Spring 2026
Web Programming (웹프로그래밍)
Instructor: Dr. Muhammad Syafrudin
Course information
This course introduces students to the fundamentals of web application development using modern technologies.
By the end of this course, students will be able to:
- Understand basic concepts of web development.
- Develop a web application using HyperText Markup Language (HTML) and Cascading Style Sheets (CSS).
- Use front-end and back-end frameworks, including Material Design for Bootstrap (MDB), Python programming language, and Flask web framework.
- Integrate Application Programming Interfaces (APIs) into a web application.
- Implement a machine learning model within a web application.
- Design and develop a complete web application through hands-on practice.
During the course:
- Students will build a web application step by step, based on a previous year version of https://web.aintlab.com (features may be added or removed depending on weekly topics).
- Students will participate in practical exercises during each session.
- Students will work in groups to propose a project idea or case study and develop a web application as a final team project.
Lecture
Friday 금 at 금10:30-13:30 KST at 광910
Office hours
Appointment by email (Office at 김원관 419호)
Prerequisites
Basic knowledge of Python programming or prior programming experience is helpful but not required.
Notes
Please note the following:
* All lectures will be conducted in English. Korean may be used in the midterm exam and final group project presentations.
* Weekly lecture topics may be adjusted based on students’ understanding and class progress.
* All lectures will be conducted offline (in person).
* Any important updates from the university will be announced through eCampus or email.
If you have any questions or concerns about the course, please contact the instructor or visit during office hours.
Schedule
| 주차(Week) | 강의내용(Class Topic & Contents) | 강의활동유형(Class Type) |
|---|---|---|
| 1 | Course overview and intro to web programming | 강의 (Lecture) |
| 2 | Introduction to HyperText Markup Language (HTML) | 강의+실습 (Lecture + Practice) |
| 3 | Introduction to Cascading Style Sheets (CSS) | 강의+실습 (Lecture + Practice) |
| 4 | Introduction to Material Design for Bootstrap (MDB), an open source front-end web user interface (UI) framework. | 강의+실습 (Lecture + Practice) |
| 5 | Introduction to Python | 강의+실습 (Lecture + Practice) |
| 6 | Introduction to Flask, an open-source backend framework based on the Python programming | 강의+실습 (Lecture + Practice) |
| 7 | Web programming with Flask I - Group Project Team Formation | 강의+실습 (Lecture + Practice) |
| 8 | Mid exam | 시험 (Exam) |
| 9 | Sejong University 86th anniversary - No class -- University holiday | No class -- University holiday |
| 10 | Integration with Database (MongoDB) | 강의+실습 (Lecture + Practice) |
| 11 | Web programming with Flask II | 강의+실습 (Lecture + Practice) |
| 12 | Web programming with Flask III | 강의+실습 (Lecture + Practice) |
| 13 | WebApp for prediction or classification & using Application Programming Interface (API)) | 강의+실습 (Lecture + Practice) |
| 14 | Group project discussion and development | Project Development & Discussion |
| 15 | Group project discussion and development | Project Development & Discussion |
| 16 | Final group project presentation (ppt, codes, and project report final submission) | Evaluation |
Grading
The final grade will be calculated using the following weights:
| # | Final Grade Weight |
|---|---|
| Attendance | 15% |
| Assignment(quiz/homework/weekly assignment) | 15% |
| Mid exam | 30% |
| Final group project | 40% |
| Total | 100% |
Assignment
There might be 3-5 assignments, which will be released at the end of each lecture.
Submitting an assignment
Instructions for turning in assignments will be posted when the semester starts (in ecampus).
Getting help
For questions about homework, course content, installation, and after you have tried to troubleshoot yourselves, the process to get help is: Post the question in ecampus/group chat and hopefully your peers will answer. Note that in ecampus questions are visible to everyone. For private matters send an email to helpline: udin [at] sju [dot] ac [dot] kr.
Course Policies
Collaboration policy
We encourage you to talk and discuss the assignments with your fellow students (and on ecampus), but you are not allowed to look at any other students assignment or code outside of your pair. Discussion is encouraged, copying is not allowed.
Late day policy
Homework is due before each class. Late submission are not allowed.
Communication to students
Class announcements will be through ecampus. All homework and quizzes will be posted in ecampus. Also all feedback forms. Important note: make sure you have your settings set so you can receive emails from ecampus.
Academic honesty
We give a strong emphasis to Academic Honesty. As a student your best guidelines are to be reasonable and fair. We encourage teamwork for problem sets, but you should not split the homework and you should work on all the problems together.
AI Usage Guidelines*
1. Permitted Use
Artificial Intelligence (AI) tools may be used in this course as a coding assistant and learning partner. Acceptable use includes:
- Assisting with programming tasks (e.g., debugging, code suggestions, optimization)
- Explaining concepts and supporting learning
- Providing examples, practice problems, or study guidance
AI should be used to support understanding, not to replace independent thinking or original work.
2. Disclosure Requirement
All use of AI tools must be clearly and specifically disclosed.
Students must include:
- The name of the AI tool used (e.g., ChatGPT, Copilot, etc.)
- The purpose of use (e.g., debugging, concept explanation, code generation)
- The extent of contribution (e.g., partial code suggestion, full function draft, idea generation)
- The modified or final usage (how the AI output was adapted or verified)
Example Disclosure
AI Tool: ChatGPT
Usage: Assisted in debugging a recursion error and suggesting code structure
Contribution: Provided initial code draft for the sorting function
Modification: Revised logic and added error handling independently
3. Academic Integrity
- Submitting AI-generated work without disclosure is considered academic misconduct.
- Over-reliance on AI that replaces personal effort or understanding is not permitted.
- Students are responsible for verifying correctness, originality, and integrity of all submitted work.
4. Responsible Use
- AI outputs may contain errors, bias, or incomplete reasoning; critical evaluation is required.
- Do not input sensitive, personal, or proprietary information into AI tools.
- Use AI in a way that aligns with the university’s academic values and learning objectives.
5. Instructor Policy Priority
Specific assignments or instructors may restrict or expand AI usage. In such cases, course-specific policies take precedence over this general guideline.
Based on [AX혁신원] 2026 세종대학교 AI 활용 가이드라인 안내/2026 Sejong University AI Usage Guideline: https://www.sejong.ac.kr/kor/intro/notice3.do?mode=view&articleNo=864878
Formed Group Projects
TBD.
Note: TL -> Team Leader.