Student Feedback Text Analytics Software Compared: Explorance MLY vs Qualtrics Text iQ vs Koji (2026)
A fair, evidence-based comparison of the leading tools for analysing open-text student feedback — Explorance MLY, Qualtrics Text iQ, manual coding, and Koji — and how to tell which job you are actually buying for.
Koji Education Team
Product ·
Short answer: If your problem is a mountain of comments you have already collected — National Student Survey (NSS) free-text, end-of-module survey boxes, PTES/PRES open comments — and you need to categorise and quantify them quickly, Explorance MLY (purpose-built for higher education) or Qualtrics Text iQ (if you already run on Qualtrics) are the strongest dedicated text-analytics engines available in 2026. If the deeper problem is that your comments themselves are thin — one-line answers, no probing, no "why" — then a text-analytics layer can only mine what students bothered to type. Koji attacks the problem one step earlier: it runs AI-moderated interviews that ask adaptive follow-up questions at the moment of collection, then applies automatic thematic analysis to the far richer transcripts that result. This guide compares all four approaches honestly and tells you when each is the right call.
The category confusion every buyer runs into
"Text analytics for student feedback" quietly describes two different jobs that are easy to conflate:
- Analysing static text you already have. You ran a Likert survey, students typed comments into open boxes, and now you have tens of thousands of sentences to read. This is a post-hoc job. Explorance MLY and Qualtrics Text iQ are built for it, and they are good at it.
- Collecting better qualitative data in the first place. A comment box is a one-shot prompt: whatever the student writes is all you get. There is no "tell me more", no clarifying question, no way to turn a vague "the module felt disorganised" into something a course team can act on. This is a collection job — and no amount of downstream NLP recovers a follow-up question that was never asked.
Most institutions buy a tool for job one while quietly suffering from job two. Working out which you actually have is the whole decision.
The comparison at a glance
| Capability | Explorance MLY | Qualtrics Text iQ | Manual coding (NVivo / ATLAS.ti) | Koji |
|---|---|---|---|---|
| Primary job | Analyse existing HE comments | Analyse existing survey open-ends | Rigorous human analysis | Collect and analyse conversational responses |
| Built for higher education | Yes — HE-specific models | No — general-purpose XM | Discipline-agnostic | Yes — student/course evaluation |
| Improves the raw input | No (analyses what exists) | No (analyses what exists) | No | Yes — adaptive follow-up probing |
| Topic categorisation | Supervised HE models | Top-down / bottom-up / automatic | Manual codebook | Automatic thematic analysis |
| Sentiment | Yes (per topic) | Yes (-2 to +2 scale) | Interpretive | Yes, in context of the exchange |
| Recommendations / alerts | Flags recommendations & critical issues | Via custom queries | Analyst judgement | Surfaced from probed responses |
| Scales to 10,000s of comments | Yes | Yes | No (labour-intensive) | Yes |
| Best fit | Large existing comment corpora (e.g. NSS) | Existing Qualtrics customers | Small, high-rigour qualitative studies | Institutions that want deeper feedback, not just faster reading |
Verified against each vendor's public product documentation as of publication. Feature availability varies by plan and configuration — confirm specifics with each vendor.
Explorance MLY: the higher-education specialist
Explorance MLY is arguably the most credible dedicated text-analytics engine for higher education. Its differentiator is genuine: rather than a generic sentiment model, MLY uses in-house machine-learning models purpose-built for student feedback. It categorises qualitative comments into relevant topics, detects sentiment, redacts sensitive content, highlights recommendations, and flags critical issues for follow-up.
What earns trust with a PhD-literate audience is the training method. Explorance trains MLY with a supervised approach using a blind annotation process: three annotators work independently, and a comment is only accepted into the training set when all three agree on its interpretation. That is a more rigorous standard than most commercial NLP tools disclose. In 2026 Explorance went further, releasing a model purpose-built to analyse National Student Survey open-ended feedback — and institutions including LJMU and the University of Newcastle, plus Advance HE for PTES and PRES, have adopted MLY at scale.
Where MLY genuinely wins: if you already run large structured surveys and your bottleneck is reading the resulting comments, MLY is the pragmatic, sector-tuned choice. It is fast, HE-aware, and defensible.
Its honest limitation: MLY analyses the comments students chose to leave. If those comments are short, vague, or absent — the reality for most under-incentivised end-of-module boxes — MLY faithfully categorises thin input into tidy themes. The insight ceiling is set by the raw text, not the model.
Qualtrics Text iQ: powerful, if you already live in Qualtrics
Qualtrics Text iQ is the text-analysis layer inside the Qualtrics XM platform. It lets you assign topics to open-text responses using three approaches — top-down (you define topics), bottom-up (build from the data), and automatic (Qualtrics recommends topics from your responses) — and it scores sentiment on a scale from -2 (very negative) to +2 (very positive), with a Mixed category for ambiguous responses. New responses flow into Text iQ automatically as they are collected.
Where Text iQ genuinely wins: if your institution already runs on Qualtrics, Text iQ is the path of least resistance. There is no integration to build, no second vendor to procure, and the analysis sits next to your quantitative dashboards.
Its honest limitation: Text iQ is general-purpose experience-management technology, not a higher-education instrument. It has no innate model of what "assessment feedback" or "seminar teaching" means in an academic context, so more of the topic scaffolding falls to you. And, like MLY, it analyses comments that already exist — it does not make students say more. For data-residency implications of running free-text student data through a US-headquartered platform, see our companion guide on GDPR-compliant course evaluation software.
Manual coding (NVivo, ATLAS.ti): the rigour benchmark
Before any AI tool, the gold standard for qualitative student feedback was — and for small studies still is — reflexive thematic analysis in software like NVivo or ATLAS.ti, following an established method such as Braun and Clarke's six phases. A trained analyst reads every comment, builds a codebook, and interprets meaning in context, catching irony, hedging, and cultural nuance that automated models still miss.
Where manual coding genuinely wins: for a focused study — a programme review, a curriculum redesign, a research paper on the student experience — where rigour and defensibility matter more than turnaround, nothing beats careful human analysis.
Its honest limitation: it does not scale. Coding 38,000 comments by hand is not a weekend job; it is months of analyst time, and inter-coder reliability is hard to maintain across that volume. This is precisely the gap MLY and Text iQ were built to fill.
Koji: fix the input, not just the reading
Every tool above shares one assumption: that the raw comments are fixed, and the only question is how to read them faster. Koji rejects that assumption. Instead of a static comment box, Koji runs an AI-moderated interview: it asks the student a question, reads the answer, and asks an adaptive follow-up — probing "the module felt disorganised" into what felt disorganised, when, and what would have helped. Because the moderation is standardised, every student gets the same calibrated, bias-aware probing, without the interviewer variability of human focus groups.
Only then does Koji apply automatic thematic analysis — to transcripts that are far richer than any one-shot comment box produces. The result is not just faster reading of thin data; it is better data to read. Koji also carries the evidence forward into closing-the-loop action tracking, so a theme becomes a documented change, which matters for accreditation evidence and quality-cycle reporting.
The honest framing: Koji is not primarily a tool for mining a legacy archive of static NSS comments — if that is your only need today, MLY is more directly suited. Koji is for institutions that have concluded their real problem is shallow feedback, and want to improve collection and analysis together. The same AI interview engine powers the main Koji platform (koji.so) for customer and user research, which is why the qualitative depth translates directly to the student-voice use case.
So which should you choose?
- Choose Explorance MLY if you run large HE surveys (NSS, PTES, PRES, module evaluations) and need sector-tuned analysis of existing comments at scale.
- Choose Qualtrics Text iQ if you are already a Qualtrics institution and want text analysis without adding a vendor.
- Choose manual coding for small, high-rigour qualitative studies where defensibility outranks speed.
- Choose Koji if the honest diagnosis is that your comment boxes produce thin, unactionable feedback, and you want conversational depth plus automatic analysis and closing-the-loop tracking.
Text analytics makes bad feedback faster to read. It does not make it better. That distinction is the difference between the four tools on this page — and the reason the smartest buyers ask what job they are really solving before they compare feature lists.