About

Mary Beth Rosson is an Interim Dean and Professor of Information Sciences and Technology (IST) at the Center for Human-Computer Interaction. She is also a Co-director of the CSCL Lab. Her research focuses on human-computer interaction, with particular involvement in collaborative learning environments and the design of interactive systems. As a distinguished member of the faculty, she contributes to advancing understanding in the field of human-computer interaction through her leadership and scholarly work.

Research topics

• Computer Science
• Artificial Intelligence
• Psychology
• Multimedia
• Computer Security
• Engineering
• Internet privacy
• Human–computer interaction

Selected publications

• Towards Dynamic Learning: A Framework for Simulating Adaptive Learning Systems

  2024-11-11 · 2 citations

  articleOpen accessSenior author

  Adaptive learning technologies offer personalized education by tailoring unique learning paths to individual students. Despite extensive study, widespread adoption remains limited due to the considerable costs associated with development and deployment. This paper presents the application of a design-based research framework, integrating Wizard of Oz techniques, intervention design, and decision trees to simulate an adaptive learning platform. This platform dynamically encourages collaborative efforts among students based on their social learning tendencies. Theories of social learning highlight the importance of social interactions in facilitating effective learning outcomes, particularly in remote learning environments. Our research methodology adopts an interdisciplinary approach, drawing insights from social learning theory to explore system concepts within a fluid framework. By employing this methodology, we seek to contribute to ongoing discussions aimed at enhancing technology-supported learning environments.

  PublisherOA PDFDOI

• Online Privacy Cues and Heuristics

  2023-04-27 · 1 citations

  book-chapterSenior author

  Online users often express grave concerns about their privacy, yet exhibit free-wheeling behaviors and self-disclosures on social media sites. This discrepancy between attitudes and behaviors (referred to as a “privacy paradox”) may arise from having too little time and cognitive resources to process privacy considerations in a systematic way. To cope with information overload, online users often make privacy decisions based on contextual cues on social media, which tend to over-emphasize potential benefits and downplay potential risks of self-disclosure. For instance, cues in the form of metrics on the interface, such as the number of other employees in a person's organization who have enrolled in LinkedIn by disclosing their personal information, can trigger the bandwagon heuristic (“if disclosing information is good for so many others, it's good for me, too”). Drawing from a decade of research on online privacy, we provide an overview of privacy heuristics, organizing the discussion by considering the benefits and risks of information disclosure. This body of work sheds light on how users make privacy decisions on social media and explains why phenomena such as “privacy paradox” sometimes exist while sometimes not. Our goal is to promote more informed privacy decisions among users with the help of better designs and literacy campaigns.

  PublisherDOI

• A SYNTHETIC LITERATURE REVIEW ON ANALYTICS TO SUPPORT CURRICULUM IMPROVEMENT IN HIGHER EDUCATION

  EDULEARN proceedings · 2023-07-01 · 1 citations

  article

  Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 2130-2143ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.0640Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain

  PublisherDOI

• Exploring Potential Contributions of Social Learning to Adaptive Learning Systems

  2023-04-19 · 3 citations

  article

  Adaptive learning systems aim to emulate how skilled educators seek to provide every student the best possible learning experience. We investigate how such systems might be enriched by activities and indicators of social learning - an aspect of learning that focuses on the influences of learners’ social context and interactions. In this paper we describe a pilot study aimed at exploring the potential for including social learning in an adaptive system. Our analysis of the social learning scale demonstrates its validity and usefulness for our ongoing work. Our qualitative analysis of students’ learning demonstrates how social learning vary among students. We discuss how the rating scale results and observations of social learning can be integrated within a student model needed to drive an adaptive system. More generally, our work illustrates how theories of learning can contribute to the design of adaptive learning systems.

  PublisherDOI

• AIGuide: Augmented Reality Hand Guidance in a Visual Prosthetic

  ACM Transactions on Accessible Computing · 2022-03-02 · 10 citations

  article

  Locating and grasping objects is a critical task in people’s daily lives. For people with visual impairments, this task can be a daily struggle. The support of augmented reality frameworks in smartphones can overcome the limitations of current object detection applications designed for people with visual impairments. We present AIGuide, a self-contained smartphone application that leverages augmented reality technology to help users locate and pick up objects around them. We conducted a user study to investigate the effectiveness of AIGuide in a visual prosthetic for providing guidance; compare it to other assistive technology form factors; investigate the use of multimodal feedback, and provide feedback about the overall experience. We gathered performance data and participants’ reactions and analyzed videos to understand users’ interactions with the nonvisual smartphone user interface. Our results show that AIGuide is a promising technology to help people with visual impairments locate and acquire objects in their daily routine. The benefits of AIGuide may be enhanced with appropriate interaction design.

  PublisherDOI

• A Scenario-based Exploration of Expected Usefulness, Privacy Concerns, and Adoption Likelihood of Learning Analytics

  2022-05-31 · 3 citations

  article

  Learning analytics has become a robust research area in the last decade, as innovative analytic models of learning data have been created with the goal of enhancing teaching and learning. However, barriers to large scale adoption of such technologies in higher education still exist. In recent years, a strand of research has begun to investigate stakeholders' expectations of learning analytics, hoping to find ways to integrate the innovations into everyday teaching practices. For instance, studies have investigated instructors' ideas about how learning analytics might be helpful, as well as concerns about student data privacy. However, most studies have taken a general approach rather than considering instructors' day-to-day experiences. Using survey methods, we presented instructors with hypothetical scenarios of learning analytics in use across disciplines, class sizes, teaching activities, and types of student data. We asked for ratings of both usefulness and privacy concerns for each proposed teaching situation. Our respondents considered scenarios involving learning outcomes-related data (e.g. grades) to be more useful than those that involve student interactions (e.g. language, social activity). In contrast, privacy concerns were lower for outcomes-oriented scenarios than interactions-focused scenarios. An interesting new finding was a negative correlation of usefulness and privacy; we discuss this in the context of instructors' possible cost-benefit reasoning. We reflect on our findings with respect to future efforts in developing and fielding learning analytics tools.

  PublisherDOI

• Smile! Positive Emojis Improve Reception and Intention to Use Constructive Feedback

  Lecture notes in computer science · 2021-01-01 · 4 citations

  book-chapterSenior author
  PublisherDOI

• EXPLORING TECHNIQUES FOR PROMOTING ENGAGEMENT WITH LECTURE CONTENT: A SYNTHETIC REVIEW

  EDULEARN proceedings · 2021-07-01

  review

  Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 9622-9632ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.1938Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference

  PublisherDOI

• What do I need to know? Designing Student Learning Tools that Aid Interaction with Recorded Lecture Content

  2021-06-07

  articleSenior author

  This paper presents an exploratory investigation of how students might benefit from recorded class lectures. Our overarching goal is to inform design of an artificial intelligence (AI) tool that adds value to lecture recordings. To study this possibility, we surveyed undergraduate students about their active learning strategies and their visions of how AI might help them to engage more richly with digitized learning materials. We report our findings and discuss implications for design of a tool that can add value to recorded lectures; we also consider more generally the possibilities of using lecture recordings to enhance student learning.

  PublisherDOI

• Exploring Feelings of Student Community across a Geographically Distributed University

  Proceedings of the ACM on Human-Computer Interaction · 2021-04-13 · 4 citations

  articleSenior author

  Options for students to learn and connect with each other have diversified in recent years, with online resources and campuses playing an increasing role. Flexibility and comfort are becoming a priority as students choose when, where and how to pursue learning goals. Nonetheless, students want to feel sense of community with their peers and instructors; institutional bonds are in turn associated with enhanced learning. In this paper, we explore the feelings of community among students studying at a geographically distributed university. We seek to understand how the students understand community, the levels of community they feel and how their campus location may affect these feelings. In this paper, we present findings from both a survey and an interview study and consider the implications for tools that might promote community.

  PublisherDOI

Recent grants

• ITR: Collaborative Research: Dependable End-User Software

  NSF · $266k · 2003–2009

• BPC-DP: A Developmental Community for Recruiting and Maintaining Women in CISE Education

  NSF · $649k · 2007–2011

Frequent coauthors

• John M. Carroll

  223 shared

• Heng Xu

  American University

  23 shared

• Pamela Wiśniewski

  17 shared

• Craig H. Ganoe

  17 shared

• Gregorio Convertino

  Palo Alto Research Center

  15 shared

• Manuel A. Pérez-Quiñones

  University of North Carolina at Charlotte

  14 shared

• Hansa Sinha

  Pennsylvania State University

  12 shared

• Lu Xiao

  12 shared

Labs

• Mary Beth Rosson's LabPI

  Not provided

Awards & honors

• Member of the CHI Academy
• ACM Distinguished Scientist