Scripting for All . Lindsay Popowski '21, Kewei Zhou '21, Zach - PowerPoint PPT Presentation

Scripting for All . Lindsay Popowski '21, Kewei Zhou '21, Zach Dodds Harvey Mudd College ZD

CS for All ? ZD

CS for All ~ Scripting for All ZD

Different views of "The World" ...

"Scripting for World Domination" ZD

Scripting for All Disciplines "All" ? ZD

Scripting for All Colleges Claremont's Petri Dish ZD

From Specialty to Literacy 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: College Computing ~ College Writing ZD

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 Computing Data 8 College Computing for Insight Who owns this? School-owned Discipline-owned What is this? ZD

Since 2009... CS1 "black" students with some CS Intro to CS in Python ( more breadth) CS1 "gold" Intro to CS in Python (breadth) students new to CS CS1 "green" Biology-owned CS1 course ZD

Biology-owned CS1 Iteration Recursion Projects CS1 in which all practice is biologically motivated… Lectures: ½ CS ½ Bio ZD

What happens beyond CS1 … ? CS1 "black" students with some CS CS2 CS3 Java & Racket C++ CS1 "gold" CS for students new to CS Insight CS1 "green" Python ZD - LP

Biology-owned CS1 ~ Aftermath Intro CS Course taken... CS majors Biology majors 9 years of data: Black 27.6% 2.0% ~300 green students: ~400 black students, and Gold 17.2% 3.1% ~2000 gold students 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 LP

Biology-owned CS1 ~ Aftermath Intro CS Course taken... CS majors Biology majors 9 years of data: Black 27.6% 2.0% ~300 green students: ~400 black students, and Gold 17.2% 3.1% ~2000 gold students 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 There is room to make CS1 half biology -- not only without harm but with considerable benefit ... LP

Of the students who took CS5 green (biology's CS1)... 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 How did interest in the biology facets of the course We asked impact the academic journey of these students? LP

Results ~ paths chosen vs. paths unchosen fraction who chose fraction who didn't choose Subsequent-course selection Course p value cs5green taking course cs5green taking course BIOL054 HM 0.38 0.24 0.046 BIOL113 HM 0.35 0.17 0.008 CSCI060 HM 0.68 0.67 0.854 CSCI070 HM 0.45 0.42 0.664 p < 0.05 CSCI081 HM 0.20 0.14 0.297 average grade average grade (o a Subsequent-course grades Course (4-pt-scale) of those 4-pt-scale) of those who p value who chose CS5 Green didn't choose CS5 Green BIOL052 HM 2.98 2.76 0.137 BIOL054 HM 3.63 3.63 0.978 no evidence of a BIOL113 HM 3.42 3.31 0.542 significant difference CSCI060 HM 3.59 3.47 0.148 CSCI070 HM 2.93 3.08 0.290 CSCI081 HM 3.13 2.63 0.088 LP

Results ~ paths chosen vs. paths unchosen fraction who chose fraction who didn't choose Subsequent-course selection Course p value cs5green taking course cs5green taking course BIOL054 HM 0.38 0.24 0.046 BIOL113 HM 0.35 0.17 0.008 CSCI060 HM 0.68 0.67 0.854 CSCI070 HM 0.45 0.42 0.664 p < 0.05 CSCI081 HM 0.20 0.14 0.297 average grade average grade (o a Subsequent-course grades Course (4-pt-scale) of those 4-pt-scale) of those who p value who chose CS5 Green didn't choose CS5 Green BIOL052 HM 2.98 2.76 0.137 BIOL054 HM 3.63 3.63 0.978 no evidence of a BIOL113 HM 3.42 3.31 0.542 significant difference CSCI060 HM 3.59 3.47 0.148 CSCI070 HM 2.93 3.08 0.290 CSCI081 HM 3.13 2.63 0.088 There is room to make CS1 half biology -- even for students not predisposed to biology ! LP

Results ~ all paths Goal : Not to make everyone be the same , but to make everyone experientially confident LP

Beyond CS1… ? CS2 identities , past decade ZD

Beyond CS1… ? CS2 raw enrollments (also F/M) CS2 growth, past decade ZD If you build it, they will come...

CS2 for non-majors : 2016, 2017, 2018 Homework Subject Topics Assignments 0 Text & File Analysis Python review, Reading/writing text files, GitHub Ongoing scavenger hunt across a broad, deep Retrieving data from Google Maps, iTunes, and 1 Webscraping and APIs directory utilizing particular skills learned in USGS Earthquake API each week 2 Web Technologies HTML/CSS, Text annotation Matplotlib, Distinguishing human-generated and 3 Data Visualization Evaluating data in relation to Benford's Law batch-mode inputs 4 Machine Learning K nearest neighbors using scikit-learn library Neural networks using scikit-learn library Decision trees & random forests using scikit-learn 5 Machine Learning Neural networks, TensorFlow library Using NLTK, gensim (Google's vector representation Predicting Amazon product review scores using 6 Natural Language Processing of word meanings), and TextBlob libraries sentiment analysis 7 Computer Vision Pixel processing, Steganography, Green-screening "Photoshopping" text algorithmically Reading pictures of letters with pixel processing 8 Computer Vision K-means image posterization/implementation and neural networks ZD

CS2 for non-majors: 2016, 2017, 2018 Homework Subject Topics Assignments 0 Text & File Analysis Python review, Reading/writing text files, GitHub Ongoing scavenger hunt across a broad, deep Retrieving data from Google Maps, iTunes, and 1 Webscraping and APIs directory utilizing particular skills learned in USGS Earthquake API each week 2 Web Technologies HTML/CSS, Text annotation " t h g i s Matplotlib, Distinguishing human-generated and n I r o 3 Data Visualization f Evaluating data in relation to Benford's Law S C " batch-mode inputs - k e a s n w o 4 Machine Learning K nearest neighbors using scikit-learn library Neural networks using scikit-learn library t s i r o f E S r o n S C r e . h h s t t e i a N p r e h Decision trees & random forests using scikit-learn t o g n i y i f 5 Machine Learning p l Neural networks, TensorFlow m a o r library f t u b Using NLTK, gensim (Google's vector representation Predicting Amazon product review scores using 6 Natural Language Processing of word meanings), and TextBlob libraries sentiment analysis 7 Computer Vision Pixel processing, Steganography, Green-screening "Photoshopping" text algorithmically Reading pictures of letters with pixel processing 8 Computer Vision K-means image posterization/implementation and neural networks ZD

Building "Overlaps" with other disciplines NYT Webscraping Professor A. Sinha Government Dept. Claremont McKenna College Data analysis Professor L. Connolly Physics Dept. Harvey Mudd College ZD - KZ

NYT article scraping 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... KZ

NYT Scraping for Insight ... … 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, and c) Offering new insights or paths ... KZ

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 KZ

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 KZ

Insights for Computing in Non-STEM majors 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 KZ