A student’s perspective on where the future of learning—with AI—should be headed.  

GUEST COLUMN | by Conrad Ingersoll Dube and William Saulsbery

Education systems, especially K-12, are the foundation of society’s future, meant to equip students with the knowledge and skills that reflect the present, grounded in lessons from the past, to prepare them for tomorrow. Yet, as the world evolves at an unprecedented pace we’re still clinging to outdated teaching methods from decades ago. It’s time to question whether we’re truly preparing the next generation for the challenges and opportunities of the future.

‘It’s time to question whether we’re truly preparing the next generation for the challenges and opportunities of the future.’

Modes of imparting education have improved. Digital, internet and media technologies are frequently employed in classrooms, homework is submitted electronically, classroom discussion and chat groups are formed online. Dissemination has improved, but the content being imparted has remained fairly constant. How can we take this well established, and irreplaceable foundation and evolve it to fully prepare the students of today for the world of tomorrow?

Beyond Fundamental Programming

Students can access courses in AI or in Python programming, but the overwhelming majority of our coursework remains consistent with the curriculum of the past. A large part of education continues to focus on memorization and regurgitation. In days where neuralink technologies are starting to make information available to us from the web at any time, we must focus on evolving education to meet the challenges of modern times. With the fourth industrial revolution upon us, we are entering into an algorithmic economy. For students to succeed in the coming world, they must be taught to think creatively and become experts at problem solving.

For example, take the curriculum around the United States Civil War.  We are commonly teaching students that; there was a Civil War, the North won, Slavery ended, President Lincoln was assassinated. This is an incredible lesson that must be taught, but we are not doing the event, and its participants, justice teaching facts and dates alone. What if instead educators talked through how the war was fought, how it was won. How did General Grant solve terrain, feeding troops, morale, delegation, and how did he grow as a leader throughout the conflict? Then, ask students to tie these learnings to either current personal challenges, or the current geopolitical landscape. Walk them through questions like “how did Lincoln build a coalition to end slavery in the legislature? What was his relationship like with Grant, Sherman, and his other generals?” What is the importance of a great leader to listen to those he has appointed and take their council? The Civil War could be used to give students skills for life and their coming careers. 

Simultaneously, our educators would unleash their creativity to its fullest potential and become excited again about their subject matter. Teachers become teachers because they want to help children learn and flourish. They want to prepare their students for the new world, they want to impart wisdom that was imparted to them, and sometimes wisdom that was not. Release them from “textbook to white board and back again,” quizzing kids on this date and that name. Place 30% of their lesson plans in foundational knowledge of events, and 70% in the hows and whys, and what this can teach their students about solving the challenges facing the world today. 

Free students for Creativity and Problem Solving

The future of work is not person and machine working at odds, or at parallel, but working directly together. We must teach our students to use machines to quickly complete all repetitive tasks, or gathering of common facts and dates. The next generation of careers require humans to act in tandem with machines to form a hybrid society.

Teach students how to leverage artificial intelligence to act as an augmenting agent assisting in tasks. How do we leverage AI to drive better decisions and improve outcomes? What are the prospective threats of AI, and how to protect against them? How do humans introduce ethics into these digital systems? How do humans best combat threats to privacy, safety and human dignity? These are the questions we must grapple with and solve for. Coursework in every dimension should include a hybrid methodology that allows human students to focus on creativity and innovation. 

Who is Leading the Way?

Estonia is emerging as a leader in digital teaching. Their “Tiger Leap” initiative, implemented over 20 years ago to introduce computers to students at an early age, has been a success. For multiple years they have been named as one of the top Programmes for International Student Assessment by the Organisation for Economic Co-operation and Development (OECD). 

The UK is introducing computers and algorithms to children as early as 5 years old. Research shows that learning new languages is easiest when you are young, and computer languages are no exception. Whether you are programming using logic languages like Prolog, or writing object-oriented code in Java, learning the various dialects to converse with our digital colleagues should be as natural as learning new languages at an early age. 

A Digital Assistant for Every Student

AI shouldn’t be the centerpiece of education, but effective leveraging of a digital AI assistant should be a priority. When learning history, we should not be challenged to recall dates and events, these should be furnished by our digital assistant. 

Geography courses should focus not on the names of various straits and gulfs, but on the geographical challenges of these areas and how to best navigate them based on situational challenges and hypotheticals. Global warming, ecological threats, and biomedical solutions would all be engrossing topics for young minds. Again, use foundational curriculum as a basis for real world creativity and innovation. Walk students through the history of the Suez Canal and how it transformed commerce and the way of life for three continents. Then, ask students “what if a climate catastrophe closed the canal for 6-12 months?” What would the global consequences be? How many people would be adversely affected? Who would profit? Get them to think creatively on possible short, medium, and long term solutions. Educators and those running these institutions will become inspired at the possibilities of what scenarios they could create for their students, gaining ownership of the new educational system.

An Incalculable Impact

The United States is behind many in the industrial world in terms of science and math education. We need top governmental focus to catapult us to the front. We need to draw the brightest brains to teaching by arming them with a rock solid foundation honed over decades of practice, topped with a new problem solving focused end game. We must as a society make the choice to acknowledge and reward our teachers at an exponentially higher grade than today. They are molding the minds of our future society; should we not compensate them at top executive levels?

Strategic impact deserves more attention than the tactical quarterly impact that Wall Street seems to be focused on. Education and its overhaul has to become a central focus, as our future depends on it. Think of the cumulative benefit to the United States on the global stage—if every child is taught how to figure things out, creative problem solve, and be an innovator, the benefit to the nation would be incalculable. 

—

Conrad Ingersoll Dube (son of Chetan Dube, renowned futurist and founder of Amelia and Quant), is currently in high school in New York and his thoughts were the genesis of this piece. 

William Saulsbery is a former teacher and tutor who co-wrote the piece with Conrad. Connect with Will on LinkedIn.

The post The Future of Learning: Empowering the Next Generation to Lead the Digital Age appeared first on EdTech Digest.

In our goal of trying to estimate a programming language’s popularity, we realized that no one can look over the shoulder of every person writing code, whether that be a child writing a Java script for a personal Minecraft server, a mobile app developer hoping to hit it big, or an aerospace engineer writing mission-critical code for a voyage to Mars. Our Top Programming Languages interactive tries to tackle the problem of estimating a language’s popularity by looking for proxy signals.

We do this by constructing measures of popularity from a variety of data sources that we believe are good proxies for active interest for each programming language. In total, we identify 63 programming languages. We then weight each data source to create an overall index of popularity, excluding some of the lowest scorers. Below, we describe the sources of data we use to get the measures, and the weighting scheme we use to produce the overall indices.

By popularity, we mean we are trying to rank languages that are in active use. We look at three different aspects of popularity: languages in active use among typical IEEE members and working software engineers (the “Spectrum” ranking), languages that are in demand by employers (the “Jobs” ranking), and languages that are in the zeitgeist (the “Trending” ranking).

We gauged the popularity of languages using the following sources for a total of eight metrics (see below). We gathered the information for all metrics in July—August 2024. The data were gathered manually to avoid results being biased due to API changes or terminations and because many of the programming language’s names (C++, Scheme) collided with common terms found in research papers and job ads or were difficult for a search engine to parse. When a large number of search results made it impractical to resolve ambiguities by examining all of the results individually, we used a sample of each data source, and determined the relevant sample size based on estimating the true mean with 95 percent confidence. Not all data sources contain information for each programming language and we interpret this information as the programming language having “no hits” (that is, not being popular).

The results from each metric are normalized to produce a relative popularity score between 0 and 1. Then the individual metrics are multiplied by a weight factor, combined, and the result renormalized to produce an aggregate popularity score.

In aggregating metrics, we hope to compensate for statistical quirks that might distort a language’s popularity score in any particular source of data. Varying the weight factors allows us to create the different results for the Spectrum, Jobs, and Trending rankings. We fully acknowledge that, while these weights are subjective, they are based on our understanding of the sources and our prior coverage of software topics. Varying the weight factors allows us to emphasize different types of popularity and produce the different rankings. We then combined each weighted data source for each program and then renormalized the resulting frequency to produce an aggregate popularity score.

The Top Programming Languages was originally created by data journalist Nick Diakopoulos. Our statistical methodology advisor is Hilary Wething. Research assistance was provided by Elizabeth Wood. Rankings are computed using R.

Google

Google is the leading search engine in the world, making it an ideal fit for estimating language popularity. We measured the number of hits for each language by searching on the template, “X programming language” (with quotation marks) and manually recorded the number of results that were returned by the search. We took the measurement in July 2024. We like this measure because it indicates the volume of online information resources about each programming language.

Stack Overflow

Stack Overflow is a popular site where programmers can ask questions about coding. We recorded the number of questions tagged to each program within the last week prior to our search (August 2024). For the Mathematica/Wolfram language, we relied on the sister “Stack” for the Mathematica platform and tallied the number of programming-related questions asked in the past week. These data were gathered manually. This measure indicates what programming languages are currently trending.

IEEE Xplore Digital Library

IEEE maintains a digital library with millions of conference and journal articles covering a wide array of scientific and engineering disciplines. We searched for articles that mention each of the languages in the template “X programming” for the years 2023 and 2024, because this is the smallest timeframe for which we could access articles. For search results that returned thousands of articles, we identified the correct sample size for a 95 percent confidence interval (usually a little over 300) and pulled that number of articles. For each language we sampled, we identified the share of articles that utilize the programming language and then multiplied the total number of articles by this share to tally the likely total number of articles that reference a given programming language. We conducted this search in July 2024. This metric captures the prevalence of the different programming languages as used and referenced in engineering scholarship.

IEEE Job Site

We measured the demand for different programming languages in job postings on the IEEE Job Site. For search results that returned thousands of listings, we identified the correct sample size for a 95 percent confidence interval (usually around 300 results) and pulled that number of job listings to manually examine. For each language we sampled, we identified the share of listings that utilize the programming language and then multiplied the total number of job listings by this share to tally the likely total number of job listings that reference a given programming language. Additionally, because some of the languages we track could be ambiguous in plain text—such as lD, Go, J, Ada, and R—we searched for job postings with those words in the job description and then manually examined the results, again sampling entries if the number of results was large. The search was conducted in July 2024. We like the IEEE Job Site for its large number of non-U.S. listings, making it an ideal to measure global popularity.

CareerBuilder

We measured the demand for different programming languages on the CareerBuilder job site. We searched for “Developer” jobs offered within the United States, as this is the most popular job title for programmers. We sampled 400 job ads and manually examined them to identify which languages employers mentioned in the postings. The search was conducted in July 2024. We like the career builder site to identify the popularity of programmer jobs in the United States.

GitHub

GitHub is a public repository for many volunteer-driven open-source software projects. We used data gathered by GitHut 2.0, which measures the top 50 languages used by the number of repositories tagged with that language and draws from GitHub’s public API. We use two metrics from GitHub: repositories that have been “starred” by users to reflect long-term interests, and the number of pull requests to indicate current activity. The data cover the second quarter of 2024. These measures indicate what languages coders choose to work in when they have a personal choice.

Trinity College Dublin Library

The library of Trinity College Dublin is one of six legal deposit libraries in Ireland and the United Kingdom. A copy must be deposited with the library of any book published or distributed in Ireland, and on request any U.K. publisher or distributor must also deposit a book. We searched for all books published in the year to date that had their subject matter categorized as computer programming and totaled the number of returns. The search was conducted in June 2024. We like this library collection because it represents a large and categorized sample of works, primarily in the English language.

Discord

Discord is popular chat-room platform where many programmers exchange information. We counted the number of tags that correspond to each language. In the case of languages that could also be names of nonprogramming topics, (many nonprogramming-related topics also have dedicated Discord servers; for example, “Julia” could refer to the programming language or the Sesame Street puppet), results were manually examined. Disboard was searched in August 2024. Disboard lists many public discord servers and many young coders use the site, contributing a different demographic of coders.

Welcome to IEEE Spectrum’s 11th annual rankings of the most popular programming languages. As always, we combine multiple metrics from different sources to create three meta rankings. The “Spectrum” ranking is weighted towards the profile of the typical IEEE member, the “Trending” ranking seeks to spot languages that are in the zeitgeist, and the “Jobs” ranking measures what employers are looking for.

You can find a full breakdown of our methodology here, but let’s jump into our results. At the top, Python continues to cement its overall dominance, buoyed by things like popular libraries for hot fields such as AI as well as its pedagogical prominence. (For most students today, if they learn one programming language in school, it’s Python.) Python’s pretty popular with employers too, although there its lead over other general purpose languages is not as large and, like last year, it plays second fiddle to the database query language SQL, which employers like to see paired with another language. SQL popularity with employers is a natural extension of today’s emphasis on networked and cloud-based system architectures, where databases become the natural repository for all the bytes a program’s logic is chewing on.

Top Programming Languages 2024 is brought to you by the IEEE Computer Society. Get connected with the world’s largest community empowering computer science and engineering professionals.

Stalwarts like Java, Javascript, and C++ also retain high rankings, but it’s what’s going on a little further down that’s particularly interesting. Typescript—a superset of Javascript—moves up several places on all the rankings, especially for Jobs, where it climbs to fourth place, versus 11th last year. Typescript’s primary differentiator over Javascript is that it enforces static typing of variables, where the type of a variable—integer, floating point, text, and so forth—must be declared before it can be used. This allows for more error checking when Typescript programs are compiled to Javascript, and the increase in reliability has proven appealing.

Another climber is Rust, a language aimed at creating system software, like C or C++. But unlike those two languages, Rust is “memory safe”, meaning it uses a variety of techniques to ensure programs can’t write to locations in memory that they are not supposed to. Such errors are a major source of security vulnerabilities. Rust’s profile has been rising sharply, boosted by things like a February cybersecurity report from the White House calling for memory safe languages to replace C and and C++. Indeed, C’s popularity appears to be on the wane, falling from fourth to ninth place on the Spectrum ranking and from 7th to 13th on the Jobs ranking.

Two languages have entered the rankings for the first time: Apex and Solidity. Apex is designed for building business applications that use a Salesforce server as a back end, and Solidity is designed for creating smart contracts on the Ethereum blockchain.

This year also saw several languages drop out of the rankings. This doesn’t mean a language is completely dead, it just means that these languages’ signal is too weak to allow them to be meaningfully ranked. Languages that dropped out included Forth, a personal favorite of mine that’s still popular with folks building 8-bit retro systems because of its tiny footprint. A weak signal is also why we haven’t included some buzzy languages such as Zig, although those proficient in it can apparently command some high salaries.

As these other languages come and go from the rankings, I have to give the shout out to the immortals, Fortran and Cobol. Although they are around 65 years old, you can still find employers looking for programmers in both. For Fortran, this tends to be for a select group of people who are also comfortable with high-energy physics, especially the kind of high-energy physics that goes boom (and with the security clearances to match). Cobol is more broadly in demand, as many government and financial systems still rely on decades-old infrastructure—and the recent paralyzing impact of the Cloudstrike/Microsoft Windows outage incident probably hasn’t done much to encourage their replacement!