Chapter 1-9 Review: Computational Thinking and Logic

Attendance, scheduling, and course logistics

• The instructor emphasizes punctuality and class rosters.
• If you’re late, you’re considered absent; doors may close, and there may be restrictions on re-entry (use the restroom before class if possible to avoid missing material).
• Friday will feature a recitation session to finalize roster adjustments; some advisers may have moved students between sections, which needs to be straightened out.
• The main course section is 1-10 with a dash and a two-digit subsection; for example, the main section is 1-10, with recitation sections like R1, R3, R4. The target is R3 or R4, not R1 or R2.
• Students should double-check their schedules, especially after printing, because capacity issues can cause changes.
• Attendance policy: by the end of the week, the instructor will review attendance; students who haven’t shown up may be dropped (no-shows policy).
• “No shows” require some form of academic contact with the professor (in-person or online assignment/discussion post) to evidence presence.
• For online courses, you must complete assigned activities to demonstrate engagement (e.g., discussion posts or introductions).
• This policy aligns with federal regulations requiring evidence of course participation.

Course goals and core themes

• The course begins by revisiting computational thinking and then centers on the most foundational aspect of computer science, IT, computation in life: logic.
• You are expected to be capable of analyzing and using logic, not just “playing around” with it; logic underpins robust software and critical thinking.
• The instructor emphasizes that real-world software (and even life-critical systems) depend on correct logical reasoning; improper logic has caused major failures (e.g., Boeing 737 MAX family crashes linked to software/logic issues).
• AI is acknowledged as a tool, but it is not a substitute for human reasoning; reliance on AI for everything is warned against, including concerns about cheating.
• There is a recent engagement with industry (FICO) on the use and limits of AI, illustrating that AI-driven systems have ethical, practical, and reliability considerations.
• Students are encouraged to use AI for inspiration and examples, but not as a panacea; critical thinking, diagramming, and formal reasoning are essential for validity.
• The instructor notes variability in AI outputs across similar prompts, highlighting the unreliability and the need for human verification.

Computational thinking vs. logic: foundational ideas

• Computational thinking hinges on three keywords: logic, algorithms, and the ability to prove or disprove ideas (where possible).
• The course emphasizes not thinking like a computer but using human reasoning to design and analyze algorithms and systems.
• The discussion includes critical thinking about claims, especially in public discourse and politics, illustrating why formal reasoning matters beyond CS.
• Students are encouraged to diagram problems, construct formal proofs, and use multiple tools to reason about questions.
• The instructor distinguishes between proof-based thinking and everyday belief; in math/CS/IT, formal proof or disproof is possible in a rigorous setting.
• The phrase “prove” is discouraged in casual talk; instead, use diagrams, formal arguments, or counterexamples to demonstrate correctness.

The role and limits of AI in CS/IT education

• AI, such as ChatGPT, can generate useful examples and explanations but is not always reliable or consistent across sessions.
• The speaker recounts an experience where the same prompts yielded different, though still useful, answers in two different sessions.
• The takeaway: AI can assist learning but should not replace original thinking, explicit reasoning, or verification.
• There is concern about AI being used for cheating; students are encouraged to use AI responsibly and to prove their understanding with concrete explanations or diagrams.
• Real-world AI discussions include fraud detection and credit scoring; AI can help, but human oversight remains essential.

Computational thinking in real-world contexts

• The material reflects on how ubiquitous computational thinking is becoming, as modern devices and services rely on algorithms and data processing.
• Examples mentioned: stock market algorithms, fraud detection in banking, and consumer apps (e.g., McDonald’s app) that rely on software and data analytics.
• The instructor notes that information technology and computer science are deeply embedded in everyday systems—from inventory and promotions to telecommunications and security.
• The distinction between CS and IT is discussed through practical lenses:
  • Computer Science (CS): systematic study of feasibility, structure, expression, and mechanization of methodical processes or algorithms underlying information processing. It emphasizes new algorithms, computational theory, and scientific analysis.
  • Information Technology (IT): application-oriented use of computers and communications to store, retrieve, transmit, and manipulate information; emphasis on deployment, networking, security, and systems support.
• Europe sometimes uses the term informatics for CS, highlighting the broader science of information processing.
• A caution: while CS may involve developing new algorithms and theories, IT focuses on applying these ideas to real systems and infrastructure (e.g., cybersecurity, data management, automation).
• The lecture connects database systems, data analytics, and data-driven decision-making to CS/IT, illustrating how data storage, retrieval, and analysis underpin many modern technologies.

Distinguishing CS vs IT with concrete examples

• Fraud detection example from a real-world model used by banks: a model that reportedly predicts fraudulent transactions with high accuracy (
  • {
    150 patents;
    }
    ) and a notable industry adoption statistic: about 95\% of banks use this kind of model. The model can predict fraud in about 30\text{ units} (the exact unit is not specified in the transcript).
• The instructor recounts personal experience with a bank credit card breach and how bank fraud monitoring helped detect unauthorized transactions, underscoring the importance of data analytics and security.
• The discussion touches on cybersecurity roles within IT and the potential for CS to drive more sophisticated anomaly detection algorithms.
• Hardware vs software emphasis: hardware-oriented work (breadboards, real devices) is more aligned with computer engineering; CS and IT primarily focus on software, networks, and information handling; there is collaboration with engineering for hardware projects.
• The department and program structure imply opportunities to cross-train (e.g., hardware-related courses) but the primary majors in this context are Computer Science (CS) and Information Technology (IT).

What is logic? Propositional calculus as the foundation

• Logic is introduced as the absolute foundation for CS, IT, math, and even practical life; it enables rigorous analysis and correct reasoning.
• Propositional calculus (Boolean logic) focuses on statements that can be evaluated as True or False (propositions).
• The session introduces essential terminology:
  • Propositions: statements that can be evaluated as true or false.
  • Variables: often denoted by letters like p, q, r, x, y to represent propositions.
  • Logical connectives: operators to combine propositions (e.g., AND, OR, NOT).
  • The goal is to determine the truth value of composite propositions using these connectives.
• The instructor warns that not all logic is simple; there exist non-classical logics (approximate, fuzzy, rough, multi-valued), but the initial focus is on simple true/false propositions.

Propositional calculus basics and the dog example

• Example setup: two propositions
  • p: "this is a dog" (based on evidence, e.g., an image)
  • q: "this is brownish" (color attribute)
• The goal is to evaluate a composite proposition such as "this is a brownish dog".
• What makes a dog? Humans rely on facial features and color cues; computers require explicit features or learned models; this highlights potential ambiguity and the need for robust reasoning.
• Truth values in this context are often represented as True (T) or False (F), or 1/0.
• The narrative emphasizes that when evidence is uncertain (e.g., a blurry image), humans may still infer, but computers need explicit criteria to decide.
• The idea of garbage in, garbage out: training data quality directly affects model performance.

Truth tables and composing propositions

• A truth table is a tool to enumerate all possible truth values of a proposition; for two propositions, there are four combinations:
  • p = T, q = T → p ∧ q = T
  • p = T, q = F → p ∧ q = F
  • p = F, q = T → p ∧ q = F
  • p = F, q = F → p ∧ q = F
• The standard conjunction operator is often denoted by the symbol \land (read as “and”): p \land q.
• In the lecture, this is referred to with a symbol similar to an upside-down check; in standard notation, this is the logical AND operator: p \land q.
• Notation hint: When representing propositions, we typically use lowercase letters like p, q, r, and we denote their truth values as T/true or F/false (or 1/0 in some contexts).

Example: evaluating a brownish dog using a truth table

• Propositions:
  • p: "this is a dog"
  • q: "this is brownish"
• Composite proposition: "this is a brownish dog" → p \land q
• Truth table outcomes:
  • If p = T and q = T, then p \land q = T.
  • If p = T and q = F, then p \land q = F.
  • If p = F and q = T, then p \land q = F.
  • If p = F and q = F, then p \land q = F.
• This concrete example shows how to determine the truth of a compound proposition based on its components.

Why this matters for computer science and IT

• Propositional calculus and truth tables underpin reasoning about algorithms and software correctness.
• They provide a practical way to reason about conditions in code (e.g., if (dog && brownish) then …).
• The instructor emphasizes the importance of diagrams and formal reasoning in proving or disproving claims, rather than relying on intuition alone.

Practical implications, exam focus, and study tips

• You should expect questions about computational thinking and logic on the first and final exams.
• The recommended strategy for proving understanding is to provide concrete examples and diagrams that illustrate why a statement is true or false, not merely to state a conclusion.
• When asked to explain computational thinking, avoid the simplistic phrase that it is simply “thinking like a computer”; instead, illustrate with explicit algorithms, logic, and a concrete example (e.g., a small program or a truth table).
• Remember to practice constructing truth tables for simple and slightly more complex propositions (e.g., conjunctions, disjunctions, implications) and translating natural-language statements into logical formulas.

Key takeaways

• Computational thinking integrates logic, algorithms, and critical reasoning to design and analyze computer-based solutions.
• Logic (propositional calculus) is the formal foundation for evaluating true/false statements and combining them with logical connectives.
• Truth tables are essential for understanding how composite propositions evaluate under all possible inputs.
• Real-world AI tools are useful but have reliability and ethical considerations; do not rely on them without human verification and evidence.
• CS vs IT differ in focus but are deeply interconnected in practice; CS emphasizes theory and algorithm development, IT emphasizes application, deployment, and system support.
• Be prepared for exam questions that require both definition recall and demonstration of reasoning with explicit examples.

Quick reference: key symbols and terms

• Propositions: statements that are either T or F
• Variables: p, q, r, ext{etc.} representing propositions
• Conjunction: p \land q (logical AND)
• Truth table: a table listing all possible truth values of propositions and their combinations
• Evidence and propositions in examples: e.g., p = ext{this is a dog''}, q = ext{this is brownish''}
• Important caveat: non-classical logics exist (approximate, fuzzy, rough, multi-valued), but the initial focus is on classical true/false propositions
• Practical notes: ensure attendance, verify schedules, use diagrams and proofs to demonstrate understanding, and treat AI as a tool—not a replacement for reasoning