TY - JOUR
T1 - Fractional Online Learning Rate
T2 - Influence of Psychological Factors on Learning Acquisition
AU - Ramirez-Arellano, Aldo
AU - Almira, José María Sigarreta
AU - Reyes, Juan Bory
N1 - Publisher Copyright:
© 2022. Society for Chaos Theory in Psychology & Life Sciences
PY - 2022
Y1 - 2022
N2 - The quantification of learning acquisition in a blended and online course is still slightly explored from the complex systems lens. The fractional online learning rate (fOLR) using fractional integrals is introduced. The notion of fOLR is based on the nonlinearity of the individual students learning pathway network, built from Learning Management System log files. Several learning pathway networks from students that pass or fail the course were constructed. The Akaike information criterion shows that the minimum number of boxes to cover these networks follow a power-law model. Further analysis shows that the fOLR model and its parameters were significantly compared with the online learning rate model. Thus, the fOLR was computing power and delayed power models, inspired by the "law of practice." The results show that the fractional definition is a better model and has a nonlinear relationship with the overall grade. Also, engagement and disengagement mould the fOLR curve. It means that the student's performance is affected by the engagement, and it is necessary that they are encouraged to pay more effort and attention to the learning activities, and those activities need to be designed to be fun and pleasant to improve the learning achievements.
AB - The quantification of learning acquisition in a blended and online course is still slightly explored from the complex systems lens. The fractional online learning rate (fOLR) using fractional integrals is introduced. The notion of fOLR is based on the nonlinearity of the individual students learning pathway network, built from Learning Management System log files. Several learning pathway networks from students that pass or fail the course were constructed. The Akaike information criterion shows that the minimum number of boxes to cover these networks follow a power-law model. Further analysis shows that the fOLR model and its parameters were significantly compared with the online learning rate model. Thus, the fOLR was computing power and delayed power models, inspired by the "law of practice." The results show that the fractional definition is a better model and has a nonlinear relationship with the overall grade. Also, engagement and disengagement mould the fOLR curve. It means that the student's performance is affected by the engagement, and it is necessary that they are encouraged to pay more effort and attention to the learning activities, and those activities need to be designed to be fun and pleasant to improve the learning achievements.
KW - Elearning
KW - Engagement
KW - Fractal network
KW - Higher education
UR - http://www.scopus.com/inward/record.url?scp=85133778501&partnerID=8YFLogxK
M3 - Artículo
C2 - 35816135
AN - SCOPUS:85133778501
SN - 1090-0578
VL - 26
SP - 289
EP - 313
JO - Nonlinear Dynamics, Psychology, and Life Sciences
JF - Nonlinear Dynamics, Psychology, and Life Sciences
IS - 3
ER -