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Computational Biology (Spring 2024)

Semester: Spring 2024
Website: pitt-biosc1540-2024s.oasci.org
Meeting time: Tuesdays and Thursday 9:30 AM - 10:45 AM
Location: G8 Cathedral of Learning

Computational biology is highly interdisciplinary
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Computational biology lies at the intersection of biology, computer science, and machine learning to analyze intricate biological data, model biological processes, and make predictive contributions to life sciences. Data often involves genetic sequencing, host-pathogen interactions, and protein dynamics and modulation; thus, computational biologists require a deep understanding of biology. The intersection with computer science is crucial for developing efficient algorithms and tools, employing programming languages and software engineering principles. Machine learning also plays a key role. Some of the largest computational biology advancements are from machine learning, and it is essential to grasp these principles to stay at the forefront of computational biology research.

Broad sampling of major fields
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I believe that by making this course a sampling of major fields of computational biology, you will gain a broad understanding of what is possible. Additionally, by exposing you to a range of topics, I am helping you develop a diverse set of skills that will be useful in many different career paths. Computational biology is a rapidly evolving field, and by providing you with a broad foundation, I am preparing you to adapt to new challenges and opportunities as they arise.

Real-world scenarios enhance learning
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As we delve into the realm of computational biology, I want to emphasize the practical relevance of each module. Instead of a traditional approach, we’ll be driven by motivating real-world scenarios that underscore the importance of the concepts you’ll be learning. Consider yourself a problem solver in a scientific expedition, applying computational tools to tackle actual challenges faced in the field. From predicting the impact of genetic variations on disease susceptibility to simulating the dynamics of biological systems under different conditions, our focus will be on tangible applications. This approach ensures that what you learn in this course is not confined to theoretical frameworks but has direct implications for understanding and addressing complex biological phenomena.

Outcomes
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The following outcomes guides my design and implementation of this course.

  1. Develop a strong foundation in computational biology concepts and methodologies.
  2. Acquire practical skills in data analysis, modeling, and interpretation using Python.
  3. Understand genomic and transcriptomic analysis techniques and their applications.
  4. Explore the principles and techniques of computer-aided drug design.
  5. Gain hands-on experience in molecular simulations and structural analysis.
  6. Evaluate and apply computational methods to solve real-world biological problems.
  7. Prepare students for further research or applications in computational biology and related fields.

Grade distribution
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Below is the final grade distribution of the 69 students in the class.

A+ (5.8%)

A (17.4%)

A- (17.4%)

B+ (5.8%)

B (18.8%)

B- (7.2%)

C+ (2.9%)

C (8.7%)

C- (1.4%)

D+ (5.8%)

D (1.4%)

D- (0.0%)

F (7.2%)

Insight
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The vast majority of students (84.2%) demonstrated proficient understanding of the material. A strong correlation exists between attendance and receiving a passing grade; students with a C- or lower in the course attended only 20% of the classes (excluding quiz days). Students who semi-routinely came to office hours make up a large portion of A’s and above.

Repository
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OASCI / 🏫 Courses / Pitt / biosc1540-2024s

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Teaching evaluations
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The immediately following text is a generative AI summary of my teaching evaluations.

Strengths:

  • The instructor was open to feedback, adapted the course based on student needs, and created an engaging, respectful learning environment.
  • Projects and checkpoints provided meaningful, real-world learning experiences.
  • The course covered a wide range of relevant, modern computational biology topics.

Areas for Improvement:

  • Some students felt the workload and expectations were too high, especially for those without prior coding experience.
  • Lectures could be more informative and better prepare students for exams and assignments.
  • More consistent expectations, clearer documentation, and additional resources for learning Python would be helpful.
  • Consider splitting the course into sections based on students’ coding background or adding prerequisites.

Recommendations:

  • Provide more hands-on coding practice and smaller, manageable assignments to build skills gradually.
  • Improve lecture content by adding more explanations, examples, and visual aids.
  • Consider recording lectures and providing more detailed slides for reference.
  • Clarify expectations and maintain consistency throughout the course.

Overall, the instructor’s passion, adaptability, and support for students were highly valued. While there is room for improvement in structuring the course and providing resources, the instructor’s teaching style and the course’s real-world applications were appreciated by many students.

The following passages are unmodified student responses (36 out of 69) to a Course Survey administered by OMET.

Results

The standards the instructor set for me were
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Too low: 0

Appropriate: 21

Too high: 14

How many hours per week did you usually spend working on this course outside of classroom time?
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Less than one hour: 1

One to three hours: 12

Four to six hours: 13

Seven to nine hours: 6

Ten or more hours: 2

The instructor created an atmosphere that kept me engaged in course content
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Strongly disagree: 1

Disagree: 4

Neutral: 8

Agree: 16

Strongly agree: 7

The instructor was prepared for class
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Strongly disagree: 1

Disagree: 1

Neutral: 12

Agree: 10

Strongly agree: 12

The instructor treated students with respect
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Strongly disagree: 0

Disagree: 0

Neutral: 2

Agree: 11

Strongly agree: 23

The instructor was available to me (in-person, electronically, or both)
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Strongly disagree: 0

Disagree: 1

Neutral: 1

Agree: 10

Strongly agree: 23

N/A: 1

The instructor evaluated my work fairly
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Strongly disagree: 0

Disagree: 0

Neutral: 2

Agree: 19

Strongly agree: 14

N/A: 1

The instructor provided feedback that was helpful to me
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Strongly disagree: 0

Disagree: 2

Neutral: 10

Agree: 12

Strongly agree: 11

N/A: 1

I learned a lot from this course. If there is no basis to judge or not applicable, answer N/A
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Strongly disagree: 1

Disagree: 1

Neutral: 8

Agree: 11

Strongly agree: 13

The instructor creates an inclusive learning environment for all students
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Strongly disagree: 0

Disagree: 1

Neutral: 1

Agree: 15

Strongly agree: 17

The guest speaker gave an effective presentation
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Strongly disagree: 0

Disagree: 0

Neutral: 8

Agree: 15

Strongly agree: 11

The guest speaker should be asked to speak again in this course
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Strongly disagree: 0

Disagree: 0

Neutral: 10

Agree: 15

Strongly agree: 9

The project(s) provided a meaningful learning experience
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Strongly disagree: 1

Disagree: 3

Neutral: 3

Agree: 12

Strongly agree: 15

This course helped to develop my ability to solve real problems in this field
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Strongly disagree: 0

Disagree: 4

Neutral: 4

Agree: 15

Strongly agree: 11

I would rather have no exams and harder/open-ended projects of real-world problems
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Strongly disagree: 1

Disagree: 10

Neutral: 5

Agree: 9

Strongly agree: 9

I would rather have fewer topics and more depth (i.e., half transcriptomics and half cadd) instead of more topics at a surface level (i.e., genomics, transcriptomics, cadd, simulations)
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Strongly disagree: 2

Disagree: 9

Neutral: 7

Agree: 9

Strongly agree: 7

If this class had CS 0011 (Introduction to Python) as a prerequisite, would you still have wanted to take this course if it meant an additional class?
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Strongly disagree: 3

Disagree: 5

Neutral: 3

Agree: 12

Strongly agree: 11

This course should be split into computational biology major and non-major sections.
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Strongly disagree: 1

Disagree: 2

Neutral: 2

Agree: 9

Strongly agree: 20

How would you rate the relevance of the course content to your personal and professional goals? (Strongly agree = Strong relevance)
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Strongly disagree: 3

Disagree: 4

Neutral: 8

Agree: 12

Strongly agree: 7

What did you like best about how the course was taught?
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  • Alex adapted to the feedback and took action fairly early and quickly.
  • I appreciated and valued how prepared the professor was for each and every class. Having everything set and laid out on a Github website made it clear and easy for me to find where and what we were doing beforehand. I thought especially in a writing class, it was extremely helpful to have all sorts of resources in an organized fashion.
  • I liked how for the final paper we had multiple drafts to gain feedback
  • I liked how the entire course was structured, from start to finish learning how to effectively read scientific papers. Then on to researching and picking our topics. To then write our final paper. While it was an over two–hour class having a break every hour made it much easier to concentrate and learn while in the classroom as well.
  • I loved the open and honestness of the classroom teaching style. There was a lot of information provided for every assignment, and we had an ample amount of help and resources.
  • I liked that a lot of the activities were more conversational and that the entire class was in a conversational format.
  • I liked how non rigid it was. There was certainly a lot of preset assignments however I really liked being able to give feedback that altered the content covered in the class.
  • I liked the structure of this course a lot. In the beginning, we learned about how to read and critique research articles, which set us up for our presentations and final papers later on. the paper was broken up by drafts and not just one big grade at the end
  • I liked the fact that the course and instructor were adaptable and listened to what the students needed
  • I liked the presentations and paper that we had to write, I feel that it has developed my skills in those areas
  • I enjoyed the interactive coursework in class. The programs helped me gain more insight into the course itself.
  • I liked the way the presentations were done. I felt liked I was able to comprehend and actually understand what was being thought in the class.
  • I liked the smaller class size because it gave me opportunities to ask questions and really be involved in the discussions
  • I like this class because it can urge me to read the research paper. And each part can be analyzed, which is very helpful in the future work or continue to study.
  • I liked how the assignments were clearly outlined at the start of the course, and how the class itself is more–so spent doing group activities and supplemental.
  • Alex was very open to suggestions and ran the class based on feedback given during the class.
  • Generally, I liked how the course was laid out with the giant paper over the semester along with the presentation.

What did you like best about how the course was taught?
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  • Learning about the newest technology so we are up to date
  • How modern it was. Computational Biology is a field that is always changing and Alex really leaned into that and did well in making the topics relevant and applicable to what would happen in the real world.
  • I enjoyed the last two checkpoints. I think that if you do keep teaching this course, incorporate more work like that.
  • I liked how engaging Alex was. It was evident that this course is what he is passionate about and it showed in the lessons. I also liked how he was very open minded when it came to feedback and adjusted assignments based on the needs of the students.
  • I liked the breadth of information and how the material actually directly applies to a lot of current research.
  • It was project heavy, which was nice because it had a real world application to it.
  • I liked how the assignments and checkpoints enhanced the learning from the classroom.
  • taught interesting material given in a productive way. made an atmosphere that was comfortable.
  • The adaptability and flexibility of assignments and course material. He had a good understanding quickly what we were capable of and what we were not and how to adjust to it all. However, he also gave options to expand on learning for those who wanted a little extra.
  • The quizzes being open note
  • More weight on checkpoints and assignments
  • Even though the professor was not always prepared to teach the class or if the lectures were lackluster, such as the salmon day, he was available for office hours M– F, excluding Thursday, which made it much more tolerable and enjoyable. Note: The professor was only given two weeks to prepare for the class and make up a syllabus, so I don’t really blame him on this.
  • There were many opportunities to get things right.
  • The outside resources which were linked in the syllabus.
  • I like how flexible he was in changing lesson plans if he saw we were struggling
  • I enjoyed the wide variety of comp bio topics that Alex introduced us to because it is evident he is passionate about the course content and wants to share it with us. I also learned a lot from completing the checkpoints and was proud of the product I ended up submitting because of the amount of effort I had put in. I know a lot of students were critical of the way the course was taught because they didn’t feel equipped to search on their own for documentation on scikit–learn etc. I think a lot of this had to do with the fact that many students had never done coding of any sort before. Alex did a great job adapting to this while still maintaining aspects of comp bio for us to be able to learn.
  • Learning about how we can use computers to create and discover drugs or do science generally was amazing.
  • how it was lots of projects and not tests
  • I love how the course was altered to help all of us. It is something that I greatly appreciated and made me enjoy the class more than I already did.
  • I liked being able to have hands–on experience with the docking simulations and Python. The reason I took this class was because I wanted to have some experience with Python and how it works in a lab, or bio kind of setting. I guess I got some of that, even though I struggled heavily with how to “do” Python in the first few classes (I have experience with C++; they’re really different…). I also liked how you outlined the assignment criteria very clearly, and responded relatively quickly to emails when I had questions. I- liked the level of python that we used I think it was a good understanding of the system without having to actually know how to code.
  • I liked the structure of how there weren’t any exams. Especially having other hard classes this semester, it made me feel better.
  • I like the wy the professor taught the class, he tried to be kind and polite to students and answer everyone question.
  • I liked the structure of the class being lectures, homeworks, projects, and quizzes
  • I enjoyed the projects because they helped reinforce what was learned in class
  • Step by step help with understanding concepts of python for the ones unfamiliar with it
  • I liked the flexibility of this course and how you adhered to the students. I think one dropped quiz takes a lot of stress off the students and the checkpoints were very fair and not overly difficult.
  • the concept of having a portfolio was initially appealing. Prof took feedback and incorporated it into class. Using tophat to anonymously ask questions was really helpful. Discussions during class was useful and engaging. Open note quizzes with plenty of time to complete them. Time to work on checkpoints during class was VERY helpful
  • The slides used a lot of real examples, including going through programs in class to better understand their function.
  • N/A

If you were teaching this course, what would you do differently?
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  • If wish it was more hands on actually learning how to do the programs we were describing instead of just learning how they worked from a chem/bio standpoint
  • I would set expectations a bit lower. I felt that on quizzes or projects that I would spend 60+ hours on, and I don’t think that the projects needed to be that stressful. Alex and the teaching team was very helpful, but I felt that on my own, I had to do a ton of self teaching outside of the classroom. Which is usually okay, but the transcriptomics checkpoint legitimately took me over 100 hours to complete.
  • I would offer a textbook (but I do know that a comprehensive computational biology textbook is hard to come by).
  • Give students answers to the practice problems before exams and provide study guides. Be more clear about what would be on exams and teach more efficiently.
  • I think I would structure it entirely differently, I know this was the first year teaching it but I couldnt find helpful resources to learn outside of class. The lectures themselves were not very informative either, and nothing could be learned from going over them again outside of class.
  • I think this happened only because this is a new course, but the expectations for what we as students should be able to do varied widely throughout the length of the class. I would make ideas of expectations clearer to students and try to stick to them as much as possible.
  • I wish the syllabus did not change every couple of weeks and the rules for exams were changing every time. I also wish it was more programming based.
  • I would have more clear writing on the lecture slides, so its easier to understanding when looking back after lectures and studying.
  • more explanations on what will be on exams. more study materials. more coding in a computational biology class!
  • We’ve already talked about this but it honestly should be 2 courses––one python and one not. They both have their benefits but it is hard for a bunch of bio majors to just go into the class knowing python at the level that may be needed for the best code. Other than that I would have a few more assignments only 5 felt too little and practice problems for quizzes which I know is not a lot but after this class, it should be kind of possible. I would also just make reading mandatory as they really help even if its just like you have to answer 5 questions on top hat for an assignment that is in the reading just to clear up some confusion come lecture. If we can’t do 2 courses just make the checkpoints have a python and excel/sheets option. (I would still go over some python it is useful) And for CADD keep it MolModa.
  • I would give more homework assignments to ensure everyone is prepared for quizzes
  • Less confusion with python at beginning of semester
  • I would change the prerequisites. The class prereqs should also include CS 0011 and STAT 1000 to get a basic understanding of the course. If that’s not allowed, I would attempt to either separate the classes into two versions or give a prerequisite quiz on the topics as a test to see if you’re ready for the course. Also, within the textbook, over the summer, I think it would help students greatly if you put sample problems on important topics(the videos you made in Unit 1 were invaluable to my understanding of the quiz). For example, on a greedy algorithm section, embed your video lesson from unit one on the page and put practice problems below. I believe this would seriously increase student understanding. I think what made this class so much harder is after unit 1, there was significantly less written content, mostly just blank sections with the header “TO DO.” Overall, you were an excellent instructor within time constraints and showed me how much I truly enjoyed this subject.
  • Organization and expectations for students.
  • I would use Canvas. Utilize more powerpoints and be more organized when it comes to day to day activities and goal for upcoming exams and assignments.
  • I would go into more detail about fewer topics and make sure that we have a solid background of the material. I would also maybe do more hands–on activities so we can see what we’re supposed to do for assignments.
  • I think unfortunately some students were still discouraged because up front the content can be intimidating. There is a lot to learn and people who haven’t done coding of any kind before can have more trouble ignoring the things that they don’t need to understand and focusing in on what they do need because it’s hard to know what to look for and what to ignore (for example all the different available parameters in the sklearn documentation for the classifiers that makes it hard to understand what you actually need to use in order for it to work). I think a lot of students aren’t used to searching online for support with coding because they’ve been taught not to rely on online resources that are not endorsed by the professor. I don’t know how plausible this is, but I think that in the beginning of the semester emphasizing that online support is a necessary aspect of this course and that it is normal to have questions and need support with coding and to get errors and how to resolve these errors on your own will be helpful. However, I think a lot of the issue which is difficult to prevent is that there isn’t a coding prereq for the course.
  • This course was brand new to me, and I had never seen any of the content in this course before. At the beginning of the course, I would go slower into Python because not everyone knows what that is. In the beginning, I would teach and go deep into the material to ensure every single student grasped the course concept.
  • maybe make more consistent assignments so we keep up with content better
  • Considering comp bio is a new field with no basis of a textbook for an intro class I would not do anything differently. I can tell that Alex put a lot of time and effort to all of his materials and assignments so they are clear and make sense.
  • I think maybe assigning us Python courses from the platforms you suggested would’ve helped with making us learn Python, especially since I tried one of the intro ones and it didn’t seem too time–consuming. I understand why you had us practice via your assignment questions, but I think the Python courses from the platforms were better at contextualizing and teaching us the content. Assignment–wise, I think criteria were outlined pretty well, barring the times that 1. the criteria wasn’t listed, and 2. the documentation for the assignments weren’t there. The second one is particularly directed to the CADD unit, with little to no documentation on the website. Yes, you gave us textbook material, but it doesn’t give us the exact phrasing / information you might be looking from us, and it made the CADD checkpoint, particularly the docking segment, hard to do. Because I wasn’t exactly sure what you were looking for, and I felt like I just repeated myself for five questions, but in different ways.
  • I understand that this was one of your first times teaching this class, and I think you did a relatively good job!! But sometimes things just felt so disorganized that I just felt like I was floating through class. Also, sometimes the quiz questions didn’t really feel like they reflected lecture material; I think I was pulling from my genetics knowledge for the first quiz, when it came to sequencing methods and things like that.
  • I would utilize class time differently, I think that class time was wasted on topics that were not needed in that moment, the timeline was skewed for what assignments were upcoming. I think that the material covered in class did not properly reflect what we were tested on. It might as well have been an asynchronous course with how much out of lecture teaching there was. Which yes, in most courses students need to research and learn on their own outside of class, but in this course all the learning and teaching was done outside of class. The information given in lecture was practically just a guiding point to know what topics to look for. Literally equivalent to a study guide, maybe less than. There was also too much information to sort through and completely understand how everything fits together.
  • Understandably, it’s hard to know what to teach when a course is so new, I just wish instead of him trying to explain for the students who are taking this as an elective, he focused on helping those who are in the major.
  • I think if i were teaching this class, I will make two classes, some people were very familiar with phyton and other computational sciences and there were people like me, that everything took extra longer. if this class was two sections one for people familiar with this and one for people who had zero familiarity, it will be better. i don’t think this is a mistake of the professor but of just being the first time this class was taught and no one was familiar or could research how the class would work.
  • I think if i were teaching this class, I will make two classes, some people were very familiar with phyton and other computational sciences and there were people like me, that everything took extra longer. if this class was two sections one for people familiar with this and one for people who had zero familiarity, it will be better. i don’t think this is a mistake of the professor but of just being the first time this class was taught and no one was familiar or could research how the class would work. iMAGINE,
  • I wish the lectures had more content on the slideshow so I could refer to something from lecture if I missed it in my notes. I would also increase groupwork as this class had a large learning curve, especially because so much of the content revolved around python without us actually being able to fully use python. I wish there were smaller and easier projects that would allow us to use minimal python as it would allow us to be more immersed in the field. I would also focus on some content rather than attempt to push it all out because it made the content very confusing since I had no background knowledge on the subject.
  • Record lectures. The content was very dense and it was hard to write everything down/retain information
  • Strongly advise people to learn programming language on the side so that work isn’t overwhelming
  • Some people may not agree with me on this, but I liked the freedom on the second checkpoint. Instead of specific outlines, you allowed us to be creative with what we learned and draw our own conclusions. It was still enough outline to make sure we did everything needed, but still gave us a lot of freedom. The resources you have on the website were more than enough to help us through that checkpoint. I would give more assignments like that if I were teaching.
  • I would forgo a class website and instead have all resources laid out on the same page on canvas. Website is stressful to navigate and not beneficial. I would record lecture and include more material on lecture slides. Lecture slides with predominantly images/screenshots are not useful. More text is needed. I would use Tophat more effectively and purposefully for class participation. I would include more lectures and guidance dedicated for how to complete checkpoints including how to code and what the code actually represents, what your expectations are etc. I would include more practice material, support in general. such as TA lead review sessions before quizzes and more drop in hours for checkpoints.
  • I believe some type of way to review for the exams is necessary, either through multiple choice questions with an answer key, or homework assignments similar to A4 and A5, which are more theory–based questions throughout the class.
  • N/A

What are the essential qualities of a successful instructor for computational biology courses?
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  • Meet the students at where they are
  • Availability, Knowledge, and Efficacy. All of which Alex had.
  • You have to know how to code, but also have a good content foundation in the field of general biology.
  • Is able to explain things on a simpler level to students.
  • Someone willing to help out their students as well as help them find opportunities within the job field and related job fields
  • Willing to explain detailed concepts. Willing to explain basic concepts/not assuming students have background knowledge in the subject matter. Understanding students may not be using computational biology in their career. Being available for questions. Consistent with expectations. (you already do a lot of this!)
  • Understand the students in the class and maybe emphasize that the class is programming heavy.
  • Open mindset when teaching students, as this is a newer field we are diving into without much of background due to a large focus in Java and Biology courses that are not necessarily very relevant to the Python, R and specific topics of computational biology not covered in the major curriculum.
  • knowledgeable on the many topics that encapsulate the course. being able to accurately answer questions.
  • Flexibility, Time (Sry), Enough knowledge to dumb it down for some. A great knowledge of food. An understanding of students’ issues.
  • To slow down and explain
  • Strong knowledge of what materials students can use to learn since there is no one concrete textbook
  • The professor does not have a problem with this.
  • knowing limitations
  • The ability to explain abstract concepts in an elementary way.
  • Someone who is flexible and able to break concepts down to understand them easily
  • Adaptability, patience, and the ability to determine the difference between when students truly feel incapable of understanding course content and when students aren’t trying to understand something because it requires a different kind of learning than the learning they are used to. A lot of students I think were unable to get over the hurdle of taking a class that felt so different from one they took before and thus weren’t able to see that once they accept that it is a different style of class it isn’t necessarily that difficult.
  • Don’t expect students to know so much of the material in the beginning. I came to class with no knowledge of this course.
  • knowledge, passion, communication
  • Willing to work with students and able to explain these complex concepts in a way that can be understood by people new to the material
  • Able to explain coding and other biological concepts succinctly and clearly, able to clearly outline expectations for assignments and tests.
  • Someone who is in the field/ has worked in the field for a good amount of time.
  • good communication ( Prof.Alex has good communication). accesibility (being available to students questions). flexibility
  • Being able to fully breakdown code and computational biology concepts on an easy to understand level.
  • efficient in teaching information, simplifies information enough for students to understand
  • Patience. It may seem overwhelming non–compbio students, but to the ones you helped, being patient allowed us to appreciate the real word computational aspect of python
  • Understanding with students not being familiar with coding. Coding takes more than a couple classes to learn, so it helps if there is an understanding that we are not experts.
  • A prof who has the ability to think like a student and someone with no prior exposure to the topic and can empathize with students who are learning the material for the first time. This involves limiting the use of Jargon, explaining acronyms, using visuals/drawings to explain concepts, explaining things in multiple ways for students to understand. A prof who can systematically present information in a clear step by step manner, including why things are done such as lines of code or sequencing technologies
  • Ability to summarize objectives/ reasons for a process. Open and available communication, flexible class layout (able to spend more time on a subject students are not understanding).

How likely are you to recommend this course or my teaching to other students, and why?
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  • 6/10 You tried your best but a lot of the work took an unnecessary amount of time to reiterate the same information over and over again
  • I don’t know about how likely I’d be to recommend this course to non Comp–Bio majors as it is very technical. But I’d recommend Alex’s teaching to anyone in STEM as he’s been one of my favorites thus far at Pitt.
  • I would highly recommend this course because you are actually applying biological concepts to work, not just listening to information you’ve already heard before (and that’s the end of it).
  • I thought your teaching was good, but it did not really help me prepare for exams and you did not provide enough resources to study for exams, which is what I struggled with the most.
  • Very likely for other computational biology majors as you provided much help for those in the major.
  • not likely at all, as a non comp bio major it was a really unapproachable class.
  • I would recommend your course to others with the idea in mind that it was your first time teaching it, and that future iterations of the course will probably go more smoothly/be more consistent.
  • VeryI would be very likely to recommend you as a teacher to other students. I think you’re grading and syllabus were more than fair and very helpful to students with other difficult classes. likely, but with tweaks in the teaching style and syllabus.
  • Highly likely; You created an environment where we could ask questions and not be afraid to ask for help or change checkpoints if we were struggling with aspects covered.
  • very likely! very knowledgeable and welcoming to discuss class and future plans
  • I would definitely recommend it to comp sci majors with an interest in biological problems. As for bio majors, I would still recommend it but with a caveat that it will be a slow start and it is different from anything you have done so far. I think this way you get more students that come in interested to learn and a good distribution between majors is set
  • I would 100% recommend, you can tell Alex cares about his students and their learning.
  • I would recommend to other students who are interested in comp bio specifically because it’s a good learning experience about the field
  • In the current state: 6/10. But I’m sure over the summer when the textbook is completed, and the class is more blue–printed out, it will be an invaluable course for computational biology majors. On the “I would rather have fewer topics and more depth (i.e., half transcriptomics and half cadd) instead of more topics at a surface level (i.e., genomics, transcriptomics, cadd, simulations)” question, I don’t think you should remove content as this is the first main computational biology class and not an advanced elective and students must get a general introduction to major areas in the field before advancing in one particular field. Maybe look into making a CADD–specific HL elective class.
  • Slightly likely because it was a difficult course with a lot of coding and machine learning. These were things not stated in the prerequisites. However, if the goal were more clarified I would probably recommend this course because there are many useful skills which can be taken from this course.
  • As this course was this year, I would not recommend it. However, I know that there will be a lot of changes made and I think it could be a very engaging class if it was a little bit more organized. I would recommend your teaching since it was very flexible and obvious you care about your students.
  • I would be wary of recommending the course to anyone who I don’t think adapts to change very well or isn’t familiar with the challenges that come with coding initially. However, I would recommend it to anyone interested in comp bio because I think the course does a great job of introducing what it is like.
  • I would recommend your teaching because, as a student, you are a very fun professor. You make the environment of the class friendly to everyone, which will make students want to learn from you. I would not recommend this class to anyone if they don’t need it. This course did not catch my attention, and it was never of my interest. It shouldn’t be required for the Biology major.
  • very likely because you are young and understanding of students, so i have no worries you will adjust your courses to accommodate for mishaps.
  • Very, Alex has been my absolute favorite professor at Pitt and will be pushing everyone I know to consider taking this class specifically with him. I only took this class because he was teaching it rather than the other comp bio professor.
  • I recommended this course to a few friends because I liked how this class felt more application–based rather than just memorizing things.
  • Very likely.
  • I would recommend the course as it is, if you have familiar with phyton. OR give you advice to prepare with some phyton before taking this class at least the basics so you could be able to get all those extra credits.
  • Semi likely, I felt like a lot of the class required me to have previous knowledge on the content and I was often left grappling with what I was supposed to learn or what was to be expected on the assessments, and the open resources were often overwhelming and confusing as well.
  • Yeah, probably would. I think there are some kinks that need ironed out in terms of course structure, which i’m sure Alex will agree with, but once that is done I think this course will be incredibly useful for those interested.
  • Not unless they are interested in comp bio
  • Still very good recommendation to anyone willing to diverse their computer major with biological aspects. I would still warn those with limited programming experience since its pretty much learning a new language + academic rigor of classes
  • I would be very likely to recommend you as a teacher to other students. I think you’re grading and syllabus were more than fair and very helpful to students with other difficult classes.
  • Likely, your teaching style is challenging but if you are willing to put in the time and visit outside of class for issues you are having you can develop a deep understanding for the course.