Emotion Data Collection and Its Implications for Affective Computing

Afzal, Shazia; Robinson, Peter

doi:10.1093/oxfordhb/9780199942237.013.002

Cited by 25 publications

(15 citation statements)

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“…While each game elicits similar playstyles across different participants, the database features unique videos with selfannotated arousal traces. AGAIN puts an emphasis on firstperson annotation as-compared to a third-person annotation scheme-is expected to yield ground truths of affect that are closer to the affect experienced [7], [63], [68]. The existing in-game footage of AGAIN, however, can be used directly for third-person annotation in future studies.…”

Section: Discussionmentioning

confidence: 99%

The Arousal video Game AnnotatIoN (AGAIN) Dataset

Melhart,

Liapis,

Yannakakis

2021

Preprint

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How can we model affect in a general fashion, across dissimilar tasks, and to which degree are such general representations of affect even possible? To address such questions and enable research towards general affective computing this paper introduces the Affect Game AnnotatIoN (AGAIN) dataset. AGAIN is a large-scale affective corpus that features over 1, 100 in-game videos (with corresponding gameplay data) from nine different games, which are annotated for arousal from 124 participants in a first-person continuous fashion. Even though AGAIN is created for the purpose of investigating the generality of affective computing across dissimilar tasks, affect modeling can be studied within each of its 9 specific interactive games. To the best of our knowledge AGAIN is the largest-over 37 hours of annotated video and game logs-and most diverse publicly available affective dataset based on games as interactive affect elicitors.

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Section: Discussionmentioning

confidence: 99%

The Arousal video Game AnnotatIoN (AGAIN) Dataset

Melhart,

Liapis,

Yannakakis

2021

Preprint

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“…For research on emotions, simultaneously collecting different kinds of data increases the validity and accuracy of the study [15]. We collected both quantitative and qualitative data.…”

Section: Data Collectionmentioning

confidence: 99%

“…Because emotions are complex phenomena, previous researchers have advocated multi-modal approaches to data collection [4], [15], which we have adopted. Researchers have also called for investigations of emotions to take an interdisciplinary approach [4].…”

Section: Introductionmentioning

confidence: 99%

A Qualitative Study of Emotions Experienced by First-year Engineering Students during Programming Tasks

Atiq

Loui

2022

ACM Trans. Comput. Educ.

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In introductory computer programming courses, students experience a range of emotions. Students often experience anxiety and frustration when they encounter difficulties in writing programs. Continued frustration can discourage students from pursuing engineering and computing careers. Although prior research has shown how emotions affect students’ motivation and learning, little is known about students’ emotions in programming courses. In this qualitative study of first-year engineering students taking an introductory programming course, we examined the emotions that these students experienced during programming tasks, and the reasons for experiencing those emotions. Our study was grounded in the control-value theory of achievement emotions. Each research participant came to two laboratory sessions: a programming session and a retrospective think-aloud interview session. In the programming session, each participant worked individually on programming problems. We collected screen capture, biometrics, and survey responses. In the interview session, each participant watched a video of their actions during the programming session. After every two minutes of viewing, the participants reported the emotions that they had experienced during this two-minute period. We performed a thematic analysis of the interview data. Our results indicate that the participants experienced frustration most frequently. Sometimes they experienced multiple emotions. For example, one participant felt annoyed because she had made a mistake, but she felt joy and pride when she fixed the mistake. To promote student learning, educators should take students’ emotions into account in the design of curriculum and pedagogy for introductory programming courses.

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“…Well-established research resources have been constructed this way. For instance, the ImageNet database offers "millions of cleanly sorted images" to train computer vision and pattern recognition algorithms (Deng et al, 2009), while in more subjective tasks such as emotion recognition, human experts are recruited to label corpuses of naturalistic expressions in order to train affective computing systems that reflect human responses (Afzal & Robinson, 2014).…”

Section: Conversational Labelingmentioning

confidence: 99%

Perception of rhythmic agency for conversational labeling

Blackwell

Cross

2021

Human–Computer Interaction

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Machine learning algorithms often need to be trained with sample datasets, prepared by humans who label the content manually. However, data labeling is tedious and repetitive, with humans often replicating trivial decisions that could have been automated, and not getting sufficient time to devote to cases where their judgment would have added more value. Much recent research has therefore focused on approaches to labeling that optimize information gain by assisting the user with trivial cases, or directing them toward more complex ones, so that the user can devote time and attention to subtle cues and ambiguous cases, potential bias (e.g., Binns et al., 2017), or inventing new label categories (e.g., Kulesza et al., 2014). The consequence of this increasingly common strategy is that labeling interfaces become more conversational. The system is providing implicit feedback to the user about the model under development, while the user's decisions on more difficult cases are implicitly challenging the system to update the model it holds. Such a perspective shifts from the relatively predictable view of machine learning, in which facts are simply repeated until the machine retains them, to one that more closely imitates teaching and learning situations between two humans, where the knowledge being acquired is dialogically shared, probed and questioned as in a conversation. We report investigations into a particular characteristic of human conversation that has not previously been explored for its relevance to interactive machine learning-rhythmic timing in conversation. This can be contrasted on one hand to earlier models of "dialog" in HCI where the user takes the initiative, issuing commands while the system responds with information as soon as it can in order to be seen as "smooth" (Miller, 1968; Nielsen, 1993), or on the other hand to task optimization models where the system takes the initiative, prompting for information that is supplied by the user as quickly and efficiently as possible, with minimum latency (Bernstein et al., 2011). In mixed initiative interaction (Horvitz, 1999), neither of these existing design models is appropriate, and we suggest that attention to rhythm and timing becomes far more important. In design approaches where the system has the potential to complete the user's actions, or even to take the initiative and make decisions, there will be a back-and-forth flow of initiative between the user and the system, resembling participatory turn-taking in human conversation. In human conversation, 1 poorly timed participation is associated with negative or inappropriate social effects (see, e.g., Benus et al., 2011; Richardson et al., 2006). We ask: during mixed-initiative interaction such as interactive labeling, how do the rhythmic timing characteristics of the interaction influence users' experience, and how should these be designed?

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Emotion Data Collection and Its Implications for Affective Computing

Cited by 25 publications

References 0 publications

The Arousal video Game AnnotatIoN (AGAIN) Dataset

The Arousal video Game AnnotatIoN (AGAIN) Dataset

A Qualitative Study of Emotions Experienced by First-year Engineering Students during Programming Tasks

Perception of rhythmic agency for conversational labeling

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