Using Heart Rate to Predict Students’ Academic Performance

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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Development of Accurate and Timely Students' Performance Prediction Model Utilizing Heart Rate Data

Advances in Computer and Electrical Engineering - Handbook of Research on Engineering Innovations and Technology Management in Organizations ◽

10.4018/978-1-7998-2772-6.ch007 ◽

2020 ◽

pp. 123-144

Author(s):

Mu Lin Wong ◽

Senthil S.

Keyword(s):

At Risk ◽

Heart Rate ◽

Prediction Model ◽

Performance Prediction ◽

Prediction Models ◽

At Risk Students ◽

Model Development ◽

Rate Data ◽

Heart Rate Data ◽

The Difference

Academic Performance Prediction models mustn't be accurate only, but timely too, to identify at-risk students at the earliest to provide remedy. Heart rate data of 50 students in 3 main courses are collected, processed, and analyzed to distinguish the difference between excellent students and at-risk students. Three of the 12 heart rate attributes were chosen to calculate the threshold values, which are used to predict at-risk students. Half of the at-risk students were identified after week 5. Later, the datasets were rebalanced. Using four Data Mining classifiers, six attributes were identified to be the best attributes for prediction model development. The datasets were then dimensionally reduced. Applying classification, half of the at-risk students were identified earliest around week 5 of the 12-week semester. J48 is the most robust classifier, compared to JRip, Multi-Level-Perceptron, and RandomForest, making accurate prediction on at-risk students earlier most of the time.

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Associations of Heart Rate Measures during Physical Education with Academic Performance and Executive Function in Children: A Cross-Sectional Study

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17124307 ◽

2020 ◽

Vol 17 (12) ◽

pp. 4307

Author(s):

Adrià Muntaner-Mas ◽

Josep Vidal-Conti ◽

Jo Salmon ◽

Pere Palou-Sampol

Keyword(s):

Heart Rate ◽

Academic Performance ◽

Executive Function ◽

Physical Education ◽

Cognitive Flexibility ◽

Cross Sectional Study ◽

Rate Data ◽

Sectional Study ◽

Cross Sectional ◽

Heart Rate Data

The current evidence for a relation between children’s heart rate measures and their academic performance and executive functioning is infancy. Despite several studies observing dose-response effects of physical activity on academic performance and executive function in children, further research using objective measures of the relative intensity of physical activity (e.g., heart rate) is warranted. The present study aimed to inspect associations between heart rate response and various academic performance indicators and executive function domains. A total of 130 schoolchildren between the ages of 9 and 13 years (M = 10.69, SD 0.96 years old; 56.9% boys) participated in a cross-sectional study. Children’s heart rate data were collected through participation in physical education classes using the polar TeamTM hardware and software. One week before heart rate measures, academic performance was obtained from the school records in maths, Spanish language, Catalan language, physical education, and Grade point average. Executive function was measured by two domains, cognitive flexibility with the Trail Making Test and inhibition with the Stroop test. Associations between children’s heart rate data and academic performance and executive function were analyzed using regression models. Academic performance was found to be positively related to four heart rate measures (β range, 0.191 to 0.275; all p < 0.040). Additionally, the hard heart rate intensity level was positively related to two academic indicators (β range, 0.183 to 0.192; all p < 0.044). Three heart rate measures were associated with two cognitive flexibility subdomains (β range, −0.248 to 0.195; all p < 0.043), and three heart rate measures were related to one inhibition subdomain (β range, 0.198 to 0.278; all p < 0.028). The results showed slight associations of heart rate responses during physical education lessons with academic performance but did not clearly indicate associations with executive function. Future experimental studies testing associations between different bouts of intensity levels are needed to disentangle the relationship with brain function during childhood.

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Correlation in the heart rate data

Nonlinear Analysis Modelling and Control ◽

10.15388/na.1998.2.0.15301 ◽

1998 ◽

Vol 2 ◽

pp. 141-148

Author(s):

J. Ulbikas ◽

A. Čenys ◽

D. Žemaitytė ◽

G. Varoneckas

Keyword(s):

Nonlinear Dynamics ◽

Experimental Data ◽

Heart Rate ◽

Time Series ◽

Cardiovascular Function ◽

Statistical Properties ◽

Rate Data ◽

Heart Rate Data ◽

Dynamical Nature ◽

Analysis Of Time Series

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.

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Estimation of Dog Emotions by Machine Learning Using Heart Rate Data of Other Dogs

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) ◽

10.1299/jsmermd.2021.2p1-m03 ◽

2021 ◽

Vol 2021 (0) ◽

pp. 2P1-M03

Author(s):

Kotaro SATO ◽

Kazunori OHNO ◽

Ryoichiro TAMURA ◽

Sandeep Kumar NAYAK ◽

Shotaro KOJIMA ◽

...

Keyword(s):

Machine Learning ◽

Heart Rate ◽

Rate Data ◽

Heart Rate Data

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Early Detection of At-Risk Undergraduate Students through Academic Performance Predictors

Higher Education Studies ◽

10.5539/hes.v7n3p42 ◽

2017 ◽

Vol 7 (3) ◽

pp. 42

Author(s):

Vikash Rowtho

Keyword(s):

At Risk ◽

Academic Performance ◽

Early Detection ◽

Undergraduate Students ◽

At Risk Students ◽

Regression Analyses ◽

Linear Discriminant ◽

Student Dropout ◽

Performance Predictors ◽

A New Technique

Undergraduate student dropout is gradually becoming a global problem and the 39 Small Islands Developing States (SIDS) are no exception to this trend. The purpose of this research was to develop a method that can be used for early detection of students who are at-risk of performing poorly in their undergraduate studies. A sample of 279 students participated in the study conducted in a Mauritian private tertiary academic institution. Results of regression analyses identified the variables having a significant influence on academic performance. These variables were used in a linear discriminant analysis where 74 percent of the students could be correctly classified into three categories: at-risk, pass or fail. In conclusion, this study has proposed a new technique that can be used by institutions to determine significant academic performance predictors and then identify at-risk students upon whom interventions can be implemented prior to exams to address the problem of dropouts.

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The Impact of Accelerometer and Heart Rate Data on Hypoglycemia Mitigation in Type 1 Diabetes

Journal of Diabetes Science and Technology ◽

10.1177/1932296813516208 ◽

2014 ◽

Vol 8 (1) ◽

pp. 64-69 ◽

Cited By ~ 30

Author(s):

Matthew Stenerson ◽

Fraser Cameron ◽

Darrell M. Wilson ◽

Breanne Harris ◽

Shelby Payne ◽

...

Keyword(s):

Heart Rate ◽

Type 1 Diabetes ◽

Rate Data ◽

Heart Rate Data ◽

The Impact

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Academic performance of secondary education students in socio-familial risk contexts

Suma Psicológica ◽

10.14349/sumapsi.2021.v28.n2.5 ◽

2021 ◽

Vol 28 (2) ◽

Author(s):

Daniel Rodríguez-Rodríguez ◽

◽

Remedios Guzmán ◽

Keyword(s):

At Risk ◽

Academic Performance ◽

Secondary Education ◽

Self Efficacy ◽

At Risk Students ◽

Familial Risk ◽

Course Evaluation ◽

Self Concept ◽

Cognitive Variables ◽

Not At Risk

Introduction: The relationship that socio-familial and non-cognitive variables have on students in regards to their academic performance is a very important element for success in Secondary Education. In this study the influence of non-cognitive variables (academic self-concept, self-efficacy and perceived family affective support) and socio-familial variables (educational level and expectations of each parent) on the academic performance of secondary school students were analysed. Method: Students were grouped according to their accumulated socio-familial risk index (at-risk students, n = 305; not-at-risk students, n = 991). To measure the variables, the scales What do you think of yourself, General Self-Efficacy and Perceived Family Support were used. Socio-family variables were measured with an ad hoc questionnaire, and academic performance with the end-of-course evaluation scores. Results: The receiver operating characteristic curve showed a decrease in students’ academic performance from three or more accumulated risks. Structural Equation Modelling (SEM) was performed for each group. The results showed that for at-risk students, academic performance was mainly determined by two variables: academic self-concept and self-concept; in contrast to the not-at-risk students in which self-efficacy was the one that had the greatest effect on performance. In both groups, the parents’ expectations were the family variable with the highest incidence being performance, although, for the at-risk group, the effect was greater. Conclusions: The relevance of the identification of non-cognitive and socio-familial variables on the academic performance of at-risk students in regards to secondary education due to socio-familial factors is discussed.

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Effectiveness of Tutoring to Improve Academic Performance in Nursing Students at the University of Seville

Journal of New Approaches in Educational Research ◽

10.7821/naer.2017.7.201 ◽

2017 ◽

Vol 6 (2) ◽

pp. 93-102 ◽

Cited By ~ 5

Author(s):

María Dolores Guerra-Martín ◽

Marta Lima-Serrano ◽

Joaquín Salvador Lima-Rodríguez

Keyword(s):

At Risk ◽

Academic Performance ◽

Nursing Students ◽

At Risk Students ◽

Intervention Group ◽

Academic Failure ◽

Program Satisfaction ◽

Tutoring Program ◽

Information Comprehension ◽

Education Support

In response to the increase of Higher Education support provided to tutoring programs, this paper presents the design, implementation and evaluation of a tutoring program to improve the academic performance of at-risk students enrolled in the last year of a nursing degree characterized by academic failure (failed courses). A controlled experimental study was carried out to evaluate a tutoring program that included a minimum of nine meetings performed by an expert professor as tutor. A questionnaire for assessing the academic needs was designed and interventions were performed when responses were: nothing, a little or something. Medium to large effects were found in the progress of failed course to passed course (p =.000, rφ = .30), improving the information about courses (p < .001, d = 2.01), the information comprehension (p < .001, d = 0.85) and the strategies to improve academic performance (p < .001, d = 1.37). The intervention group students’ response highlighted program satisfaction and effectiveness. The significance of the study lies in reinforcing the formal tutoring as a tool to improve academic performance in at-risk students.

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