Artificial intelligence (AI) systems are increasingly prevalent in youths’ lives, despite documentation and concern of potential algorithmic harm. With responsible AI (RAI) efforts aiming to address harms, youth are largely overlooked as contributors, despite being stakeholders of AI. This paper explores perspectives on challenges, barriers, and opportunities of youth participation in responsibly creating AI. Through workshops with 16 teens and 11 parents, as well as interviews with 8 AI practitioners, we find that while all groups recognize the value of youth perspectives, the opinions of those not directly paying for AI are not prioritized. Many parents were optimistic about their youths’ ability to contribute, while youth showed a desire to contribute, coupled with an awareness of their strengths and limitations. Practitioners saw the potential of youth to contribute but not necessarily as empowered decision makers in RAI processes. We offer insights on involving youth in RAI, balancing protection with agency.

Persistence after failure is critical for learning—but when students make mistakes in intelligent tutoring systems, they often choose not to try again. How can digital platforms encourage students to persist at these moments? We conducted a randomized controlled trial in an intelligent tutoring system for math and science, involving 164,532 students (Grades 8-12) who completed 17 million practice problems. We tested two scalable interventions: a brief persuasive prompt encouraging students to try again, and a visual default nudge that highlighted the retry option. Both interventions increased persistence after failure, and when combined, their effects were additive—suggesting they operate through distinct psychological mechanisms. The nudge had a much larger immediate effect, but the prompt showed proportionally greater spillover to untreated problems. These findings advance theories of persuasive design, demonstrating that implicit, interface-level nudges and explicit motivational prompts can be combined to avoid redundancy while amplifying impact.

Educational technology (EdTech) solutions have shown promise for disseminating educational opportunities to last-mile learners, particularly in the Global South. Low-infrastructure EdTech as a digital learning resource is especially critical to understand in remote contexts where educational opportunities and resources are limited. Our work investigated insights from 81 learners who engaged with a remote course that provided engineering education through radios and mobile phones in rural Uganda. Findings revealed that the course facilitated goal-driven and practical motivations in safe, adaptable environments. Our work goes beyond the idea that low-infrastructure EdTech can easily facilitate learning, highlighting diverse learner experiences navigating radio and phone use and presenting novel findings on community skepticism towards the course. Our research extends the EdTech and HCI literature by bringing light to the underrepresented voices of last-mile learners, sharing their insights on interacting with low-infrastructure EdTech, and how these insights can guide the design of contextually aligned EdTech.

Most learners worldwide are multilingual, yet implementing multilingual education remains challenging in practice. EdTech offers an opportunity to bridge this gap and expand access for linguistically diverse learners. We conducted a quasi-experiment in Uganda with 2,931 participants enrolled in a non-formal radio- and mobile-based engineering course, where learners self-selected instruction in Leb Lango (a local language), English, or a Hybrid option combining both languages. The Leb Lango version of the course was used disproportionately by learners from rural areas, those with less formal education, and those with lower prior knowledge, broadening participation among disadvantaged learners. Moreover, the availability of Leb Lango instruction was associated with higher active participation, even among learners who registered for English instruction. Although Leb Lango learners began with lower performance, they demonstrated faster learning gains and achieved comparable final examination outcomes to English and Hybrid learners. These results suggest that providing local language options to learners is an effective way to make EdTech more accessible.

The Doer Effect states that completing more active learning activities, like practice questions, is more strongly related to positive learning outcomes than passive learning activities, like reading, watching, or listening to course materials. Although broad, most evidence has emerged from practice with tutoring systems in Western, Industrialized, Rich, Educated, and Democratic (WEIRD) populations in North America and Europe. Does the Doer Effect generalize beyond WEIRD populations, where learners may practice in remote locales through different technologies? Through learning analytics, we provide evidence from N = 234 Ugandan students answering multiple-choice questions via phones and listening to lectures via community radio. Our findings support the hypothesis that active learning is more associated with learning outcomes than passive learning. We find this relationship is weaker for learners with higher prior educational attainment. Our findings motivate further study of the Doer Effect in diverse populations. We offer considerations for future research in designing and evaluating contextually relevant active and passive learning opportunities including leveraging familiar technology, increasing the number of practice opportunities, and aligning multiple data sources.

Expanding access to education in rural African communities remains difficult, largely due to limited internet connectivity. Mobile learning courses delivered via radio and offline mobile phones offer a promising, scalable solution. However, it is challenging to track student engagement in these environments due to the absence of tools that monitor students' interactions with the radio. In this study, we investigate the potential of ''Prize Codes'' -- codes read aloud during broadcasts that students enter via text message -- to serve as a real-time measure of student engagement with mobile-learning broadcasts. Using data from a 2024 implementation of Yiya AirScience, a mobile engineering course in Uganda, we evaluate the validity of Prize Codes as an engagement metric. Specifically, we test whether Prize Code measures (1) demonstrate reliability, with students who enter correct codes in one lesson being more likely to do so in subsequent lessons; (2) demonstrate convergent validity with existing measures of engagement; and (3) demonstrate predictive validity, predicting learning outcomes in the course. Our findings suggest that Prize Codes are a reliable and valid measure of engagement. Prize-Code accuracy demonstrates strong internal consistency (alpha = .97) and moderate test-retest reliability (ICC = .44). The measure aligns closely with synchronous participation (87% agreement, Cohen's kappa = .50), indicating it captures similar engagement patterns. Importantly, students who consistently enter correct Prize Codes perform significantly better on assessments, with Prize Code engagement predicting final exam scores above and beyond other engagement metrics. After establishing the measure's validity, we use it to (1) characterize patterns of engagement with Yiya broadcasts, (2) investigate early engagement with the broadcasts as a predictor of course persistence, and (3) replicate findings about the benefits of learning by doing. This study suggests that Prize Codes can be a feasible, scalable approach for tracking real-time engagement in resource-limited mobile learning settings at scale.

Artificial intelligence (AI) is ubiquitous in K-12 youths' everyday lives. However, it has become increasingly well-documented that AI can cause harm by reflecting and amplifying societal biases. While many youth are not currently empowered to engage in broader responsible AI discourse and processes, there is great potential. Foundational to engaging in critical conversations is ability to critique AI. We present the RAD framework, designed to scaffold critique of AI in three steps: Recognize (harms of AI), Analyze (societal aspects of AI harms), and Deliberate (what more responsible AI could be). We ran a workshop study with racially diverse middle school girls (N = 21) to investigate its effectiveness. We found that through being scaffolded with the framework, the youth could articulate biases that they saw in an AI scenario and consider how biases may impact different stakeholders. They then could contemplate how different stakeholders had varying amounts of power in the AI scenario and what that meant in terms of creating more responsible AI systems and processes. After participating in the study, the youth felt more strongly about voicing their opinions about AI with others. The RAD framework and activities work toward emboldening youths' engagement in critical discourse about AI.

Despite the best intentions to support equity with educational technologies, they often lead to a “rich get richer” effect, in which communities of more advantaged learners gain greater benefit from these solutions. Effective design of these technologies necessitates a deeper understanding of learners in understudied contexts and their motivations to pursue an education. Consequently, we studied a 15-week remote course launched in 2021 with 17,896 learners that provided engineering education through a radio and phone-based system aimed for use in rural settings within Northern Uganda. We address shifts in learners’ motivations for course participation and investigate the impact of demographic features and motivations of students on persistence and performance. We found significant increases in student motivation to learn more about and pursue STEM. Importantly, the course was most successful for learners in demographics who typically experience fewer educational opportunities, showing promise for such technologies to close opportunity gaps.

Teaching is one of many professions for which personalized feedback and reflection can help improve dialogue and discussion between the professional and those they serve. However, professional development (PD) is often impersonal as human observation is labor-intensive. Data-driven PD tools in teaching are of growing interest, but open questions about how professionals engage with their data in practice remain. In this paper, we present ClassInSight, a tool that visualizes three levels of teachers’ discussion data and structures reflection. Through 22 reflection sessions and interviews with 5 high school science teachers, we found themes related to dissonance, contextualization, and sustainability in how teachers engaged with their data in the tool and in how their professional vision, the use of professional expertise to interpret events, shifted over time. We discuss guidelines for these conversational support tools to support personalized PD in professions beyond teaching where conversation and interaction are important.

Mentoring is crucial for teachers' professional development but challenges of access, resources and capacity make it difficult for regular in-person mentorship in low-infrastructure contexts. Mobile technologies such as social media have the potential to scale teacher professional development and communities. This paper explores the culture of teacher mentorship in rural Cote d'Ivoire and the motivations and challenges mentors face in using mobile technology in their work through semi-structured interviews with 33 teacher mentors. We found that mentors valued social connections and knowledge sharing in using mobile technology. However, power dynamics between teachers and mentors affected how they could collaborate. Mentors also expressed frustrations with the lack of moderation within social media groups. Lack of infrastructure and connectivity also raise potential issues of inclusion and access to rural areas and schools. We conclude with design recommendations for technologies that scale culturally-relevant teaching mentorship in low-infrastructure contexts.

Multi-modal classroom sensing systems can collect complex behaviors in the classroom at a scale and precision far greater than human observers to capture learning insights and provide personalized teaching feedback. As students are critical stakeholders in the adoption of smart classrooms for the improvement of teaching, open questions remain in understanding student perspectives on the use of their data to provide insights to instructors. We conducted a Speed Dating with storyboards study to explore student values and boundaries regarding the acceptance of classroom sensing systems in STEM college courses. We found that students have several emergent beliefs about teaching and learning that influence their views towards smart classroom technologies. Students also held contextual views on the boundaries of data use depending on the outcome. Our findings have implications for the design and communication of classroom sensing systems that reconcile student and instructor beliefs around teaching and learning.

Robot technologies have been introduced to computing education to engage learners. This study introduces the concept of co-creation with a robot agent into culturally-responsive computing (CRC). Co-creation with computer agents has previously focused on creating external artifacts. Our work differs by making the robot agent itself the co-created product. Through participatory design activities, we positioned adolescent girls and an agentic social robot as co-creators of the robot’s identity. Taking a thematic analysis approach, we examined how girls embody the role of creator and co-creator in this space. We identified themes surrounding who has the power to make decisions, what decisions are made, and how to maintain social relationship. Our findings suggest that co-creation with robot technology is a promising implementation vehicle for realizing CRC.

Artificial intelligence (AI) literacy is especially important for those who may not be well-represented in technology design. We worked with ten Black girls in fifth and sixth grade from a predominantly Black school to understand their perceptions around fair and accountable AI and how they can have an empowered role in the creation of AI. Thematic analysis of discussions and activity artifacts from a summer camp and after-school session revealed a number of findings around how Black girls: perceive AI, primarily consider fairness as niceness and equality (but may need support considering other notions, such as equity), consider accountability, and envision a just future. We also discuss how the learners can be positioned as decision-making designers in creating AI technology, as well as how AI literacy learning experiences can be empowering.

Families and youth often interact with artificial intelligence (AI) and are stakeholders in technology. However, while AI can enhance interactions with technology, it is well-known that AI can also be biased and cause harm. Therefore, AI literacy is important, such that users understand both some technical components (e.g., training data, what is and is not AI and why), as well as ethical aspects (e.g., bias in society reflected in AI-powered technology) in order to make informed and empowered decisions about interacting with technologies. AI learning opportunities are scarce, and few leverage informal intergenerational learning as an opportunity for youth to engage with topics in AI and ethics. Games are an underexplored yet promising route for intergenerational play and learning for families and youth without much prior exposure to computing or AI. This talk highlights how and why games are particularly fitting to support families in gaining AI literacy through (1) games as an educational tool to build stronger mental models of AI, (2) intergenerational play as a way to scaffold adults and children in having a dialogue about more complex ethical considerations of AI, and (3) position families in roles of influence, where they may be empowered as techno-social change agents. We believe that games can help fill a gap by making AI literacy, an important family matter, accessible to a range of adult and youth learners.

Children interact frequently with artificial intelligence (AI) in their everyday lives. Their understanding of AI technology, however, is often very limited. AI literacy is vital to make informed and empowered decisions about engaging with technology. The ability to imagine future technology designs and applications is a core competency of AI literacy, but few studies have used booklets, a well-studied design fiction tool, as a technique with children in imagining future AI. We explored how a booklet-based design fiction method can support AI literacy with fifth graders (N = 7) from marginalized backgrounds. We describe two benefits of using booklets to create AI design fiction with children in our educational workshop: (1) grounding abstract AI concepts, (2) prolong post session dialogues. This work contributes a new type of scaffolded activity that supports youth learners in AI education.

Youth regularly use technology driven by artificial intelligence (AI). However, it is increasingly well-known that AI can cause harm on small and large scales, especially for those underrepresented in tech fields. Recently, users have played active roles in surfacing and mitigating harm from algorithmic bias. Despite being frequent users of AI, youth have been under-explored as potential contributors and stakeholders to the future of AI. We consider three notions that may be at the root of youth facing barriers to playing an active role in responsible AI, which are youth (1) cannot understand the technical aspects of AI, (2) cannot understand the ethical issues around AI, and (3) need protection from serious topics related to bias and injustice. In this study, we worked with youth (N = 30) in first through twelfth grade and parents (N = 6) to explore how youth can be part of identifying algorithmic bias and designing future systems to address problematic technology behavior. We found that youth are capable of identifying and articulating algorithmic bias, often in great detail. Participants suggested different ways users could give feedback for AI that reflects their values of diversity and inclusion. Youth who may have less experience with computing or exposure to societal structures can be supported by peers or adults with more of this knowledge, leading to critical conversations about fairer AI. This work illustrates youths' insights, suggesting that they should be integrated in building a future of responsible AI.

As governments in developing countries race to solve the global learning crisis, a key focus is on novel teaching approaches as taught in pedagogical programs. To scale, these pedagogical programs rely on government teacher training infrastructure. However, these programs face challenges in rural parts of Africa where there is a lack of advisor support, teachers are isolated and technology infrastructure is still emerging. Conversational agents have addressed some of these challenges by scaling expert knowledge and providing personalized interactions, but it is unclear how this work can translate to rural African contexts. To explore the use of such technology in this design space, we conducted two related studies. The first was a qualitative study with 20 teachers and ministry officials in rural Côte d’Ivoire to understand opportunities and challenges in technology use for these stakeholders. Second, we shared a conversational agent probe over WhatsApp to 38 teachers for 14-weeks to better understand what we learned in the survey and to uncover realistic use cases from these stakeholders. Our findings were examined through a theoretical lens of aspirations to discover sustainable design directions for conversational agents to support teachers in low infrastructure settings.

Youth regularly interface with AI technology that leverages supervised machine learning. However, it is well-known that biased training data can result in harmful algorithmic bias. Thus, it is important that youth and families understand training data in machine learning. We present Data Detectives, a child-friendly tabletop card game about training data. Based on three research-based design principles: low-stakes experimentation to support curiosity, games facilitating conversation, and tangible and embodied learning for abstract concepts, the game supports learning the high-level mechanics of training data in supervised machine learning, as well as practicing critical discussion of training data related to algorithmic bias. Contributing to AI literacy opportunities, this game aims to facilitate playful peer-peer and child-parent learning.

Computing education is important for K-12 learners, but not all learners resonate with common educational practices. Culturally responsive computing initiatives center and empower learners from diverse and historically excluded backgrounds. Recently, a number of educational programs have been developed and curated for an online experience. In this paper, we describe an online synchronous culturally responsive computing (CRC) camp for middle school girls (ages 11-14 years old) and report on challenges and successes from running the camp curriculum four times over the course of a year. We also describe core iterative changes we made between our runs. We then discuss lessons learned related to building rapport and connection among learners, centering learners of different backgrounds in an online synchronous environment, and facilitating reflection on power and identity aimed at positioning learners as techno-social change agents. Lastly, we offer recommendations for running online CRC experiences.

Computer science (CS) education is an important subject for K-12 students in an increasingly computational world. However, common CS education practices may not be inclusive of all learners. Culturally responsive computing (CRC) initiatives aim to center and empower learners from diverse and historically excluded backgrounds. With a sudden shift to online learning, virtual educational experiences have been developed. We describe three main findings from running three iterations of an online synchronous CRC camp for middle school girls, which are: (1) Integration of power, identity, and CS concepts, (2) Participation in CS vs. power and identity activities based on learners' backgrounds, and (3) Adjusting instructor expectations about learner engagement to be more open-ended.

As schools begin offering more opportunities for computer science education, it is important to consider the viewpoints of all stakeholders. Parents, in particular, can have an outsize impact on their children's educational experience and attitudes towards STEM topics, but few studies investigate parent perceptions of CS, beliefs around whether and why learning CS is important for their children, and preferences for tools to learn programming. We ran a survey with 133 parent respondents of children in 6th to 12th grade to investigate perspectives on computing and CS education. Most parents viewed CS as important for their children to learn; however, fewer actively encouraged their child to engage with CS, and those who did tended to have higher prior knowledge in CS. In considering three types of educational programming interfaces (block, text, and hybrid), parents of varying experience levels preferred the hybrid tool, commonly because they believed that it could provide the best educational value. This work contributes to understanding parent stances and preferences on CS education, as they play an important role in supporting and facilitating their children's access to and experience in CS learning opportunities.

Culturally responsive STEM and computing initiatives aim to engage and embolden a diverse range of learners, center their identity and experiences in curriculum, and connect learners to each other and their communities. With an abrupt pivot to online learning at the beginning of 2020, more educational experiences have taken place virtually. We ran a virtual synchronous culturally responsive computing camp and saw that establishing the right environment online to support a good sense of connectedness was challenging. To investigate this further, we interviewed eight K-12 instructors of culturally responsive STEM and computing programs. Three themes emerged on defining and cultivating connectedness in learning experiences, the role of equity in supporting community online, and affordances of being online specific to culturally responsive perspectives. We support our thematic findings with vignettes from the camp data. In this study, we address K-12 culturally responsive STEM and computing instructors' beliefs, experiences, and approaches regarding cultivating connectedness online. This work fills a gap in understanding instructor perspectives on building in-program and broader community connections online from a culturally responsive STEM and computing lens.

Classroom sensing is an important and active area of research with great potential to improve instruction. Complementing professional observers – the current best practice – automated pedagogical professional development systems can attend every class and capture fine-grained details of all occupants. One particularly valuable facet to capture is class gaze behavior. For students, certain gaze patterns have been shown to correlate with interest in the material, while for instructors, student-centered gaze patterns have been shown to increase approachability and immediacy. Unfortunately, prior classroom gaze-sensing systems have limited accuracy and often require specialized external or worn sensors. In this work, we developed a new computer-vision-driven system that powers a 3D “digital twin” of the classroom and enables whole-class, 6DOF head gaze vector estimation without instrumenting any of the occupants. We describe our open source implementation, and results from both controlled studies and real-world classroom deployments.

Adaptive Collaborative Learning Support (ACLS) systems improve collaboration and learning for students over individual work or collaboration with non-adaptive support. However, many ACLS systems are ill-suited for rural contexts where students often need multiple kinds of support to complete tasks, may speak languages unsupported by the system, and require more than pre-assigned tutor-tutee student pairs for more equitable learning. We designed an intervention that fosters more equitable help-seeking by automatically detecting student struggles and prompts them to seek help from specific peers that can help. We conducted a mixed-methods experimental study with 98 K-3 students in a rural village in Tanzania over a one-month period, evaluating how the system affects student interactions, system engagement, and student learning. Our intervention increased student interactions by almost 4 times compared to the control condition, increased domain knowledge interactions, and propelled students to engage in more cognitively challenging activities.

Teachers’ perspectives are critical for understanding classroom culture. They create and enforce rules in classrooms and are responsible for educating students using methods that they perceive to be most effective. Therefore, creating supplementary education technologies without understanding teachers and the culture they promote may lead to interventions that are underutilized or ineffective. Our research specifically investigates how technologies that foster student collaboration fit into teachers’ views of learning in a rural context with limited existing collaboration scaffolds. We interviewed 24 teachers and observed 39 classrooms in a rural Tanzanian village to understand how teachers value peer-peer collaboration in their teaching practice, and the unique challenges they face educating students in rural classroom settings. We uncover insights that inform the design and deployment of supplementary education technologies to support teachers in rural Tanzania and similar demographics.

Dialog with a social pedagogical robot or agent is a powerful way for kids to learn [1, 5] but may limit the formation of an agentic relationship with the technology [9]. One main purpose of conversational agents is to allow the user to have a natural interaction that reduces the need to learn artificial conventions [6], but dialog systems fall short with respect to failure recovery, vocabulary diversity, remembering conversational history, and other measures [2, 3]. Further, Hill et. al. [4] found that people adapt their model of communication to match a chatbot’s in the same way they do with a child or non-native speaker. Thus, users conversing with a pedagogical agent are implicitly trained to shape their behavior to suit the technology rather than shaping the technology. For young learners, particularly among populations that have been historically excluded from technology fields, this limits agency and reinforces marginalizing power structures [9].

Access to literacy is critical to children's futures, but formal education may be insufficient for fostering early literacy, especially in low-resource contexts. Educational technologies used at home may be able to help, but it is unclear whether or how children (and families) will use such technologies at home in rural communities, particularly in low-literate families. In this paper, we investigate these questions with a voice-based literacy technology deployed with families in 8 rural communities in Côte d'Ivoire for 4 months. We use interviews and observations with 37 families to investigate motivations, methods, and barriers for rural families' engagement with a literacy technology accessible via feature phones. We contribute insights into how families view digital literacy as a learning goal, leverage networks of supporters, and over time, transition from explicit to implicit support for children's learning.

Teaching is challenging in rural areas in developing countries because of large classroom sizes, limited basic infrastructure, and lack of teacher training. Teachers' impact on educational outcomes can be improved by teacher training programs but these programs are limited by poor infrastructure in rural areas. Although infrastructural challenges can be overcome by information technology today, it is unclear how such technology can support teacher training. Therefore to explore opportunities for teacher training in rural developing contexts with technology, we conducted interviews and observations with 22 primary school teachers in two regions of rural Côte d'Ivoire. We followed an aspirations-based approach that focuses on the user's aspirations (long term desires) to help understand teachers' intrinsic desire to grow in their professional life. We found that teacher aspirations conflict with their current role but their solidarity helps them find education role models who balance this conflict. We found that teachers face challenges in teacher training but they handle issues through their solidarity and by creating solutions. We then discuss ways to measure impact on aspirations, designing for conflicting aspirations and to leverage aspirations to create a social change in teacher training using technology.

Social media messaging applications(i.e. WhatsApp, Facebook) have reached 2.3 billion users in 2019, with the majority of users emerging from developing countries. The high usage among emergent users opens the possibility of designing text-based interventions for social change but such interventions rely on experts (i.e. doctors, educators, and moderators) knowledge which is scarce in developing contexts. Expert knowledge can be scaled up using chatbots but more research is needed to support emergent users who need context-specific support such as local language interventions or may not have regular internet connectivity. Therefore to support the design of chatbot based interventions in low resource contexts, we built DIA a chatbot architecture for low resource contexts to scale expert knowledge and support localization. DIA is a human-chatbot (humbot) hybrid system that organically learns topic-specific knowledge and local language from user interactions. We built a preliminary version of DIA on WhatsApp and deployed it to mentor 38 teachers in a rural context of Côte d'Ivoire. Through our preliminary deployment, we show that DIA can help (1) build a data-set of a topic and language-specific dialogues (2) understand users' online smartphone usage through chat logs and (3) collect survey data for through conversational interaction.

Significant research has demonstrated the crucial role that parents play in supporting the development of children's literacy, but in contexts where adults may lack sufficient literacy in the target language, it is not clear how to most effectively scaffold parental support for children's literacy. Prior work has designed technologies to teach children literacy directly, but this work has not focused on designing for low-literate parents, particularly for multilingual and developing contexts. In this paper, we describe findings from a qualitative study conducted in several regions of rural Côte d'Ivoire to understand Ivorian parents' beliefs, desires, and preferences for French literacy. We discuss themes that emerged from these interviews, surrounding ideas of trust, collaboration, and culturally-responsive design, and we highlight implications for the design of technology to scaffold low-literate parental support for children's literacy.

Low levels of childhood literacy in global contexts may be mitigated by educational technologies, however, these technologies often rely on parents of sufficient literacy to effectively support their children. Given low levels of adult literacy in many low-resource contexts, we investigate the nature of low-literate adult support for children's use of a literacy technology designed to foster early literacy precursors. We deployed an interactive voice response (IVR) system with 38 families in a rural village in Côte d'Ivoire using the IVR for 5 weeks in their homes. Using call log data and grounded theory analyses of IVR observations and interviews, we find evidence that families leverage complex support networks where family members support children's use of the IVR in different ways, via a collective network of intermediaries. These results suggest opportunities to scaffold low-literate family supporters for educational technologies.

Tablet-based educational technologies provide a supplement to traditional classroom-based early literacy education, especially in regions with limited schooling resources. Prior work has probed how children generally interact with and learn from these technologies, however, there is limited research on student engagement with applications that utilize valuable input techniques such as automatic handwriting and speech recognition. In our study, we designed and field-tested early literacy speech and handwriting recognition applications with the primary aim of maximizing student engagement. We designed the applications based on prior research insights and classroom observations from our target population and field-tested the applications with 283 children living in rural Tanzania. We found that observing a small set of classrooms can produce design insights that increase engagement on tablet-based learning systems on a much larger scale. We also demonstrate the importance of domain familiarity in students' choice to persist through activities while learning with technology.

Smartphone- and tablet-based learning systems are often posited as solutions for closing early literacy gaps between rural and urban regions in emerging economies. These systems are often developed based on experiences with students in urban contexts, limiting their success rates with children from rural areas who have had little to no prior exposure to technology. To explore how such technologies are used in different learning contexts, we deployed an early literacy learning application in school and home settings in a rural village in Tanzania. We use Rogoff's theory of instructional models to understand and describe the interaction between learners, adults, and peers. We found that in the presence of a school teacher, the instructional model was primarily "adult-run" where information was almost entirely disseminated by the teacher, while in home settings, the instructional model was similar to a "community-of-learners" model where children collaborate with other peers and adults to achieve their learning goals. We use these instructional models to surface six themes of support and scaffolding that were expressed differently across settings, and discuss the benefits and drawbacks of the instructional models observed in providing support across these themes.

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.

The sheer amount of human-centric multimedia content has led to increased research on human behavior understanding. Most existing methods model behavioral sequences without considering the temporal saliency. This work is motivated by the psychological observation that temporally selective attention enables the human perceptual system to process the most relevant information. In this paper, we introduce a new approach, named Temporally Selective Attention Model (TSAM), designed to selectively attend to salient parts of human-centric video sequences. Our TSAM models learn to recognize affective and social states using a new loss function called speaker-distribution loss. Extensive experiments show that our model achieves the state-of-the-art performance on rapport detection and multimodal sentiment analysis. We also show that our speaker-distribution loss function can generalize to other computational models, improving the prediction performance of deep averaging network and Long Short Term Memory (LSTM).

It has been shown in numerous studies that the application of Augmented Reality (AR) to teaching and learning is beneficial, but determining the reasons behind its effectiveness, and in particular the characteristics of students for whom an AR is best suited, can bring forth new opportunities to integrate adaptive instruction and AR in the future. Through a quasi-experimental research design, our study recruited 66 participants in an 8-week long AR-assisted learning activity, and lag sequential analysis was used to analyze participants' behavior in an AR learning environment. We found that AR was more effective in enhancing the learning gains in elementary school science of learners who prefer a Kinesthetic approach to learning. We hypothesize that these effects are due to the increase in opportunity for hands-on activities, effectively increasing learners' concentration and passion for learning.

Although xMOOCs are not designed to directly engage students via social media platforms, some students in these courses join MOOC-associated Facebook groups. This study explores the prevalence of Facebook groups associated with courses from MITx and HarvardX, the geographic distribution of students in such groups as compared to the courses at large, and the extent to which such groups are location and/or language homophilous. Results suggests that a non-trivial number of MOOC students engage in Facebook groups, that learners from a number of non-U.S. locations are disproportionately likely to participate in such groups, and that the groups display both location and language homophily. These findings have implications for how MOOCs and social media platforms can support learners from non-English speaking contexts.

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.

Massive Open Online Courses (MOOCs) have the potential to bridge education and literacy gaps by offering high quality, free courses to anyone with an Internet connection. MOOCs in their present state, however, may be relatively inaccessible to non-native English speakers, as a majority of MOOC content is in the English language. While a potential solution is to translate all MOOC content into all languages, it is not known whether this solution will satisfy the learning goals of all English as a Second Language (ESL) speakers. Through a series of interviews, we investigate ESL speakers' motivations for taking MOOCs and other online courses. Our findings show that ESL speakers have a variety of motivations for taking online courses that are not captured in current surveys, which implies that current one-size-fits-all approaches to increasing MOOC accessibility for learners with a first language other than English may not be effective. Rather, offering learners individualized tools based on their motivation and needs may be more effective.

Existing datasets tell us only a partial story about the contextual factors that impact learners in Massive Online Open Courses (MOOCs). Information about race/ethnicity, education, and income helps us understand socioeconomic status, but such data is notoriously difficult to collect in an international context. Extant MOOC studies have not paid due attention to socioeconomic variables; they have either taken a U.S.-centric approach, ignored important country-specific dimensions of variables, or failed to ask about certain variables altogether, such as race/ethnicity. During a qualitative study of 24 self-regulated learners from population groups underrepresented in MOOCs, we piloted a short U.S.-centric demographic questionnaire. Preliminary results suggest that a large-scale survey designed for both cross-national and country-specific analyses would provide valuable information to MOOC researchers.

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.

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 student's 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 Stacy's behavior in the third-person and formal tutoring statements reduce learning gains. Additionally, we found that the agent's mistakes were a significant predictor for students shifting away from alignment with the agent.

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.

Educational games may be particularly suited to teaching social learning skills with virtual humans. We investigate the importance of social goals and engaging social interactions in learning from such games. In one experiment, students played a cultural negotiation game with an explicit social goal or only negotiation task goals. While the group without the explicit social goal learned significantly more, students who reported having social goals in a manipulation check learned the most. In future work, we will develop an intervention built into a virtual learning environment to implicitly scaffold social goals.

Games motivate through challenge, which is often supported by giving proximal, task-related goals. In an educational game for social skills, however, these may not be the most appropriate goals. We ran an experiment in which 54 students played a game designed to teach cross-cultural negotiation skills, with either negotiation task goals or with an additional social goal. The group not given the social goal performed significantly better on most measures. However, students who reported having social goals, regardless of condition, learned significantly more than students who did not report social goals. This experiment provides evidence that while explicit goals may not be the right scaffold, social goals are important in learning cross-cultural negotiation from an educational game.

Previous research shows that video viewing (a frequent activity in language courses) is more effective when students receive guidance. We investigate how to support students in an on-line environment in acquiring cultural knowledge and intercultural competence by viewing clips from feature films from the target culture. To test the effectiveness of a set of attention-focusing techniques (pause-predict-ponder), some of which have been shown to be effective in other contexts, we created ICCAT, a simple tutor that enhances an existing classroom model for the development of intercultural competence. We ran a study in two French Online classrooms with 35 participants, comparing ICCAT versions with and without attention-focusing techniques. We found that the addition of the pause-predict-ponder seemed to guide students in acquiring cultural knowledge and significantly increased students' ability to reason from an intercultural perspective. We discuss possible implications for intelligent tutoring systems in such difficult and ill-defined domains.