Proceedings of the International AAAI Conference on Web and Social Media
We present a computational framework for understanding the social aspects of emotions in Twitter ... more We present a computational framework for understanding the social aspects of emotions in Twitter conversations. Using unannotated data and semisupervised machine learning, we look at emotional transitions, emotional influences among the conversation partners, and patterns in the overall emotional exchanges. We find that conversational partners usually express the same emotion, which we name Emotion accommodation, but when they do not, one of the conversational partners tends to respond with a positive emotion. We also show that tweets containing sympathy, apology, and complaint are significant emotion influencers. We verify the emotion classification part of our framework by a human-annotated corpus.
Proceedings of the International AAAI Conference on Web and Social Media
For many people who speak more than one language,their language proficiency for each of the langu... more For many people who speak more than one language,their language proficiency for each of the languagesvaries. We can conjecture that people who use onelanguage (primary language) more than another wouldshow higher language proficiency in that primary language.It is, however, difficult to observe and quantifythat problem because natural language use is difficultto collect in large amounts. We identify Wikipedia asa great resource for studying multilingualism, and weconduct a quantitative analysis of the language complexityof primary and non-primary users of English,German, and Spanish. Our preliminary results indicatethat there are indeed consistent differences of languagecomplexity in the Wikipedia articles chosen by primaryand non-primary users, as well as differences in the editsby the two groups of users.
Proceedings of the Fifth Annual ACM Conference on Learning at Scale
In programming education, instructors often supplement lectures with active learning experiences ... more In programming education, instructors often supplement lectures with active learning experiences by offering programming lab sessions where learners themselves practice writing code. However, widely accessed instructional programming screencasts are not equipped with assessment format that encourages such hands-on programming activities. We introduce Elicast, a screencast tool for recording and viewing programming lectures with embedded programming exercises, to provide hands-on programming experiences in the screen-cast. In Elicast, instructors embed multiple programming exercises while creating a screencast, and learners engage in the exercises by writing code within the screencast, receiving auto-graded results immediately. We conducted an exploratory study of Elicast with five experienced instructors and 63 undergraduate students. We found that instructors structured the lectures into small learning units using embedded exercises as checkpoints. Also, learners more actively engaged in the screencast lectures, checked their understanding of the content through the embedded exercises, and more frequently modified and executed the code during the lectures.
Multiplayer online battle arena games provide an excellent opportunity to study team performance.... more Multiplayer online battle arena games provide an excellent opportunity to study team performance. When designing a team, players must negotiate a proficiency-congruency dilemma between selecting roles that best match their experience and roles that best complement the existing roles on the team. We adopt a mixed-methods approach to explore how users negotiate this dilemma. Using data from League of Legends, we define a similarity space to operationalize team design constructs about role proficiency, generality, and congruency. We collect publicly available data from 3.36 million users to test the influence of these constructs on team performance. We also conduct focus groups with novice and elite players to understand how players' team design practices vary with expertise. We find that player proficiency increases team performance more than team congruency. These findings have implications for players, designers, and theorists about how to recommend team designs that jointly pri...
Individuals who successfully make their livelihood by talking with others, for example travel age... more Individuals who successfully make their livelihood by talking with others, for example travel agents, can be presumed to have optimized their language for the task at hand in terms of conciseness and intelligibility. It makes sense to exploit this effort for the purpose of building better generation components for a spoken dialog system. The Stochastic Generation technique, introduced by<br>Oh and Rudnicky (2002), is one such approach. In this approach, utterances in a corpus of domain expert utterances are classified as to speech act and individual concepts tagged. Statistical n-gram models are built for each speech-act class then used generatively to create novel utterances. These have been shown to be comparable in quality to human productions. The class and tag scheme is concrete and closely tied to the domain at hand; we believe this produces a distinct advantage in speed of implementation and quality of<br>results. The current paper describes the classification and...
Proceedings of the International AAAI Conference on Web and Social Media
We present a computational framework for understanding the social aspects of emotions in Twitter ... more We present a computational framework for understanding the social aspects of emotions in Twitter conversations. Using unannotated data and semisupervised machine learning, we look at emotional transitions, emotional influences among the conversation partners, and patterns in the overall emotional exchanges. We find that conversational partners usually express the same emotion, which we name Emotion accommodation, but when they do not, one of the conversational partners tends to respond with a positive emotion. We also show that tweets containing sympathy, apology, and complaint are significant emotion influencers. We verify the emotion classification part of our framework by a human-annotated corpus.
Proceedings of the International AAAI Conference on Web and Social Media
For many people who speak more than one language,their language proficiency for each of the langu... more For many people who speak more than one language,their language proficiency for each of the languagesvaries. We can conjecture that people who use onelanguage (primary language) more than another wouldshow higher language proficiency in that primary language.It is, however, difficult to observe and quantifythat problem because natural language use is difficultto collect in large amounts. We identify Wikipedia asa great resource for studying multilingualism, and weconduct a quantitative analysis of the language complexityof primary and non-primary users of English,German, and Spanish. Our preliminary results indicatethat there are indeed consistent differences of languagecomplexity in the Wikipedia articles chosen by primaryand non-primary users, as well as differences in the editsby the two groups of users.
Proceedings of the Fifth Annual ACM Conference on Learning at Scale
In programming education, instructors often supplement lectures with active learning experiences ... more In programming education, instructors often supplement lectures with active learning experiences by offering programming lab sessions where learners themselves practice writing code. However, widely accessed instructional programming screencasts are not equipped with assessment format that encourages such hands-on programming activities. We introduce Elicast, a screencast tool for recording and viewing programming lectures with embedded programming exercises, to provide hands-on programming experiences in the screen-cast. In Elicast, instructors embed multiple programming exercises while creating a screencast, and learners engage in the exercises by writing code within the screencast, receiving auto-graded results immediately. We conducted an exploratory study of Elicast with five experienced instructors and 63 undergraduate students. We found that instructors structured the lectures into small learning units using embedded exercises as checkpoints. Also, learners more actively engaged in the screencast lectures, checked their understanding of the content through the embedded exercises, and more frequently modified and executed the code during the lectures.
Multiplayer online battle arena games provide an excellent opportunity to study team performance.... more Multiplayer online battle arena games provide an excellent opportunity to study team performance. When designing a team, players must negotiate a proficiency-congruency dilemma between selecting roles that best match their experience and roles that best complement the existing roles on the team. We adopt a mixed-methods approach to explore how users negotiate this dilemma. Using data from League of Legends, we define a similarity space to operationalize team design constructs about role proficiency, generality, and congruency. We collect publicly available data from 3.36 million users to test the influence of these constructs on team performance. We also conduct focus groups with novice and elite players to understand how players' team design practices vary with expertise. We find that player proficiency increases team performance more than team congruency. These findings have implications for players, designers, and theorists about how to recommend team designs that jointly pri...
Individuals who successfully make their livelihood by talking with others, for example travel age... more Individuals who successfully make their livelihood by talking with others, for example travel agents, can be presumed to have optimized their language for the task at hand in terms of conciseness and intelligibility. It makes sense to exploit this effort for the purpose of building better generation components for a spoken dialog system. The Stochastic Generation technique, introduced by<br>Oh and Rudnicky (2002), is one such approach. In this approach, utterances in a corpus of domain expert utterances are classified as to speech act and individual concepts tagged. Statistical n-gram models are built for each speech-act class then used generatively to create novel utterances. These have been shown to be comparable in quality to human productions. The class and tag scheme is concrete and closely tied to the domain at hand; we believe this produces a distinct advantage in speed of implementation and quality of<br>results. The current paper describes the classification and...
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Papers by Alice Oh