Amy Ogan

ZoomBoard: a diminutive qwerty soft keyboard using iterative zooming for ultra-small devices

The proliferation of touchscreen devices has made soft keyboards a routine part of life. However, ultra-small computing platforms like the Sony SmartWatch and Apple iPod Nano lack a means of text entry. This limits their potential, despite the fact they are quite capable computers. In this work, we present a soft keyboard interaction technique called ZoomBoard that enables text entry on ultra-small devices. Our approach uses iterative zooming to enlarge otherwise impossibly tiny keys to comfortable size. We based our design on a QWERTY layout, so that it is immediately familiar to users and leverages existing skill. As the ultimate test, we ran a text entry experiment on a keyboard measuring just 16 x 6mm - smaller than a US penny. After eight practice trials, users achieved an average of 9.3 words per minute, with accuracy comparable to a full-sized physical keyboard. This compares favorably to existing mobile text input methods.

Toward a Framework for the Analysis and Design of Educational Games

We describe and illustrate the beginnings of a general framework for the design and analysis of educational games. Our students have used it to analyze existing educational games and to create prototype educational games. The framework is built on existing components: a method for precisely specifying educational objectives, a framework for relating a game’s mechanics, dynamics, and aesthetics, and principles for instructional design grounded in empirical research in the learning sciences. The power of the framework comes from the components themselves, as well as from considering these components in concert and making connections between them. The framework coordinates the many levels at which an educational game must succeed in order to be effective. We illustrate the framework by using it to analyze Zombie Division and to generate some redesign ideas for this game.

Evaluating the Effectiveness of a Tutorial Dialogue System for Self-Explanation

Previous research has shown that self-explanation can be supported effectively in an intelligent tutoring system by simple means such as menus. We now focus on the hypothesis that natural language dialogue is an even more effective way to support self-explanation. We have developed the Geometry Explanation Tutor, which helps students to state explanations of their problem-solving steps in their own words. In a classroom study involving 71 advanced students, we found that students who explained problem-solving steps in a dialogue with the tutor did not learn better overall than students who explained by means of a menu, but did learn better to state explanations. Second, examining a subset of 700 student explanations, students who received higher-quality feedback from the system made greater progress in their dialogues and learned more, providing some measure of confidence that progress is a useful intermediate variable to guide further system development. Finally, students who tended to reference specific problem elements in their explanations, rather than state a general problem-solving principle, had lower learning gains than other students. Such explanations may be indicative of an earlier developmental level.

Oh dear stacy!: social interaction, elaboration, and learning with teachable agents

Understanding how children perceive and interact with teachable agents (systems where children learn through teaching a synthetic character embedded in an intelligent tutoring system) can provide insight into the effects of so-cial interaction on learning with intelligent tutoring systems. We describe results from a think-aloud study where children were instructed to narrate their experience teaching Stacy, an agent who can learn to solve linear equations with the students help. We found treating her as a partner, primarily through aligning oneself with Stacy using pronouns like you or we rather than she or it significantly correlates with student learning, as do playful face-threatening comments such as teasing, while elaborate explanations of Stacys behavior in the third-person and formal tutoring statements reduce learning gains. Additionally, we found that the agents mistakes were a significant predictor for students shifting away from alignment with the agent.

Rudeness and rapport: insults and learning gains in peer tutoring

For 20 years, researchers have envisioned artificially intelligent learning companions that evolve with their students as they grow and learn. However, while communication theory suggests that positivity decreases over time in relationships, most tutoring systems designed to build rapport with a student remain adamantly polite, and may therefore inadvertently distance the learner from the agent over time. We present an analysis of high school friends interacting in a peer tutoring environment as a step towards designing agents that sustain long-term pedagogical relationships with learners. We find that tutees and tutors use different language behaviors: tutees express more playfulness and face-threat, while tutors attend more to the task. This face-threat by the tutee is associated with increased learning gains for their tutor. Additionally, a small sample of partners who were strangers learned less than friends, and in these dyads increased face-threat was negatively correlated with learning. Our findings support the idea that learning companions should gradually move towards playful face-threat as they build relationships with their students.

Collaboration in cognitive tutor use in latin America: field study and design recommendations

Technology has the promise to transform educational prac-tices worldwide. In particular, cognitive tutoring systems are an example of educational technology that has been ex-tremely effective at improving mathematics learning over traditional classroom instruction. However, studies on the effectiveness of tutor software have been conducted mainly in the United States, Canada, and Western Europe, and little is known about how these systems might be used in other contexts with differing classroom practices and values. To understand this question, we studied the usage of mathematics tutoring software for middle school at sites in three Latin American countries: Brazil, Mexico, and Costa Rica. While cognitive tutors were designed for individual use, we found that students in these classrooms worked collaboratively, engaging in interdependently paced work and conducting work away from their own computer. In this paper we present design recommendations for how cognitive tutors might be incorporated into different classroom practices, and better adapted for student needs in these environments.

The Effects of Culturally Congruent Educational Technologies on Student Achievement

Dialectal differences are one explanation for the systematically reduced test scores of children of color compared to their Euro-American peers. In this work, we explore the relationship between academic performance and dialect differences exhibited in a learning environment by assessing 3rd grade students’ science performance after interacting with a “distant peer” technology that employed one of three dialect use patterns. We found that our participants, all native speakers of African American Vernacular English (AAVE), demonstrated the strongest science performance when the technology used AAVE features consistently throughout the interaction. These results call for a re-examination of the cultural assumptions underlying the design of educational technologies, with a specific emphasis on the way in which we present information to culturally-underrepresented groups.

Infusing Cultural Awareness into Intelligent Tutoring Systems for a Globalized World

In a global economy, with increasing immigration and cross-cultural interaction, the impact of culture in educational settings cannot be ignored. The impact is two-fold: students from diverse cultural backgrounds will be using the same educational technologies, and intercultural competence will become an increasingly important domain of instruction. In response, this chapter introduces what it means to adapt Intelligent Tutoring Systems for users with diverse cultural backgrounds, and how Intelligent Tutoring Systems can be used to support instruction in culture. We then discuss the major research issues involved in modifying Intelligent Tutoring Systems in support of these efforts. To provide insight into the current landscape of the field, we briefly outline several recent research achievements. In conclusion, we highlight significant current and future issues that arise in the integration of cultural concerns and educational technology.

Towards Understanding How to Assess Help-Seeking Behavior Across Cultures

In recent years, there has been increasing interest in automatically assessing help seeking, the process of referring to resources outside of oneself to accomplish a task or solve a problem. Research in the United States has shown that specific help-seeking behaviors led to better learning within intelligent tutoring systems. However, intelligent tutors are used differently by students in different countries, raising the question of whether the same help-seeking behaviors are effective and desirable in different cultural settings. To investigate this question, models connecting help-seeking behaviors with learning were generated from datasets from students in three countries – Costa Rica, the Philippines, and the United States, as well as a combined dataset from all three sites. Each model was tested on data from the other countries. This study found that models of effective help seeking transfer to some degree between the United States and Philippines, but not between those countries and Costa Rica. Differences may be explained by variations in classroom practices between the sites; for example, greater collaboration observed in the Costa Rican site indicates that much help seeking occurred outside of the technology. Findings indicate that greater care should be taken when assuming that the models underlying AIED systems generalize across cultures and contexts.

Browser Language Preferences as a Metric for Identifying ESL Speakers in MOOCs

Open access and low cost make Massively Open Online Courses (MOOCs) an attractive learning platform for students all over the world. However, the majority of MOOCs are deployed in English, which can pose an accessibility problem for students with English as a Second Language (ESL). In order to design appropriate interventions for ESL speakers, it is important to correctly identify these students using a method that is scalable to the high number of MOOC enrollees. Our findings suggest that a new metric, browser language preference, may be better than the commonly-used IP address for inferring whether or not a student is ESL.