论文标题
反思计算机科学教育的演变
Reflections on the Evolution of Computer Science Education
论文作者
论文摘要
多年来，计算机科学教育一直在不断发展，以反映应用现实。直到大约十年前，计算，算法设计和系统软件的理论主导了课程。大多数课程被认为是核心，因此是强制性的。程序结构不允许选择或多种多样。本专栏分析了为什么这改变了2010年大约在许多主题的选修课成为主流教育的一部分以反映计算机科学的横向加速度的一部分。人工智能，机器学习，虚拟化和云计算的基本发现已有数十年历史。数据科学中的许多核心理论都是数百年历史的。然而，他们的杠杆作用只有在2010年左右之后才爆发，当时舞台以大规模以人为中心解决问题。这部分是由于创新的现实世界应用程序的匆忙，这些应用程序通过无处不在的智能手机到达了普通人。 AI/ML模块以流行的编程语言到达；它们可以用来通过高速互联网连接到达公共云上的强大（但价格合理）来建立和培训模型。 Academia通过调整计算机科学课程以使其与不断变化的技术格局保持一致。这种体验式作品的目的是引发有关计算机科学教育的过去和未来的生动讨论。
Computer Science education has been evolving over the years to reflect applied realities. Until about a decade ago, theory of computation, algorithm design and system software dominated the curricula. Most courses were considered core and were hence mandatory; the programme structure did not allow much of a choice or variety. This column analyses why this changed Circa 2010 when elective subjects across scores of topics become part of mainstream education to reflect the on-going lateral acceleration of Computer Science. Fundamental discoveries in artificial intelligence, machine learning, virtualization and cloud computing are several decades old. Many core theories in data science are centuries old. Yet their leverage exploded only after Circa 2010, when the stage got set for people-centric problem solving in massive scale. This was due in part to the rush of innovative real-world applications that reached the common man through the ubiquitous smart phone. AI/ML modules arrived in popular programming languages; they could be used to build and train models on powerful - yet affordable - compute on public clouds reachable through high-speed Internet connectivity. Academia responded by adapting Computer Science curricula to align it with the changing technology landscape. The goal of this experiential piece is to trigger a lively discussion on the past and future of Computer Science education.
