teaching and Learning Research Center
Research Staff:
Kameryn Denaro, University of California, Irvine, School of Education
Tamara Tate, University of California, Irvine, Teaching and Learning Research Center
Graduate Students:
Dennis Dang (Education)
Joseph Aubele (Education)
Yiwen Lin (Education)
Mariela Rivas (Education)
Renzhe Yu (Education)
Affiliates:
Nia Dowell, Assistant Professor, University of California, Irvine
Qiujie Li, Post-Doc, New York University
Peter McPartlan, Post-Doc, San Diego State University
Funding Source: NSF Grant Number 1535300, 2015-2020
Online Learning Research Center: olrc.us
Summary: With 69% of higher education institutions stating that online learning is a critical part of their long-term strategy and 32% of higher education students taking at least one course online (Allen & Seaman, 2013), new forms of virtual learning are raising the specter of radically transformed undergraduate education. This is particularly true for lower-division courses in science, technology, engineering, and mathematics (STEM), which disproportionately rely on large lectures. The possibilities of providing lecture material online, as is done through the popular Khan Academy and new massive online open courses (MOOCs), suggest to many that virtual learning may have the potential to serve as a comparable or even advantageous alternative to traditional in-person lectures. Debates on this issue highlight both the high hopes and potential pitfalls. To date, few rigorous studies have convincingly demonstrated the effects of virtual learning. There have been a number of studies, including some meta-analyses, that have found benefits linked with virtual learning; however, only a handful have used rigorous experimental or quasi-experimental methods to identify causal effects (e.g., Figlio, Rush, & Yin, 2010; Xu & Jaggars, 2013).
In sum, despite their increasingly important role and fast growth in the national education landscape, the field has limited information about the impacts of different course delivery formats on student learning outcomes and on potential strategies to improve their effectiveness. To fill this research gap, the project systematically explored the effectiveness of virtual learning in the particular setting of STEM courses in large research universities. This setting offers the means to test moderation hypotheses regarding student populations, course content, and instructional practices within a context that plays a special role in the STEM pipeline to graduate study and advanced careers. In keeping with the theory of affordances (see Warschauer, 2003) in which we set our work, by conducting both experiments and observations across a broad swath of courses, we aimed to flesh out the heterogeneous effects of virtual learning environments and identify what kinds of environments and instructional techniques are most potent for aiding diverse learners.
This project had three major goals:
1) to explore how different types of virtual learning environments affect teaching and learning processes, outcomes, and attitudes toward STEM, both in general and for particular groups of underrepresented learners;
2) to examine how variation in instructional practices is associated with student learning gains for the purposes of distilling potential strategies to design high-quality virtual learning environments and improve student learning;
3) to understand the student experience of learning within virtual learning environments through investigation of learning behaviors and the ways in which these behaviors relate to student outcomes.
Training and Software Products
Intro to Python for Data Science
Python for Clickstream Analysis
Canvas API Crawler for Accessing Learning Analytic Data
