Learner Engagement Measurement and Classification in 1:1 Learning

Aslan, Sinem; Çataltepe, Zehra; Diner, Itai; Dundar, Onur; Esme, Asli Arslan; Ferens, Ron; Kamhi, Gila; Oktay, Ece; Soysal, Canan; Yener, Murat

doi:10.1109/icmla.2014.111

Cited by 27 publications

(8 citation statements)

References 19 publications

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“…The study recommended the triangulation of other sensors and indices for better detection of student engagement and attention. Aslan et al used feature selection, including gaze, body posture, and facial points to create a student engagement classification with accuracies up to 85% to 95% [30]. However, the study only specified that the students were in a learning environment and failed to address the specific educational context where this model is beneficial.…”

Section: Indices Of Attentionmentioning

confidence: 99%

Evaluating the Effectiveness of Physical Exercise in Improving Standardized Test Scores Through Attention Indices

Huang¹,

Huang²,

Chang³

et al. 2022

Preprint

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In the US, standardized tests have risen in prevalence, extending their importance from education placement to employment. Attention is crucial to improving testing performance. Past studies have established that acute, coordinative, aerobic exercise improves attention, which is measured by the D2 Attention Test, emotional analysis, reading time, and eye movement tracking. No studies have drawn connections between physical exercise’s quantifiable improvement in attention to improvements in standardized tests; therefore, this study would attempt to do so. This study defines attention to be positively related to reading speed and negatively related to the number of eye drifts. High school students were selected to read and answer two reading passages from an SAT (Scholastic Assessment Test) exam, before and after a short 80% intensity run. Their reading times, facial video, and test scores were recorded. Dlib plots the facial landmark and OpenCV tracks the movement of the pupil. Through paired-samples t-tests, this study found out that after exercise, subjects displayed increased reading speed and fewer eye drifts, coupled with increased mean scores. Thus, this study demonstrated that running, as an acute, coordinative, aerobic exercise, helps increase the testing performance of the SAT reading section by measuring attention. Future research could focus on including head movement as an attention index, replicate the experiment on different standardized tests or exercises, and conduct natural experiments to better simulate real-life conditions to increase applicability.

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Section: Indices Of Attentionmentioning

confidence: 99%

Evaluating the Effectiveness of Physical Exercise in Improving Standardized Test Scores Through Attention Indices

Huang¹,

Huang²,

Chang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Aslan et al. ( 2014 ) used feature selection, including gaze, body posture, and facial points to create a student engagement classification with accuracies up to 85%–95%. However, the study only specified that the students were in a learning environment and failed to address the specific educational context where this model is beneficial.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating the effectiveness of physical exercise in improving standardized testing performances through attention indices

Huang¹,

Huang²,

Chang

et al. 2022

Brain and Behavior

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Introduction: In the United States, standardized tests have risen in prevalence, extending their importance from education placement to employment. Attention is crucial to improving testing performance. Past studies have established that acute, coordinative, aerobic exercise improves attention, which is measured by the D2 Attention Test, emotional analysis, reading time, and eye movement tracking. No studies have drawn connections between physical exercise's quantifiable improvement in attention to improvements in standardized tests; therefore, this study would attempt to do so.Methods: This study defines attention to be positively related to reading speed and negatively related to the number of eye drifts. High school students were selected to read and answer two reading passages from an SAT (Scholastic Assessment Test) exam, before and after a short 80% intensity run. Their reading times, facial video, and test scores were recorded. Dlib plots the facial landmark and OpenCV tracks movement of the pupil.Results: Through paired-samples t-tests, this study found out that after exercise, subjects displayed increased reading speed and fewer eye drifts, coupled with increased mean scores. Conclusion:Thus, this study demonstrated that running, as an acute, coordinative, aerobic exercise, helps increase the testing performance of the SAT reading section by measuring attention. Future research could focus on including head movement as an attention index, replicate the experiment on different standardized tests or exercises, and conduct natural experiments to better simulate real-life conditions to increase applicability.

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“…In this study, student performance was evaluated by supervised ML models. Studies that had similar data have used random forest [42][43][44], gradient boosting [41], support vector machines (SVM) [45][46][47], elastic net [12], naive Bayes [15,34,48], logistic regression [12,41,49], decision tree [42,50,51], and ANN [52][53][54] algorithms. Therefore, the preprocessed data executed on previously mentioned models and best performance with the default parameters were obtained by RF, SVM and LR models; Thus, these models were selected for more tuning and analysis.…”

Section: Model Selectionmentioning

confidence: 99%

A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning

et al. 2021

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The objective of this research is to develop an machine learning (ML) -based system that evaluates the performance of high school students during the semester and identify the most significant factors affecting student performance. It also specifies how the performance of models is affected when models run on data that only include the most important features. Classifiers employed for the system include random forest (RF), support vector machines (SVM), logistic regression (LR) and artificial neural network (ANN) techniques. Moreover, the Boruta algorithm was used to calculate the importance of features. The dataset includes behavioral information, individual information and the scores of students that were collected from teachers and a one-by-one survey through an online questionnaire. As a result, the effective features of the database were identified, and the least important features were eliminated from the dataset. The ANN accuracy, which was the best accuracy in the original dataset, was reduced in the decreased dataset. On the contrary, SVM performance was improved, which had the highest accuracy among other models, with 0.78. Moreover, the LR and RF models could provide the same performance in the decreased dataset. The results showed that ML models are influential for evaluating students, and stakeholders can use the identified effective factors to improve education.

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Learner Engagement Measurement and Classification in 1:1 Learning

Cited by 27 publications

References 19 publications

Evaluating the Effectiveness of Physical Exercise in Improving Standardized Test Scores Through Attention Indices

Evaluating the Effectiveness of Physical Exercise in Improving Standardized Test Scores Through Attention Indices

Evaluating the effectiveness of physical exercise in improving standardized testing performances through attention indices

A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning

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