Document Type


Date of Award


Degree Name

Doctor of Philosophy in Information Systems - (Ph.D.)



First Advisor

Quentin Jones

Second Advisor

Starr Roxanne Hiltz

Third Advisor

Michael J. Lee

Fourth Advisor

Jacob Chakareski

Fifth Advisor

Xianlian Alex Zhou

Sixth Advisor

Vivek K. Singh


Since the 1940s, researchers have envisioned lifelogging as the systematic capture and utilization of lived experiences for augmenting learning, performance, and community Unfortunately, this vision was never actualized since few, if any, systems support lifelogging in the term's original sense. Technologies that emerged through the Quantified-Self (QS) movement allowed users to monitor and track almost every life aspect. However, the decontextualized self-tracking data QS systems produced are unsuitable for supporting learning and community engagement, and therefore have not made lifelogging a reality yet. Central to this dissertation is understanding how to augment learning and community through lifelogging. This is particularly a problem in learning in collaborative physical-recreation communities (CPRC) (e.g., regional volleyball communities, college campus-based dance communities) because CPRC members must work together in performance and learning.

This dissertation addresses this motivating problem by proposing Collaborative Lifelogging (CLL), a conceptual framework of lifelogging systems inspired by Collective Computing. By facilitating collaborative procedural learning in CPRC, CLL solutions could support participation in collaborative physical recreation, leading to physical and mental health benefits such as physical fitness improvements, disease prevention, and stress relief. CLL solutions could also bring members together and improve social connectivity in the communities.

This dissertation answers the following research questions. (i) Why do individuals engage with CPRC? (ii) What types of intrinsic or extrinsic feedback do community members use for procedural learning? (iii) How do community members use current technologies to gain extrinsic feedback and support procedural learning? (iv) What community-based processes support collaborative procedural learning in CPRC? (v) What do individuals identify as teachable moments? (vi) What is the perceived utility of viewing videos of teachable moments? (vii) What are the perceived benefits of CLL solutions compared to QS systems among community members? The above research questions are addressed through a series of empirical studies of recreational volleyball and dance communities in New Jersey, USA.

Study I is a qualitative study with semi-structured interviews (n=32) and qualitative diaries (n=13) focusing on self-reported participation in community activities. The study findings show that individuals have multiple reasons for their engagement with CPRC, and they rely on various forms of intrinsic feedback for their procedural learning. However, they do not view QS systems as an effective way to support their procedural learning. While they do see value in using cameras (e.g., GoPros) for their learning, they face significant challenges in effectively using them. The study highlights the enormous potential of showing individuals' teachable moments through video snippets to support their procedural learning.

Study II is an observational study of how members collaboratively perform and learn during community activities. Findings show that community-based processes in CPRC, such as feedback exchange among members, depend on the roles of teammates, skills levels, and personal connections in the communities. These findings inform how to incorporate these factors in the design of CLL processes for supporting feedback exchange among individuals.

Study III is a contextual inquiry of teachable moments (n=15). In the study, community members identify moments during their matches and discuss them in collaborative video viewing sessions. Findings show that individuals identify moments of their successes, unsuccessful attempts and long rallies as teachable moments. These moments are also associated with improvement areas in their individual and team performance. These insights inform how to create CLL processes for identifying, contextualizing, and categorizing video snippets of teachable moments.

Using a research-through-design approach, this dissertation translates the insights from studies I, II, and III into representations of CLL solutions by defining personas and pre-intervention scenarios, ideating post-intervention scenarios, and iterative user-interface prototyping. The final study, Study IV, uses a video-prototyping research method (n=11) examining the comparative utility of CLL solutions and QS systems. Findings show the perceived benefits of CLL solutions over QS systems for their support of collaborative procedural learning. The insights also highlight that integrated CLL-QS solutions are desired among high-skilled individuals. Collectively, these studies advance the understanding of the requirements for lifelogging systems supporting collaborative procedural learning in CPRC.



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