This course will meet in person two days a week. While attendance will not be recorded, there will be activities that must be completed in class to receive credit.
The hybrid designation on this course means that there is also an online component to the course, approximately one hour per week. The online component may consist of readings, videos, tutorials and other activities. All assignments and announcements are posted in the course Canvas shell.
Students build a data-driven web application. Focus on understanding and integrating the various technology components of modern web applications. Survey of security practices in the web technology stack.
Students will create a data-driven web application. Overall, you should expect to spend about 10 hours per week outside of class completing class assignments and projects.
At the end of the course, you should be able to:
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.
There are tutors available both at Auburn Center and the main campus (Holman Library) for all IT Software Development classes. View the Tutoring Schedule
my.greenriver.edu contains information and links for important student resources.
LinkedIn Learning provides a wide range of technical video tutorials, and is free to Green River students.
All assignments will have a 24-hour grace period during which no points will be deducted. After that, an assignment may be turned in up to 3 calendar days after the due date for 50% credit. After 3 days, an assignment will not receive credit.
As an example, if an assignment is due Monday at midnight and you turn it in on Tuesday, you will receive full credit. If you turn it in before Thursday at midnight you will receive 50% credit. If you turn it in Friday or later, you will not receive credit.
Pair programs may be turned in within one week of the due date and still receive full credit. Pair programs submitted more than one week late will not receive credit.
All assignments are posted well in advance. Be sure to get an early start so that you have plenty of time to get help when you need it.
Regular attendance and participation are required to succeed in this course. Absences have a huge impact on your team productivity, as well as your individual learning. If missing a class is unavoidable, you are responsible for asking a classmate to take notes, and pick up any handouts you may have missed. You do not need to notify the instructors if you will be missing a class.
You may earn up to 12 points in extra credit. Each extra credit activity is worth 4 points. If you have other ideas for extra credit, please share them for consideration. To receive extra credit, submit a brief 1-2 paragraph writeup of your experience in Canvas within one week of the event. The link is under Course Resources.
No extra credit will be accepted more than a week after the event, or beyond the last day of class.
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 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 |