Description:MACHINE LEARNING APPROACHES FOR ANALYZING STUDENTS AND TEACHERS’ PERSPECTIVES IN HIGHER EDUCATION MATHEMATICS

Technical Field
[0001] The embodiments herein generally relate to a method for machine learning approaches for analyzing students and teachers’ perspectives in higher education mathematics.
Description of the Related Art
[0002] The present invention relates to the interdisciplinary fields of educational data mining, learning analytics, and artificial intelligence, with particular emphasis on the application of machine learning techniques to analyze students’ learning experiences and teachers’ instructional practices in higher education mathematics, wherein diverse data sources such as structured surveys, free-text feedback, performance records, and classroom interactions are processed using natural language processing, clustering, predictive modeling, and sentiment analysis to extract meaningful insights, which are further utilized by intelligent recommendation systems to provide personalized learning pathways, adaptive teaching strategies, and real-time analytics, thereby offering an automated, scalable, and intelligent framework to improve pedagogy and enhance the relationship between teaching methodologies and student learning outcomes.
[0003] The present invention fulfills this need by introducing a comprehensive machine learning framework that employs natural language processing, clustering techniques, predictive modeling, and sentiment analysis to analyze educational feedback and interaction data. By bridging the gap between student experiences and teacher practices, the invention provides an intelligent, scalable, and automated solution designed to enhance mathematics education at the higher education level.
[0004] Mathematics is widely regarded as a cornerstone of higher education, providing the foundation for disciplines such as engineering, computer science, economics, and data science. Despite its critical role, the teaching and learning of mathematics at the tertiary level face enduring challenges. Students often struggle with comprehending abstract concepts, linking theoretical knowledge to real-world applications, and adapting to diverse instructional methods. At the same time, teachers encounter difficulties in addressing varied learning styles, managing large groups of learners, and ensuring their teaching practices are equally effective across different student populations.
[0005] Conventional approaches to evaluating teaching effectiveness and student learning experiences—such as end-of-semester surveys, feedback forms, and examination performance—have been widely used but remain inadequate. These methods are often narrow in scope, subjective in nature, and limited to static results, offering little opportunity for real-time feedback or predictive insights that could guide continuous pedagogical improvement.
[0006] The proliferation of digital learning platforms, learning management systems, and online educational resources has led to the generation of vast amounts of educational data. This data includes structured formats (grades, attendance, test scores), unstructured content (free-text feedback, discussion posts, digital assignments), and behavioral information (interaction logs, study patterns, and time spent on tasks). However, most existing analytical systems are fragmented, underutilized, or overly simplistic, lacking the ability to integrate these heterogeneous sources and deliver actionable intelligence to educational stakeholders.
[0007] Recent advances in artificial intelligence (AI) and machine learning (ML) offer promising solutions to these shortcomings. AI-driven educational data mining has shown the potential to reveal hidden patterns, detect knowledge gaps, and predict student outcomes. Yet, existing research has generally focused on either student-centered analytics (such as dropout prediction or performance forecasting) or teacher-centered analytics (such as instructional quality assessment), with minimal emphasis on integrating the perspectives of both groups. This lack of holistic analysis creates a critical gap in understanding the complex interplay between teaching methods and student learning outcomes, particularly in higher education mathematics, where cognitive complexity and conceptual depth are substantial.
[0008] Therefore, there exists a strong need for a machine learning–based system capable of systematically capturing, analyzing, and interpreting both student and teacher perspectives, while integrating multiple data types and employing advanced analytical techniques. Such a system must move beyond descriptive feedback to deliver predictive insights and intelligent recommendations, enabling adaptive teaching approaches, personalized learning pathways, and data-informed decisions for curriculum and policy development.
SUMMARY
[0009] In view of the foregoing, an embodiment herein provides a method for machine learning approaches for analyzing students and teachers’ perspectives in higher education mathematics. In some embodiments, wherein the present invention provides a comprehensive machine learning–based framework for analyzing, interpreting, and evaluating the perspectives of both students and teachers in higher education mathematics. Unlike conventional assessment methods such as surveys, feedback forms, and exam scores—which are often limited, subjective, and retrospective, this invention leverages educational data mining, natural language processing (NLP), predictive modeling, clustering techniques, and sentiment analysis to provide actionable, data-driven insights.
[0010] The system is designed to process diverse educational data types, including: Structured data: grades, attendance, test scores, and course completion metrics.
[0011] Unstructured data: open-ended feedback, discussion forum posts, essay responses, and teacher reflections.
[0012] Behavioral data: online learning interaction logs, time spent on learning platforms, clickstream data, and engagement patterns.
[0013] Through a multi-stage analytical pipeline, the invention performs the following key functions: Data Acquisition and Integration – Aggregates data from multiple sources, including learning management systems (LMS), digital assessments, classroom observation tools, and institutional databases, creating a unified dataset for analysis.
[0014] In some embodiments, wherein the Data Preprocessing and Feature Engineering – Cleans, standardizes, and transforms raw data, extracting meaningful features such as engagement indices, conceptual mastery levels, sentiment scores, and teaching effectiveness metrics. NLP techniques are applied to textual feedback to detect sentiment, key themes, and qualitative perceptions.
[0015] Machine Learning–Driven Analysis – Applies supervised learning to predict student performance and identify at-risk learners, unsupervised learning to detect patterns, clusters, and correlations between student learning styles and teaching strategies, and deep learning/NLP models to understand semantic nuances in feedback and teacher reports.
[0016] Predictive Insights and Recommendations – Generates actionable outcomes such as identification of learning gaps, personalized learning pathways, adaptive teaching strategies, and policy-oriented recommendations for curriculum planning and institutional decision-making.
[0017] Visualization and Dashboard Interface – Provides intuitive dashboards for multiple stakeholders: Students receive personalized recommendations, progress tracking, and resources tailored to their learning needs. Teachers access analytics to improve instructional methods and address student engagement challenges. Institutions gain aggregated insights to optimize curriculum design, allocate resources efficiently, and monitor overall academic outcomes.
[0018] Key Advantages of the Invention: Dual-Perspective Analysis– Simultaneously considers student and teacher perspectives, enabling a holistic understanding of teaching-learning dynamics. Scalable and Multi-Source Integration– Processes large volumes of structured, unstructured, and behavioral data in real time, providing comprehensive evaluation beyond traditional static methods.
[0019] Automated and Real-Time Processing– Allows continuous monitoring, prediction, and intervention rather than retrospective assessment. Intelligent Recommendations Converts raw analytics into actionable strategies to improve student outcomes and teaching effectiveness. Domain-Specific Application– Specifically addresses the complexities of higher education mathematics, which involve abstract concepts, varied learning styles, and diverse teaching approaches.
[0020] In summary, this invention provides an automated, intelligent, and scalable framework for bridging the gap between students’ learning experiences and teachers’ instructional practices in higher education mathematics. By combining advanced machine learning algorithms with comprehensive educational data analysis, the system offers predictive insights, adaptive recommendations, and actionable intelligence, ultimately enhancing pedagogy, student engagement, and institutional decision-making.
[0021] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.

BRIEF DESCRIPTION OF THE DRAWINGS
[0022] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0023] FIG. 1 shows a block diagram representing the overall system architecture of the machine learning–based framework designed to analyze students’ and teachers’ perspectives in higher education mathematics.
[0024] Illustrates the workflow for data collection and preprocessing, demonstrating how structured, unstructured, and behavioral data are acquired from multiple educational sources and transformed for further analysis.
[0025] It presents the machine learning engine, comprising supervised learning models, unsupervised learning models, and natural language processing modules, which collectively process and interpret the collected data.
[0026] It shows depicts the predictive analysis and recommendation module, which provides insights into student learning difficulties, evaluates teaching effectiveness, and generates adaptive instructional strategies along with personalized learning pathways.
[0027] It shows an example of a user interface/dashboard that displays analytical outcomes, predictive insights, and system-generated recommendations for use by students, instructors, and educational administrators.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0028] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0029] FIG. 1 shows a block diagram representing the overall system architecture of the machine learning–based framework designed to analyze students’ and teachers’ perspectives in higher education mathematics. In some embodiments, the present invention discloses a machine learning–based analytical framework designed to systematically capture, process, and evaluate the perspectives of both students and teachers in higher education mathematics. The framework integrates educational data mining, natural language processing (NLP), predictive modeling, clustering techniques, and sentiment analysis into a cohesive system that provides actionable insights to enhance mathematics teaching and learning at the tertiary level.
[0030] System Overview: The framework comprises five primary modules:
[0031] Data Collection and Integration Module – Responsible for aggregating structured data (e.g., grades, attendance records, performance metrics), unstructured data (e.g., open-ended survey responses, discussion posts), and behavioral data (e.g., interaction logs, usage patterns on digital learning platforms). Data is sourced from learning management systems, classroom tools, online assessments, and institutional surveys.
[0032] Data Preprocessing and Feature Engineering Module – Raw data is cleaned, anonymized, and standardized. This module handles missing values, removes noise, and applies NLP techniques such as tokenization, stemming, and lemmatization to textual responses. Feature engineering is employed to derive key indicators, including sentiment polarity, engagement levels, concept mastery scores, and teaching effectiveness metrics.
[0033] Machine Learning Engine – Serving as the core analytical component, this module utilizes: Supervised Learning Models (e.g., classification and regression) to predict student learning outcomes, identify at-risk students, and evaluate instructional effectiveness.
[0034] Unsupervised Learning Models (e.g., clustering, dimensionality reduction) to group students by learning styles, categorize teaching strategies, and detect latent patterns in feedback.
[0035] Natural Language Processing (NLP) Techniques for semantic analysis, sentiment assessment, and keyword extraction to interpret qualitative perceptions from both students and teachers.
[0036] Predictive Analysis and Recommendation Module – This module transforms the processed data into actionable insights, including: Identification of learning gaps among students.
[0037] Assessment of teaching effectiveness across different instructional methods.
[0038] Personalized learning pathways tailored to individual student needs.
[0039] Adaptive teaching strategies for educators to address diverse learning preferences.
[0040] Policy-oriented decision support for institutional administrators for curriculum planning and resource allocation.
[0041] Visualization and User Interface Module – Analytical results are presented through an intuitive dashboard tailored to various stakeholders:
[0042] Students receive individualized study recommendations, performance analytics, and learning resource suggestions.
[0043] Teachers gain insights into instructional effectiveness, student engagement, and areas requiring pedagogical adjustments.
[0044] Institutions access aggregated reports, predictive trends, and strategic recommendations to enhance overall academic performance.
[0045] Technical Advancements: The invention introduces several significant innovations:
[0046] Integrated Dual-Perspective Analysis – Simultaneously evaluates and correlates both student and teacher perspectives, offering a comprehensive understanding of the teaching-learning process.
[0047] Scalable Multi-Source Data Integration – Capable of processing structured, unstructured, and behavioral data streams in real time, providing richer insights than conventional static surveys.
[0048] Intelligent Recommendation Engine – Converts analytical outcomes into actionable strategies for personalized learning and adaptive teaching.
[0049] Automated Real-Time Processing – Enables continuous monitoring and predictive analytics, supporting timely interventions rather than relying on retrospective evaluations.
[0050] Industrial Application: The invention is applicable to universities, colleges, e-learning platforms, and educational policy-making bodies. It can be deployed as a standalone solution or integrated with existing learning management systems to enhance student performance, improve teaching effectiveness, and support data-driven decision-making in higher education mathematics.
, Claims:I/We Claim:
1. A machine learning–based system for analyzing students’ and teachers’ perspectives in higher education mathematics, comprising:
A data collection module,
A preprocessing module,
A machine learning engine for classification and clustering,
A predictive analysis module, and
A recommendation engine.
2. The system of claim 1, wherein the machine learning engine employs supervised learning models such as logistic regression, decision trees, random forests, or neural networks.
3. The system of claim 1, wherein the machine learning engine employs unsupervised learning models such as k-means clustering, hierarchical clustering, or latent semantic analysis.
4. The system of claim 1, wherein natural language processing is used to analyze qualitative and textual data from students’ and teachers’ feedback.
5. The system of claim 1, wherein the recommendation engine generates adaptive teaching methodologies and personalized learning pathways.
6. The system of claim 1, wherein predictive analysis identifies academic performance trends and learning gaps.