Panda / Singh / Ramasamy Behavioural Analytics: Machine Learning Approaches for Predictive Insights

1. Auflage 2026
ISBN: 979-8-89881-393-2
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, Band 3, 201 Seiten

Reihe: Applied Machine Learning for IoT and Data Analytics

Applied Machine Learning for IoT and Data Analytics (Volume 3) offers a comprehensive exploration of how machine learning transforms behavioural data into actionable intelligence. In an era where data-driven strategies shape competitive advantage, this volume examines how organisations can harness predictive analytics to understand patterns, anticipate risks, and unlock hidden opportunities.
The book introduces the foundational principles of behavioural analytics and advances into practical machine learning applications across diverse domains. It addresses critical integration challenges such as data quality, model reliability, privacy protection, and ethical considerations-highlighting transparency and responsible data governance as essential pillars of modern analytics frameworks.
Through empirical research and real-world case studies, the volume demonstrates how predictive insights can enhance employee engagement, improve customer experiences, optimise marketing performance, and support public safety initiatives. Bridging theory with applied implementation, the book equips readers with both conceptual clarity and practical strategies for deploying machine learning-driven behavioural intelligence in dynamic organisational environments.
Key Features:
-Comprehensive overview of behavioural analytics and predictive modelling foundations.
-Application of machine learning techniques with real-world perspectives on implementation and management.
-Insights into improving employee retention, customer engagement, and operational efficiency.
-Discussion of integration challenges, including data quality and governance frameworks, with a focus on ethical AI, transparency, data privacy, and responsible analytics practices

Autoren/Hrsg.

Panda, Shradhanjali

Singh, Leena

Ramasamy, V. Ram

S, Balamurugan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Leseproben

Enhancing Academic Development Through Automatic Speech Recognition and Natural Language Processing

Ananya Mitra1, *

1 School of Economics & Commerce, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India

Abstract

The rapid advancement of Speech Recognition and Natural Language Processing (NLP) technologies offers transformative potential for academic development, aligning with the United Nations Sustainable Development Goal (SDG) 4: Quality Education. These technologies can revolutionize education by providing innovative tools that enhance inclusive and equitable quality education for all. Speech Recognition and NLP enable the automation of academic tasks, improve accessibility for diverse learners—including those with disabilities—and offer data-driven insights to improve student outcomes. The integration of these technologies into educational environments presents significant challenges like limited accessibility in under-resourced educational settings, accuracy issues that affect their reliability, and ethical concerns regarding data privacy and algorithmic bias. This study aims to explore the current state of Automatic Speech Recognition (ASR) and NLP in academia, identify key challenges, and propose solutions to enhance their effectiveness in promoting equitable and inclusive education. The research employs a mixed-methods approach, combining quantitative and qualitative methods. Surveys and interviews with educators, students, and administrators are conducted to gather insights into the use and challenges of ASR and NLP technologies. Additionally, experimental studies are carried out to test the effectiveness of existing ASR systems in educational contexts. By offering workable solutions using usable frequencies from a student perspective to improve the integration of speech recognition and Natural Language Processing (NLP) technologies in education, the study's findings will help achieve SDG 4, guaranteeing that all students have access to high-quality, inclusive, and equitable educational opportunities.

Keywords: Automatic speech recognition, Academic sector, NLP, Primary survey, SDG-4.

* Corresponding author Ananya Mitra: School of Economics & Commerce, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India; E-mail: [e-mail]

Introduction

ASR and NLP technologies have the potential to greatly improve education and support SDG-4. These tools can make learning and teaching more inclusive and equitable. NLP can create personalized educational content that meets the specific needs of each student. It can also translate materials in real time, helping students understand content in languages they are not familiar with. This fosters a more diverse and accessible classroom environment. In addition to enhancing learning, these technologies can make teaching easier by automating tasks like grading and taking attendance. This allows teachers to spend more time on engaging activities that connect with students. NLP tools can also help educators create and update educational materials quickly, ensuring students have access to the best resources available. Another important advantage of ASR and NLP is their ability to improve accessibility for all learners, especially those with disabilities. For instance, speech-to-text tools provide real-time captions for students who are hard of hearing, while text-to-speech tools help those with visual impairments. Additionally, these technologies can support research by speeding up data analysis and encouraging collaboration among educators. By using insights from these advancements, teachers can identify students who may be struggling and provide timely help, creating a more effective and supportive learning environment for everyone [1-3].

While ASR and NLP technologies bring many benefits to education, their use in classrooms poses significant challenges. One major issue is that schools in poorer areas often do not have the necessary resources, training, or funding to use these tools effectively. This can create a larger gap between well-funded schools and those that struggle, making it important to find ways to ensure that all students can benefit from these advancements. Another key challenge is the accuracy of ASR and NLP technologies [4]. When these tools do not work consistently, they can cause misunderstandings, especially in classrooms with students who have different language skills. For teachers and students to trust and use these technologies fully, they need to keep improving the accuracy and understanding of context. This means not just advancing the technology itself, but also providing training to help users make the most of these tools in real-life situations. Lastly, there are important ethical concerns related to data privacy and bias. Using student data raises questions about how that information is collected, stored, and used, so strong privacy protections are necessary. Additionally, algorithms can sometimes reflect biases from the data they were trained on, which can unfairly impact different groups of students. Addressing these challenges is essential to make sure that ASR and NLP technologies truly support fair and equal education for everyone. By addressing these problems, we attempted to develop a more

effective and inclusive educational system that maximizes the use of these technologies. This type of research is completely new [5, 6].

Objectives

This study aimed to examine how ASR and NLP technologies were being used in education. It sought to understand the challenges these tools faced and suggested ways to improve their effectiveness for all students. By enhancing the use of ASR and NLP, the study hoped to promote more equitable and inclusive education. To achieve these goals, the research employed a mixed-methods approach, combining both quantitative and qualitative methods. Primary surveys and interviews were conducted with educators, students, and administrators to gather their thoughts and experiences regarding ASR and NLP technologies. This helped understand the benefits and problems encountered when using these tools in real classrooms. In addition to surveys and interviews, the study also carried out experimental studies to assess how well current ASR systems perform in educational settings. By analyzing the results of these experiments, researchers aimed to gain insights into the effectiveness of these technologies in supporting student learning and identifying areas for improvement.

Methodology

The research was conducted in various technical institutes across Bhubaneswar, Odisha. Bhubaneswar is a vibrant educational hub, making it an ideal location for this research. Students from different regions, speaking various dialects, come together in one place. By studying these diverse groups, the research explored how ASR and NLP technologies worked for speakers of different languages. By examining how these technologies were used in such a dynamic environment, the study aimed to gather insights that could help improve these tools for all students. By including voices from different dialect speakers, the research aimed to identify specific challenges they faced with ASR and NLP technologies. This understanding can guide future improvements, ensuring that all students, regardless of their language background, have the same opportunities to succeed in their studies. Overall, conducting the research in Bhubaneswar allowed for a rich exploration of language diversity in education.

Students are more likely to engage in research when they are referred by someone they know, creating a comfortable environment for sharing their experiences. This is particularly crucial when talking about personal difficulties with technologies like ASR and NLP, as it may make participants feel more comfortable sharing their experiences [7]. For this reason, snowball sampling was used in this research to effectively gather students from different regions and dialects. By starting with a few participants and expanding through their networks, snowball sampling ensured a more representative mix of students from different regions and dialects. This approach is effective in educational settings where students might have friends or classmates from similar backgrounds, making it easier to reach a wide range of dialect speakers.

In the initial phase of the research, using the Delphi Method, interviews with educators from language and computer science departments were conducted to gather their expert opinions on the potential of ASR and NLP technologies in education. The iterative approach allowed for deep discussions and the refinement of ideas, providing valuable insights into how these tools can enhance teaching and learning. Anonymity of the educators was essential for the study to ensure honest and open responses without the fear of judgment or repercussions. Educators might hesitate to share their true opinions or experiences with ASR and NLP technologies if they felt their identities were known, especially if their views were critical of institutional policies or...