Predicting students’ satisfaction using a decision tree

Skrbinjek, Vesna; Dermol, Valerij

doi:10.1007/s11233-018-09018-5

Cited by 23 publications

(6 citation statements)

References 21 publications

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“…Decision trees are widely used in several sectors. Skrbinjek and Dermol [69] applied decision trees in education to determine the relationship between student satisfaction and their performance in an e-classroom. They proposed five student satisfaction factors: student satisfaction (SATISFACT), grade (GRADE), number of students' attempts to pass the examination (EXAPPROACH), average student responses, and active engagement (views and posts) (EINVOLV), and student workload (WORKLOAD).…”

Section: Decision Treementioning

confidence: 99%

Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics

2020

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The purpose of this research was to create a Matching Consignees/Shippers Recommendation System (MCSRS). We used the association rule to identify product associations, the clustering technique to group shippers and consignees according to behaviors when receiving goods from similar shipper groups, and the decision tree to identify possible matches between shippers and consignees. Finally, Monte Carlo simulation was used to estimate potential revenue. The case study is a courier company in Thailand. The results showed that garment products and clothes were the products with the highest association. Shippers and consignees of these products were segmented according to recency, frequency, monetary factors, number of customers, number of product items, weight, and day. Three rules are proposed that enabled the assignment of 8 consignees to 56 shippers with an estimated increase in revenue by 36%. This approach helps decision-makers to develop an effective cost-saving new marketing, inclusive strategy quickly.

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Section: Decision Treementioning

confidence: 99%

Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics

2020

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“…Del mismo modo, los algoritmos Machine Learning (regresión lineal y árbol de decisión) facilitaron la predicción del rendimiento académico a través de los sistemas de gestión de aprendizaje (KOYUNCU; KILIC y GOKSUN, 2022). De hecho, el algoritmo árbol de decisión permite encontrar las relaciones y condiciones entre los fenómenos de estudio con el propósito de predecir los eventos (ALENEZI y FAISAL, 2020;CHADAGA et al, 2021;SKRBINJEK y DERMOL, 2019).…”

Section: Introductionunclassified

Uso de los algoritmos Machine Learning para analizar Moodle y los teléfonos inteligentes en el proceso educativo de la Física

2023

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Resumen El objetivo de este estudio mixto es analizar las percepciones de los alumnos sobre el uso de Moodle y los teléfonos inteligentes en el proceso educativo de la Física a través de la Ciencia de Datos. Los algoritmos Machine Learning utilizados son regresión lineal, árbol de decisión y deep learning. En este estudio, la incorporación de Moodle facilitó la entrega de tareas, la consulta de los contenidos, la comunicación y la revisión de los recursos multimedia. Incluso, los teléfonos inteligentes permitieron el acceso a las plataformas virtuales de aprendizaje, el uso de las aplicaciones móviles y la comunicación desde cualquier lugar. Los resultados de los algoritmos regresión lineal y deep learning indican que el uso de Moodle y los teléfonos inteligentes influye positivamente la motivación de los alumnos, la asimilación del conocimiento y la satisfacción en el curso Física. Por otro lado, el algoritmo árbol de decisión determina 6 modelos predictivos. Las limitaciones son las técnicas de Machine Learning utilizadas y el análisis de las herramientas tecnológicas para la asimilación del conocimiento, la motivación y la satisfacción. Los futuros estudios pueden analizar el uso de Moodle y los teléfonos inteligentes para el rol activo y el desarrollo de las habilidades en diversas preparatorias y universidades. Asimismo, los algoritmos Machine Learning sobre los bosques aleatorios y la regresión logística pueden ser empleados para analizar el impacto de estas herramientas tecnológicas considerando el rendimiento académico. Por último, la incorporación de Moodle y los teléfonos inteligentes permite actualizar los cursos y diseñar creativas actividades a distancia.

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“…In terms of assessment tools, research has focused on environmental attitude scales and observational methods, while research on the use of decision trees to predict pro-environmental behaviour has been rare. Decision-tree models, one of the data-mining algorithms in machine learning, have high predictive accuracy and the ability to decompose a complex decision process into a series of simpler decisions, thus providing a more easily interpretable solution [ 10 , 11 , 12 ]. Researchers have used decision-tree models to accurately predict the factors influencing the success and failure of innovation in the Korean manufacturing industry [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Predictive Analysis of the Pro-Environmental Behaviour of College Students Using a Decision-Tree Model

Wang

Kou

Sun

et al. 2022

IJERPH

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The emergence of the COVID-19 pandemic has hindered the achievement of the global Sustainable Development Goals (SDGs). Pro-environmental behaviour contributes to the achievement of the SDGs, and UNESCO considers college students as major contributors. There is a scarcity of research on college student pro-environmental behaviour and even less on the use of decision trees to predict pro-environmental behaviour. Therefore, this study aims to investigate the validity of applying a modified C5.0 decision-tree model to predict college student pro-environmental behaviour and to determine which variables can be used as predictors of such behaviour. To address these questions, 334 university students in Guangdong Province, China, completed a questionnaire that consisted of seven parts: the Perceived Behavioural Control Scale, the Social Identity Scale, the Innovative Behaviour Scale, the Sense of Place Scale, the Subjective Norms Scale, the Environmental Activism Scale, and the willingness to behave in an environmentally responsible manner scale. A modified C5.0 decision-tree model was also used to make predictions. The results showed that the main predictor variables for pro-environmental behaviour were willingness to behave in an environmentally responsible manner, innovative behaviour, and perceived behavioural control. The importance of willingness to behave in an environmentally responsible manner was 0.1562, the importance of innovative behaviour was 0.1404, and the perceived behavioural control was 0.1322. Secondly, there are 63.88% of those with high pro-environmental behaviour. Therefore, we conclude that the decision tree model is valid in predicting the pro-environmental behaviour of college student. The predictor variables for pro-environmental behaviour were, in order of importance: Willingness to behave in an environmentally responsible manner, Environmental Activism, Subjective Norms, Sense of Place, Innovative Behaviour, Social Identity, and Perceived Behavioural Control. This study establishes a link between machine learning and pro-environmental behaviour and broadens understanding of pro-environmental behaviour. It provides a research support with improving people’s sustainable development philosophy and behaviour.

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Predicting students’ satisfaction using a decision tree

Cited by 23 publications

References 21 publications

Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics

Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics

Uso de los algoritmos Machine Learning para analizar Moodle y los teléfonos inteligentes en el proceso educativo de la Física

Predictive Analysis of the Pro-Environmental Behaviour of College Students Using a Decision-Tree Model

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