Enter Online Training Platforms

Where do the online training platforms, such as Coursera, edX and Udacity that sprang up to offer MOOCs in computer science related-topics and introduced mass audiences to coding, fit into this picture?

These platforms are characterized by features not found on traditional institutions. Worldwide and decentralized with flexible e-learning formats and environments, no setup is required on the user's part, and despite their physical absence, they encourage cooperation between their students.

As strongly community driven platforms, they promote student interaction through chat rooms where students hang out and ask questions, seek help or look to find a programming partner. In addition, through real world "coffee-and-code" events, one can meet his colleagues face to face for the purpose of exchanging ideas, helping each other out or bonding as a team.

Students are also encouraged to go online and post all their work on their GitHub repos , that way incrementally building their portfolios as well as learning to cooperate and coordinate. Post study facilities include free preparation for job interviews as well as job boards where businesses directly hire graduates.

But traditional institutions seek to modernize too. Let's check how a brick and mortar college, the Supercomputer Department of the University of Toronto, aims to teach Computing Disciplines to graduate students of emerging computational fields such as biology and medical science.

A departure from the usual, is that attendance is not mandatory. The lectures and their material are delivered in such a way that gives students the flexibility of attending in person or watching remotely.

Secondly, rather than basing and working on some predetermined data samples that potentially mean nothing to the student, the educators encourage them bring their own data to the table. This way students can apply the techniques and tools presented in a way that is meaningful to their own research field hence achieving immediate feedback as they are basically work on their own research questions and problems. This though poses the issue that grading cannot be done quantitatively as there aren't standard solutions to the handed out homework since the data that it is based upon is custom. This issue is sidestepped by providing very detailed guidelines on the things to look for when grading.

Another ingredient to the successful recipe as well as particularly invaluable in classes with a great number of students, is to delegate grading to TAs or Teaching Assistants, a practice also employed by popular MOOCs. Thanks to TAs the luxury of looking at each student assignment individually and not in bulk can be afforded. Therefore each assignment is checked against the logic of the implementation and not just the outcome, while students also receive individual feedback and detailed comments on it.

TAs are often students who have taken the course before, therefore are already trained on the best practices, philosophy and requirements the course is after. Hence their experience can be leveraged in helping new students by anticipating their needs. This is a scheme also employed by Udacity's Nanodegrees as qualified graduates are promoted to the positions of "Mentors".

Depending on the subject at hand, more practice or more theory is offered. For example, when covering topics such as the Linux Shell, the basics of the R language, data visualization, etc, a more practical outlook is employed.

However, if the topic to be covered requires some theoretical background, we a more traditional lecture can be delivered – e. g. reviews on probability and statistics, introduction to machine learning and neural networks, etc.

Whatever the case, computer use is required in order for the students to actively follow the material's computational implementation. It is preferable for the students to carry their own laptop than to work on the standard pc's of the computer lab. That way they feel more comfortable because they work on their own machines, while at the same time gaining much needed practice in setting up the environment and tools on their own. But the computer lab is not preferred for one more reason. The ideal location of running the lectures is the traditional lecture rooms due to their plentiful space which allows both students and lecturers to comfortably use their own laptops as well as move easily around, something especially desired for lectures that contain hands-on components

At the end of the day, the question that must be asked should be: "What makes MOOC platforms less respected than a bricks and walls materialized institution such as a real world College?"

They're missing that tight physical involvement of the student in the software development life cycle, and as the study suggests,
the integration with the experts of the industry. Nevertheless, MOOCs teaching the aspect of coding is what they are best for.

This about colleges and education. What about LLM-based agents coming for our jobs? I think the answer is definitive as far as LLMs replacing software engineers goes. A chatbot cannot posses that multi-skilled paradigm necessary nor can it replace any of those skills by automating them. AI could be very useful as the developer's side kick but not as his replacement, and I don't see this status quo change. Coding is rarely the issue and SE is not just coding.

In conclusion, Software Engineering will never die or be replaced. It might shift shape, adapt and embrace technologies such as generative AI, but there never will be a substitute for the human programmer. 

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