Inggriani Liem (Bu Inge) - Computational Thinking | BukaTalks

Main ideas and lessons conveyed

1) Computers are everywhere; developers won’t be eliminated by automation

• Computers (including IoT and smartphones) are already embedded in daily life, so society cannot “live without computers.”
• With Industry 4.0 and more robotics, some repetitive human tasks may be automated—but new roles and demand will grow for:
  • Software developers
  • Hardware developers
• The speaker argues that demand for developers remains high both in Indonesia and abroad.
• She critiques current hiring outcomes with examples:
  • In one recruitment company example placing Indian developers in America, only ~25% passed the test.
  • For Indonesia, only ~1% passed, which she calls unacceptable.
• Implication: talent pipelines and education/test readiness need improvement.

2) “Computational Thinking” is the missing skill (not just coding or ICT)

• The talk presents Computational Thinking as a growing international trend, but still less known in Indonesia.
• Key distinctions:
  • ICT (Information and Communication Technology) = the “edge”/applications relying on informatics.
  • Informatics = creating informatics products/tools, which requires deeper thinking (algorithms, programs, applications).
• She argues people shouldn’t only be “ICT users”; the goal is informatics creation.
• Computational thinking is emphasized as:
  • a way of thinking, not a checklist to memorize.

3) 21st-century learning requires more than absorbing information

• The speaker references a broader 21st century skills framework, including:
  • digital citizenship
  • constructing knowledge
  • innovative design
  • computational thinking
  • creative communication
  • global collaboration
• Core claim: modern education must produce problem solvers—and coding is only a medium for problem solving, not the whole goal.

4) Education systems should adopt computing standards earlier (K-12 focus)

• She argues advanced computing should not wait until university.
• Examples include:
  • robotics/programming/software development as part of high school curricula elsewhere
  • Germany examples: language and automata; algorithm learning in high school
• She mentions an international “computing standard” aligned with ACM’s computing curriculum.
• She encourages Indonesia to build informatics education for children via:
  • teacher guidance
  • curriculum alignment
  • student/teacher involvement in spreading informatics education

5) Motivation and national improvement: Indonesia must raise competence

• She discusses PISA performance and Indonesia’s low ranking (“6th from the bottom”).
• She criticizes the habit of treating ranking improvements as “success,” since comparisons are relative.
• She links this to the need for better:
  • problem interpretation
  • abstraction
  • computational thinking

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Computational Thinking definition and components

What Computational Thinking is (as described)

• Connected to Seymour Papert’s LOGO (a moving cursor language used for drawing and learning to think).
• Later revisited/re-innovated around 2006 by Janet (as referenced in subtitles; likely Janet Wing in the broader context).
• Definition used in the talk:
  • A thinking process for formulating problems with solutions, including:
    • identify the problem
    • suggest solutions
    • represent solutions so they can be executed by an information-processing agent (computer or human)

“Mind’s eyes” metaphor

• The speaker stresses:
  • understanding and abstraction (choosing key relevant aspects)
  • not memorizing details
  • extracting the essential part of a situation (e.g., traffic/crowds → the core problem)

The “4 pillars” of computational thinking

The talk explicitly lists the commonly used 4-part model:

1. Decomposition
2. Abstraction
3. Algorithm
4. Pattern recognition

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Detailed methodology / instructional framework (practical steps)

A) How to solve problems as a developer using computational thinking

• Decomposition
  • Break a complex problem into smaller parts that each have their own functions.
  • Example: building a bike vs a car requires different components.
• Abstraction
  • Focus on what matters and ignore irrelevant details.
  • Example: a table shaped like a train still functions as a table—keep the functional essence.
• Algorithm
  • Create a clear step-by-step plan before coding.
  • The speaker highlights programming building blocks as part of constructing algorithms:
    • input
    • output
    • assignment
    • if
    • loop (referenced as “look”; context implies iteration)
  • Emphasis: plan first, don’t “code first.”
• Pattern recognition
  • Recognize recurring structures so you can reuse solutions instead of starting from zero.
  • Efficiency benefits (e.g., commuting routes learned over time).
  • Without pattern recognition, she suggests you won’t truly become a developer.

B) Generalizing to transfer skill to new problems

• After gaining experience with similar problems:
  • introduce parameters so the solution generalizes to a wider class of problems.
• Computational thinking should become reflexive knowledge, not something memorized short-term.

C) Automate work and optimize performance

• Move from manual steps to automation:
  • use scripts to reduce repeated effort (e.g., repeatedly downloading/crawling data)
• For complex tasks:
  • divide into parallel parts
  • simulate where needed
• Optimization after evaluating alternatives:
  • decide whether a graph is “disconnected,” since it impacts performance
  • compare sorted vs unsorted approaches

D) Evaluate and improve solutions continuously

• Don’t settle for the first solution.
• Use multiple approaches if needed.
• Choose the most effective solution for the objective (not just something “different”).

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Software development practices emphasized beyond “writing code”

Quality, correctness, and readability

• Workflow: specifications → design solutions → implement
• Coding is only part of the process. She emphasizes:
  • read and listen
  • code review
  • refactoring
  • proving program correctness (not relying only on “running it”)
• “Debugging/compilation culture” example:
  • In her earlier education era, failing tests after compilation could mean not passing the semester.
  • Modern idea: “let compilers find bugs” is not enough.

Teamwork and continuous integration

• Software change is compared to “building a boat while it’s sailing”:
  • changes can break what others already use.
• Emphasizes:
  • continuous integration
  • automation for re-testing and re-integration after changes
  • modular design so you can locate the impact of changes
  • agile-like collaboration principles

DevOps and related concepts (mentioned as next keywords to learn)

She ends by pointing to learning keywords such as:

• DevOps
• Continuous integration
• Configuration management

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Additional curriculum/competency guidance and recruitment stance

Competition and learning approach

• She advocates learning computational thinking through competitions rather than “add more cases” ad-hoc fixes.
• Participant scale mentioned:
  • “2.4 million participants”
  • comparisons across years (e.g., 2015: 1.3 million; last year: 2.4 million), framed as still too little for world standard.

Hiring criteria (strongly stated)

• She suggests evaluating applicants with a structured set of knowledge chapters.
• If hiring developers:
  • require passing 7 chapters (mentioned as Toki-related training/structure)
  • warn that hiring “only coders” is wasteful if they lack computational thinking principles
• She also insists candidates know core software engineering principles:
  • mentions SOLID
  • argues you shouldn’t hire someone who doesn’t know it

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Speakers / sources featured (as stated in the subtitles)

Speakers / people

• Inggriani Liem (“Bu Inge,” main presenter)
• Mr. Fajrin (event inviter mentioned)
• Mr. Fajri (mentioned in a developer-management question context)
• Janet (named in subtitles; associated with computational thinking “around since 1980”)
• Mr. Seymour (associated with LOGO in subtitles; likely Seymour Papert)

Organizations / standards / platforms (mentioned as standards or references)

• ITB (Bandung Institute of Technology)
• Bukalapak (and its CEO mentioned)
• ACM (computing standard/curriculum)
• UNESCO
• PISA
• LOGO
• WIKIPEDIA (mentioned as a place to look up computational thinking)
• Google (mentioned as supporting computational thinking)
• DevOps / Continuous Integration / Configuration Management (terms referenced; not tied to specific organizations)

Websites / learning platforms

• A site mentioned that allows studying from elementary level abroad (exact name not provided in subtitles)
• Toki/TOKI alumni (a free learning site mentioned; exact organization name unclear beyond “TOKI/Toki” in subtitles)