Course Syllabus

SDEV 106 - Front-End Web Dev

Fall 2020 (Item #5531)

Course Information

Instructor Information

Course Format

This is an online course. It is imperative that you check Canvas for Announcements regularly!

There are optional class meetings every Thursday at 10am. This is an opportunity to meet with the instructor and your classmates, and to ask questions. These sessions will be recorded and made available in Canvas.

This course is designed so that you may work at your own pace. At the end of the course, you will have certificates of completion for a series of HTML and CSS video tutorials, and a professional personal online portfolio.

Course Description

This is an introductory course that assumes no prior experience. Covers the fundamentals of web page production. Students learn the three layers of front-end web development: HTML for structure, CSS for styling and JavaScript for behavior. Emphasizes design for usability and accessibility. Students learn how the Internet works, how a web page is processed, and how to launch a website on the Internet. Previously IT 206.

Course Outcomes

At the end of the course, you should be able to:

  1. Use HTML5 to properly mark up a web page using a basic text editor.
  2. Apply both internal and external CSS to control the look and feel of a web page.
  3. Utilize classes and IDs, and know when it is appropriate to use each.
  4. Explain the importance of usability and perform usability testing.
  5. Explain the importance of accessibility and integrate accessibility features into a web page.
  6. Navigate directory structures and properly incorporate images and internal and external links.
  7. Utilize proper document structure and formatting standards.
  8. Follow proper file and folder naming conventions.
  9. Explain and utilize the Document Object Model (DOM).
  10. Write JavaScript functions to create interaction within a web page and control its behavior.

Program and Campus Learning Outcomes

Course Resources

Required Textbook

Canvas

All assignments, supplementary materials, the course schedule, due dates, and updates to this syllabus will be posted to the course web site in Canvas at https://egator.greenriver.edu/

Check the course web site and your @mail.greenriver.edu email account daily for important announcements.

If you have any questions about the course, reading, or the homework, please post them to Canvas Discussions. This will enable you to get an answer to your questions more quickly, and also help classmates who might have the same question. If you see a question in the Discussions that you think you can answer, please do so! 

If you have questions of a personal nature, such as regarding a specific grade or scheduling an appointment, then either email me or visit me during office hours.

Tutors

There are tutors available both at Auburn Center and the main campus (Holman Library) for all IT Software Development classes. View the Tutoring Schedule

Tutoring Protocols

Student Portal

my.greenriver.edu contains information and links for important student resources.

LinkedIn Learning

LinkedIn Learning provides a wide range of technical video tutorials, and is free to Green River students.

Late Work

There are due dates on assignments in order to help you stay on track. However, there is no penalty for late work in this course. This course is designed so that you may work at your own pace, and finish as quickly (or as slowly) as you wish! However, no work will be accepted beyond the last day of the quarter, no exceptions.


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Academic Integrity and Collaboration

Plagiarism occurs when you knowingly submit someone else's work (ideas, words, code) as your own. Plagiarism is an act of intentional deception that is not only dishonest, it robs you of the most important product of education - the actual learning. Should I suspect that you have plagiarized, I will talk with you one-on-one and ask you to prove the work in question is your own. 

You may use AI tools for learning or research, but you are responsible for verifying the accuracy of any AI-generated information. All submitted work must be your own. AI-generated submissions will be considered academic dishonesty.

The purpose of this restriction is to ensure that students develop a fundamental understanding of technical concepts and problem-solving skills.

Software Development and Data Analytics are skills that demands active engagement, critical thinking, and hands-on practice. By prohibiting the use of AI text generators, we aim to promote a genuine learning experience where students grapple with challenges, debugging issues, and algorithmic thinking on their own. This approach encourages the development of analytical skills, creativity, and the ability to translate conceptual knowledge into practical solutions.

Furthermore, fostering a learning environment that relies solely on individual effort and peer collaboration prepares students for real-world scenarios where coding proficiency is essential. While tools like ChatGPT have their place in certain applications, this course aims to lay a strong foundation in skills that students can build upon throughout their academic and professional journeys.

Students are encouraged to seek assistance from the instructor, tutors, and peers, as well as to utilize the provided course materials and resources to enhance their understanding and overcome challenges. Embracing the learning process, persevering through difficulties, and honing problem-solving abilities are key objectives of this course, and refraining from the use of AI text generators supports the achievement of these goals.

If your work is not your own, you will receive a failing grade of zero on the assignment. If your work continues to be plagiarized during the quarter, you will receive a failing grade for the course.

Grading

Grading in this course consists of your demonstrated competency and professionalism. If you have any questions or concerns about a course grade, talk to the instructor within two weeks of receiving the grade.

Grades will be converted according to the following scale:

Decimal %
4.0 95
3.9 94
3.8 93
3.7 92
3.6 91
3.5 90
3.4 89
3.3 88
3.2 87
3.1 86
Decimal %
3.0 85
2.9 84
2.8 83
2.7 82
2.6 81
2.5 80
2.4 79
2.3 78
2.2 77
2.1 76
2.0 75
Decimal %
1.9 74
1.8 73
1.7 72
1.6 71
1.5 70
1.4 69
1.3 68
1.2 67
1.1 66
1.0 65
0.0 <65