Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation

Autor: Carlos Felipe Rodríguez-Hernández, Mariel Musso, Eva Kyndt, Eduardo Cascallar

Publikováno v: Computers and Education: Artificial Intelligence, Vol 2, Iss , Pp 100018- (2021)

The applications of artificial intelligence in education have increased in recent years. However, further conceptual and methodological understanding is needed to advance the systematic implementation of these approaches. The first objective of this

Externí odkaz: https://doaj.org/article/585bffea987044c795c1df571c3581de

Predicting key educational outcomes in academic trajectories: a machine-learning approach

Autor: Eduardo Cascallar, Mariel Musso, Carlos Felipe Rodríguez Hernández

Publikováno v: CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICET

Predicting and understanding different key outcomes in a student's academic trajectory such as grade point average, academic retention, and degree completion would allow targeted intervention programs in higher education. Most of the predictive model

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2fc49fda0a1412d4dfc973c42f3789d0
https://doi.org/10.1007/s10734-020-00520-7

The well-fit for the FET model: Understanding training transfer factors in Spain

Autor: Anna Ciraso-Calí, Mariel Musso, Carla Quesada-Pallarès, Eduardo Cascallar

Publikováno v: Educar (2021)
Dipòsit Digital de Documents de la UAB
Universitat Autònoma de Barcelona

Learning transfer evaluation is a necessary process for practitioners to assess the effectiveness of training, and the outcomes of training produces in workers' behaviors. In this paper, we explore an alternative way to evaluate transfer: through the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a836fe7cf1b65a1a8ac10d410a61fefb
https://educar.uab.cat/article/view/1327

Identifying Reliable Predictors of Educational Outcomes Through Machine-Learning Predictive Modeling

Autor: Mariel Musso, Michael F. Crawford, Eduardo Cascallar, Neda Bostani

Publikováno v: Frontiers in Education, Vol 5 (2020)

Results-based financing has guided the development of policies with measurable results improving learning outcomes at micro/macro levels. However, it is then necessary to identify factors which predict early and accurately favorable or challenging co

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9c6518cc250db08732bd15e98c67ef7
https://www.frontiersin.org/article/10.3389/feduc.2020.00104/full

Validación de la versión en español de la Escala de remoralización

Autor: Gertrudis Wyss, Wiede Vissers, Mariel Musso, Eduardo Cascallar, Elena Diana Scherb

Publikováno v: Anuario de Psicología/The UB Journal of Psychology; Vol. 47, No 1 (2017); 1-8
Anuario de Psicología/The UB Journal of Psychology; Vol. 47 No 1 (2017); 1-8
Anuario de Psicología/The UB Journal of Psychology; Vol. 47 No. 1 (2017); 1-8
Anuario de Psicología/The UB Journal of Psychology; Vol. 47 Núm. 1 (2017); 1-8
RCUB. Revistas Científicas de la Universidad de Barcelona
instname
Anuario de psicología / The UB Journal of psychology; Vol. 47, Núm. 1 (2017); p. 1-8

The Remoralization Scale (RS) was developed in order to measure an individual's state in terms of how the person perceives him or herself in relation to his/her self-concept, self-value, hope, empowerment and positive anticipation. However, there is

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::06136297059a0de5d72b88e8a4bc706f
http://revistes.ub.edu/index.php/Anuario-psicologia/article/view/20754