UBC Faculty Research and Publications

Artificial Intelligence for Video-based Learning at Scale Seo, Kyoungwon; Fels, Sidney; Yoon, Dongwook; Roll, Ido; Dodson, Samuel; Fong, Matthew 2020-08

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Artificial Intelligence for Video-based Learning at Scale    Kyoungwon Seo University of British Columbia Vancouver, BC, Canada kwseo@ece.ubc.ca  Sidney Fels University of British Columbia Vancouver, BC, Canada ssfels@ece.ubc.ca  Dongwook Yoon University of British Columbia Vancouver, BC, Canada yoon@cs.ubc.ca  Ido Roll Technion — Israel Institute of Technology, Haifa, Israel roll@technion.ac.il Samuel Dodson University of British Columbia Vancouver, BC, Canada dodsons@mail.ubc.ca Matthew Fong University of British Columbia Vancouver, BC, Canada mfong@ece.ubc.ca  ABSTRACT Video-based learning (VBL) is widespread; however, there are numerous challenges when teaching and learning with video. For instructors, creating effective instructional videos takes considerable time and effort. For students, watching videos can be a passive learning activity. Artificial intelligence (AI) has the potential to improve the VBL experience for students and teachers. This half-day workshop will bring together multi-disciplinary researchers and practitioners to collaboratively envision the future of VBL enhanced by AI. This workshop will be comprised of a group discussion followed by a presentation session. The goal of the workshop is to facilitate the cross-pollination of design ideas and critical assessments of AI approaches to VBL. Author Keywords Artificial intelligence; video-based learning; machine learning; natural language processing; computer vision CSS Concepts • Applied computing~Education; Interactive learning environments; Learning management systems  INTRODUCTION Video-based learning (VBL) is a major area of research within the Learning @ Scale community, and has implications to pedagogies, such as Massive Open Online Courses (MOOCs) and flipped classrooms [4,11]. Nonetheless, the effectiveness and efficiency of VBL has been questioned [1,8]. Some instructors are hesitant to use video because it takes a significant amount of time and effort to record and edit instructional videos [2]. For students,  1 https://videx.ece.ubc.ca/research  watching video is often a consumptive and passive activity, limiting learning outcomes [7]. There are many challenges to be tackled in order to better support teaching and learning with video. Copying, pasting, and editing, which can all be easily done with text and word processors, are difficult tasks with most video and VBL systems. How can VBL systems be designed for active learning [3,9]? While instructors may have access to student trace data from VBL systems, data can be hard to make sense of. How can instructional dashboards be designed so instructors can provide their students with timely, personalized feedback in ways that are sensitive to students’ interests and abilities [8]?  Artificial intelligence (AI) applications in the research areas of computer vision, information retrieval, and natural language processing will likely power new approaches to VBL [10,12]. For instance, AI could help instructors determine students’ understanding of video by analyzing student trace data, and then use this insight to recommend video content that may be useful for students. Also, AI-based text and / or image recognition and classification techniques could assist students in navigating instructional video by detecting changes in topic. AI-based VBL could support instructors in assessing individual student’s knowledge gaps based on trace data in order to provide personalized feedback and recommendations tailored to student’s abilities, experiences, and interests. Along with this, many concerns are raised when applying AI in the classroom, such as privacy, ethics, and agency vs. automation, which should be investigated [5,6,10]. These are a sample of the research questions that will be explored during this workshop. Our interest in organizing this workshop stems from our work at the University of British Columbia (UBC) designing systems for learning with video. Through our partnership with Microsoft, we have focused heavily on designing AI-based VBL systems. 1  We have benefited greatly from discussions with members of the Learning @ Scale Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.  L@S '20, August 12–14, 2020, Virtual Event, USA © 2020 Association for Computing Machinery. ACM ISBN 978-1-4503-7951-9/20/05 $15.00 https://doi.org/10.1145/3386527.3405937  community, and expect this workshop will develop this area of research further. We see this workshop benefiting the Learning @ Scale community in three ways. First, the workshop will bring together researchers and practitioners applying AI technologies to the design of systems for video-based teaching and learning. We will distribute a call for participation to researchers from computer science, education, information science, psychology, and other fields that share an interest in pedagogies and tools for video-based teaching and learning. Second, the workshop will explore priorities for future work on AI-based VBL. Third, we expect to publish a paper outlining the workshop discussions, describing how the Learning @ Scale community can take steps towards realizing the potential of AI for video-based teaching and learning. We anticipate the insights coming out of the workshop will be useful to many researchers and practitioners beyond the Learning @ Scale community who are working on educational technology. ORGANIZERS Kyoungwon Seo Kyoungwon is a Postdoctoral Research Fellow at UBC. He designs and evaluates AI-based educational technologies to support students’ engagement with learning material and social interaction with their peers. Kyoungwon’s work explores how AI can support students’ learning activities, such as viewing video, searching online, and participating in forum discussions. Sidney Fels Sidney is a Professor and Distinguished University Scholar at UBC. He is internationally known for his work on new interfaces for musical expression and interactive arts. He is currently leading the ViDeX project at UBC in partnership with the Microsoft Development Center. Dongwook Yoon Dongwook is an Assistant Professor at UBC. He focuses on building rich collaboration systems that offer expressive multimodal interactions (i.e., interactions through multiple communication channels). Dongwook’s design approach translates natural human interactions into novel combinations of input modalities that serve as building blocks for fluid, rich, and lightweight interfaces. Ido Roll Ido is an Associate Professor at the Technion. He studies how learning environments can support students in becoming better learners and scientists, focusing on their development of information literacies, creativity, and sense making. Ido’s work seeks to bridge research and teaching by identifying evidence-based practices that are effective in the context of higher education.  2 http://learningatscale.acm.org/las2020/ Samuel Dodson Samuel is a PhD candidate at UBC. He investigates how people find, manage, and use different types of information (e.g., audio, images, text, and video). Samuel explores the tensions and breakdowns in human information interaction, and then provides implications for the design of practices and tools to address these needs. Matthew Fong Matthew is a PhD candidate at UBC. His research explores student trace data from VBL systems used within and beyond the classroom. Matthew is also studying the potential of video analytics and instructor dashboards in teaching with video. PRE-WORKSHOP PLANS The organizers will recruit workshop participants from both a variety of communities. The workshop materials will be posted on the Learning @ Scale conference website.2 WORKSHOP STRUCTURE 12:00–12:15 (15 minutes) Welcome The organizers will introduce the agenda and the goals. 12:15–13:00 (45 minutes) Position Paper Presentations To increase awareness among the attendees, organizers will coordinate a presentation session where each attendee will introduce their research interests and the abstract of their position paper. 13:00–13:15 (15 minutes) Break 13:15–14:15 (60 minutes) Discussing AI-based VBL Participants will breakout into groups to discuss challenges and opportunities with VBL, and the implications of applying AI in the context of VBL. 14:15–15:00 (45 minutes) Synthesizing Participants will be asked to synthesize their discussions (e.g., through a mockup, scenario, skit, Wizard-of-Oz, and so on). The organizers will provide a variety of resources and tools to help participants express themselves. 15:00–15:45 (45 minutes) Reporting with Q&A Breakout groups will report back to the group through presentations on their discussion, and will provide their take-away message(s) on the role(s) of AI in VBL. 15:45–16:00 (15 minutes) Closing The workshop will conclude with a group discussion about the outcomes of the day, summarizing the challenges and opportunities for designing the future of AI video-based teaching and learning at scale. POST WORKSHOP PLANS Following the workshop, the organizers plan to write a special issue paper on the insights gained during the workshop. CALL FOR PARTICIPATION We invite position papers for a half-day workshop exploring the potential of artificial intelligence (AI) for video-based learning (VBL) at scale. This workshop offers an interdisciplinary forum for all interested in designing and / or critiquing AI approaches, such as machine learning, natural language processing, computer vision, and big data analytics. Discussions of the potential ethical challenges of AI in education, such as student privacy, bias and discrimination, and labor, are strongly encouraged. This workshop aims to identify research topics for researchers and practitioners and to build a community around this area. We welcome two to four page position papers in the CHI Extended Abstracts Format. All papers will be single-blind peer reviewed by the program committee, in order to assess the relevance, quality, and topical diversity of submissions. Participants from all backgrounds are welcome. Accepted submissions will be presented at the workshop. All submissions are due by July 12, 2020 via email to Kyoungwon Seo.3 Notifications of acceptance will be sent no later than July 28, 2020. At least one author of each accepted position paper must attend the workshop, and all participants must register for both the workshop and at least one day of the 2020 Learning @ Scale conference. We intend to publish a special issue paper summarizing the results of the workshop, and outlining how the Learning @ Scale community can move towards realizing the potential of AI for video-based teaching and learning. ACKNOWLEDGMENTS We appreciate the comments and suggestions of members from the ViDeX team. We are also grateful for our collaboration with the Microsoft Development Center in Vancouver and the Centre for Teaching, Learning and Technology at UBC. REFERENCES [1] Elaine I Allen and Jeff Seaman. 2013. Changing course: ten years of tracking online education in the United States. (2013). Retrieved October 14, 2019 from http://www.onlinelearningsurvey.com/reports/gradechange.pdf [2] Huber Bernd, Hijung Valentina Shin, Bryan Russell, Oliver Wang, and Gautham J. Mysore. 2019. B-Script: Transcript-based B-roll Video Editing with Recommendations. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 81.  [3] Samuel Dodson, Ido Roll, Matthew Fong, Dongwook Yoon, Negar M. Harandi, and Sidney Fels. 2018. An active viewing framework for video-based learning. In  3 kwseo@ece.ubc.ca Proceedings of the Fifth ACM Conference on Learning @ Scale, 1-4. [4] Yousef Ahmed Mohamed Fahmy, Mohamed Amine Chatti, and Ulrik Schroeder. 2014. The state of video-based learning: A review and future perspectives. Int. J. Adv. Life Sci 6, 3/4: 122–135. [5] Jessica Fjeld, Nele Achten, Hannah Hilligoss, Adam Nagy, and Madhulika Srikumar. Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-based Approaches to Principles for AI. Berkman Klein Center Research Publication, 1-39. [6] Anna Jobin, Marcello Ienca, and Effy Vayena. 2019. The global landscape of AI ethics guidelines. Nature Machine Intelligence 1, 9: 389-399. [7] Koedinger Kenneth R., Jihee Kim, Julianna Zhuxin Jia, Elizabeth A. McLaughlin, and Norman L. Bier. 2015. Learning is not a spectator sport: Doing is better than watching for learning from a MOOC. In Proceedings of the second ACM conference on Learning @ Scale, 111–120.  [8] Fong Matthew, Samuel Dodson, Negar Mohaghegh Harandi, Kyoungwon Seo, Dongwook Yoon, Ido Roll, and Sidney Fels. 2019. Instructors Desire Student Activity, Literacy, and Video Quality Analytics to Improve Video-based Blended Courses. In Proceedings of the Sixth ACM Conference on Learning @ Scale, 122–135.  [9] Antonija Mitrovic, Vania Dimitrova, Lydia Lau, Amali Weerasinghe, and Moffat Mathews. 2017. Supporting constructive video-based learning: requirements elicitation from exploratory studies. In International Conference on Artificial Intelligence in Education, 224-237. [10] Amy Ogan. 2019. Reframing classroom sensing: promise and peril. interactions 26, 6: 26-32. [11] Poquet Oleksandra, Lisa Lim, Negin Mirriahi, and Shane Dawson. 2018. "Video and learning: a systematic review (2007–2017)." In Proceedings of the 8th International Conference on Learning Analytics and Knowledge, 151–160.  [12] Holmes Wayne, Maya Bialik, and Charles Fadel. 2019. Artificial intelligence in education: Promises and implications for teaching and learning. Boston, MA: Center for Curriculum Redesign. 


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