Evaluation of learning performance by quantifying user's engagement investigation through low-cost multi-modal sensors

Sandhu, Vedant; Wai, Aung Aung Phyo; Ho, Chih Ying

doi:10.1109/icot.2017.8336117

Cited by 4 publications

(2 citation statements)

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“…Currie et al [22] highlight the use of eye movement as a mean of gauging student engagement for predicting academic outcome. Many recent studies [23]- [25] reveal the need to use engagement data in the predictive model development. Pardo et al [26] emphasizes the need to combine self-report data and online engagement data to develop prediction model.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Heart Rate to Predict Students’ Academic Performance

Wong*¹,

Senthil²,

Robert³

2019

IJRTE

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Timeliness was a missing factor in many studies on Academic Performance Prediction to identify at-risk students. This study embarked on a search to evaluate the feasibility of predicting students’ performance based on heart rate data collected during classes. This dimension of data was collected in the first four weeks after semester commencement to validate accurate prediction that will enable educationists to introduce remedial intervention to at-risk students. Another aim of this study is to determine the best threshold values for the different types of heart rate fluctuations that can be used in predicting academic achievements. The threshold values were tested further to verify whether the prediction model for individual course or combined courses was more accurate. Results revealed that heart rate data alone can achieve a maximum prediction accuracy of 88% and recall of 100%. Threshold values calculated in derived heart rate fluctuation types produces the best results. Prediction models for individual courses outperform the model using average threshold values of all courses.

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Section: Literature Reviewmentioning

confidence: 99%

Using Heart Rate to Predict Students’ Academic Performance

Wong*¹,

Senthil²,

Robert³

2019

IJRTE

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“…Though no statistical significant differences, attention modeling with Flanker tests achieves 4−6% higher accuracy than other two tests as shown in Figure4(b).We use six EEG band power features in our evaluation analysis. Among these features, different combination of θ, α and β band power features are used to derive focus or engagement index without modeling[14]. Also, the inclusion of δ in features must be careful as different artifacts may contain in the EEG signals resulting biases towards potential artifacts…”

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confidence: 99%

Generalizability of EEG-based Mental Attention Modeling with Multiple Cognitive Tasks

Wai

Dou

Guan

2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Attention is the foundation of a person's cognitive function. The attention level can be measured and quantified from the electroencephalogram (EEG). For the study of attention detection and quantification, we researchers usually ask the subjects to perform a cognitive test with distinct attentional and inattentional mental states. Different attention tasks are available in the literature, but there is no empirical evaluation to quantitatively compare the attention detection performance among the tasks. We designed an experiment with three typical cognitive tests: Stroop, Eriksen Flanker, and Psychomotor Vigilance Task (PVT), which are arranged in a random order in multiple trials. Data were collected from ten subjects. We used six standard band power features to classify the attention levels in four evaluation scenarios for both subject-specific and subject-independent cases. With cross-validation for the subject-independent case, we achieved a classification accuracy of 61.6%, 63.7% and 65.9% for PVT, Stroop and Flanker tasks respectively. We achieved the highest accuracy of 74.1% and 65.9% for the Flanker test in the subject-dependent and subject-independent cases respectively. Our evaluation shows no statistically significant differences in classification accuracy among the three distinct cognitive tasks. Our study highlights that EEG-based attention recognition can generalize across subjects and cognitive tasks.

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