Carrie J. Cai

Data-driven interaction techniques for improving navigation of educational videos

With an unprecedented scale of learners watching educational videos on online platforms such as MOOCs and YouTube, there is an opportunity to incorporate data generated from their interactions into the design of novel video interaction techniques. Interaction data has the potential to help not only instructors to improve their videos, but also to enrich the learning experience of educational video watchers. This paper explores the design space of data-driven interaction techniques for educational video navigation. We introduce a set of techniques that augment existing video interface widgets, including: a 2D video timeline with an embedded visualization of collective navigation traces; dynamic and non-linear timeline scrubbing; data-enhanced transcript search and keyword summary; automatic display of relevant still frames next to the video; and a visual summary representing points with high learner activity. To evaluate the feasibility of the techniques, we ran a laboratory user study with simulated learning tasks. Participants rated watching lecture videos with interaction data to be efficient and useful in completing the tasks. However, no significant differences were found in task performance, suggesting that interaction data may not always align with moment-by-moment information needs during the tasks.

Chain Reactions: The Impact of Order on Microtask Chains

Microtasks are small units of work designed to be completed individually, eventually contributing to a larger goal. Although microtasks can be performed in isolation, in practice people often complete a chain of microtasks within a single session. Through a series of crowd-based studies, we look at how various microtasks can be chained together to improve efficiency and minimize mental demand, focusing on the writing domain. We find that participants completed low-complexity microtasks faster when they were preceded by the same type of microtask, whereas they found high-complexity microtasks less mentally demanding when pre-ceded by microtasks on the same content. Furthermore, participants were faster at starting high-complexity microtasks after completing lower-complexity microtasks, but completion time and quality were not affected. These findings provide insight into how microtasks can be ordered to optimize transitions from one microtask to another.

Wait-Learning: Leveraging Wait Time for Second Language Education

Competing priorities in daily life make it difficult for those with a casual interest in learning to set aside time for regular practice. In this paper, we explore wait-learning: leveraging brief moments of waiting during a persons existing conversations for second language vocabulary practice, even if the conversation happens in the native language. We present an augmented version of instant messaging, WaitChatter, that supports the notion of wait-learning by displaying contextually relevant foreign language vocabulary and micro-quizzes just-in-time while the user awaits a response from her conversant. Through a two week field study of WaitChatter with 20 people, we found that users were able to learn 57 new words on average during casual instant messaging. Furthermore, we found that users were most receptive to learning opportunities immediately after sending a chat message, and that this timing may be critical given user tendency to multi-task during waiting periods.

Wait-learning: leveraging conversational dead time for second language education

Second-language learners are often unable to find time for language practice due to constraints in their daily lives. In this paper, we examine how brief moments of waiting during a persons existing social conversations can be leveraged for second language practice, even if the conversation is exchanged in the first language. We present an instant messaging (IM) prototype, WaitChatter, that supports the notion of wait-learning by displaying contextually relevant foreign language vocabulary and micro-quizzes while the user awaits a response from her conversant. The foreign translations are displayed just-in-time in the context of the conversation to promote incidental learning. In a preliminary study of WaitChatter, we found that participants were able to integrate second language learning into their existing instant messaging activities, and that a particularly opportune time to embed foreign language elements may be immediately after the learner sends a chat message.

Productivity Decomposed: Getting Big Things Done with Little Microtasks

It is difficult to accomplish meaningful goals with limited time and attentional resources. However, recent research has shown that concrete plans with actionable steps allow people to complete tasks better and faster. With advances in techniques that can decompose larger tasks into smaller units, we envision that a transformation from larger tasks to smaller microtasks will impact when and how people perform complex information work, enabling efficient and easy completion of tasks that currently seem challenging. In this workshop, we bring together researchers in task decomposition, completion, and sourcing. We will pursue a broad understanding of the challenges in creating, allocating, and scheduling microtasks, as well as how accomplishing these microtasks can contribute towards productivity. The goal is to discuss how intersections of research across these areas can pave the path for future research in this space.

Adapting arcade games for learning

Although memory exercises and arcade-style games are alike in their repetitive nature, memorization tasks like vocabulary drills tend to be mundane and tedious while arcade-style games are popular, intense and broadly addictive. This suggests an opportunity to modify well-known, existing arcade games for the purpose of memorization and learning. A key challenge to modifying any existing arcade-style game is the incorporation of learning on top of an already fast-paced, mentally demanding game. This paper presents a web-based vocabulary-drill game, based on Tetris and augmented with speech recognition (Figure 1). To help adult learners acquire new vocabulary, we embed an in-game mechanism that presents vocabulary items to be learned via word-picture associations. Using our working speech recognition prototype, we investigate the extent to which retrieval practice, an educationally effective but cognitively demanding strategy, impacts learning and engagement in this fast-paced game environment.

Wait-Learning: Leveraging Wait Time for Education

Competing priorities in daily life make it difficult for those with a casual interest in learning to set aside time for regular practice. Yet, learning often requires significant time and effort, with repeated exposures to learning material on a recurring basis. Despite the struggle to find time for learning, there are numerous times in a day that are wasted due to micro-waiting. In my research, I develop systems for wait-learning, leveraging wait time for education. Combining wait time with productive work opens up a new class of software systems that overcomes the problem of limited time while addressing the frustration often associated with waiting. My research tackles several challenges in learning and task management, such as identifying which waiting moments to leverage; how to encourage learning unobtrusively; how to integrate learning across a diversity of waiting moments; and how to extend wait-learning to more complex domains. In the development process, I hope to understand how to manage these waiting moments, and describe essential design principles for wait-learning systems.

An analysis of the dialogic complexities in designing a question/answering based conversational agent for preschoolers

Parents are well aware that pre-school children are incessantly inquisitive, and the high ratio of questions to statements suggests that questions are a primary method utilized by children for language acquisition, cognitive development, and formulating knowledge structures. Question-asking is furthermore a comfortable medium for a child to stay engaged in natural discourse and the activity at hand. To take advantage of the naturalness and learning benefits of question-answer exchanges, there could be intelligent agents that can engage a child in activities while setting children in the mood to ask meaningful, information-seeking questions. There are currently multiple intelligent agents that can interact with older children and adults to promote literacy or teach topics in specific domains. This paper thus focuses on the complexities of designing an intelligent agent for younger children, by collecting and analyzing data and categorizing childrens questions, which are often ill-formed.

WaitSuite: Productive Use of Diverse Waiting Moments

The busyness of daily life makes it difficult to find time for informal learning. Yet, learning requires significant time and effort, with repeated exposures to educational content on a recurring basis. Despite the struggle to find time, there are numerous moments in a day that are typically wasted due to waiting, such as while waiting for the elevator to arrive, wifi to connect, or an instant message to arrive. We introduce the concept of wait-learning: automatically detecting wait time and inviting people to learn while waiting. Our approach is to design seamless interactions that augment existing wait time with productive opportunities. Combining wait time with productive work opens up a new class of software systems that overcome the problem of limited time. In this article, we establish a design space for wait-learning and explore this design space by creating WaitSuite, a suite of five different wait-learning apps that each uses a different kind of waiting. For one of these apps, we conducted a feasibility study to evaluate learning and to understand how exercises should be timed during waiting periods. Subsequently, we evaluated multiple kinds of wait-learning in a two-week field study of WaitSuite with 25 people. We present design implications for wait-learning, and a theoretical framework that describes how wait time, ease of accessing the learning task, and competing demands impact the effectiveness of wait-learning in different waiting scenarios. These findings provide insight into how wait-learning can be designed to minimize interruption to ongoing tasks and maximize engagement with learning.