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can the next generation of scientists become computational thinkers?

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can the next generation of scientists become “computational thinkers”?

- eScience workshop • december 2008
- rosalind reid
- executive director
- harvard initiative in innovative computing

Fact 1: Computation will be at the core of all science within the next decade.Fact 2: Today’s undergraduates are tomorrow’s research scientists.Fact 3: Computational thinking generally is not integrated into undergraduate science curricula at Harvard.Is this a problem?We asked the faculty. (At Harvard, always ask the faculty.)

narrative responses to an informal online survey* of Harvard science faculty on “computational thinking”

* conducted June 2008

modest hypothesis: computational thinking (as defined by Jeannette M. Wing*) can be a unifying theme for catalyzing curriculum innovation to improve the preparation of tomorrow’s scientists.

*of Carnegie Mellon University, now in charge of Computer and Information Sciences and Engineering (CISE) at NSF

Wing’s examples of computational thinking in science*

- “machine learning has revolutionized statistics”
- “algorithms and data structures, computational abstractions and methods will inform biology”

*Microsoft Faculty Summit, Hangzhou, China, October 31, 2005

notes on these data

- quantitative data were collected, but the survey was informal and unscientific
- faculty from several departments took the time to offer thoughtful comments
- respondents self-labeled their fields of research
- special thanks to Lynn Stein for her help, to Microsoft Research for funding, and to Rob Lue for his continuing work to organize conversation among science faculty on these questions
- and thanks to the EECS faculty for an open and supportive discussion of co-teaching possibilities

q1: We are interested in how computation has or has not transformed research in your field.

- theoretical mechanics/earth science: “People with [deep, fundamental understanding of ... science and math] are able to do marvelous things with modern computation.”
- climate: “Computation is a matter of necessity... as real-scale experiments are not possible.”
- earth science: “Numerical solution of large-scale problems...crystal structures, high-pressure phases.”
- language/cognition: “More precise and rigorous formulation and testing of theories... large-scale databases can be analyzed for patterns of human behavior.”

q1: We are interested in how computation has or has not transformed research in your field.

- cosmology(n=2): [Computation is]“the engine of progress in our field.” “High-end computation has become both necessary and critical for data analysis”
- geophysics: “My group.... is doing science that other groups can’t because we have embraced a computational approach... computational geometry and [GUIs] enable us to build and run more realistic models.”
- materials/surfaces: “Computation makes it possible to ‘see’ molecular detail.”
- astrophysics:“Totally transformed.”

q1: We are interested in how computation has or has not transformed research in your field.

- paleobiology:“Forward modeling, simulation of complex systems that cannot be addressed analytically, solutions to NP-complete problems such as DNA sequence alignment....”
- nanotechnology: “Computerized data acquisition; data analysis; graphic presentation... of data; simulations of experiments; fundamental understanding of electrons inside small structures....”
- evolutionary biology: “analyses of large data sets.”
- evolutionary developmental biology: “As more and more genomic sequence data becomes available, computational methods are necessary to deal with the data.”

q1: We are interested in how computation has or has not transformed research in your field.

- paleobiology:“Forward modeling, simulation of complex systems that cannot be addressed analytically, solutions to NP-complete problems such as DNA sequence alignment....”
- nanotechnology: “Computerized data acquisition; data analysis; graphic presentation... of data; simulations of experiments; fundamental understanding of electrons inside small structures....”
- evolutionary biology: “analyses of large data sets.”
- evolutionary developmental biology: “As more and more genomic sequence data becomes available, computational methods are necessary to deal with the data.”

q1: We are interested in how computation has or has not transformed research in your field.

- exoplanets:“The discovery of exoplanets was enabled by sensitive optical detectors and by the ability to undertake massive modeling efforts... and identify best models over a large parameter space.”

q2: What types of computational thinking do you expect to become important to scientific investigation in the coming decade?

- theoretical mechanics/earth science: “...intelligent algorithms and a deep understanding of aspects of the physics that cannot be represented accurately due to limitations on computer resources.”
- climate: “...how to reduce and abstract a real-world problem into a computationally solvable problem... and how to map the results back to the real-world problem.”
- earth science: “...numerical solution of problems, use of tools such as MATLAB and Mathematica...”
- language/cognition:“Intelligent searching and parsing of language databases.”

q2: What types of computational thinking do you expect to become important to scientific investigation in the coming decade?

- theoretical mechanics/earth science: “...intelligent algorithms and a deep understanding of aspects of the physics that cannot be represented accurately due to limitations on computer resources.”
- climate: “...how to reduce and abstract a real-world problem into a computationally solvable problem... and how to map the results back to the real-world problem.”
- earth science: “...numerical solution of problems, use of tools such as MATLAB and Mathematica...”
- language/cognition:“Intelligent searching and parsing of language databases.”

q2: What types of computational thinking do you expect to become important to scientific investigation in the coming decade?

- cosmology (n=2): “... ability to exploit large databases... write, debug and run programs. Proficiency with a scripting language. “The ability to conceptualize (and visualize) large data sets.”
- geophysics: “General procedural programming... models and data visualization... ”
- materials/surfaces: “ simulations of complex systems... solving mathematically intractable problems....manipulating datasets... capturing time-dependent phenomena.”
- evo-devo biology: “the ability to compare many genomes at once”

q2: What types of computational thinking do you expect to become important to scientific investigation in the coming decade?

- cosmology (n=2): “... ability to exploit large databases... write, debug and run programs. Proficiency with a scripting language. “The ability to conceptualize (and visualize) large data sets.”
- geophysics: “General procedural programming... models and data visualization... ”
- materials/surfaces: “ simulations of complex systems... solving mathematically intractable problems....manipulating datasets... capturing time-dependent phenomena.”
- evo-devo biology: “the ability to compare many genomes at once”

q2: What types of computational thinking do you expect to become important to scientific investigation in the coming decade?

- exoplanets: “novel analyses... of temporal variability surveys and [categorization of] the variability in these large data sets.”
- paleobiology: “...a wealth of more complex, easy-to-use packages that can handle Bayesian analyses...”
- nanotechnology: “..techniques...to handle many [processors] at once.”
- evo-devo biology: “...analyses of large data sets... smart systems that bring together relevant data from disparate sources.”

q3: What computational skills and abilities would allow today’s undergraduates to tackle tough problems in your field 10 or 20 years from now?

- geophysics: “...general programming skills are the key that allow tomorrow’s researchers to create their own tools... and think differently.”
- materials/surfaces: “...both applied math and skill in numerical simulations and manipulations...”
- astrophysics: “ability to use whatever programs are standard [and] be able to modify them.”
- cosmology “...what is becoming harder and harder is to get students to understand the very basics of how astronomical data is collected.”

q3: What computational skills and abilities would allow today’s undergraduates to tackle tough problems in your field 10 or 20 years from now?

- geophysics: “...general programming skills are the key that allow tomorrow’s researchers to create their own tools... and think differently.”
- materials/surfaces: “...both applied math and skill in numerical simulations and manipulations...”
- astrophysics: “ability to use whatever programs are standard [and] be able to modify them.”
- cosmology “...what is becoming harder and harder is to get students to understand the very basics of how astronomical data is collected.”

q3: What computational skills and abilities would allow today’s undergraduates to tackle tough problems in your field 10 or 20 years from now?

- evo-devo biology: “statistical analysis, programming, large-dataset management.”
- paleobiology: “the big problems, the importance of first principles”
- nanostructures: “pattern recognition in the most general sense”
- evolutionary biology“... familiarity with... ‘informatics’ approaches”

miscellaneous comments

- “Programming seems here to stay.” geophysics
- “The larger problem is eliminating innumeracy among Harvard undergrads. I routinely have students in my core class that are marginally able, or unable, to deal with quantitative material.” earth science
- “The major problem with this [“computational thinking”] approach is that it is concerned with teaching skills, rather than building a CV for medical/professional school, and is thus a slightly unusual vector for our undergraduates.” materials/surfaces

miscellaneous comments

- “You know, ironically, students are beginning to lose track of the fundamentals that underlie the computational tools they are using.” paleobiology
- “These subjects [applied math and numerical simulation] are difficult, and Harvard undergrads are not terrifically fond of difficult subjects.” materials/surfaces
- “Harvard... is the perfect place to pursue this type of education.” geophysics

some conclusions

- Computing challenges in the sciences will focus on large data sets, but not just on large data sets.
- The ability to bring data together from disparate sources will be increasingly critical.
- Some faculty fear that students using sophisticated tools will lose touch with first principles or the understanding of nature that comes from direct observation and experimentation.
- There is concern about levels of quantitative skill among science students and cynicism about motivation.
- Scale is seen as a growing challenge across the sciences, and computational skill as necessary for meeting that challenge.

experimentation at Harvard

- research experiences provided by IIC (18 internships, 4 REUs in first 3 years)
- physical sciences and life sciences now have integrated first-year courses
- first winter session: January 2010
- new undergraduate laboratories will combine wet labs with computer labs
- IIC Director Efthimios Kaxiras convening interdisciplinary faculty committee to launch co-teaching workshops
- planned addition of science projects to CS 50/51; new numerical methods courses in School of Engineering and Applied Sciences (lack of departmental boundaries helps!)

what can “computational thinking” not do for science?

- replace observation; scientists must first “take their dictation from Nature”
- provide young scientists knowledge of science’s laws, principles and method

what can “computational thinking” do for science?

- help conceptualize, manipulate and analyze novel and large databases
- lead to different formulations of theory
- cleave observations/data/interactions/natural systems into computable pieces; abstract them; represent and model them; map results back to the real world

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