Judith Uchidiuno

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.

Personalized Assistive Web for Improving Mobile Web Browsing and Accessibility for Visually Impaired Users

Mobile web browsing has become a daily routine for many people, including those with visual impairments. However, usability and accessibility challenges of mobile handheld devices may compromise the benefits of mobile web access, particularly for users with visual impairments. To improve mobile web accessibility, we propose a Personalized Assistive Web (PAW) that aims to improve skimming in mobile web browsing for users with visual impairments through hierarchical outline view and personalization adaptations in this research. We empirically evaluated PAW via a controlled lab experiment with 21 blind participants and 34 sighted participants. The empirical results provide strong evidence for the positive impacts of the hierarchical outline view adaptation on user performance of information search (i.e., search time) and perceptions (i.e., perceived ease of use and perceived usefulness) across the two groups of participants and demonstrate that the positive effects of adaptation personalization vary with participants. The findings not only demonstrate the effectiveness of the hierarchical outline view adaptation for blind and sighted participants but also reveal some important similarities and interesting differences in the effect of personalized adaptation between the two groups of participants. This research provides design and technical insights that are instrumental for improving mobile web accessibility.

Going Global: Understanding English Language Learners’ Student Motivation in English-Language MOOCs

Massive Open Online Courses (MOOCs) offer high quality, free courses to anyone with an Internet connection. However, these courses may be relatively inaccessible to the large global population of students who are English Language Learners (ELLs). Current efforts to understand student motivation in MOOCs do not take into account the specific needs of ELL students. Through interviews with 12 ELL online students, and a survey with 20,084 ELL respondents, we investigate ELL students’ motivations for taking online courses. We show that ELL students’ motivations are highly socialized strategies for achieving long-term goals of economic, social, and geographic mobility. Although research studies show that ELLs interact sparingly with other students in MOOCs, we present evidence that they have unmet needs for interaction, and discuss how student interaction systems in MOOCs can better address these needs. Finally, we show evidence that ELLs deliberately use English MOOCs to improve their language skills, even when the content is not language-related. This implies that meeting ELL students’ needs and access to MOOCs involves translating MOOCs to their local languages, but also providing language support in English-language MOOCs.

How Do English Language Learners Interact with Different Content Types in MOOC Videos

English Language Learners (ELLs) are a substantial portion of the students who enroll in MOOCs. In order to fulfill the promise of MOOCs – i.e., making higher education accessible to everyone with an internet connection – appropriate interventions should be offered to students who struggle with the language of course content. Through the analysis of clickstream log data gathered from two MOOC courses deployed on Coursera, Introduction to Psychology and Statistical Thermodynamics, we show that compared to native English speakers, ELL students have distinct behavioral patterns in how they engage with MOOC content including increased interaction with content that contains text, increased seeking away from content without visual support, and decreased video play rates. These patterns are expressed differently in response to different types of course content and domains. Our findings not only suggest more fine-grained methods for automatically identifying students who need language interventions, but also have further implications for the design of language support interventions and MOOC videos.

Characterizing ELL Students' Behavior During MOOC Videos Using Content Type

Making MOOCs accessible to English Language Learners (ELLs) requires that students understand the language of instruction, and that instructional strategies address their unique learning challenges. Through the analysis of clickstream log data gathered from two MOOC courses deployed on Coursera, Introduction to Psychology and Statistical Thermodynamics, we show that ELL students exhibit distinct struggle behaviors in video portions without visual aids e.g., narrations without slides. Our findings challenge widely accepted multimedia design principles such as the split attention effect, provide insights into designing MOOC videos, and emphasize the need for adaptivity to increase MOOC access for ELLs.

Privacy and Fear in the Drone Era: Preserving Privacy Expectations Through Technology

The increasing use of unmanned aerial vehicles (i.e. drones) is raising a number of privacy issues. The high-resolution cameras that drones can carry may pose serious privacy hazards to the general public. Numerous media stories highlight incidents of drones spying on people, often with negative consequences (e.g. lawsuits and arrests). Our research seeks to understand how incorporating privacy-preserving technologies in drone design can mitigate privacy fears. We identify several classes of privacy-preserving technological solutions and evaluate their potential efficacy across a variety of situations through a Mechanical Turk survey of 7,200 respondents. Our results inform both drone designers and regulators who wish to develop, operate, and/or regulate novel drone services while assuaging public fears of privacy violations.

The search for my computer science

A journey spanning Nigeria, the United States, and Tanzania, is one womans search for meaning and validation as a computer scientist.