Virtual Session 2020
The Bay Area Learning Analytics Network (BayLAN) provides a forum for researchers and professionals in the San Francisco Bay Area to present and discuss new developments in analytics for education and educational technology and their impact on learning, instruction, assessment, and equity. Due to COVID-19, our annual meeting, originally scheduled for May 2020 at the University of California, Berkeley, had to be canceled.
To facilitate continued conversation and information exchange within the community, we will be hosting a special online meeting titled “Bridging the distance: Learning, Analytics, & COVID-19.” The event will take place on August 6 (1-5 p.m. PDT) and will include short talks and a panel discussion. The primary focus will be the impact of COVID-19 on education and educational technologies, including both near-term and long-term changes and demands.
The ongoing pandemic has resulted in a dramatic increase in the use of educational technology in both K-12 and university spaces as teachers help students learn despite shuttered classrooms. Many platforms offer universal free access. How has that increased usage worked? What trends and patterns are emerging in student data? How has student retention, engagement, or achievement shifted due to the disruption of COVID-19? What lessons can be learned for the future of digital and blended education? In this meeting, we’ll discuss these issues, with a focus on how they have affected educators and students from underserved communities.
1.15-2:05: K-12 presentations
2:05-2.50: K-12 Panel
3:00-4.00: Higher Ed presentations
4.00-5:00: Higher Ed Panel
*Note - Due to the level of interest in this meeting, we have expanded the original event schedule to allow more time for interactive discussion.*
Moderator: John Whitmer, Former Senior Director for Data Science and Analytics, ACTNext by ACT"
The abrupt emergency transition to online education brought by COVID created challenges for students, teachers and educational institutions seeking to continue learning and teaching under these difficult conditions . Educational technology providers experienced unprecedented demand for their services and different usage patterns. How did educators and edTech providers adapt and innovate? What have we learned and what questions remain unanswered? Most important, what was the experience of students from low-income and minoritized communities? In this panel, members of educational institutions and educational technology analytics leaders will discuss their observations, experiences, and responses to this situation.
Rody Boonchouy, Associate Superintendent of Instruction, Davis Joint Unified School District
Amanda Baker, Head of Analytics, Quizlet
Mel Restori, Director of Analytics and Data Engineering at Khan Academy
Jamie Studwell, Manager, Product Research & Analytics; Lexia Learning
Kim Wallace, Professional Education Consultant, Process Makes Perfect, Former Superintendent, Fremont Unified School District
Panelists Higher Ed :
Iain Harlow, VP Science, Cerego, Inc.
Julie Neisler, Quantitative Researcher and Data Scientist, Digital Promise
Vanessa Peters, Senior Learning Sciences Researcher, Digital Promise
Inger Stark, Distance Education Coordinator, Peralta Community College District
Jenn Stringer, Associate Vice Chancellor for IT and Chief Information Officer, UC Berkeley
Professional Education Consultant, Process Makes Perfect, Former Superintendent, Fremont Unified School District -
Overcoming Barriers to Ed-Tech Integration
Integrating innovations into instructional practices in “normal times” required a substantial amount of effort—pedagogically, physically, and psychologically—and today’s conditions for educating our youth are even more complex. Technology leaders should be aware of three orders of barriers to overcome to shift public education to online platforms for the foreseeable future: 1) first-order barriers—equipment, resources, training, and support; 2) second-order barriers—knowledge, skills, beliefs, and attitudes; and 3) third-order barriers--organizational context and culture such as conventions, contracts, and expectations. By first identifying the obstacles, the ed-tech industry can better understand how to implement and measure success.
Director of Analytics and Data Engineering at Khan Academy
Effects of COVID-19 on Online Learning at Khan Academy
Since mass school closures as a result of the COVID-19 pandemic, Khan Academy has seen dramatic changes in its usage and user base. This talk will discuss some of the biggest differences.
Manager, Product Research & Analytics, Lexia Learning
Challenges and Successes of Blended Learning at a Distance
Due to the pandemic school closures, Lexia educators across the world have had to take their blended learning classrooms fully online, many for the first time. In this short talk, we'll explore some of the interesting shifts in student and educator behavior that Lexia has seen in its program usage data and how we are combining those patterns with educator survey data to understand the challenges and successes of blended learning in this new remote learning paradigm.
Head of Analytics, Quizlet
Resilience in the face of remote learning: effects of income level on Quizlet studying
U.S. schools, students, and parents weren’t prepared for COVID-19’s impact to learning and the percentage of students studying compared to pre-COVID levels did not recover after remote learning started. The motivation was there for some students, however, resources post-shutdown were not equally available. We examine Quizlet's study behaviors post-shutdown as a function of different income levels and offer some theories on what effect device availability had on low-income learners' performance during remote learning.
VP Science, Cerego, Inc.
Measuring Learning Remotely
COVID-19 has accelerated an existing trend towards remote learning, bringing related challenges to the attention of instructors and institutions. Some of those challenges revolve around measuring whether and how much a learner knows. Are course attendance and test scores still the best we can do? How do we know we’ve really provided value for students? Modern cognitive science and machine learning provide us with some surprisingly robust and predictive measures of learning. I’ll include both general guidance and show some concrete examples of how we measure knowledge and understanding in Cerego.
Kevin Kelly & Inger Stark
Peralta Community College District
Equity Online Course Design Rubric
As advanced as distance education (DE) technologies are, most are not set up to support disproportionately impacted students. The abrupt shift to “emergency remote teaching and learning” due to COVID-19 further amplified equity issues like access to technology, the Internet and support services. To meet these students’ needs the Peralta Community College District DE Committee created a research-based rubric to help teachers increase learning equity in their online and hybrid courses. This session will describe briefly the district’s decisions based on student success data, its plans to research the rubric’s impact, and challenges related to avoiding biases and assumptions during those research efforts.
Vanessa Peters, Senior Learning Sciences Researcher, Digital Promise
& Julie Neisler, Quantitative Researcher and Data Scientist, Digital Promise
Investigating Students’ Online Course Experiences During the COVID-19 Pandemic: Opportunities for Increasing Equity in Higher Education
This past spring, colleges and universities across responded to the COVID-19 pandemic by moving all their courses online. Across the U.S., faculty and instructors who had never taught fully online courses before suddenly found themselves adapting all their instruction for online
delivery. In this presentation, we share the results of the “Survey of Student Perceptions of Remote Teaching and Learning” to capture the experiences of undergraduates taking courses that transitioned to online instruction in response to the COVID-19 pandemic. The survey
explores the nature of college courses as they were taught during the COVID-19 outbreak, the pervasiveness of various challenges undergraduates faced after the transition to remote instruction, and course features associated with higher levels of student satisfaction. Data
analyses compared experiences of students from low-income, underrepresented, or rural backgrounds to those of students with none of these characteristics. This survey was administered to a random national sample of 1,008 undergraduates, age 18 and older, who were taking college courses for credit that included in-person class sessions when the COVID-19 pandemic hit and had to finish the course by learning at a distance. The sample included 717 students attending four-year colleges and 271 students attending two-year colleges. Survey data are supplemented with in-depth interview data from 92 students enrolled in 2- and 4-year institutions. In this presentation, we share findings from quantitative and qualitative analyses and explore implications for creating more equitable learning experiences in higher education.