In a previous post, I shared several practical ways to boost engagement in virtual Excel sessions, including structured prompts, clearly defined breakout roles, targeted questions tied directly to the dataset on screen, and deliberate use of chat as an interaction channel rather than a passive sidebar. Those techniques still work and I continue to rely on them.
But before engagement can be boosted, it has to exist at all.
And that is where many virtual sessions stall. The room goes quiet. You ask a question about a formula choice or a PivotTable setup and nothing comes back. No voices, no chat messages, just a long pause and a grid of muted microphones. If you train Excel regularly, you have almost certainly experienced this moment.
Part of the context is technological. With AI tools now embedded directly into Excel and available in any browser tab, many professionals understandably assume that if they do not fully grasp something in the moment, they can revisit it later. The perceived cost of staying quiet has dropped considerably.
I think that is a dangerous gamble. AI can generate explanations, but it cannot build the habit of articulating your reasoning in real time. It cannot simulate the experience of defending a calculation to a skeptical stakeholder or explaining a model to a colleague. In fact, strong participation in a learning community may be more important now than it was before.
With that framing in mind, here are several practical strategies I use to move a room from silence to participation without turning the session into an awkward performance exercise.
Replace vague invitations with concrete actions
One of the most common mistakes in virtual training is asking overly broad questions. “Any questions?” or “What do you think?” sounds inclusive, but in practice it places all the social risk on the participant and signals that engagement is optional.
A more reliable approach is to convert participation into a specific task. Instead of asking for general feedback, ask participants to type the value they calculated into chat, paste the formula they used, or respond with a quick indicator such as “1” if their PivotTable matches yours and “2” if it does not. These prompts feel procedural rather than performative. Participants are not volunteering a speech; they are completing a defined step. At the same time, you gain immediate feedback on whether people are following along or drifting off track.
Design exercises that require visible output
Breakout rooms are especially vulnerable to silence if expectations are not explicit. Simply asking participants to “discuss what you found” often leads to minimal conversation because there is no shared sense of responsibility for producing something.
We often assume that adults will naturally organize themselves, but that rarely happens without structure. In theory, everyone knows how to collaborate. In practice, people hesitate, wait for someone else to start, or avoid taking the lead.
You see the same thing when a traffic light stops working. Everyone knows the rule is to treat it like a four-way stop, yet the intersection often becomes awkward and hesitant because nobody is quite sure who should go first.
Breakout rooms work the same way. Instead, define the deliverable before the breakout begins. Assign one person to share their screen, one person to explain the approach the group used, and one person to identify the most important result. Let participants know each room will report back with something concrete, such as a PivotTable, chart, or one-sentence insight. When the outcome is clear and visible, participation tends to follow.
Use chat as a primary participation channel
Many professionals are more comfortable typing than speaking in a virtual setting. Rather than fighting that dynamic, it often makes sense to lean into it. I frequently pause during demonstrations and ask short, targeted questions that can be answered quickly in chat. For example, I might ask which column participants grouped by first, which function they used to clean a text field, or what assumption underlies a particular summary metric.
Reading and synthesizing those responses out loud reinforces that chat contributions are not incidental. They are part of the analytical conversation. Over time, participants begin to see chat as a normal way to participate rather than a side feature that only a few people use.
Normalize mistakes without lowering expectations
Excel can be unforgiving in small ways. A misplaced reference or incorrect aggregation can produce the wrong answer immediately. Participants may hesitate to speak because they are unsure whether their answer is correct and do not want to appear careless.
It helps to address this directly by acknowledging that even experienced analysts occasionally make these mistakes. Sharing a quick example of how you diagnosed a broken formula or tracked down an incorrect range reference can make the process feel less intimidating. At the same time, it is important to keep the expectation that reasoning should ultimately be clear and defensible. The goal is not to eliminate standards but to make it easier for participants to surface partial understanding so it can be refined.
Set expectations at the start of the session
If participation matters to you, it helps to say that directly at the beginning. Let participants know the session is meant to be a working environment rather than a passive webinar. Explain that you will ask for responses in chat, that breakout rooms will include short report-backs, and that visible thinking is part of the design.
It also helps to be honest about the limits of teaching online. In a classroom you can read the room: confused looks, nods, people falling behind. Online those signals mostly disappear. Because of that, I tell participants that I need them to be my eyes and ears. If something is unclear, say so in chat. If something works well, mention it.
When these expectations are set early, interaction feels normal rather than surprising, and people are less likely to default to silence.
Why this matters
In a world where AI can generate formulas, summaries, and even slide decks quickly, the differentiator is no longer the mechanical output. It is the ability to reason about that output, validate it, and communicate it clearly. Virtual Excel training should reflect that reality. If participants leave with a list of new features but without having practiced explaining their thinking, the long-term impact is limited.
If you are interested in learning more about how I design Excel and analytics training to build durable capability rather than short-term familiarity, take a look at my “How I Work” page:
I outline my approach to developing Excel and data skills within teams and organizations, and you can also get in touch there if you would like to explore relevant training or consulting services.