Developing Curriculum

The Institute for Computing in Research works with teachers to implement curriculum on various topics.

Goals and nature of our curriculum packages

Our goal is to prepare students from your district for research in STEM and non-STEM academic areas, or for a research-minded approach to technology jobs.

Advantages of the Institute’s curriculum

Hands-on: the problem of getting students to have true hands-on experience is an old one. We carefully prepare a custom approach to using programming tools, retrieving data, and visualizing information based on a school’s specific hardware and network constraints. This custom preparation allows students to do hands-on work from the start.

Customized: we re-work our curriculum for every teacher who uses it in their courses.

Expertise in answering teacher questions: we have domain-specific experts who publish regularly in their field, as well as generalists who are available to teachers throughout the course, when questions, doubts, and opportunities for special topics come up.

World-class guest lecturers: the scientists, scholars, and technologists from our community are available to give special-topic guest lectures at various times in the curriculum. This adds “vision of depth” in a way that inspires students and instills the material more firmly.

Support is always there, and more is available: our scholars are actively involved and interested in curriculum development at a research level. This means that curriculum is never complete, and ongoing feedback with teachers is welcome, and a part of our core offering. In addition we offer further tiers of support and opportunity to support ongoing instruction and mentor teachers, if needed.

Pricing: our pricing is intended to be accessible to any district: we charge districts based on what they already pay for comparable curriculum packages. If a school requests a higher level of support, we negotiate that price based (once again) on the district’s typical expenses.

Link to the “next level”: our curriculum always makes clear what direction takes students to the next level of expertise. This makes it the correct basis for future employment or university work, and avoids that common feeling of “I did not know what I was doing when I got to [next level job/academic endeavor]”.

No proprietary tools/instructional-materials, and no lock-in: the tools used in our hands-on work, and the teaching materials we write, are entirely free/open-source/open-access. A teacher who has used our curriculum can continue to teach off of it without needing to renew with us, or they can collaborate with us on future work. There is no “vendor lock-in” when using our materials, consulting, or customization.

Curriculum packages available

The list below is not complete: the scientists, scholars, and technologists at the Institute work in many other areas as well, which allows us to prepare custom workshops for schools or other groups.

Data Science

Data science has risen to prominence in recent years: it leads to rewarding professions, and combines expertise from a specific research domain with a generalist’s skill in software development, visualization, and machine learning.

Our machine learning curriculum starts with the simple level of Python knowledge that students can pick up in our free Serious Computer Programming for Youth weekend workshop (although there are alternative ways of picking up that basic Python experience). We then continue with a hands-on tour of types of data repair and visualization, using examples from climate measurement stations and music.

Once students are comfortable with the basic data science tools we explore domain applications, using as examples data sets from astronomy, bioinformatics, and quantitative finance.

If the course allows going beyond this basic level, our curriculum then introduces the next level of mathematical skill (Fourier Analysis and some statistics), as well as machine learning techniques. We have developed an effective curriculum to teach Fourier Analysis to high school students, which allows students to learn the basics of digital signal processing, and the preparation of data sets for machine learning techniques.

Sysadmin, IT, DevOps

The support of a company’s or department’s computing infrastructure is the job of the system administrator, or the IT department. “Sysadmins” are the ultimate generalists, learning a variety of techniques and accumulating long-term knowledge, as well as awareness of the state of the computer industry.

Few high schools teach system administration. We believe that this is a serious flaw, based on at least the following arguments: (a) system administrators become extraordinarily rapid and subtle software users and developers; (b) it is an excellent job possibility for students who do not have college temperament or opportunity; (c) the problem-solving skills are intellectually fascinating; (d) it leads to some of the best campus jobs for future college students.

Our system administration curriculum addresses the difficult challenge of keeping up-to-date on both techniques and trends. We do this through interaction with our community of experts in the various areas (operating systems, networking, cybersecurity, software engineering, …), and revising the material constantly.

The curriculum starts with building computers from bins of old hardware, then moves on to installing an operating system on that old hardware. It then teaches basic command line and scripting skills – an enabler for all that comes next.

Then comes a hands-on introduction to computer networking, delving in to the fundamental protocols and typical home/office/facility network layouts. We introduce tools that probe networks and report on them.

Next we work toward an understanding of cybersecurity, starting from the taxonomy of types of exploits, and moving on to prescriptions and case studies.

The final phase is a collection of special topics, which can include web site deployment, cloud computing (local and remote), container deployment, support for DevOops, …

Computing in social science, humanities, and the arts

Students in non-STEM fields seldom told that their future university work will be heavily computational. This has a general effect of slowing down their academic preparation, and making research opportunities more difficult.

We offer this curriculum for an elective high school course to give hands-on computational experience to advanced high school students in non-STEM fields.

The curriculum starts with a tour that demonstrates how a small amount of program can give interesting results and insight in various fields, including history, psychology, linguistics, analysis of literary text, music, and visual arts.

Then we step back to prepare students for hands-on work that allows them to reproduce the entire tour.

Then the biggest section: a series of case studies. This is a customized collection of problems from these fields which can be illustrated by computer programs. Examples include curve fitting (with data sets coming from anthropology and other social sciences), universal power law behavior (such as Zipf’s law) in social science and digital humanities, the analysis of sound and music, image filtering, and approaches to digital art.

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