Sleepers' Lag – Study on Motion and Attention

Raca, Mirko; Tormey, Roland; Dillenbourg, Pierre

doi:10.18608/jla.2016.32.12

Cited by 13 publications

(16 citation statements)

References 21 publications

(26 reference statements)

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“…Until less invasive technologies become available, alternative strategies and sources of information will have to be adopted. Raca, Tormey and Dillenbourg (2014) studied attention through a camera system, instead of through eye‐tracking or a wristband, to measure the reaction times of students listening to a lecture. The authors observed that inattentive students have later reactions than focused students.…”

Section: Resultsmentioning

confidence: 99%

Multimodal Learning Analytics research with young children: A systematic review

Lanna¹

2020

Brit J Educational Tech

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Learning Analytics and Multimodal Learning Analytics are changing the way of analysing the learning process while students interact with an educational content. This paper presents a systematic literature review aimed at describing practices in recent Multimodal Learning Analytics and Learning Analytics research literature in order to identify tools and strategies useful for the assessment of the progress and behaviour of children under 6 years old in respect of their learning. The purpose is to provide guidance for Multimodal Learning Analytics research with children under 6 years old to assess their engagement in a task, their emotions, attention, understanding and achievement of a goal. The current state of knowledge on Multimodal Learning Analytics research suggests how performance analytics, face and speech recognition systems, eye tracking, Kinect analytics and wristbands could be used with children. The results show the complexity of collecting data using non‐invasive methodologies with children under 6 years old. Ethical implications related to multimodal data from audio, visual, biometric and quantitative measures of child behaviour are discussed.

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Section: Resultsmentioning

confidence: 99%

Multimodal Learning Analytics research with young children: A systematic review

Lanna¹

2020

Brit J Educational Tech

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“…They found that immediate neighbors had a significant influence on a student's attention, whereas students' motion was not directly connected with reported attention levels ). Furthermore, Raca et al (2014) analyzed students' reaction time upon presentation of relevant information (sleeper's lag). In addition to estimating head pose, they considered the class period, student's row, how often faces were automatically detected (as a precursor to eye contact), head movement, and the amount of still time (i.e., 5-s periods without head movement) because these features had previously been shown to be good predictors of engagement in learning (Raca et al 2015).…”

Section: Using Machine Learning To Assess Visible Indicators Of (Dis)mentioning

confidence: 99%

Attentive or Not? Toward a Machine Learning Approach to Assessing Students’ Visible Engagement in Classroom Instruction

et al. 2019

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Teachers must be able to monitor students' behavior and identify valid cues in order to draw conclusions about students' actual engagement in learning activities. Teacher training can support (inexperienced) teachers in developing these skills by using videotaped teaching to highlight which indicators should be considered. However, this supposes that (a) valid indicators of students' engagement in learning are known and (b) work with videos is designed as effectively as possible to reduce the effort involved in manual coding procedures and in examining videos. One avenue for addressing these issues is to utilize the technological advances made in recent years in fields such as machine learning to improve the analysis of classroom videos. Assessing students' attention-related processes through visible indicators of (dis)engagement in learning might become more effective if automated analyses can be employed. Thus, in the present study, we validated a new manual rating approach and provided a proof of concept for a machine vision-based approach evaluated on pilot classroom recordings of three lessons with university students. The manual rating system was significantly correlated with selfreported cognitive engagement, involvement, and situational interest and predicted performance on a subsequent knowledge test. The machine vision-based approach, which was based on gaze, head pose, and facial expressions, provided good estimations of the manual ratings. Adding a synchrony feature to the automated analysis improved correlations with the manual ratings as well as the prediction of posttest variables. The discussion focuses on challenges and important next steps in bringing the automated analysis of engagement to the classroom.

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“…1 The classroom is the system. Above, the classroom is the output (Lantern, Alavi and Dillenbourg 2012); below, the classroom is the input (Raca et al 2014) Trend 2: Less Semantic…”

Section: Trend 1: More Physicalmentioning

confidence: 99%

“…abstractions of raw data, and these features do actually convey some semantics. For instance, Raca et al (2014) estimated the global level of learners' attention in a lecture room by placing two cameras in front of students. The ratio of co-movements (students rotating their head in a near-synchronous way) predicted self-estimated attention levels.…”

Section: Trend 1: More Physicalmentioning

confidence: 99%

The Evolution of Research on Digital Education

Dillenbourg

2016

Int J Artif Intell Educ

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How does AI&EdAIED today compare to 25 years ago? This paper addresses this evolution by identifying six trends. The trends are ongoing and will influence learning technologies going forward. First, the physicality of interactions and the physical space of the learner became genuine components of digital education. The frontier between the digital and the physical has faded out. Similarly, the opposition between individual and social views on cognition has been subsumed by integrated learning scenarios, which means that AIED pays more attention today to social interactions than it did at its outset. Another trend is the processing of learners' behavioural particles, which do not carry very many semantics when considered individually, but are predictive of knowledge states when large data sets are processed with machine learning methods. The development of probabilistic models and the integration of crowdsourcing methods has produced another trend: the design of learning environments has become less deterministic than before. The notion of learning environment evolved from a rather closed box to an open ecosystem in which multiple components are distributed over multiple platforms and where multiple stakeholders interact. Among these stakeholders, it is important to notice that teachers play a more important role than before: they interact not only at the design phase (authoring) but also in the runtime phase (orchestration). These trends are not specific to AIED; they depict the evolution of learning technologies as a whole.

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Sleepers' Lag – Study on Motion and Attention

Cited by 13 publications

References 21 publications

Multimodal Learning Analytics research with young children: A systematic review

Multimodal Learning Analytics research with young children: A systematic review

Attentive or Not? Toward a Machine Learning Approach to Assessing Students’ Visible Engagement in Classroom Instruction

The Evolution of Research on Digital Education

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