AN ADVANCED EEG-BASED HYBRID DEEP LEARNING BRAIN-COMPUTER INTERFACE MODEL FOR ENHANCED KNOWLEDGE TRACING

Abstract

This invention provides a hybrid deep learning BCI model for real-time e-learning assessment, utilizing EEG data to monitor student engagement and learning outcomes. By processing EEG signals through CNN and LSTM networks, the system provides tailored feedback and adaptive learning paths, enhancing knowledge tracing in online education.

Specification

Description:FIELD OF THE INVENTION
This invention relates to educational technology and neuroscience, focusing on a brain-computer interface (BCI) model for knowledge tracing in e-learning. The invention integrates electroencephalogram (EEG) data with a hybrid deep learning model to assess student engagement and understanding in real-time, enabling personalized feedback and progress tracking.
BACKGROUND OF THE INVENTION
In online education, platforms often lack mechanisms for personalized monitoring and validation of student learning progress. Standard e-learning courses provide a uniform approach without adaptive capabilities, failing to account for individual learning rates and comprehension levels. This limitation hampers effective knowledge tracing, leaving students without the tailored guidance that is essential for optimized learning. This invention addresses these issues by using real-time EEG data to monitor students' cognitive responses during Massive Open Online Courses (MOOCs). The EEG signals, processed through a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model, provide insights into engagement, attention, and knowledge retention. This novel application of deep learning in e-learning personalization offers a scalable, learner-centered solution, facilitating improved knowledge tracing and adaptive content delivery.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The invention provides a hybrid deep learning model that integrates EEG data collected from students using BCI headsets during online classes and quizzes. The model processes EEG signals to monitor cognitive states and assess learning outcomes, categorizing students into performance levels (e.g., Grade A for excellent, Grade D for needs improvement). By utilizing EEG metrics like attention, sensory integration, and problem-solving, the model provides actionable feedback to learners, enabling a more tailored educational experience. The system's architecture includes an EEG headset for data collection, a deep learning model for real-time signal processing, and a dashboard for progress visualization.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: ILLUSTRATES THE FRAMEWORK FOR EEG DATA COLLECTION AND PROCESSING WITHIN THE E-LEARNING PLATFORM, HIGHLIGHTING CANDIDATE ENROLLMENT AND HEADSET PLACEMENT.
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a"," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", "third", and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
This Advanced EEG-Based Hybrid Deep Learning Brain-Computer Interface Model for Enhanced Knowledge Tracing provides a real-time e-learning assessment framework that leverages EEG data to evaluate learning engagement and performance. The process begins with candidates enrolling in an E-learning BCI-Portal (EBP), where they select topics of interest. Participants wear neuro-headsets during online sessions, which capture EEG signals reflecting their cognitive states. These signals undergo pre-processing, including Fast Fourier Transform (FFT), to prepare the data for analysis.
The deep learning model, comprising CNN and LSTM networks, classifies EEG data by analyzing cognitive metrics like focus, cognitive load, and problem-solving abilities. The hybrid model outperforms traditional methods, offering high accuracy in learning classification and enabling knowledge tracing at multiple levels of engagement. Learners' cognitive responses during quizzes are also analyzed, allowing the system to compare in-class engagement with quiz performance to measure learning effectiveness.
The results are displayed on a dashboard accessible to both students and educators. The system provides personalized recommendations based on EEG classifications, guiding learners on areas needing improvement. Additionally, the model's scalability ensures compatibility with various EEG headsets, supporting different learning contexts and content formats. By offering real-time feedback and adaptive learning paths, this invention enhances the online educational experience, bridging gaps in conventional knowledge tracing systems.
, C , Claims:1. An advanced EEG-based hybrid deep learning brain-computer interface model for e-learning, integrating EEG data from students with CNN and LSTM networks to assess cognitive states and monitor learning outcomes.
2. The model as claimed in Claim 1, wherein EEG signals are collected in real-time during online classes and quizzes, providing insights into engagement, attention, and learning comprehension.
3. The model as claimed in Claim 1, wherein CNN and LSTM networks are used to classify EEG data, enabling the system to categorize learning performance levels and generate personalized feedback.
4. The model as claimed in Claim 1, further comprising a dashboard that displays learner progress, performance levels, and tailored recommendations based on EEG classifications.
5. The model as claimed in Claim 1, wherein EEG pre-processing includes Fast Fourier Transform (FFT), optimizing data for accurate deep learning classification.
6. A method of monitoring student engagement and learning outcomes as claimed in Claim 1, involving real-time EEG data acquisition, deep learning classification, and adaptive feedback generation.
7. The model as claimed in Claim 1, designed to support various EEG headsets, allowing application across multiple educational platforms and content formats.

8. The model as claimed in Claim 1, facilitating enhanced e-learning personalization by integrating EEG cognitive metrics and adaptive recommendations into knowledge tracing.

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