Start exactly how most great live sessions start: Where are you joining from?-but make it feel relevant to Predictive Modelling. How to use it in this session: - Ask: Where are you joining from today? and let StreamAlive plot everyone live. - Then tie it back to the topic: Cool-look at our spread. Now imagine were predicting training outcomes across all these regions. The patterns wont be the same everywhere. Predictive Modelling-flavored prompts you can use: - Drop the city where your learners are mostly based (your training HQ). - Type a location where you *wish* you had better learning performance data. - If you could teleport to any site to observe training impact firsthand, where would you go? Trainer tip: If youre doing cohort comparisons later (regions, business units, countries), this map becomes a perfect visual callback.

Before you teach anything, find out where people *actually* are. Rating Polls are perfect for that because theyre fast and you can react in real time. How to use it in this session: - Run a quick confidence check early, then run the same rating again near the end to show growth. - Use the results to decide if you should stay high-level or go deeper into model evaluation. Questions that work really well: - Rate your confidence in Predictive Modelling (1 = what is this, 10 = I can explain it to others). - How confident are you in choosing the right metric (accuracy vs precision/recall vs AUC)? - How confident are you that your org has the data needed for prediction? - Rate how comfortable you are explaining overfitting in plain English. Trainer move: If the average is low, say it out loud-Perfect. Were not here to impress anyone, were here to get usable. That line alone relaxes the room.

Predictive Modelling can trigger big feelings-excitement for some, intimidation for others. A word cloud lets you surface that instantly and normalize it. How to use it in this session: - Ask for a 12 word response and let Wonder Words visualize the room. - Use the biggest words as your facilitation roadmap (I see confusing and excited-great, well tackle both.). Prompts to try: - Predictive Modelling in one word: how does it feel? - One word: what do you hope prediction helps L&D do better? - Whats the biggest risk with predictive models in L&D? One word. - One word: what data source do you wish you trusted more? (LMS, HRIS, survey, manager ratings, etc.) Trainer tip: Use Combine Similar Answers so scared, fear, and nervous dont split the signal.

This is where your session stops being theoretical. Talking Tiles is amazing for pulling richer, real-world answers-then displaying them in a way that feels energetic, not like reading chat. How to use it in this session: - Ask a practical question and let answers fall in as tiles. - Read a few out loud and group them: retention, performance, compliance, leadership programs, sales enablement, etc. Scenario-based prompts for L&D Leaders: - Where could Predictive Modelling make the biggest difference in your role this year? - Name one decision youre currently making with gut feel that you wish you could predict. - Whats one outcome youd love to predict: dropout risk, time-to-competency, post-training performance, promotion readiness? - Whats the hardest part-data quality, stakeholder buy-in, privacy, or actually taking action on predictions? Trainer move: Pick 23 tiles and say, Lets build a model concept around THIS. People feel seen-and they lean in.

Instead of guessing what L&D leaders want from Predictive Modelling, just ask-and show the results live. This also helps you manage time because the group helps you prioritize. How to use it in this session: - Run a poll right after the intro: Where should we spend the most time today? - Run another later: Which model type should we demo with? Poll questions (with options) you can copy-paste: - What do you want most from Predictive Modelling in L&D? 1) Predict learner dropout risk 2) Predict performance improvement after training 3) Predict time-to-competency 4) Predict who needs coaching support 5) Prove ROI / business impact - What should we go deeper on? 1) Choosing features (inputs) 2) Avoiding bias + ethics 3) Model evaluation (metrics) 4) Turning predictions into interventions 5) Communicating results to stakeholders - Whats your data reality right now? 1) We have data, its clean-ish 2) We have data, its messy 3) Data is scattered across systems 4) We dont have access yet Trainer tip: If you want the group to suggest options you didnt list, use an Open-Ended Poll version and let StreamAlive pick answers straight from chat.

Youll always have a few people who answer everything and a bunch who stay quiet. The Winner Wheel makes participation feel random and playful-so it doesnt come off like youre putting someone on the spot. How to use it in this session: - Tell them: Type anything in chat-your example, your vote, your question-because Im going to spin the wheel for a couple of voices. - Spin from people who participated in the last interaction so it rewards engagement. Ways to use it during Predictive Modelling training: - Lets spin for someone to share a training decision they wish they could predict. - Spin for a volunteer: Ill describe a model result, you tell me how youd explain it to an exec. - Spin for a quick feature brainstorm: what input variables would you use to predict dropout risk? Trainer tip: Offer a low-pressure out: If you get picked and dont want to speak, just type pass-no big deal. Participation still stays high.

Predictive Modelling has a bunch of terms people nod at but dont always understand. A fast Quiz interaction lets you correct misunderstandings immediately-and it keeps attention up. How to use it in this session: - Use quizzes every 1015 minutes as mini reset points. - Reveal the correct answer and give a simple real-world explanation tied to L&D. Example quiz questions (single correct answer): - Which is the best example of a *prediction*? 1) Sales improved after training 2) This learner is likely to drop out next week 3) The course was rated 4.6/5 4) Managers liked the program - Overfitting basically means: 1) The model is too simple 2) The model memorized training data and wont generalize 3) The data has too few rows 4) The dashboard is confusing - If dropout is rare, which metric becomes more important than accuracy? 1) Precision/Recall 2) Total learners 3) Mean 4) Completion rate Trainer move: After the reveal, ask: Where would this show up in an L&D dashboard? Thats how you turn trivia into application.

Before you teach anything, find out where people *actually* are. Rating Polls are perfect for that because theyre fast and you can react in real time. How to use it in this session: - Run a quick confidence check early, then run the same rating again near the end to show growth. - Use the results to decide if you should stay high-level or go deeper into model evaluation. Questions that work really well: - Rate your confidence in Predictive Modelling (1 = what is this, 10 = I can explain it to others). - How confident are you in choosing the right metric (accuracy vs precision/recall vs AUC)? - How confident are you that your org has the data needed for prediction? - Rate how comfortable you are explaining overfitting in plain English. Trainer move: If the average is low, say it out loud-Perfect. Were not here to impress anyone, were here to get usable. That line alone relaxes the room.

In technical sessions, questions fly by in chat-and trainers miss half of them while screensharing. StreamAlives Q&A pulls questions out and organizes them so you can actually manage them. How to use it in this session: - Tell participants: Drop questions anytime. Ill pause every 15 minutes and hit the Q&A board. - Use it to spot themes: data access, bias, tools, stakeholder alignment, privacy. Prompts that encourage better questions: - What part of Predictive Modelling feels fuzzy right now? Ask it plainly. - Whats one this would never work in my company concern you have? - If you could ask a data scientist one thing about predictive models in L&D, what would it be? Trainer tip: When you answer, reference the persons name-people feel acknowledged, and more questions come in.

After the session, StreamAlive Analytics helps you see what actually worked-not just what you *think* worked. How L&D Leaders and trainers can use it: - Check minute-by-minute engagement to see where attention dipped (often during heavy theory) and where it spiked (usually during polls, quizzes, or real examples). - Use chat replay to find which Predictive Modelling topics triggered the most discussion-those are your keep sections. - Identify your most engaged participants (your future champions). These are the folks you can invite to pilot a predictive use case. Concrete ways to apply it: - If engagement drops during model evaluation metrics, next time add a Quiz right before it and a real L&D example right after. - Share interaction results with your team via email reports (great for showing stakeholder buy-in: Heres what leaders said they need most). - Use the reports to refine your run-of-show: keep the interactive moments that drove the highest chat and participation. Bottom line: Analytics turns your ILT into something you can continuously improve-like a product, not a one-off workshop.