RYEL: An Experimental Study in the Behavioral Response of Judges Using a Novel Technique for Acquiring Higher-Order Thinking Based on Explainable Artificial Intelligence and Case-Based Reasoning

Oconitrillo, Luis Raúl Rodríguez; Vargas, José A.; Camacho, Arturo; Burgos, Álvaro; Corchado, Juan M.

doi:10.3390/electronics10121500

Cited by 7 publications

(3 citation statements)

References 98 publications

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“…Interpretable and explainable ML has already been investigated within disciplines in which inference, variable importance, and understanding prediction results for single, new observations are of particular importance. Specifically, such methods have been investigated in the fields of healthcare (e.g., [13,[22][23][24][25]), finance (e.g., [26][27][28]), and law (e.g., [26,29,30]). Explainable ML has seen less application and investigation in geospatial science and geoscience [31][32][33].…”

Section: Background 21 Explainable Machine Learningmentioning

confidence: 99%

Explainable Boosting Machines for Slope Failure Spatial Predictive Modeling

Maxwell

Sharma²,

Donaldson³

2021

Remote Sensing

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Machine learning (ML) methods, such as artificial neural networks (ANN), k-nearest neighbors (kNN), random forests (RF), support vector machines (SVM), and boosted decision trees (DTs), may offer stronger predictive performance than more traditional, parametric methods, such as linear regression, multiple linear regression, and logistic regression (LR), for specific mapping and modeling tasks. However, this increased performance is often accompanied by increased model complexity and decreased interpretability, resulting in critiques of their “black box” nature, which highlights the need for algorithms that can offer both strong predictive performance and interpretability. This is especially true when the global model and predictions for specific data points need to be explainable in order for the model to be of use. Explainable boosting machines (EBM), an augmentation and refinement of generalize additive models (GAMs), has been proposed as an empirical modeling method that offers both interpretable results and strong predictive performance. The trained model can be graphically summarized as a set of functions relating each predictor variable to the dependent variable along with heat maps representing interactions between selected pairs of predictor variables. In this study, we assess EBMs for predicting the likelihood or probability of slope failure occurrence based on digital terrain characteristics in four separate Major Land Resource Areas (MLRAs) in the state of West Virginia, USA and compare the results to those obtained with LR, kNN, RF, and SVM. EBM provided predictive accuracies comparable to RF and SVM and better than LR and kNN. The generated functions and visualizations for each predictor variable and included interactions between pairs of predictor variables, estimation of variable importance based on average mean absolute scores, and provided scores for each predictor variable for new predictions add interpretability, but additional work is needed to quantify how these outputs may be impacted by variable correlation, inclusion of interaction terms, and large feature spaces. Further exploration of EBM is merited for geohazard mapping and modeling in particular and spatial predictive mapping and modeling in general, especially when the value or use of the resulting predictions would be greatly enhanced by improved interpretability globally and availability of prediction explanations at each cell or aggregating unit within the mapped or modeled extent.

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Section: Background 21 Explainable Machine Learningmentioning

confidence: 99%

Explainable Boosting Machines for Slope Failure Spatial Predictive Modeling

Maxwell

Sharma²,

Donaldson³

2021

Remote Sensing

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“…This is in accordance with the results of previous studies where programming is close to higher order thinking skills, including critical thinking skills. In addition, learning by programming provides many benefits, including acquiring computer knowledge, problem-solving skills, and skills (Barr, 2011;Choi, 2013;Rodriguez, 2021;Rouhani & Jørgensen, 2022;Tasneem, 2012;). The other side shows that scratch supports wave learning by providing a variety of shape representations.…”

Section: Improving Student Thinking Skillsmentioning

confidence: 99%

Scratch-assisted waves teaching materials: ICT literacy and students' critical thinking skills

Negoro¹,

Rusilowati²,

Aji³

2024

tused

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The rapid development of technology requires education to provide learning that can improve students' critical thinking and ICT (information and communication technology) skills. One of the lessons that can be applied is ICT-based. The material is packed with state-of-the-art technology and computer programming. For this reason, a study was carried out which had the objectives of (1) developing Scratch-assisted Wave teaching materials, (2) testing their validity, (3) testing the practicality of using teaching materials, and (4) testing their effectiveness in improving students' ICT literacy and critical thinking skills. This research is an R&D that has 4D procedures, namely define, design, develop, and disseminate. The product trial used the pretest-posttest control group design. The research instruments consist of the gap test, ICT literacy test and critical thinking skills test. The data analysis technique used in the study are percentage and t-tests. The product in this research is in the form of ICT-based wave teaching materials. Programming applications in teaching materials are in the form of Scratch with a contextual learning approach, namely physical symptoms and phenomena that are closely related to life. The average percentage validity of teaching materials in terms of content is very decent category. The level of readability of teaching materials through gap tests and questionnaires is very easy-to-understand categories. The effectiveness of teaching materials is seen from the increase in ICT literacy and critical thinking skills of high school students who use Scratch-assisted Wave teaching materials are higher than those using ordinary teaching materials.

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“…La página web permite a los usuarios enviar los datos de entrada requeridos por el modelo predictivo y recibir la predicción de la sentencia en respuesta. Se utilizó HTML, CSS y JavaScript para desarrollar la página web y garantizar una experiencia de usuario intuitiva y agradable [4][5][6] [7].…”

Section: Fig 2 Arquitectura Del Juez Robotunclassified