A decade in computer industry is a long time. Many things relevant a decade ago are obsolete today. So comparing what I studied, with what today’s collegians study, is meaningless. However, what is comparable is the method of learning computing. I have had the good fortune of meeting lot of people of all ages in the field of computing (read computer science, IT, software, hardware etc) and I have had something to learn from many of them. From those interactions, I noticed how various individuals had similar or dissimilar computing habits than mine. After giving some thought to it, it was easy to correlate the habits to the era in which they learned computing. Once I could understand their habits, I could predict or at least anticipate their next action with fairly good success ratio. This proved useful in building good professional relations at work.
In this post, I am going to make observations about changes in computing technology and how it has affected learning styles of people. I want to keep it short and simple, so I am going to exempt the times preceding the era in which I learned (studied) computing. Since that’s approximately the year 2000, this post is going to contain comments relevant between then and now (2011).
So let’s start the comparison
| Factors | Parameters | Then (around year 2000) | Now (2011) |
| Economic | Affordability | A PC used to cost 10x to 15x monthly saving of a middle class Indian family | Typically, costs less than 3x monthly savings of a middle class Indian family |
| Availability | At least a month of wait time | Immediate availability. At worst, next day delivery | |
| Maintainability | People used to opt for maintenance plans | Hardly anyone opts for maintenance plans in PC segments, but expect 100% reliability | |
| Social | Status | Considered luxury | Considered necessity |
| Primary consumers | Kids for gaming and students and professionals for institutional work | All kinds of people for plenty of different kinds of usages | |
| Wow factor | Tremendous | Almost zero | |
| Special attractions | Internet | Broadband, VOIP | |
| Technology | Computer sizes | 233 MHz processor, 64 MB RAM, 1.2 GB HDD, 56 Kbps modems | 3 GHz processor, 2 GB RAM, 1 TB HDD, 2 Mbps broadband connections |
| Operating Systems | DOS or Windows 3.1 / 95 / 98 | Windows 7 / Linux / Mac | |
| Typical applications | Documents, Spreadsheets | Browser-based |
So it’s fair to say that a decade ago computing resources were scarce and had to be hard-earned whereas acquiring computing resources is easy pickings today. It’s basic human psychology that hard-earned resources tend to get valued more than the ones acquired without much hard work. Naturally then, the returns that one gets on it, is proportional to the value that one places on it. In the next table let’s substantiate the efforts part of the equation.
| Factors | Parameters | Then (around year 2000) | Now (2011) |
| Pedagogical | Knowledge Acquisition | Students used to refer tens of books and compare and contrast literature within. Computer books were costly, hence people used to subscribe to libraries and photocopy parts of books they wanted to refer | Mumbai university has “text-books” for computer science engineering courses (what a pity). I seriously doubt if such a system can produce students who can objectively evaluate merits and demerits of comp sci concepts |
| Technology-enabled knowledge acquisition | There were no Google, Wikipedia or blog sites to learn from other people’s experiences | Everything is available at click of a button. Absolutely zero efforts in acquiring knowledge resources | |
| Knowledge retention and reproduction | Students used to do projects which reflected the concepts one had studied. Some of the projects that me and my friends did in our college days were · MathCAD library · Multi-tasking in DOS · Chess programming · Code beautifiers · C to Basic conversion etc. | Today’s projects are much application oriented and very few systems oriented projects are original work for e.g. · Hotel management system · Railway reservation system · Compressing JPEG images etc. |
The Outcome
As a result of the hard-work, time and money spent in learning the science of computing, a student graduating from engineering college, a decade ago, would be brimming with confidence and eager to apply his theoretical knowledge in solving the real world problems. Also, because of his deeper understanding he is well-versed with the concepts and can objectively evaluate merits and demerits of a computing solution.
On the other hand, because of his focus on applications, an engineer who graduates in today’s era is more adept at aspects like Usability and domain-specific things. However, when such an engineer is scratched beneath the surface one sees poor fundamentals and lower levels of confidence. This corroborates with my observation that instead of knowing what he can offer to the world, he is in search of doing a course on the next in-thing like a new language or a new technology. So now, after graduating, he is thinking of spending money instead of earning money, to stay relevant
If I come across as saying - Cooking one's own food is better than eating cooked food - then let me be more explicit here. I am just saying that the value of the food is directly proportional to the hunger one has. Staying hungry is the key here.
My Verdict
According to me, various socio-economic and technological factors have severely impacted the learning approaches towards computing science. The seriousness and the formality required in studying the subject is quickly getting lost in a world which needs direct applications and / or commercialization. In the process, it has actually widened the skills gap instead of closing it. As a consequence, we have more people who can use a finished product and less people who can actually build a good product and that’s dangerous in the long run.
By way of analogy, I say, engineering a car and driving a car are two different aspects of the same car. Both are important in its own rights, but I am strongly biased that students who study computing science should engineer the cars and leave the driving to others who have not formally studied the subject.
DISCLAIMER: - These are vast generalizations based on personal observations and exceptions exist.
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