Course Reviews for
Indiana Wesleyan University
10/10
average rating
no filters applied
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9 /10
Major: M.S. Computer Information Systems - Artificial Intelligence
AIML-500 Machine Learning Fundamentals
Instructor: Cliff Birdsell
Delivery: online
9 /10
AIML-500 (Machine Learning Fundamentals) with Professor Cliff Birdsell provides an excellent, well-structured online learning experience. The coursework is clear, practical, and offers a manageable workload that reinforces core AI concepts effectively. Professor Birdsell’s engaging instruction and strong organization make this course highly valuable. Highly recommended!
07/26/2026
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10 /10
Major: M.S. Computer Information Systems - Artificial Intelligence
AIML-510 Responsible Application of Artificial Intelligence
Instructor: Scott McCullough
Delivery: online
10 /10
Professor is good and easy when it comes to grading, assignments are feasible.
07/24/2026
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10 /10
Major: M.S. Computer Information Systems - Artificial Intelligence
AIML-500 Machine Learning Fundamentals
Instructor: Scott McCullough
Delivery: online
10 /10
The course is informative and assignments are feasible, the professor is engaging and not rough on grading.
07/24/2026
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10 /10
Major: M.S. Computer Information Systems - Artificial Intelligence
AIML-500 Machine Learning Fundamentals
Instructor: Scott McCullough
Delivery: online
10 /10
My professor was knowledgeable and made the class engaging. The hands-on activities and practical assignments helped reinforce the concepts and made learning more interactive. Overall, the course was informative and provided a strong foundation in machine learning.
07/18/2026
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10 /10
Major: M.S. Computer Information Systems - Artificial Intelligence
CSCI-505 Object-oriented Programming
Instructor: Dr. Clifford Birdsell
Delivery: online
10 /10
My professor explained the concepts and material really well. They are helpful, and encourage students to learn. Overall, I had a positive experience in this class.
07/18/2026
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9 /10
Major: M.S. Computer Information Systems - Artificial Intelligence
CSCI-505 Object-oriented Programming
Instructor: Daniel parrell
Delivery: online
9 /10
Good
07/17/2026
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10 /10
Major: Master’s in Data Analytics
DTAN-500 Foundations of Data Analytics
Instructor: Paola Saibene
Delivery: online
10 /10
Professor Paola Saibene has made a very positive first impression. Although the course has just started, I have really enjoyed the class so far. She explains concepts in a clear and organized way and is always willing to answer questions, which makes it easier to follow the material and stay engaged. The workload seems appropriate for a graduate-level course. The assignments are challenging enough to reinforce what we learn in class without feeling overwhelming. So far, the expectations have been well communicated, which has helped me understand what is expected throughout the course. Professor Saibene encourages participation and creates a welcoming classroom environment where students feel comfortable asking questions and sharing their ideas. I also appreciate that she takes the time to clarify concepts whenever someone needs additional explanation instead of simply moving on. Attendance seems important because each class builds on previous discussions and activities. Being present allows students to better understand the material and benefit from the classroom interactions. Overall, my experience has been very positive so far. Even though we are only at the beginning of the semester, I appreciate Professor Saibene's teaching style and her willingness to support students. I am looking forward to the rest of the course and would recommend taking a class with her based on my experience so far.
07/02/2026
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10 /10
Major: Master’s in Data Analytics
DTAN-505 Data Visualization
Instructor: Dr. Brayton Smith
Delivery: onsite
10 /10
Professor Brayton has made a very positive first impression. Although the course has just started, I have enjoyed the class so far. He explains the material clearly and takes the time to answer students' questions, making the learning environment comfortable and engaging. The workload seems reasonable for a graduate-level course. The assignments appear to reinforce the concepts covered in class without feeling overwhelming. So far, the expectations have been clear, which has helped me stay organized. Classroom participation is encouraged, and Professor Brayton creates an atmosphere where students feel comfortable asking questions and sharing their thoughts. He is patient when explaining concepts and makes sure everyone understands before moving on. Attendance seems important because each class builds on previous discussions and activities. Being present allows students to participate, ask questions, and benefit from the explanations provided during class. Overall, my experience has been very positive so far. Even though we are only at the beginning of the course, I appreciate Professor Brayton's teaching style and his willingness to help students understand the material. I look forward to learning more throughout the semester and would recommend this course based on my experience so far.
07/02/2026
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10 /10
Major: MS in Artificial Intelligence - Data Analytics
AIML-505 Large Language Models and Generative Artificial Intelligence
Instructor: Melisa Snyder
Delivery: onsite
10 /10
I am currently pursuing the MS in Artificial Intelligence – Data Analytics, and I recently started the course AIML-505: Large Language Models and Generative Artificial Intelligence with Professor Melisa Snyder. Since this review is based on the beginning of the course, my evaluation focuses on the initial course structure, first impressions, expectations, and early learning experience rather than final outcomes. At the start of the course, AIML-505 appears to be a highly relevant and valuable class for students in the Artificial Intelligence and Data Analytics major. The subject matter is especially important because large language models and generative AI are becoming central to many areas of technology, business, analytics, automation, and decision-making. The course seems designed to help students understand not only how these models work but also how they can be applied responsibly in real-world situations. The early course materials give a clear indication that students will be expected to think critically about AI tools, model behavior, prompt design, limitations, and practical applications. The quality of the class so far seems strong. The course content is organized in a way that introduces students to important concepts step by step. Since this is the start of the course, the material feels challenging but manageable. The topics are modern and directly connected to current developments in artificial intelligence, which makes the class engaging. For a student in the MS in Artificial Intelligence – Data Analytics program, this course feels useful because it connects technical AI knowledge with analytical thinking and practical problem-solving. The homework load at the beginning of the class appears reasonable, but it also requires consistent attention. Students should expect to spend time reading course materials, understanding concepts, completing assignments, and applying ideas through written or practical work. The workload does not seem overwhelming at the start, but it is clear that students need to stay organized and avoid falling behind. Because generative AI is a broad and fast-moving topic, completing assignments carefully will likely require both independent study and thoughtful reflection. Classroom discipline and participation expectations also seem important. Whether the course is taken online or onsite, students are expected to remain engaged, follow instructions, meet deadlines, and participate professionally. Attendance requirements appear to be an important part of the learning process because the early sessions and course introductions help students understand expectations, assignment structure, and how the subject will be developed throughout the term. Being present and attentive at the beginning of the course is especially valuable because it sets the foundation for later topics. Professor Melisa Snyder’s teaching style, based on the start of the class, appears structured, supportive, and focused on helping students understand complex AI concepts in a clear way. The course expectations are presented professionally, and the teaching approach encourages students to connect theory with practical applications. This is helpful for students who may have different levels of prior experience with artificial intelligence or generative AI tools. Overall, my initial impression of AIML-505 is positive. The course appears to be well-aligned with the MS in Artificial Intelligence – Data Analytics major and provides a strong foundation in one of the most important areas of modern AI. At this early stage, I believe the class will be valuable for developing both technical understanding and practical awareness of large language models and generative artificial intelligence.
07/01/2026
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9 /10
Major: Master’s in Data Analytics
ADM-545 Organizational Development and Change
Instructor: Daniel Hall
Delivery: online
9 /10
Feedbacks are a little slow but overall good communication and good teaching provided by professor.
07/01/2026
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