scripting for all icsi presentation .pdf · gold 17.2% 3.1% green 15.0% 18.7% intro cs course...

Scripting for All .

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Lindsay Popowski '21, Kewei Zhou '21, Zach DoddsHarvey Mudd College

CS for All ?

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CS for All ~ Scripting for All

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Different views of "The World" ...

"Scripting for World Domination"

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"All" ?

Scripting for All Disciplines

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Scripting for All Colleges

Claremont's Petri DishZD

Premise ~ Computing is (becoming) a professional literacy.

Challenges:

[Q1] How can CS departments support computing skills? … while encouraging students to retain and grow in other academic identities?

[Q2] What does a CS-for-All college curriculum look like? Our answer:

From Specialty to Literacy

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College Computing ~ College Writing

Literacy, not a specialty

College Writing

Many ways in Many ways through Many ways from

Writing department?ZD

Literacy, not a specialty

College Computing

Many ways in Many ways through Many ways from

Computing department?ZD

Claremont ~ 2025

Discipline-ownedSchool-owned

College ComputingData 8Computing for Insight

Who owns this?

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What is this?

Since 2009...

CS1 "gold"

CS1 "black"

CS1 "green"

students new to CS

students with some CS

Biology-owned CS1 course

Intro to CS in Python (breadth)

Intro to CS in Python (more breadth)

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Biology-owned CS1

Projects

Recursion

Iteration

CS1 in which all practice is biologically motivated…

Lectures: ½ CS ½ Bio ZD

What happens beyond CS1 … ?

CS1 "gold"

CS1 "black"

students new to CS

students with some CS

CS1 "green"

CS2 CS3Java & Racket C++

CS for Insight

Python

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Intro CS Course taken... CS majors Biology majors

Black 27.6% 2.0%

Gold 17.2% 3.1%

Green 15.0% 18.7%

Biology-owned CS1 ~ Aftermath

Intro CS Course taken... Avg # CS courses taken Avg # Bio courses taken

Black 5.6 1.6

Gold 4.0 1.8

Green 4.3 3.8

9 years of data:~300 green students:

~400 black students, and ~2000 gold students

LP

Biology-owned CS1 ~ Aftermath

There is room to make CS1 half biology -- not only without harm but with considerable benefit...

9 years of data:~300 green students:

~400 black students, and ~2000 gold students

LP

Intro CS Course taken... CS majors Biology majors

Black 27.6% 2.0%

Gold 17.2% 3.1%

Green 15.0% 18.7%

Intro CS Course taken... Avg # CS courses taken Avg # Bio courses taken

Black 5.6 1.6

Gold 4.0 1.8

Green 4.3 3.8

● 100 students requested to take the biology-flavored CS1

● 71 students did not request to take the biology-flavored CS1 either they opted for a different section or did not express a preference

Of the students who took CS5 green (biology's CS1)...

How did interest in the biology facets of the course impact the academic journey of these students?We asked

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Course fraction who chose cs5green taking course

fraction who didn't choose cs5green taking course p value

BIOL054 HM 0.38 0.24 0.046BIOL113 HM 0.35 0.17 0.008CSCI060 HM 0.68 0.67 0.854CSCI070 HM 0.45 0.42 0.664CSCI081 HM 0.20 0.14 0.297

Courseaverage grade

(4-pt-scale) of those who chose CS5 Green

average grade (o a 4-pt-scale) of those who didn't choose CS5 Green

p value

BIOL052 HM 2.98 2.76 0.137BIOL054 HM 3.63 3.63 0.978BIOL113 HM 3.42 3.31 0.542CSCI060 HM 3.59 3.47 0.148CSCI070 HM 2.93 3.08 0.290CSCI081 HM 3.13 2.63 0.088

no evidence of a significant difference

p < 0.05

Subsequent-course selection

Results ~ paths chosen vs. paths unchosen

Subsequent-course grades

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Course fraction who chose cs5green taking course

fraction who didn't choose cs5green taking course p value

BIOL054 HM 0.38 0.24 0.046BIOL113 HM 0.35 0.17 0.008CSCI060 HM 0.68 0.67 0.854CSCI070 HM 0.45 0.42 0.664CSCI081 HM 0.20 0.14 0.297

Courseaverage grade

(4-pt-scale) of those who chose CS5 Green

average grade (o a 4-pt-scale) of those who didn't choose CS5 Green

p value

BIOL052 HM 2.98 2.76 0.137BIOL054 HM 3.63 3.63 0.978BIOL113 HM 3.42 3.31 0.542CSCI060 HM 3.59 3.47 0.148CSCI070 HM 2.93 3.08 0.290CSCI081 HM 3.13 2.63 0.088

no evidence of a significant difference

p < 0.05

Subsequent-course selection

Results ~ paths chosen vs. paths unchosen

There is room to make CS1 half biology -- even for students not predisposed to biology !

Subsequent-course grades

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Results ~ all paths

Goal: Not to make everyone be the same, but to make everyone experientially confidentLP

CS2 identities, past decade

Beyond CS1… ?

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CS2 growth, past decade

CS2 raw enrollments (also F/M)

If you build it, they will come...

Beyond CS1… ?

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CS2 for non-majors: 2016, 2017, 2018Homework Subject Topics Assignments

0 Text & File Analysis Python review, Reading/writing text files, GitHub

Ongoing scavenger hunt across a broad, deep directory utilizing particular skills learned in each week

1 Webscraping and APIsRetrieving data from Google Maps, iTunes, and USGS Earthquake API

2 Web Technologies HTML/CSS, Text annotation

3 Data VisualizationMatplotlib, Distinguishing human-generated and batch-mode inputs

Evaluating data in relation to Benford's Law

4 Machine Learning K nearest neighbors using scikit-learn library Neural networks using scikit-learn library

5 Machine LearningDecision trees & random forests using scikit-learn library

Neural networks, TensorFlow

6 Natural Language ProcessingUsing NLTK, gensim (Google's vector representation of word meanings), and TextBlob libraries

Predicting Amazon product review scores using sentiment analysis

7 Computer Vision Pixel processing, Steganography, Green-screening "Photoshopping" text algorithmically

8 Computer Vision K-means image posterization/implementationReading pictures of letters with pixel processing and neural networks

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CS2 for non-majors: 2016, 2017, 2018Homework Subject Topics Assignments

0 Text & File Analysis Python review, Reading/writing text files, GitHub

Ongoing scavenger hunt across a broad, deep directory utilizing particular skills learned in each week

1 Webscraping and APIsRetrieving data from Google Maps, iTunes, and USGS Earthquake API

2 Web Technologies HTML/CSS, Text annotation

3 Data VisualizationMatplotlib, Distinguishing human-generated and batch-mode inputs

Evaluating data in relation to Benford's Law

4 Machine Learning K nearest neighbors using scikit-learn library Neural networks using scikit-learn library

5 Machine LearningDecision trees & random forests using scikit-learn library

Neural networks, TensorFlow

6 Natural Language ProcessingUsing NLTK, gensim (Google's vector representation of word meanings), and TextBlob libraries

Predicting Amazon product review scores using sentiment analysis

7 Computer Vision Pixel processing, Steganography, Green-screening "Photoshopping" text algorithmically

8 Computer Vision K-means image posterization/implementationReading pictures of letters with pixel processing and neural networks

"CS for Insight"

Neither CS nor SE for its own sake -

but for amplifying other paths.

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Building "Overlaps" with other disciplines

Professor A. SinhaGovernment Dept.Claremont McKenna College

Professor L. ConnollyPhysics Dept.Harvey Mudd College

NYT Webscraping

Data analysis

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● Using scripting to scan a wide breadth of texts for historical information

● Internet Archive and New York Times APIs

● Examining word frequencies over time

● Finding clusters of words that frequently appear together

● Can generate questions for further and closer investigation...

NYT article scraping

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… into government and international relations, via Professor Sinha

How can we improve process of data collection and analysis in non-STEM fields?

Goal ~ speed up / automate it, while:

a) Maintaining accuracy, b) Keeping the process transparent, andc) Offering new insights or paths ...

NYT Scraping for Insight...

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Connective computing

● Scanning for articles related to a specific topic (e.g., India) or of a specific type (e.g., Letters to the editor)

● Using Google Cloud Natural Language API for more detailed text analysis

○ Finding overall article sentiments and entity sentiments

○ Analyzing syntax of the texts, including using morphology and dependency tree

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Connective computing

● Scanning for articles related to a specific topic (e.g., India) or of a specific type (e.g., Letters to the editor)

● Using Google Cloud Natural Language API for more detailed text analysis

○ Finding overall article sentiments and entity sentiments

○ Analyzing syntax of the texts, including using morphology and dependency tree

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● How do we maintain accuracy?

We want an effective search that removes articles we don’t want

What would a human be looking for or sorting out?

● How do we keep transparency?

We run into this issue with Google Natural Language Processing;

The more advanced and complicated the analysis, the less transparent

● What additional insight can we offer?

Text analysis and identifying key phrases/sentences

Insights for Computing in Non-STEM majors

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● Switch to python● Leveraging matplotlib, scikit-

learn, and Google's suite● Curve fitting, plus pixel

processing, spreadsheet handling, … computing!

Challenge: How to incorporate computing clearly into a non-CS course without distracting from the primary academic focus, physics.

Physics Lab - Data Analysis

Don't overrun!LP

Physics Lab ~ Physics, not CS

LP

● What parts do we have the students do manually?

We let them choose - based on time

● How do we teach the students enough so that they can replicate the analyses, without distracting from the lab?

● What is the best way to introduce python libraries' capabilities, while keeping the lab clear and concise?

Physics Lab - Challenges & Decisions

LPColab: Live-python Google Docs

We asked, "How can computing serve all disciplines well ?"

Our project has

1. Explored and assessed our current landscape: CS's transition-to-service2. Developed tools to support and encourage collaborations3. Verdict The future's directive is

Looking back, looking forward...

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Overlap! Don't Overrun.

We asked, "How can computing serve all disciplines well ?"

Our project has

1. Explored and assessed our current landscape: CS's transition-to-service2. Developed tools to support and encourage collaborations3. Verdict The future's directive is

Looking back, looking forward...

Overlap! Don't Overrun.

Computing-as-Literacy ~ More and more, IntroX will be/include CS1

Slides we're not using...

Expanding HMC

Introductory CS courses

Benford's Law!

It works - remarkably well!

Introducing dictionaries

R versus PythonIntroducing R as a programming language

Side-by-side Comparisons

Survey of 50 "influential" computer science programs*

Big-picture Landscape

Most commonLeast common

*Niche ranking 2017

Flavored CS1CS2 for non-CS majors Interdisciplinary Majors CS+X Majors & Courses

21 out of 50 . 8 out of 50 . 27 out of 50 . 50 out of 50 .

Survey of 50 "influential" computer science programs*

Big-picture Landscape

Most commonLeast common

*Niche ranking 2017

Flavored CS1CS2 for non-CS majors Interdisciplinary Majors CS+X Majors & Courses

21 out of 50 . 8 out of 50 . 27 out of 50 . 50 out of 50 .

Computing is growing outward ~

faster than CS departments can adapt!

Survey of 50 "influential" computer science programs*

Big-picture Landscape

Most commonLeast common

*Niche ranking 2017

Flavored CS1CS2 for non-CS majors Interdisciplinary Majors CS+X Majors & Courses

21 out of 50 . 8 out of 50 . 27 out of 50 . 50 out of 50 .

Local experiments in

this part of CS space...

Total Number of Students

Choosing biology...

CS2 for non-CS-majors: First tryHomework Subject Current Topics New Assignments

0 Text & File AnalysisPython review, Reading/writing text files, GitHub

Ongoing scavenger hunt across a broad, deep directory utilizing particular skills learned in each week

1 Webscraping and APIsRetrieving data from Google Maps, iTunes, and USGS Earthquake API

2 Web Technologies HTML/CSS, Text annotation

3 Data VisualizationMatplotlib, Distinguishing human-generated and batch-mode inputs

Evaluating data in relation to Benford's Law

4 Machine Learning K nearest neighbors using scikit-learn library Neural networks using scikit-learn library

5 Machine LearningDecision trees & random forests using scikit-learn library

Neural networks, TensorFlow

6 Natural Language ProcessingUsing NLTK, gensim (Google's vector representation of word meanings), and TextBlob libraries

Predicting Amazon product review scores using sentiment analysis

7 Computer VisionPixel processing, Steganography, Green-screening

"Photoshopping" text algorithmically

8 Computer Vision K-means image posterization/implementationReading pictures of letters with pixel processing and neural networks

Machine Learning

Files: Local & Online

Applications

InterviewsMaduka Ogba

Chemistry @ PomonaRobin Melnick

Linguistics @ PomonaVivien HamiltonHistory @ Mudd

Takeaways:● Non-CS majors want computational skills.● CS can come across as a challenge to students’ and profs’ academic identities● Richest applications of computing happen after students have developed academic identities● Opportunity to pursue overlap, while preserving -- and supporting -- deeply held priorities.

Should there be a CS2 for non-CS majors?

Opportunities to “bridge” elsewhere...