Nonverbal Behavior in the Digital Age: Meanings, Models, and Methods

Montepare, Joann M.

doi:10.1007/s10919-014-0187-z

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…In the context of educommunication, each addressed digital environments of learning, such as face-to-face ICT-enhanced classroom, e-learning, b-learning, mlearning and MOOC is considered complex and quite unique (Montepare, 2014). It would not be a novelty to conclude that in each one of them the usage of technology would be different and can also be differentiated both in related theory and practice.…”

Section: Discussionmentioning

confidence: 99%

Educommunication in digital environments: an interaction´s perspective inside and beyond the classroom

Laverde

Amado

Leguizamón

2020

IJTEI

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In a highly interconnected and technology-mediated world, the way in which education is recognized as a communicative phenomenon is highly relevant to understand its development and future possibilities. In this text, educommunication is analyzed from the point of view of interactions that occur in different digital learning environments, especially in ICT-enhanced classrooms, blended learning, e-learning, mobile learning and Massive Open Online Courses (MOOC). This analysis was conducted from a systematic literature review of 240 papers that describe research generated on these topics, during the last 20 years.The results show that beyond the importance of interaction for the development of educational practices, different digital environments involve different ways of conceiving and deploying interaction processes, inside and outside the classroom.This implies the imperative need to adjust the current processes of teacher training in such a way that teachers can understand these differences and recognize both their theoretical and practical implication.

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Section: Discussionmentioning

confidence: 99%

Educommunication in digital environments: an interaction´s perspective inside and beyond the classroom

Laverde

Amado

Leguizamón

2020

IJTEI

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“…Digital technologies such as camcorders and the Microsoft Kinect have the strength of being able to capture large volumes of continuous non-verbal channels, which can be stored as a multimedia file and used for single or repeated post analysis. On the other hand, digital recordings are limited to environments that facilitate the setup of the technology [22]. Therefore, a niche exists for a computer-based system that is able to automatically monitor multiple channels of non-verbal behaviour from digital video recordings such as FATHOM described in Section IV.…”

Section: Non-verbal Behaviourmentioning

confidence: 99%

FATHOM: A neural network-based non-verbal human comprehension detection system for learning environments

Buckingham

Crockett

Bandar

et al. 2014

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

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This paper presents the application of FATHOM, a computerised non-verbal comprehension detection system, to distinguish participant comprehension levels in an interactive tutorial. FATHOM detects high and low levels of human comprehension by concurrently tracking multiple non-verbal behaviours using artificial neural networks. Presently, human comprehension is predominantly monitored from written and spoken language. Therefore, a large niche exists for exploring human comprehension detection from a non-verbal behavioral perspective using artificially intelligent computational models such as neural networks. In this paper, FATHOM was applied to a video-recorded exploratory study containing a learning task designed to elicit high and low comprehension states from the learner. The learning task comprised of watching a video on termites, suitable for the general public and an interview led question and answer session. This paper describes how FATHOM's comprehension classifier artificial neural network was trained and validated in comprehension detection using the standard backpropagation algorithm. The results show that high and low comprehension states can be detected from learner's non-verbal behavioural cues with testing classification accuracies above 76%.

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“…Recent technological advancements such as wearable sensing devices and RGB-depth cameras provided the foundational basis for capturing objective measures and indices (e.g., interpersonal distance, gestures) in a fully automatic and continuous way. This could: sensibly reduce subjective influences introduced by human coders (people annotating psychological experiments), and increase the number of measures (derived from the video, audio, or physiological domains) eventually undertaken at the same time and their temporal resolution [11], [21], [20]. This is not only a change of methodology, but could represent the beginning of a revolution in both clinical and social psychology.…”

Section: Introductionmentioning

confidence: 99%

Spotting prejudice with nonverbal behaviours

Palazzi

Calderara

Bicocchi

et al. 2016

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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Despite prejudice cannot be directly observed, nonverbal behaviours provide profound hints on people inclinations. In this paper, we use recent sensing technologies and machine learning techniques to automatically infer the results of psychological questionnaires frequently used to assess implicit prejudice. In particular, we recorded 32 students discussing with both white and black collaborators. Then, we identiﬁed a set of features allowing automatic extraction and measured their degree of correlation with psychological scores. Results conﬁrmed that automated analysis of nonverbal behaviour is actually possible thus paving the way for innovative clinical tools and eventually more secure societies

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Nonverbal Behavior in the Digital Age: Meanings, Models, and Methods

Cited by 3 publications

References 7 publications

Educommunication in digital environments: an interaction´s perspective inside and beyond the classroom

Educommunication in digital environments: an interaction´s perspective inside and beyond the classroom

FATHOM: A neural network-based non-verbal human comprehension detection system for learning environments

Spotting prejudice with nonverbal behaviours

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