Sunil Gunderia

Chief Innovation Officer, @AgeofLearning | Board Member, @ChildrensInstLA | Innovating for Transformative Impact

[Sunil Gunderia @DigitalMasala]

Age of Learning Wins SIIA CODiE Award for Most Innovative Edtech Company https://t.co/NNek5ieCLa @AgeofLearning @YahooFinance #innovation #impact #purpose #education #edtech

[Sunil Gunderia @DigitalMasala]

@jencarolan @DorisKGoodwin She is an American treasure.

[Brookings Global @BrookingsGlobal]

Support for children and families is key to the future health of a nation. @SwetaShahECD, @KathyandRo1, and co-authors outline six areas for bipartisan investment to deliver on the professed commitment of both Democrats and Republicans: https://t.co/QB9Z6qbEfm

[Mark Swartz @SwartzMark]

Voluntary PreK students in Florida who used @AgeofLearning’s My Reading Academy program scored significantly higher on the Florida Assessment of Student Thinking compared to students who did not use the program.
@ELCPalmBeach

[Jamie Clark @XpatEducator]

EXPLICIT TEACHING ESSENTIALS! Boosting learning and participation with these evidence-based methods:

✅ 1. TAPPLE: A reliable method for checking understanding and keeping lessons moving.

🤝 2. Active Participation: Engage students by getting them to say, do, or write something to elicit evidence of learning.

⚖️ 3. Rule of Two: Provide a worked example, then give a similar matching problem for students to tackle.

🏆 4. 80% Success Rate: Strive for high success to ensure readiness for the next step.

🏗️ 5. Fade Scaffolds: Gradually shift support—first tell (fade), then ask (fade), and remind students as needed.

[Jamie Clark @XpatEducator]

📒 Barak Rosenshine’s Principles of Instruction offers 10 evidence-based strategies that all teachers should know based on key research from cognitive science and research on master teachers. This poster outlines the lot!

👊 REPOST and grab a free HQ copy here: https://t.co/Xj2XpPGvnu

[Susan Neuman @SusanBneuman]

What I hope does not get lost in this shuffle is that Mississippi began reforms in 2013, but did not see NAEP grains for six years…

[Age of Learning @AgeofLearning]

We're excited to announce that Sunil Gunderia (@DigitalMasala), our Chief Innovation Officer, will be delivering the keynote at The Center for Outcomes Based Contracting at SEF's Provider Summit, tomorrow!

#OBCProviderSummit #OBCConvening2024 #AgeofLearning #Leadership

[Javaid Siddiqi @jsiddiqi7]

The North Dakota Teacher Retention and Recruitment Task Force has adopted critical recommendations to address the teacher shortage.

Among the initiatives are reducing barriers to on-site childcare and more.

https://t.co/opc6x6K0uF

#teachershortage #educationreform

[Marci Houseman @marcihouseman]

The Pareto Principle guides us to prioritize outcomes - carefully weigh how we spend our time & money to ensure we focus on the right 20% that yields 80% of our outcomes. OBC ensures that both sides of a partnership are dedicated to outcomes.

@DigitalMasala

#OutcomesMatter

[Georgia Football @GeorgiaFootball]

.@StarksMalaki took flight 🛫

#GoDawgs

[Justin Skycak @justinskycak]

Many people know that active learning is superior to passive learning, and that deliberate practice is the most effective type of active learning.

However, many of those people still don't understand just how much of a practice session should constitute active learning.

Even if you're active half the time during practice, that's still not enough to capture anywhere near the full benefits of active learning.

Don't believe it? Let me tell you about this study on the practice habits of figure skaters:

A Search for Deliberate Practice by Deakin & Cobley (2003), pp.124-136 in https://t.co/53gd0915Ey.

Deakin & Cobley studied the practice habits of figure skaters who had been practicing for a similar number of years and had achieved varying degrees of success.

A defining attribute separating the elite and non-competitive skaters was the proportion of active practice.

During practice,
-- elite skaters were over 6 times more active than passive, while
-- non-competitive skaters were nearly as passive as they were active.

The elite skaters spent a greater proportion of their practice time actively practicing some of the trickiest, most taxing moves (jumps & spins) --

and even when resting from those taxing activities, they were more likely to continue actively practicing less taxing movements like footwork and arm positions.

Deakin & Cobley noted specific percentage breakdowns, which I organized into a table (screenshot attached) to illustrate how each group of skaters would use 100 minutes of practice time.

(The screenshot is a snippet from The Math Academy Way: https://t.co/Pri8OdO5vR)

Essentially, the elite skaters were on the ice for the same amount of time as the non-competitive skaters, but during that on-ice time, the elite skaters allocated their practice time far more efficiently.

The key takeaway is that, while some amount of active learning is certainly better than no active learning...

the BEST outcomes are achieved by fully maximizing the amount of productive active learning.

Of course, some passive instruction will generally be needed to demonstrate to a learner what it is that they need to practice...

but that passive instruction should be kept to a minimum effective dose before launching into more extensive active learning.

And remember that it's not just any type of active learning that we're talking about. There are a million ways to do active learning wrong,

but the way to do it right is deliberate practice: mindful repetition on performance tasks just beyond the edge of one's capabilities.

Deliberate practice is about making performance-improving adjustments on every single repetition.

Any individual adjustment is small and yields a small improvement in performance --

but when you compound these small changes over a massive number of action-feedback-adjustment cycles, you end up with massive changes and massive gains in performance.

Deliberate practice is superior to all other forms of training. That is a "solved problem" in the academic field of talent development. It might as well be a law of physics.

There is a mountain of research supporting the conclusion that the volume of accumulated deliberate practice is the single biggest factor responsible for individual differences in performance among elite performers across a wide variety of talent domains.

(The next biggest factor is genetics, and the relative contributions of deliberate practice vs genetics can can vary significantly across talent domains.)

Further Reading
Chapter 10: Active Learning in The Math Academy Way: https://t.co/Pri8OdO5vR

[Nate Joseph @NateJoseph19]

When you're leading reading interventions, should you:
A. Teach a comprehensive approach?
B. Teach to the students assessed weaknesses?

Many argue that (A) is the better option, but: A 2018 meta-analysis by Mathew Hall and Mathew Burns, of 26 experimental or quasi-experimental studies on reading interventions found: “Interventions were more effective if they were targeted to a specific skill (g = 0.65), then as part of a comprehensive intervention program that addressed multiple skills (g = 0.35).” In other words, interventions that targeted student deficits were approximately twice as effective!

[Justin Skycak @justinskycak]

Interleaving and spaced repetition help students maximize their learning speed and retention. However, when using these strategies while teaching, they can feel counterintuitive.

Why? Because they go against our human instincts about conversations.

During a conversation, people generally want to focus on a single thought, explore it to its fullest extent, and completely finish the thought before moving on to other things. We converse depth-first.

But interleaving and spaced repetition are about stopping the flow of thought, doing other things for a while, and then coming back to remember the flow of thought just before you’ve forgotten it. Breadth-first, not depth-first.

Granted, when creating content for a course, it’s easiest to proceed in the form of a story and “close the loop” each time before moving on and opening another loop. That’s how to create good content, but it’s not how the content should be presented.

It’s natural to think that teaching should mirror content creation, but in reality, they should be very different.

[Age of Learning @AgeofLearning]

Our Chief Innovation Officer, Sunil Gunderia (@DigitalMasala) sharing more about our programs at @CityAndStateNY 's Education Summit. #NYC #NYCEducation

[Daniel Willingham @DTWillingham]

The Benefits of Everyday Math for Kids https://t.co/KObV7LSai4

[Andrew Ng @AndrewYNg]

I wrote last week about why working on a concrete startup or project idea — meaning a specific product envisioned in enough detail that we can build it for a specific target user — lets you go faster. In this letter, I’d like to share some best practices for identifying promising ideas.

AI Fund, which I lead, works with many corporate partners to identify ideas, often involving applications of AI to the company’s domain. Because AI is applicable to numerous sectors such as retail, energy, logistics and finance, I’ve found working with domain experts who know these areas well immensely helpful for identifying what applications are worth building in these areas.

Our brainstorming process starts with recommending that a large number of key contributors at our partner corporation (at least 10 but sometimes well over 100) gain a non-technical, business-level understanding of AI and what it can and can’t do. Taking https://t.co/zpIxRSuky4’s “Generative AI for Everyone” course is a popular option, after which a company is well positioned to assign a small team to coordinate a brainstorming process, followed by a prioritization exercise to pick what to work on. The brainstorming process can be supported by a task-based analysis of jobs in which we decompose employees’ jobs into tasks to identify which ones might be automated or augmented using AI.

Here are some best practices for these activities:

(i) Trust the domain expert’s gut. A domain expert who has worked for years in a particular sector will have well honed instincts that let them make leaps that would take a non-expert weeks of research.

Let’s say we’re working with a financial services expert and have developed a vague idea (“build a chatbot for financial advice”). To turn this into a concrete idea, we might need to answer questions such as what areas of finance to target (should we focus on budgeting, investing, or insurance?) and what types of user to serve (fresh graduates, mortgage applicants, new parents, or retirees?) Even a domain expert who has spent years giving financial advice might not know the best answer, but a choice made via their gut gives a quick way to get to one plausible concrete idea. Of course, if market-research data can be obtained quickly to support this decision, we should take advantage of it. But to avoid slowing down too much, we’ve found that experts’ gut reactions work well and are a quick way to make decisions.

So, if I’m handed a non-concrete idea, I often ask a domain expert to use their gut — and nothing else — to quickly make decisions as needed to make the idea concrete. The resulting idea is only a starting point to be tweaked over time. If, in the discussion, the domain expert picks one option but seems very hesitant to disregard a different option, then we can also keep the second option as a back-up that we can quickly pivot to if the initial one no longer looks promising.

(ii) Generate many ideas. I usually suggest coming up with at least 10 ideas; some will come up with over 100, which is even better. The usual brainstorming advice to go for volume rather than quality applies here. Having many ideas is particularly important when it comes to prioritization. If only one idea is seriously considered — sometimes this happens if a senior executive has an idea they really like and puts this forward as the “main” idea to be worked on — there’s a lot of pressure to make this idea work. Even if further investigation discovers problems with it — for example, market demand turns out to be weak or the technology is very expensive to build — the team will want to keep trying to make it work so we don’t end up with nothing.

In contrast, when a company has many ideas to choose from, if one starts to look less interesting, it’s easy to shift attention to a different one. When many ideas are considered, it’s easier to compare them to pick the superior ones. As explained in the book Ideaflow, teams that generate more ideas for evaluation and prioritization end up with better solutions.

Because of this, I’ve found it helpful to run a broad brainstorming process that involves many employees. Specifically, large companies have many people who collectively have a lot of wisdom regarding the business. Having a small core team coordinate the gathering of ideas from a large number of people lets us tap into this collective fountain of invention. Many times I’ve seen a broad effort (involving, say, ~100 people who are knowledgeable about the domain and have a basic understanding of AI) end up with better ideas than a narrow one (involving, say, a handful of top executives).

(iii) Make the evaluation criteria explicit. When evaluating and prioritizing, clear criteria for scoring and ranking ideas helps the team to judge ideas more consistently. Business value and technical feasibility are almost always included. Additionally, many companies will prioritize projects that can be a quick win (to build momentum for their overall AI efforts) or support certain strategic priorities such as growth in a particular part of the business. Making such criteria explicit can help during the idea-generation phase, and it’s critical when you evaluate and prioritize.

In large companies, it can take a few weeks to go through a process to gather and prioritize ideas, but this pays off well in identifying valuable, concrete ideas to pursue. AI isn’t useful unless we find appropriate ways to apply it, and I hope these best practices will help you to generate great AI application ideas to work on.

[Original text: https://t.co/7pwXMGEbpI ]

[Brendan O'Sullivan 🇮🇪🇪🇺 @ImtaBrendan]

Very nice Bloom's Taxonomy cheat sheet! Fantastic for planning, questioning and skill development.

[Javaid Siddiqi @jsiddiqi7]

Teachers across the country are beginning their new school years. Your dedication and passion are inspiring. Here’s to a successful and enriching school year ahead. #EducatorSaturday

[Evidence Based Education @EvidenceInEdu]

The science of learning 📕

This resource summarises key research on how students learn and links it to practical teaching strategies.

Download your free copy 👇
https://t.co/TOJTcM7B3t