Class Introduction to Programming

Introduction to Programming develops the ability to turn Civil Engineering questions into computational models that can be explored and checked. During the semester, each team of three to four students selects a phenomenon - such as the response of buildings to an earthquake, traffic in a road network, or signals from an instrumented bridge - and formulates a question that can be investigated with a MATLAB program. The team states a hypothesis, makes the model's assumptions explicit, defines inputs and outputs, implements the program, and tests whether the results are consistent with the hypothesis and available evidence. Deliverables include the code, tests, interpretation of results, and a lesson for classmates explaining the phenomenon, the evidence, when the hypothesis holds, and the model's limits. AI may support research, modelling, and programming; each student records relevant uses, checks claims and code, understands key decisions, and individually defends the work.  

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1. Civil Engineering problems and modelling: phenomena, questions, hypotheses, variables, relationships, and assumptions. 2. Learning and responsible work: working and long-term memory, interruptions, active recall, explanation, and deliberate practice; responsibilities of students, teachers, and AI. 3. AI and critical thinking: language models, context, and answer generation; plausible errors, bias, source checking, and records of tools and decisions. 4. MATLAB programming: workspace, variables, expressions, built-in functions, vectors and matrices, plots, scripts, inputs and outputs, logical operators, conditionals, loops, functions, and debugging. 5. Collaborative project: deliverables, tasks, and contributions; Git repository, version control, and code integration. 6. Testing and communication: functional tests, edge cases, validation and model limits; evidence analysis, lesson for classmates, peer review, presentation, and individual defence.

The unit follows a project sequence: each team of three to four students selects a Civil Engineering phenomenon, formulates a question and hypothesis, builds a model, develops a MATLAB program, tests the results, and prepares a lesson for classmates. Classes combine short demonstrations with experiments, problem solving, output prediction, code reading and debugging, and checkpoints with verifiable deliverables. Learning is supported by active recall, attempting a solution before consulting one, explaining without reading, practising with varied data and conditions, and spaced review. AI may support any stage, but its suggestions are treated as claims to verify: students record relevant prompts and decisions, run the code, check sources, and justify what they accept, change, or reject. Peer review and oral defence make each member accountable for the team product.

Chapman, S. J. (2018). Essentials of MATLAB programming (3rd ed.). Cengage Learning. MathWorks. MATLAB documentation e tutoriais oficiais. Materiais didáticos da unidade curricular.