Study Notes on Scientific and Computational Thinking

Introduction to Scientific & Computational Thinking

  • Carl Sagan's Quote: Emphasizes society's dependence on science and technology despite widespread ignorance.

  • Definition of Computational Thinking:

    • A process of recognizing computation in the world.

    • Involves using computing tools and techniques for understanding natural, social, and artificial systems.

    • Reference: "Assessing Computational Thinking Across the Curriculum" by Julie Mueller, Danielle Beckett, Eden Hennessey, and Hasan Shodiev.

  • Overview of Chapter C4: Historical perspective of computers and computer science.

  • This chapter focuses on the history of science and scientific thinking's role in our lives.

    • Definition of Science:

    • "a system of knowledge covering general truths or the operation of general laws" (Merriam-Webster).

    • Importance of Science:

    • Advances understanding of the world.

    • Leads to practical applications improving quality of life (medicine, agriculture, technology).

  • Limits of the Chapter:

    • Brief overview of the history from ancient Greece to present; highlights key contributors.

A (Very) Brief History of Science

  • Roots predating written history: Early humans observed and sought to understand nature.

  • Ancient Greece's Contribution:

    • Thales (6th century B.C.): First to use hypothesis/theory instead of mythology for natural phenomena.

    • Plato (4th century B.C.): Proposed cosmology with perfect circular orbits.

    • Believed truth was found through contemplation rather than observation.

    • Aristotle:

    • Studied various subjects; dominant worldview for 2000 years.

    • Valued observation but did not incorporate experimentation, hence Greek philosophy is termed "pre-scientific."

  • Roman Contributions:

    • Building on Greek notions with figures like:

    • Pliny (1st century): Classifications of plants, animals, and minerals.

    • Galen (2nd century): Anatomy and physiology studies.

    • Ptolemy (2nd century): Revised Aristotle's cosmological theories.

  • Engineering vs. Theoretical Science: Romans excelled in practical applications such as roads and aqueducts.

European Renaissance and Scientific Revolution

  • Dark Ages (476 AD onwards):

    • Loss of Greek knowledge in Western Europe due to plague and literacy decline.

    • Eastern Europe: Knowledge suppressed by orthodox Christianity.

  • Islamic Golden Age (7th-13th centuries):

    • Preservation and advancement of Greek writings by Arab scholars.

    • Important figure: Muhammad ibn Musa al-Khwarizmi (9th century): Named the term "algorithm".

  • Crusades (11th century):

    • Rediscovery of Greek knowledge in Europe.

    • Renewed learning leading to the Renaissance (14th-16th centuries).

    • Cultural revolution encompassing political reform and humanism.

  • Leonardo da Vinci (1452-1519):

    • Renaissance figure blending sciences and arts.

  • Johannes Gutenberg (1439):

    • Invention of the printing press, enhanced literacy, and rapid knowledge spread.

Modern Science

  • Scientific Revolution (16th-17th centuries):

    • Viewed the universe as a complex machine understood through observation and experimentation.

    • Key figures:

    • Nicolaus Copernicus (1473-1543): Proposed heliocentrism.

    • Johannes Kepler (1571-1630): Established elliptical orbits.

    • Galileo Galilei (1564-1642): Pioneered experimentation, considered the father of modern science. He studied motion and gravity, and faced persecution for his support of heliocentrism.

    • Isaac Newton (1642-1726): Formulated laws of motion and universal gravitation; invented calculus.

  • 19th Century Professionalization: Institutions and societies formed for scientific endeavors.

    • Louis Pasteur: Principles of microbiology (vaccines, pasteurization).

    • James Watson and Francis Crick: Structure of DNA.

    • Significant contributions in chemistry (John Dalton, Dmitri Mendeleev) and physics (James Clerk Maxwell, Albert Einstein, Marie Curie).

The Scientific Method

  • Distinction from Pre-science:

    • Integration of experimentation.

  • Defined Steps of the Scientific Method (Mnemonic: OHDEAR):

    1. Observe: Notice phenomena, such as planetary motion.

    2. Hypothesize: Formulate a hypothesis about the phenomena (e.g., planetary orbits).

    3. Design: Create an experiment to test hypothesis (model or predictions).

    4. Experiment: Conduct the experiment to gather data.

    5. Analyze: Review data to confirm or refute hypothesis.

    6. Revise: Adjust hypothesis as needed, retesting to refine results.

  • Flexibility and Iterative Nature of Science:

    • Error correction and revision based on new findings is crucial.

Consistency vs. Accuracy in Experiments

  • Consistency: Reliability of results under the same conditions.

    • Example: Timing a falling ball, consistency matters in achieving reproducibility.

  • Accuracy: Closeness of experimental results to expected outcomes.

  • Example Calculations:

    • Marie's timings: 8.1s, 7.7s, 7.9s (consistent).

    • Isaac's timings: 8.1s, 8.2s, 8.3s (more consistent).

  • Importance of publication and reproducibility in science.

    • Quote: "Once is never. Twice is always" (James S.A. Corey).

Computational Thinking

  • Evolution of Problem-Solving Approaches:

    • Acknowledges limitations of the scientific method for practical problems.

  • Definition of Computational Thinking:

    • Coined by Seymour Papert in 1980.

    • Described by Jeannette Wing: Problem-solving that can be expressed algorithmically.

  • Characteristics of Computational Thinking:

    1. Decomposition: Breaking complex problems into manageable parts.

    2. Pattern Recognition: Identifying similarities in solutions across problems.

    3. Abstraction: Focusing on essential features, excluding irrelevant details.

    4. Algorithms: Creating step-by-step instructions for solutions.

  • Real-World Application:

    • Furniture assembly: Breaking down tasks to streamline the process, recognizing past experiences, abstracting details, and writing algorithms for future reference.

Computational Thinking Example: Finding the Tallest Person in a Room

  • Understanding Problem Complexity:

    • Identify criteria for height, how to measure accurately, duplicates.

Decomposition:

  • Tasks to determine height: Asking, measuring, and tracking results.

Pattern Recognition:

  • Organizing participants simplifies the comparison process and utilizes known methods for tracking results.

Abstraction:

  • Focus on relevant aspects: only height matters, not other traits.

Algorithms:

  1. Algorithm 1:

    • Ask each person their height in sequence and keep notes on the tallest.

  2. Algorithm 2:

    • Pair up people and eliminate shorter individuals progressively until one remains (in essence, finer efficiency via decomposition).

Efficiency Comparison of Algorithms

  • Algorithm 1 (linear approach) vs. Algorithm 2 (divisive reduction).

  • Example Time Measures:

    • Algorithm 1 makes time scale directly proportional to participants.

    • Algorithm 2 enhances efficiency, cutting time needed drastically for larger groups.

Conclusion

  • Key Outcomes: Science defined as knowledge through methods; Modern science arises from early thinkers through experimentation.

  • Scientific method, critical in validating hypotheses, distinct from computational thinking's focus on problem-solving through structured reasoning.

  • Both notions highlight essential skills for navigating complexities in both scientific and everyday challenges.