Natural Language Processing of Student's Feedback to Instructors: A Systematic Review

Sunar, Ayse Saliha; Khalid, Md Saifuddin

doi:10.1109/tlt.2023.3330531

Cited by 3 publications

(1 citation statement)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite explorations like those mentioned above, research to date has not focused on the feasibility and quality of LLMs' results in performing a broad array of common qualitative education survey analysis tasks, leaving a gap that we focus on in this study. For example, a review published in 2024 focusing on natural language processing of students' feedback to instructors makes no mention of studies using LLMs for this purpose (Sunar & Khalid, 2024). Prior work has primarily focused on the use of encoder models like BERT for their clustering and feature extraction capabilities and have not explored the current generation of decoder-only auto-regressive models like the GPT models mentioned above.…”

Section: Llm Background and Related Researchmentioning

confidence: 99%

A Large Language Model Approach to Educational Survey Feedback Analysis

Parker,

Anderson,

Stone

et al. 2024

Int J Artif Intell Educ

View full text Add to dashboard Cite

This paper assesses the potential for the large language models (LLMs) GPT-4 and GPT-3.5 to aid in deriving insight from education feedback surveys. Exploration of LLM use cases in education has focused on teaching and learning, with less exploration of capabilities in education feedback analysis. Survey analysis in education involves goals such as finding gaps in curricula or evaluating teachers, often requiring time-consuming manual processing of textual responses. LLMs have the potential to provide a flexible means of achieving these goals without specialized machine learning models or fine-tuning. We demonstrate a versatile approach to such goals by treating them as sequences of natural language processing (NLP) tasks including classification (multi-label, multi-class, and binary), extraction, thematic analysis, and sentiment analysis, each performed by LLM. We apply these workflows to a real-world dataset of 2500 end-of-course survey comments from biomedical science courses, and evaluate a zero-shot approach (i.e., requiring no examples or labeled training data) across all tasks, reflecting education settings, where labeled data is often scarce. By applying effective prompting practices, we achieve human-level performance on multiple tasks with GPT-4, enabling workflows necessary to achieve typical goals. We also show the potential of inspecting LLMs’ chain-of-thought (CoT) reasoning for providing insight that may foster confidence in practice. Moreover, this study features development of a versatile set of classification categories, suitable for various course types (online, hybrid, or in-person) and amenable to customization. Our results suggest that LLMs can be used to derive a range of insights from survey text.

show abstract

Section: Llm Background and Related Researchmentioning

confidence: 99%

A Large Language Model Approach to Educational Survey Feedback Analysis

Parker,

Anderson,

Stone

et al. 2024

Int J Artif Intell Educ

View full text Add to dashboard Cite

show abstract

A Systematic Survey of Multilingual Speech Transcription and Translation

Vaibhav Ravindra,

Dheeraj K N,

Prof. Vinay Raj

2024

IJARSCT

View full text Add to dashboard Cite

This paper presents an innovative initiative dedicated to revolutionizing multilingual communication by leveraging state-of-the-art technologies such as Artificial Intelligence (AI), NLP, or natural language processing and Machine Learning. With a primary focus on Indian languages, the research aims to develop an advanced system capable of seamlessly transcribing speech across diverse linguistic landscapes. Through the incorporation of cutting-edge algorithms and parallel processing techniques, the proposed system facilitates real-time transcription and translation of multiple languages concurrently. Rigorous experimentation and analysis demonstrate the efficiency of the developed framework in breaking down language barriers and fostering inclusive communication. Furthermore, the paper emphasizes the cultural significance of this technology in promoting global connectivity and celebrating linguistic diversity. Ultimately, this research underscores the transformative potential of technology in facilitating cross-cultural understanding and enabling meaningful interactions within a multilingual society

show abstract

Multilingual Speech Transcription and Translation System

Dheeraj K N,

Vaibhav Ravindra,

Prof. A S Vinay Raj

2024

IJARSCT

View full text Add to dashboard Cite

This project implements a multilingual speech recognition and translation system using Python. The system leverages libraries such as `pygame` for audio playback, `gTTS` for text-to-speech conversion, `speech_recognition` for capturing and recognizing spoken language, and Machine Translation model for translating text between languages. Users interact with the system via a Streamlit-based web interface, where they can select the input and target languages and control the start and stop of the translation process. The program can auto-detect the input language if the user chooses, and it provides real-time feedback by displaying the recognized text, translating it into the chosen target language, and converting the translated text into speech. At the core of the system is a continuous loop that listens for speech input through the microphone, processes the audio to recognize the spoken words, and then translates the recognized text into the desired language. The translated text is not only displayed but also spoken aloud using the text-to-speech functionality. The interface is user-friendly, with clear indications of the current status (listening, processing, recognized text, translated text) and handles errors gracefully, providing feedback in case of issues. This project demonstrates a practical application of speech recognition and translation technologies, offering a seamless and interactive multilingual communication tool

show abstract

Natural Language Processing of Student's Feedback to Instructors: A Systematic Review

Abstract: How to cite:Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 3 publications

References 51 publications

A Large Language Model Approach to Educational Survey Feedback Analysis

A Large Language Model Approach to Educational Survey Feedback Analysis

A Systematic Survey of Multilingual Speech Transcription and Translation

Multilingual Speech Transcription and Translation System

Contact Info

Product

Resources

About