This article will give you tips on how to analyze responses from an online course student survey about platform usability. Whether you’re working with raw responses or using the latest AI tools, getting clear insights is easier than you think.
Choosing the right tools for analysis
How you approach and analyze your survey data depends on its structure and type. Here’s a quick breakdown:
Quantitative data: If students mainly answered with choices (like rating features or selecting “yes/no”), you can easily tally results or percentages using Excel or Google Sheets. It’s straightforward, fast, and works for basic stats.
Qualitative data: When you get open-ended responses—students writing about what worked, what didn’t, or sharing detailed feedback—manual review becomes overwhelming fast. AI tools can help here, letting you surface core ideas and trends without reading each answer yourself.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Export and copy: It’s possible to export survey responses and copy them into ChatGPT or other language models. You can then “chat” with the AI about themes, pain points, or ideas.
Convenience trade-off: While this works, it’s not the most convenient. You need to handle exports, worry about formatting, and hit limits on how much text you can paste at once. You may also lose track of which answer came from which student. Still, it can be a good starting point—especially for short or one-off surveys.
All-in-one tool like Specific
Purpose-built analysis: Tools like Specific are designed for this job. They don’t just analyze responses; they collect survey data conversationally and power their analysis using AI. This means better context, higher quality responses (thanks to real-time follow-ups), and more accurate insights.
Follow-up logic: Specific stands out by automatically asking targeted follow-up questions during collection, making it easy to later group and summarize feedback by topic, choice, or theme. This makes surveys richer than a traditional static form. Here’s how AI follow-up questions work in practice.
Instant summaries and easy AI chat: Instead of spreadsheets, Specific gives you instant AI-powered summaries, uncovers core ideas, and makes survey response analysis interactive—you can chat directly with AI about your results, just like using ChatGPT, but with all the right data at hand. You can refine what data is being analyzed and instantly rerun your analysis. Learn more about AI survey response analysis in Specific.
Main value: If you’re serious about survey analysis, especially for qualitative data, tools purpose-built for this workflow let you skip manual work entirely. Various studies show that leveraging automated user feedback analysis can help drive continuous improvements to e-learning platforms and boost student satisfaction [1].
Useful prompts that you can use to analyze online course student responses about platform usability
Prompts can make or break your analysis, especially when working with AI. Here are battle-tested prompts I use (and many of these are built into Specific). Use them whether you’re using ChatGPT, another AI, or a specialized survey analysis tool.
Prompt for core ideas: The best starting point—quickly discover top themes. Paste your data and use this:
Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.
Output requirements:
- Avoid unnecessary details
- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top
- no suggestions
- no indications
Example output:
1. **Core idea text:** explainer text
2. **Core idea text:** explainer text
3. **Core idea text:** explainer text
Pro tip: AI does better work if you share context about your survey, the situation, the type of students, and your goals. Example—prepend this before your prompt:
The following are survey responses from online course students about the usability of a specific e-learning platform. Our goal is to understand the top pain points, motivations, and possible improvements. Summarize in the required format below:
Prompt to drill deeper: Once you’ve found a core idea that matters (say “mobile navigation issues”), ask: “Tell me more about mobile navigation issues”. The AI will expand on examples, quotes, or supporting data.
Prompt for specific topics: To validate whether anyone mentioned a specific idea, use: “Did anyone talk about live chat support? Include quotes.” This is incredibly helpful when stakeholders want evidence for their hunches.
Prompt for personas: Identify user types and their motivations with: “Based on the survey responses, identify and describe a list of distinct personas—similar to product management personas. For each, summarize their key characteristics, motivations, goals, and any relevant quotes.”
Prompt for pain points and challenges: Uncover what students found hardest: “Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each, and note patterns or frequency.”
Prompt for motivations and drivers: If you want to understand why students act a certain way, use: “From the survey conversations, extract the primary motivations, desires, or reasons participants express for their behaviors or choices. Group similar motivations together and provide supporting evidence from the data.”
Prompt for sentiment analysis: To get a pulse on mood: “Assess the overall sentiment expressed in the survey responses (e.g., positive, negative, neutral). Highlight key phrases or feedback that contribute to each sentiment category.”
Prompt for unmet needs and opportunities: Spot what’s missing: “Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.”
You can combine multiple prompts for richer results—and if you want premade, research-backed question templates for this exact audience and topic, try the best questions for online course student surveys about platform usability.
How Specific analyzes qualitative data by question type
With qualitative data, the way responses are structured by question type makes a big difference. Here’s what happens in Specific (and how you can approach it manually):
Open-ended questions (with or without follow-up): Specific summarizes responses and any follow-ups together, synthesizing the core message from all the student replies for each question. You get an instant overview, organized by the original question and any clarifying responses.
Multiple choice with follow-up: For each specific answer choice, Specific generates a separate summary of follow-up responses. So if students who rated "Navigation" poorly get an extra prompt (“What did you find confusing?”), you’ll see all those responses grouped and summarized by choice.
NPS: Student replies to “Why did you give this score?” are grouped by NPS category—detractors, passives, promoters. Specific creates a separate clarity-rich summary for each group’s feedback, making it simple to see what makes champions happy (or what annoys detractors).
If you’re analyzing this yourself in ChatGPT, expect a bit more work: you’ll need to filter and structure the exports for each group ahead of time before using the right prompts for each subset.
Working around the AI context limit challenge
AI models, including ChatGPT and even advanced survey platforms, have context length limits. If your survey draws hundreds or thousands of responses, not all can be analyzed in one go. Here’s how to manage this (Specific includes both options out of the box):
Filtering responses: Before sending data to the AI, filter conversations so only those where students answered specific questions or shared feedback on a particular feature are included. This zooms in on relevant replies and retains quality.
Cropping questions: Select just the questions (and follow-ups) you care about most for the AI to analyze. This sidesteps context limits and enables much deeper dives on select topics or features. You can do more analysis, with more detail, focusing on less data at a time.
Using these two approaches ensures you won’t lose track of meaningful feedback—even in large cohorts or multi-stage surveys. The result: sharper insights and less wasted time.
Collaborative features for analyzing online course student survey responses
Analyzing platform usability feedback is rarely a solo act. When teams need to align on next steps, debate findings, or break down opinions by department, collaboration becomes a challenge.
AI chat for team analysis: In Specific, you can chat with AI about survey data—no need to pull results into Slack or Google Docs. Anyone can create a new chat, filter data by segment (for example, only students who completed a certain course), and dig into the data that matters for their team.
Multiple chats, individual threads: Each chat can have its own filters and displays who kicked off the conversation, streamlining handoff between product owners, UX researchers, or support leads.
See the contributors: Avatars next to each chat message show who’s speaking, making teamwork visible and keeping collaboration organized—especially handy as you cycle through hypotheses or review feedback with a larger group.
Better context, fewer mix-ups: By chatting directly with AI, all team members have access to the same, up-to-date synthesis drawn from the actual survey data. No more lost context or email chains. Here’s a guide on creating online course student surveys for platform usability if you need a starting point.
Create your online course student survey about platform usability now
Kickstart actionable platform improvements by creating your survey on platform usability—capture better student insights, analyze responses with AI, and turn feedback into growth. Get started in minutes with conversation-driven survey tools that work for you, not against you.