This article will guide you on how to create an Online Course Student survey about Likelihood To Recommend. With Specific, you can generate a tailored survey in seconds—no hassle or technical know-how required.
Steps to create a survey for Online Course Students about likelihood to recommend
If you want to save time, just generate a survey with Specific right now.
Tell what survey you want.
Done.
You really don’t need to read further; AI will create the entire survey for you with expert-level logic. The best part? It also asks smart, relevant follow-up questions, diving deeper to capture the insights you need.
Why gather feedback with these surveys?
Gathering Likelihood To Recommend feedback from online course students gives you a roadmap for improving your courses and ultimately boosting satisfaction. Skipping these surveys isn’t just a missed tick-mark—it means missing actionable ways to refine lessons, understand student pain points, and increase word-of-mouth recommendations.
Surveys can significantly improve learning outcomes by spotlighting what’s working and what isn’t, so you can take targeted action. Research shows that well-designed surveys are crucial for driving course engagement and student satisfaction.[1]
If you don’t run these, you risk leaving “unknown” issues unsolved—while others get clearer on what students appreciate or struggle with.
The real benefit is more than just scores: you hear the actual voice of your students, uncovering the “why” behind their Likelihood To Recommend ratings.
The importance of student feedback can’t be overstated. When you use feedback as an ongoing process (not a one-off), you build a culture of improvement that directly increases enrollment, retention, and course quality.
Your survey becomes a living feedback loop—which fosters trust and shows students you care about their experience.
What makes a good survey on likelihood to recommend?
Let’s keep it simple—good Likelihood To Recommend surveys hinge on a few principles: clarity, bias-free language, and a conversational tone. This makes it easy for students to respond honestly and quickly, giving you more accurate data to work with.
Every question should help you measure both the quantity (number of completed surveys) and the quality (depth and precision) of responses. High quality means students really explain their reasoning. High quantity means your data is statistically meaningful.
Bad practices | Good practices |
|---|---|
Vague or double-barreled questions | Clear, single-purpose questions |
Loaded or leading language | Neutral, unbiased tone |
Overly formal or robotic wording | Conversational, human language |
Diversifying question types—mixing closed and open formats—drives up completion rates and makes sure you’re hearing more than just numbers. Always focus on questions that drive both insight and action. Specific’s conversational surveys help ensure your questions are both effective and welcoming to students.
What are question types with examples for Online Course Student survey about likelihood to recommend?
The best surveys use a blend of question types, all working together to reveal not just the “what” but also the “why” of a student’s Likelihood To Recommend.
Open-ended questions are great when you want students to describe their reasoning in their own words. They shine when your goal is to surface rich insights or unexpected themes. Use these to unpack stories and root causes. Example questions:
What factors most influenced your likelihood to recommend this course?
What is one thing you’d change to improve your recommendation score?
Single-select multiple-choice questions are efficient for structured feedback. They’re best when you need quick diagnostics or statistical tracking across responses—for example, understanding primary drivers of satisfaction. A good example:
What is the most important aspect influencing your recommendation?
Content quality
Instructor engagement
Ease of use
Peer community
NPS (Net Promoter Score) question is the gold standard for measuring Likelihood To Recommend and segmenting respondents (promoters, passives, detractors). It standardizes results and provides actionable follow-up. If you want to use this approach, try this NPS survey template for online course students:
On a scale from 0 to 10, how likely are you to recommend this course to a friend or colleague?
Followup questions to uncover “the why” are powerful for drilling down into answers. Use them after critical questions—especially when a student gives a low or high Likelihood To Recommend rating—to clarify what’s behind their score. For example:
What’s the main reason for your rating?
What would make you more likely to recommend this course?
Want to explore more question ideas, tips, or examples? Check out our practical guide featuring the best questions for online course student surveys about likelihood to recommend.
What is a conversational survey?
A conversational survey engages your students like a chat, not a stale form. Each question feels natural, adapting to previous answers and encouraging richer, more meaningful feedback. This format dramatically improves response rates—and the quality of insight you capture.
Here’s how AI survey generation with Specific’s AI survey builder stands apart from traditional, manual survey creation:
Manual survey | AI-generated survey |
|---|---|
Requires drafting every question by hand | AI builds expert-quality surveys from a single prompt |
Static and impersonal experience | Conversational, adaptive, and engaging |
Follow-up is manual (often impossible) | Smart, contextual follow-up questions built in |
Hard to analyze qualitative responses | Built-in AI for fast, deep response analysis |
Why use AI for online course student surveys? AI saves you hours by generating and personalizing surveys in seconds. The conversational format keeps students engaged, and advanced features—like automatic probing—surface deeper insights than you could ever get from a static form.
If you’re curious about designing a survey—from scratch or using templates—you’ll find step-by-step advice in our guide on survey creation. Specific’s conversational AI surveys provide a best-in-class experience for both creators and students—everyone wins when feedback feels like a natural conversation.
The power of follow-up questions
Follow-up questions turn a “checkbox” survey into a dynamic dialogue. With automatic AI follow-up questions, Specific asks smart, open-ended probes the instant someone gives an ambiguous or interesting answer. No more chasing people on email for clarity—AI handles it for you, smoothly (and without extra work).
Online Course Student: “It was ok, but not amazing.”
AI follow-up: “Could you share what would have made the course amazing for you?”
How many followups to ask? We’ve found that 2–3 follow-up questions are usually enough to explore each answer deeply—while keeping the conversation moving. You can always set rules in Specific for follow-up depth or let students skip ahead when they’ve said enough.
This makes it a conversational survey—it’s not just a static exchange. The AI learns and reacts, making feedback richer and much less robotic.
Analyze survey responses with AI. When you collect pages of detailed text, AI-powered tools like Specific’s response analysis instantly summarize and distill the main themes—no matter how much feedback you gather.
Follow-up questions are new to most survey software. If you haven’t tried them, generate an AI survey in seconds and experience what modern, context-rich feedback feels like.
See this likelihood to recommend survey example now
Experience the difference when you create your own conversational AI survey—going beyond the basics to unlock deeper insights and higher quality feedback in less time.