Teaching Control Theory: A Selection of Methodology Based on Learning Styles

Rojas-Palacio, Cinthia Viviana; Arango-Zuluaga, Eliana I.; Botero-Castro, Héctor

doi:10.15446/dyna.v89n222.100547

Cited by 2 publications

(2 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent study delves into teaching control theory by selecting methodologies based on various well recognized learning styles, including the Kolb, Felder and Silverman, and VARK schemes (Rojas-Palacio et al, 2022). The plethora of available schemes for selection is precisely indicative of the absence of a unified definition.…”

Section: Introductionmentioning

confidence: 99%

Manual Label and Machine Learning in Clustering and Predicting Student Performance

Yin,

Cao,

et al. 2024

International Journal of Web-Based Learning and Teaching Technologies

View full text Add to dashboard Cite

This study presents the Academic Investment Model (AIM) as a novel approach to predicting student academic performance by incorporating learning styles as a predictive feature. Utilizing data from 138 Marketing students across China, the research employs a combination of machine learning clustering methods and manual feature engineering through a four-quadrant clustering technique. The AIM model delineates student investment into four quadrants based on their time and energy commitment to academic pursuits, distinguishing between result-oriented and process-oriented investments. The findings reveal that the four-quadrant method surpasses machine learning clustering in predictive accuracy, highlighting the robustness of manual feature engineering. The study's significance lies in its potential to guide educators in designing targeted interventions and personalized learning strategies, emphasizing the importance of process-oriented assessment in education. Future research is recommended to expand the sample size and explore the integration of deep learning models for validation.

show abstract

Section: Introductionmentioning

confidence: 99%

Manual Label and Machine Learning in Clustering and Predicting Student Performance

Yin,

Cao,

et al. 2024

International Journal of Web-Based Learning and Teaching Technologies

View full text Add to dashboard Cite

show abstract

“…The students have relative preferences along the four perceptual modes but can learn to function in the other modes. It focuses on classifying how the students receive and gather the information [23]. It helps the students identify their learning styles and understand their personalities [24].…”

Section: Introductionmentioning

confidence: 99%

Effective Blended Learning Model Selection Based on Student Learning Style using Analytic Hierarchy Process for an Undergraduate Engineering Course

Maidin,

Shahrum,

Qian

et al. 2023

IJE

View full text Add to dashboard Cite

Blended learning is a flexible method conducted through face-to-face and online learning. It requires students to learn by attempting the classes physically and allows them to learn virtually at different times and places. It has become more evident and common after the Movement Control Order (MCO) as most of the lectures at the university are carried out in hybrid mode. The blended learning models create problems and opportunities for students as they need to explore and adapt to different lecturers' different blended learning methods in terms of teaching styles, planning, and timing. Therefore, the objective of this research is to investigate the best-blended learning models for an undergraduate engineering course based on student learning style by using the Analytic Hierarchy Process (AHP) method, as it is a big challenge to select the most effective approach for universities to educate, tutor and bring out quality students according to their learning styles. The AHP method is used to aid the students in finding the best-blended learning model based on their learning style. AHP analysis is then conducted to validate and verify its accuracy by comparing it with Visual, Auditory, Read/write, and Kinesthetic (VARK) models. As a result, most students are kinaesthetic learners (72%) based on VARK results, and the faceto-face driver model is the most preferred blended learning model with the priority vector at 31.33% through the AHP analysis. The accuracy of the AHP result is 74% by comparing it with the VARK result. In summary, the data can be deployed in the UTeM blended learning system to improve the course design and student learning experience.

show abstract

Teaching Control Theory: A Selection of Methodology Based on Learning Styles

Cited by 2 publications

References 50 publications

Manual Label and Machine Learning in Clustering and Predicting Student Performance

Manual Label and Machine Learning in Clustering and Predicting Student Performance

Effective Blended Learning Model Selection Based on Student Learning Style using Analytic Hierarchy Process for an Undergraduate Engineering Course

Contact Info

Product

Resources

About