Review Assessment Report
Part 1: Contact & Program Identification
| Report Year and Contact Information | ||
|---|---|---|
| Academic Year | Modified By | Date Modified |
| 2022-2023 | [email protected] | 2023-10-23T19:50:52.563Z |
| School | Name of Program | Courses |
|---|---|---|
| BIT | Artificial Intelligence & Machine Learning AAS | None |
Part 2: Program Summary
| Provide a high level review of the program to include highlights, successes, challenges, significant changes, and significant resources needed to support the program |
|---|
This is a new program. We have no graduates as of yet. We have multiple sections of the introductory course. Some students continue with AIML 2010. We are introducing two follow on classes: AIML 2110 and AIML2120. We anticipate our first graduates in Spring term 2024. |
Part 3: Data Review
| 2020-2021 | 2021-2022 | 2022-2023 | |
|---|---|---|---|
| Annual number of graduate awards is greater than 10 | 0 | 0 | 0 |
| Number of declared majors | 0 | 0 | 31 |
| Average Class Size | n/a | n/a | n/a |
| Annual Average Class withdrawal rate is 30% or below (SAGE 35%) | n/a | n/a | n/a |
| Annual C-Pass rate for coursework is 60% or above | n/a | n/a | n/a |
| Average class fill rate at 60% or above capacity within a term or over a year | n/a | n/a | n/a |
| Graduate Transfer to 4-year Schools | n/a | n/a | n/a |
| Full-time Faculty Coverage by Section | n/a | n/a | n/a |
| Summarize how your program met or did not meet the target measures based on the data above |
|---|
There are 31 declared majors.
There is no other data to compare yet.
Data will be evaluted in the next cycle.
|
Part 4: Program Learning Outcome Analysis
| Learning Outcome | Population or Course(s) Assessed | Description | Summary of Assessment Results |
|---|---|---|---|
| 1. Apply common artificial intelligence (AI) concepts and methodologies, including neural networks/Deep Learning, machine learning, Natural Language Processing, Computer Vision, and data science, for analysis and decision making. | AIML 2010 | ||
| 2. Apply artificial intelligence (AI) project development and machine learning life cycle to address social and business issues, opportunities, and problems. | AIML 1010 | ||
| 3. Apply statistical analysis and machine learning algorithms to predict usefulness of artificial intelligence (AI) programming solutions. | AIML 2110,
AIML 2120 | ||
| 4. Use appropriate programming languages to implement artificial intelligence (AI) solutions. | AIML 1010, AIML 2010 | ||
| 5. Evaluate issues of bias, culture, environment, ethics, regulations, and professional expectations in the field of artificial intelligence (AI) and machine learning. | AIML 1010 |
| Interpretation of Assessment findings |
|---|
This is brand new program. There are no findings to interpret because there were only 2 courses were offered over the past year therefore there are no assesment values. |
Part 5: Additional Action Plan in Support of Student Learning (If Appropriate)
| Upcoming Year | Changes Planned for the upcoming year | Data Motivating this change |
|---|---|---|
| 2022-2023 | Two new courses will be added to the program - Natural Language Processing and Deep Learning AIML 2010 and AIML 2020 | There was some reorganization done this cycle |
| 2022-2023 | ||
| 2022-2023 |