EEG Based Learner’s Learning Style and Preference Prediction for E-learning

Deenadayalan, D.; Kangaiammal, A.; Poornima, B. K.

doi:10.1109/i-smac.2018.8653693

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…(5) Frog with global best fitness is identified as Xg. (6) The frog with worst fitness is improved according to the following Eqs. ( 5) and (6).…”

Section: Shuffled Frog Leaping Algorithm (Sfla)mentioning

confidence: 99%

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User Feature Similarity Supported Collaborative Filtering for Page Recommendation Using Hybrid Shuffled Frog Leaping Algorithm

2023

IJIES

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The rapid growth of e-commerce has caused product overload where customers on the Web are no longer able to effectively choose the products they are exposed to. To overcome the product overload of online shoppers, a variety of recommendation methods have been developed. Collaborative Filtering (CF) is the most successful recommendation method, but its widespread use has exposed some well-known limitations, such as sparsity and scalability, which can lead to poor recommendations. This work proposes a recommendation methodology based on Web Usage Mining (WUM), and machine learning methods to enhance the recommendation quality and the system performance of current CF-based recommender systems. WUM populates the rating database by tracking learners' behaviors on the Web, thereby leading to better quality recommendations. The data is collected from user profile and their preferences and also the weblink and usage. Based on the CF and WUM data, the recommendations are provided. The Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers is used to improve the performance of searching for nearest neighbors through Shuffled Frog Leaping Algorithm (SFLA). Experimental results show that the proposed model is effective and can enhance the performance of recommendation. Results show that the proposed SFLA-XGBoost has higher average sensitivity: rating <=15 for CF by 11.49% for RF, by 7.52% for XGBoost and by 4.58% for proposed SFLA-RF respectively.

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“…(5) Frog with global best fitness is identified as Xg. (6) The frog with worst fitness is improved according to the following Eqs. ( 5) and (6).…”

Section: Shuffled Frog Leaping Algorithm (Sfla)mentioning

confidence: 99%

“…Web Usage Mining (WUM) [6] is the process of extracting knowledge from Web user's access data by exploiting data mining technologies. It can be used for different purposes such as personalization, recommendation system improvement and site etc.…”

Section: Introductionmentioning

confidence: 99%

User Feature Similarity Supported Collaborative Filtering for Page Recommendation Using Hybrid Shuffled Frog Leaping Algorithm

2023

IJIES

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“…Moreover, BCI can offer information through several motor controls and complex cognitive features for measuring people's memory [80], attention [81], concentration [82], cognitive skills [83], and learning style [84]. Measuring attention, cognitive skills, emotion, and other factors that influence student learning provide many advantages for monitoring the student's performance in academic subjects.…”

Section: Cognitive and Affective Brain-computer Interfaces In Educationmentioning

confidence: 99%

Cognitive and Affective Brain–Computer Interfaces for Improving Learning Strategies and Enhancing Student Capabilities: A Systematic Literature Review

Jamil

Belkacem²,

Ouhbi

et al. 2021

IEEE Access

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Brain-computer interface (BCI) technology has the potential to positively contribute to the educational learning environment, which faces many challenges and shortcomings. Cognitive and affective BCIs can offer a deep understanding of brain mechanisms, which may improve learning strategies and increase brain-based skills. They can offer a better empirical foundation for teaching-learning methodologies, including adjusting learning content based on brain workload, measuring student interest of a topic, or even helping students focus on specific tasks. The latest findings from emerging BCI technology, neuroscience, cognitive sciences, and psychology could be used in learning and teaching strategies to improve student abilities in education. This study investigates and analyzes the research on BCI patterns and its implementation for enhancing cognitive capabilities of students. The results showed that there is insufficient literature on BCI that addresses students with disabilities in the learning process. Further, our analysis revealed a bias toward the significance of cognitive process factors compared with other influential factors, such as the learning environment and emotions that influence learning. Finally, we concluded that BCI technology could improve students' learning and cognitive skills-when consistently associated with the different pedagogical teaching-learning strategies-for better academic achievement.INDEX TERMS 21st century abilities, Applications in subject areas, Brain computer interface, Improving classroom teaching, Neurofeedback, Teaching/learning strategies.

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“…More precisely, when using EEG technology in some innovative ways, it could capture brain signals and process them to determine learners' learning and memory during learning [49]. Studies identified that the EEG data was used successfully in detecting the learners learning style and learning preferences and the correlation between them [50].…”

mentioning

confidence: 99%

The EEG-Based Attention Analysis in Multimedia m-Learning

Wang

Liu

2020

Computational and Mathematical Methods in Medicine

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In recent years, research on brain-computer interfaces has been increasing in the field of education, and mobile learning has become a very important way of learning. In this study, EEG experiment of a group of iPad-based mobile learners was conducted through algorithm optimization on the TGAM chip. Under the three learning media (text, text + graphic, and video), the researchers analyzed the difference in learners’ attention. The study found no significant difference in attention in different media, but learners using text media had the highest attention value. Later, the researchers studied the attention of learners with different learning styles and found that active and reflective learners’ attention exhibited significant differences when using video media to learn.

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EEG Based Learner’s Learning Style and Preference Prediction for E-learning

Cited by 4 publications

References 16 publications

User Feature Similarity Supported Collaborative Filtering for Page Recommendation Using Hybrid Shuffled Frog Leaping Algorithm

User Feature Similarity Supported Collaborative Filtering for Page Recommendation Using Hybrid Shuffled Frog Leaping Algorithm

Cognitive and Affective Brain–Computer Interfaces for Improving Learning Strategies and Enhancing Student Capabilities: A Systematic Literature Review

The EEG-Based Attention Analysis in Multimedia m-Learning

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