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Yes, I've found dashboards useful.

Generalizations or problematic, since so much of this depends on industry, company, and role.

I'm in an industry (education) where at-a-glance dashboards have a proven track record of dramatically improve learning outcomes. It turns out that:

(1) Teachers, while believing they know their students, actually don't. Teachers also regularly fail to grasp what students do and don't understand.

(2) Allowing teachers to monitor their students, both individually and classroom wide, leads to much better outcomes.

In the context of a classroom, teachers are busy, non-technical (and sometimes incompetent), so an at-a-glance view is critical. For an example of real-time dashboarding done well, see Learning Catalytics. It grew out of a highly-respected physics education research project (Peer Instruction, by Eric Mazur), and allows teachers to monitor student understanding during lecture. Showing where misconceptions exist allows for deeper dives and deeper conversation. There are many systems like it, and ample evidence of improved student growth when they're used.

That's very specific, I know, but there are similar examples in other industries I've worked in.

Real time monitoring during a live task is certainly a use case I've never thought of.

A very good point.

I remember a piece on programming education research awhile ago. Said education outcomes improved considerably if you constantly bombard students with multiple choice questions.

Administering that through a dashboard would be essential.

> education outcomes improved considerably if you constantly bombard students with multiple choice questions

I would love to read more about that, if you happen to have a link to share.

https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...

Much obliged.

I see the relevant part seems to be the following, in the "Tip 4" section:

""" The key to making demonstrations more effective is to make learners predict the outcome of the demonstration before performing it. Crucially, their prediction should be in some way recorded or public, e.g., by a show of hands, by holding up cue cards marked with A, B, C, or D, or by talking to their neighbour. We speculate that the sting of being publicly wrong leads learners to pay more attention and to reflect on what they are learning; regardless of whether this hypothesis is true, instructors should be careful not to punish or criticise students who predicted wrongly but rather to use those incorrect predictions as a spur to further exploration and explanation. """

That part has been replicated (see e.g. vicarious learning, from Derek Muller as well as Miki Chi, studies on Khan Academy in-video questions, etc.).

However, it's not the most relevant part. The most relevant part is simple use of active learning. The classic paper here is Hake (Interactive-Engagement vs. Traditional Methods: A Six-Thousand-Student Survey of Mechanics Test Data for Introductory Physics Courses). However, it's been replicated discipline-by-discipline (lots of papers in PNAS). Best summary is ICAP by Miki Chi. Miki didn't invent it, and ICAP isn't the most rigorous, but she's pretty good at these summaries.

Having students do anything beyond listening leads to huge improvements in student learning, basically no matter what it is. Some things lead to bigger improvements than others, of course. Even clicking a "next" button occasionally shows a (very modest) gain. For good interactive engagement, doubling of learning gains is a good lower-bound over passive listening.

Note that it's Active Recall according to other education research.

Not only active participation, but actively being forced to remember the knowledge, in a different format to the original, is the key.

Teaching another student is definitely an approach to this. But a gentle touch multiple choice question also works.

No, that's not really fair.

There's a broad range of techniques which have significant learning gains beyond passive learning. Active recall is one of them. We have no clear sense of how they compare to each other or how they work in combination. There is ample evidence for active recall, but there is equally ample evidence for a dozen other techniques much like it. We don't have great studies comparing them side-by-side.

The best hypothesis for how they compare is ICAP, referenced. It's still a hypothesis. You can look at the evidence behind it.

Much of this is context-dependent too. Active recall is great for factoids. If you're trying to learn a date in history, memorize a word in a foreign language, or a formula in math, your best bet is a spaced repetition system (how you time active recall makes a big difference too; spaced repetition > simple active recall > passive recall).

If you're trying to understand a concept like force (in physics), what an integral means (in math), make sense of the tradition away from serfdom, or compare ethical systems between two cultures, active recall is almost irrelevant. At that point, the best methods are probably constructive and interactive learning.

I agree.

In my mind, active recall is an excellent technique that's appropriate for the first lecture covering a topic.

Which is why I like it as a prompt in a lecture, not so much later on.

Constructive learning in my mind comes later.

Re: the balance between constructive learning and other types.

I feel that we often accelerate the students too quickly to constructive learning, whilst they can still benefit from rote learning some worked examples.

The balance between rote learning and "copying" worked examples, and constructive learning from first principles, is a very difficult one.

I truly think that, certainly for programming education, all my professional life I've gotten huge mileage out of genuinely copying in example projects and tutorials into a text editor.

I think we underestimate just how much students benefit from rote learning.

I think programming education would benefit far more from 19 year old rote learning from retired 65 year olds. I find the college education system is fairly broken, in that it deploys researchers who are excellent lecturers, but far too few 65 year olds.

And there's an ocean of extremely willing to teach 65 year olds out there.

honestly this is a nightmare'ish point of view for anything but hard science at senior level. The emotional environment of being monitored constantly is toxic. Control-oriented people gravitate towards constant monitoring in similar ways as the Executive and their Dashboard are described above. How can thinking and feeling humans delegate interaction in a learning environment to machinery ?

That can be true, but it's not true in this case. Eric Mazur's classroom is sort of a visitor sport -- people come from around the world to observe. I'd encourage you to do the same. It's very human.

As originally implemented, the technique was pretty simple:

- You give a traditional lecture

- Every five minutes or so, students are asked a question to see if they understand concepts

- The system peers students who answered differently to discuss. Students can change their answers at the end.

- The instructor and class see aggregated statistics (e.g. 90% of people answered 27, 5% answered 54, and 5% gave other answers)

- Based on the feedback, the instructor can do a deeper dive into places where there are common misconceptions

Today's class is very different. Mazur juggles all sorts of peer learning models, 360 reviews, etc. in ways which are superhuman. But that's the original model.

Most of the uses of formative assessment lead to classrooms feeling more personal, more personalized, and more human.

There are control-oriented systems which scare the heck out of me, like GoGuardian and Securely, but this isn't it. There is a complex ethics question of how to build systems which don't dehumanize students.

The problem with voluntary question and answer is that the loud kids ask questions.

The shy kids sit there and be quiet.

"Prompt" questions, like multiple choice, are a good way of encouraging reluctant students to come out of their shell."

It's not voluntary. 100% of students are asked a question. In the eighties, this was entered into a classroom response system (a clicker where kids press a button on multiple choice) and an eighties-era computer would process their responses for an eighties-era dashboard, presumably using one of those $20,000 CRT projectors.

Today, it's cell phones and laptops, and they do a lot more than multiple choice. Students might be asked to highlight text in a passage (and the system shows a heatmap), to draw vectors, etc.

I didn't have that growing up at all, but I think that's an excellent way to get 11 year olds to learn something. Minus the $20k projector.

Mate it's a classroom of 9 year olds.

They can't be trusted to dress themselves.

I quite like monitoring dashboards and use them frequently. Mainly for examining system health and debugging operational issues.

My previous team had some business metric charts/dashboards related to system we worked on that we reviewed weekly. Most weeks review was it looks normal and was pretty fast review. Occasionally we'd see an unexpected deviation in some top level metric and that could trigger follow up work. Ideally all interesting deviations an alert would catch, but alerts are never complete.

Most of dashboards I've cared for my team/department was primary user of. I have not made dashboards much for other stakeholders.

> I've only had dashboards requested by users with a dysfunctional relationship with engineering, in companies where the data is irrelevant.

Sry, but this completly ... not true! Maybe in your subjective business environment, but definitely not for the whole community of python users.

We are placed in a scientific environment, teamworking with highly dimensional data. Here there is not a single person who has time to tinker around with another ones code nor data structure.

This will be a great benefit for regular report sessions - THX to the author!

Whenever I see a "dashboard" I think of something like this:

https://www.amazon.com/Fisher-Price-DYW53-Rollin-Strollin-Da...

It's a kids toy that lets them pretend they're driving the car.

Dashboards are really useful when you’re trying to distribute a set of metrics that large numbers of people actually need to know and agree on in order to do their jobs. Most metrics aren’t like this, so there are a lot of bad dashboards, but they definitely have their place.

Edit: A good example of this is something like incoming shipments for a warehouse. Management all the way down to warehouse workers need to know what is slated for delivery, what’s already been delivered in the last few days, and what’s late.

Streamlit[0] was created specifically to create dashboards for ML/data science groups, and I've found it pretty useful. I've used it for research (model inspection and development), as well as teaching and it's been pretty useful for that.

[0] - https://streamlit.io/

Ditto, although for my applications they are not live (typical business processes lag by a month or more). So most of my stakeholders are happy that I just send a monthly PDF with updates.

The endless tinkering is still requested with the static reports, but that is much easier to deal with than serving up dashboards and tinkering with that.

You are lucky! There are many people that don't want to touch any code. Setting up a Python environment locally might be a big pain. That's a huge blocker for notebooks sharing.

I treat dashboard as visualization part. What is more, visualized data is used to trigger further actions - for example, send alert email. Yes, I found them useful.

You can share notebooks using hosted solutions

Yes, that's the solution but only if you share with people that understand Python and if you are allowed to upload code + data to the cloud.

Docker?

You’re definitely right that metrics are often used as post-hoc justification of decisions already made.

An example of actual dashboard culture is AWS’s operational metrics meetings. The construction of the dashboards themselves is scrutinized just as much as any latency or availability metric (may God have mercy on your soul if use a pie chart or display the median of metrics without justification). And it’s actually useful for surfacing issues and knowledge sharing. The frequent cadence can be a big cause if stress though.

https://aws.amazon.com/blogs/opensource/the-wheel/

People that run businesses and care about data don't want dashboards, they want spreadsheets. Lately I've been having some success with creating "user friendly" data views in big query and giving out access to them with connected Google sheets. If anyone wants to make a nice graph, pivot table or add their formulas to the workbooks they're free to do so using a tool they're already familiar with. The the data is backed by the database and can update on its own so we're not constantly sending out new versions of exported files.

Excel can get data from APIs nowadays (and even use modern auth!).

I've been reasonably successful at creating a data environment for my company in which all the data is exposed through REST APIs.

The existing tooling for APIs in general is top notch. Also, you get a lot of architectural flexibility.

I would say it depends on data type and size. There are data sizes that would not fit in spreadsheets and need to be presented in a different way.

BTW, I love spreadsheets. Spreadsheets + Python is very powerful connection :)

From the non-technical side: it isn’t necessarily that dashboards are not useful, it’s just that most people who want them don’t actually know what data is useful.

I've certainly had your skepticism and these same challenges. Nonetheless, one study of Shopify storeowners who adopted dashboards saw a 4-10% increase in weekly revenue post adoption: https://pubsonline.informs.org/doi/abs/10.1287/mksc.2022.135...

But to your point there's often a principal agent problem in larger organizations. A decision-maker wants to appear "data-driven" so asks for a dashboard because it costs nothing for them to ask--someone else does the work. And when the dashboard shows something different than expected they dismiss the concern or find a reason that the dashboard is "wrong."

Non-techies wielding SQL is a big advantage for sure, but a moderately complex dataset sends SQL aggregations complexity through the roof. While modern data warehouses can handle such queries, I can hardly imagine non-technical person being able to write it.

That's where dashboards come in handy.

Denormalized views? Every dashboard is an exercise in denormalization anyway.

Certainly I would never expect people to be experts. SQL Group By is nontrivial.

Still limited to 2-dimensional data. Data warehouses support structs and arrays, but it's not easy for a human to read.

“I find your ideas interesting and would like to subscribe to your newsletter." Sadly, your common sense observations will get belittled by the growing crowd of Liliputians who seem to enjoy celebrating their ignorance.

I don't mean this as a criticism of the link author. Nobody wants to use a toilet plunger. You still want a good one.

Merely an observation about the dashboard industry.

Look at my latest gizmo...on a drone! Look at my latest data crunch app...on the web! Yep, smells like management all over.

Yes I've seen many good uses of dashboards. That said, I totally get where are you coming from, been there as well.

I've found them very useful at impressing clients and getting contracts XD

Tons of good experience with dashboards.

Managing Oracle databases? Enterprise Manager Cloud Control.

Managing your investments? Schwab has a good interface.