Grading

See also: SyllabusSyllabus
See also: Lectures · Class Schedule · Grading · AI Policy

Course Title: Tech 1 — Web & Mobile Ecosystem Fundamentals

Course Objective: Gain a foundational understanding of the... · LecturesLectures
See also: Syllabus · Class Schedule · Grading

One 2-hour session per week, Tuesdays 2:15–4:15pm. No class on 8 and 15 September.


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Tech 1 Grading Plan

Assessment and Grading Breakdown

# Component Weightage (%)
1 Attendance and Class Participation 10%
2 Class Exercises 50%
3 Final Project 40%

(If the university requires written midterm/endterm exams, weightages will be adjusted and announced in class.)

1. Attendance and Class Participation (10%)

Attendance is recorded each session. I have an open door policy — if you need to take a break or leave, feel free to do so. The penalty is that you don't get the attendance credit and don't learn from the material. No distinction is made between excused and unexcused absences.

2. Class Exercises (50%)

Weekly build exercises, done mostly in class and finished as homework:

  1. Exercise - Ship a Page Note does not exist
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  2. Exercise - Web Page Anatomy Note does not exist
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  3. Exercise - Trace a Request Note does not exist
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  4. Exercise - Figma to Code Note does not exist
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  5. Exercise - Add a Database Note does not exist
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  6. Exercise - API Mashup Note does not exist
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  7. Exercise - Add an AI Feature Note does not exist
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  8. Exercise - Mobile Ecosystem Comparison Note does not exist
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  9. Exercise - SDLC Stage Mapping Note does not exist
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Each is graded out of 8 on outcome quality, understanding demonstrated, and process documentation (including prompt logs). I will consider your best 6 scores.

3. Final Project (40%)

Group project (up to 3 people), built over the last third of the course and presented at Demo Day. See Project - Final Project Note does not exist
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for the brief and rubric.

A note on AI and academic integrity

See the full AI PolicyAI Policy
See also: Grading · Syllabus

In many courses, an AI policy is a list of things you can't do. This course is different: using AI to generate code is the expected way of working here. The sk.... In this course, using AI to generate code is not just allowed — it's the expected way of working. What is dishonest:

  • Presenting work you can't explain. If you shipped it, you should be able to answer "how does this part work?" at the level this course teaches.
  • Hiding your process. Prompt logs and iteration history are part of every submission.
  • Copying another student's project or prompts wholesale.

Cite third-party assets, datasets, templates, and any substantial code you didn't generate yourself. For group projects, each member's contributions must be documented.