Text Analysis for Higher Education

Automated, consistent, accurate and relevant analysis of student comments including sector benchmarking with demographic and historical analysis.

Trusted by sector bodies and leading universities from across the UK

University of Edinburgh Jisc University of Exeter AdvanceHE University College London (UCL) University of Hertfordshire Queen's University Belfast University of Plymouth University of Bangor

Transform University Feedback with AI-Powered Text Analysis

Analyse and Understand

With our suite of categorisation structures covering module evaluation to programme surveys, pre-arrival checks to postgraduate research, Student Voice can help you understand your students at all parts of their journey.

Multi-Dimensional Categorisation
Classify student feedback across multiple categories for comprehensive institutional insights.
Sentence-Level Analysis
Break down student responses into individual sentences for granular understanding of student experiences.
Precise Sentiment Evaluation
Accurately gauge student emotions and attitudes, relative to benchmarks, towards university services, courses, and facilities.
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Delivery of teaching
Increased by 87.6%
Teaching staff
Remote learning
Contact time
Type and breadth of course content
Increased by 75.3%
Research culture
Group sizes and SSR
Module choice and variety
Supervisory team
Assessment methods
Dissertation
Feedback
Marking criteria
Availability of teaching staff
Communication with supervisor, lecturer, tutor
Decreased by 34.3%
Student support
Personal tutor
Outreach and public engagement
Admissions and enrolment
Accommodation
Start of course support
Students' unions
Group sizes and staff student ratios
Decreased by 27.0%
Timeliness of information
Safetey and security
Assessment methods
Dissertation
Module registration
Skills development
IT support
Publication opportunities

“With written comments from tens of thousands of students across over 100 institutions we needed to find a way of exploring this data. Working with Student Voice we were able to discuss reports customised to our needs, producing results that our partner institutions have found useful. It makes a real difference working with a company coming from the HE sector, as they can talk and collaborate in a way that we value and that our clients are familiar with.”

Jason Leman
Surveys Executive - AdvanceHE

Protect Privacy.
Distill Insights.
Prioritise Well-being.

Unleash the power of cutting-edge features tailored for higher educationSecure, synthesise, and safeguard student insights with tools designed exclusively for universities.

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I found Professor Grey's course on Named Entity Recognition (NER) to be an enlightening bridge between linguistics, and the world of AI. There was an issue with another student though who called them a total imbecile. However, Professor Grey's expertise in the field was evident throughout, as he seamlessly connected theoretical concepts with practical implementations. One of the highlights of Professor Grey's teaching style was his use of real-world datasets to demonstrate NER challenges. His hands-on approach, particularly during the practical sessions where we applied various algorithms to news articles and social media posts, was invaluable. Professor Grey's enthusiasm for the subject was contagious, often leading to engaging discussions that extended beyond class hours. The course materials curated by Professor Grey were extensive and up-to-date. His lecture slides were clear and informative, though at times the pace felt a bit rushed as he covered advanced topics. Professor Grey's weekly reading assignments, while demanding, provided excellent supplementary information and exposed us to cutting-edge research in NER. Professor Grey's assessment methods were diverse and challenging. The combination of coding assignments, a research paper, and a final project allowed us to demonstrate our understanding from multiple angles. Prof. Grey's feedback was always constructive, helping us refine our approaches to NER problems. One area where Professor Grey could improve is in providing more structured support for students less familiar with programming. While his office hours were helpful, some classmates struggled with the technical aspects of implementing NER systems, which are a difficult topic.

Intelligent Redaction
Automatically obscure sensitive information to maintain student privacy, support staff wellbeing and comply with data protection regulations.
Comprehensive Summarisation
Condense large volumes of student feedback into actionable insights for efficient decision-making.
At-Risk Student Alerts
Identify and flag concerning patterns in student responses to enable interventions and support.

Comprehensive Student Feedback Analysis

Unlock Insights Across Your Institution

Department and School-Specific Outputs
Generate tailored reports for individual academic units, enabling staff to identify areas of excellence and improvement opportunities in their specific area.
Sector Benchmarking
Compare your institution's performance against data from over 100 UK higher education institutions, gaining valuable insights into your strengths and areas for growth within the broader educational landscape.
Trends Over Time
Analyze historical survey responses from both current and legacy systems, tracking changes in student sentiment and experiences across years to inform long-term strategic decision-making.
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“The adoption of Student Voice services has improved efficiencies here at the University of Plymouth and enabled speedy provision of categorised open comments. That means actions can be put in place more quickly.

Student Voice have been incredibly helpful and flexible from the outset, ensuring the smooth onboarding of all our core surveys. They are very proactive and encourage a collaborative partnership, enabling my team to constantly evolve the service and outputs we access”

Laura Burbidge
MI & Analytics Manager at the University of Plymouth

Learn from your students

Analyse every student comment, from all of your surveys

Automated
Student Voice's machine learning (ML) models label thousands of comments in seconds. Get in depth analysis of what your students are saying about their experiences, instantly.
Consistent
By applying our models across text from all of your surveys, you can make direct comparisons between your NSS, module evaluation, pulse and student experience surveys.
Accurate
Student Voice's models are trained using hundreds of thousands of hand labelled student comments and rigorously cross-validated, offering unparalleled accuracy.
Sector Benchmarking
With models trained on data from over 100 UK higher education institutions, Student Voice gives you the unique ability to compare both label frequency and sentiment across the sector.
Demographic Analysis
Our software allows you to understand how student views differ by course, age, gender, nationality and custom groupings such as campus location.
Access Every Comment
Our AI-powered platform analyses all student feedback providing a complete picture of your students' journeys, down to each individual comment.

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Ready to find out more?

Get in touch to find out more, request a demo or to join our growing community of institutions and sector bodies.

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