2018
Olivares-Rodriguez, Cristian; Guenaga, Mariluz; Garaizar, Pablo
Using children’s search patterns to predict the quality of their creative problem solving Journal Article
In: Aslib Journal of Information Management, 2018.
Abstract | Links | BibTeX | Tags: Children’s search patterns, Creative thinking, Educational data mining, Elementary education, Information search behaviour, query pattern
@article{Olivares-Rodriguez2018b,
title = {Using children’s search patterns to predict the quality of their creative problem solving},
author = {Cristian Olivares-Rodriguez and Mariluz Guenaga and Pablo Garaizar},
url = {https://doi.org/10.1108/AJIM-05-2018-0103},
doi = {10.1108/AJIM-05-2018-0103},
year = {2018},
date = {2018-08-14},
journal = {Aslib Journal of Information Management},
abstract = {Purpose
The purpose of this paper is to propose a computational model that implicitly predict the children’s creative quality of solutions by analyzing the query pattern on a problem-solving-based lesson.
Design/methodology/approach
A search task related to the competencies acquired in the classroom was applied to automatically measure children’ creativity. A blind review process of the creative quality was developed of 255 primary school students’ solutions.
Findings
While there are many creativity training programs that have proven effective, many of these programs require measuring creativity previously which involves time-consuming tasks conducted by experienced reviewers, i.e. far from primary school classroom dynamics. The authors have developed a model that predicts the creative quality of the given solution using the search queries pattern as input. This model has been used to predict the creative quality of 255 primary school students’ solutions with 80 percent sensitivity.
Research limitations/implications
Although the research was conducted with just one search task, participants come from two different countries. Therefore, the authors hope that this model provides detection of non-creative solutions to enable prompt intervention and improve the creative quality of solutions.
Originality/value
This is the first implicit classification model of query pattern in order to predict the children’ creative quality of solutions. This model is based on a conceptual relation between the concept association of creative thinking and query chain model of information search.},
keywords = {Children’s search patterns, Creative thinking, Educational data mining, Elementary education, Information search behaviour, query pattern},
pubstate = {published},
tppubtype = {article}
}
The purpose of this paper is to propose a computational model that implicitly predict the children’s creative quality of solutions by analyzing the query pattern on a problem-solving-based lesson.
Design/methodology/approach
A search task related to the competencies acquired in the classroom was applied to automatically measure children’ creativity. A blind review process of the creative quality was developed of 255 primary school students’ solutions.
Findings
While there are many creativity training programs that have proven effective, many of these programs require measuring creativity previously which involves time-consuming tasks conducted by experienced reviewers, i.e. far from primary school classroom dynamics. The authors have developed a model that predicts the creative quality of the given solution using the search queries pattern as input. This model has been used to predict the creative quality of 255 primary school students’ solutions with 80 percent sensitivity.
Research limitations/implications
Although the research was conducted with just one search task, participants come from two different countries. Therefore, the authors hope that this model provides detection of non-creative solutions to enable prompt intervention and improve the creative quality of solutions.
Originality/value
This is the first implicit classification model of query pattern in order to predict the children’ creative quality of solutions. This model is based on a conceptual relation between the concept association of creative thinking and query chain model of information search.
2017
Olivares-Rodriguez, Cristian; Guenaga, Mariluz; Garaizar, Pablo
Automatic Assessment of Creativity in Heuristic Problem-solving Based on Query Diversity Journal Article
In: DYNA, vol. 92, no. 4, pp. 449-455, 2017.
Abstract | Links | BibTeX | Tags: aprendizaje automático, Búsqueda de información, information search, machine learning, patrón de consultas, problem solving, query pattern, resolución de problemas
@article{Olivares-Rodriguez2017,
title = {Automatic Assessment of Creativity in Heuristic Problem-solving Based on Query Diversity},
author = {Cristian Olivares-Rodriguez and Mariluz Guenaga and Pablo Garaizar},
url = {http://home/learninglabdeust/public_html.revistadyna.com/Articulos/Ficha.aspx?IdMenu=a5c9d895-28e0-4f92-b0c2-c0f86f2a940b&Cod=8243&Codigo=b25ba3bd-bef9-4391-8317-6a8209d9309e},
doi = {10.6036/8243},
year = {2017},
date = {2017-07-03},
journal = {DYNA},
volume = {92},
number = {4},
pages = {449-455},
abstract = {Creative problem-solving emerges as one of the most relevant skill of the 21st century knowledge society. Fortunately, there are many creativity training programmes that have proven effective. However, most of these programmes require a previous measurement of creativity, which involves time-consuming tasks conducted by experienced reviewers, i.e. far from primary school classroom dynamics. In this study, we propose a model to predict the creative quality of students’ solutions based on the analysis of query patterns and the use of Wikipedia. This model has been able to predict the creative quality of solutions produced by 226 school students, aged 10 to 12 years old, reaching a sensitivity of 78.43%. The agreement among reviewers regarding students’ creative characteristics has also been evaluated using two rubrics. We hope this model can be used to foster prompt detection of non-creative solutions in order to enable intervention and improve the final result in terms of creativity.},
keywords = {aprendizaje automático, Búsqueda de información, information search, machine learning, patrón de consultas, problem solving, query pattern, resolución de problemas},
pubstate = {published},
tppubtype = {article}
}