Originally published in “Notes of Excellence” newsletter, issue 008 – January 2013.
Hufeza Rangwala is an Assistant Professor of Computer Science in the Volgenau School of Engineering. Since arriving at Mason in 2008, he has taught and developed a variety of graduate courses on data mining, computer architecture, and biological sequence analysis. A rising star in his discipline, he won his department’s Outstanding Teaching Award in 2012, is the principal investigator for Mason’s NSF-funded Machine Learning in Biomedical Informatics (MLBio+) Laboratory, and co-wrote an OSCAR-funded proposal to revise and integrate scholarship into the Computer Science undergraduate curriculum.
What is the most innovative thing you do with your students and/or your classes? Why do you think it is effective?
I design class assignments to supplement the understanding of the subject material. Good assignments are meant to evoke questions that were not raised in class. I believe that a student’s performance on assignments is a reflection of the instructor’s teaching performance. I have always found that the “learning by doing” idea pays rich dividends in understanding important concepts or principles. I developed assignments where the students develop state-of-the-art algorithms explained in class and compare the performance to available public or commercial versions. The classes I teach have a predictive modeling component. I have participated in blind protein structure prediction competitions (CASP) and data mining competitions like KDD Cup. Motivated by this model, I have implemented these forms of competitions in my bioinformatics and current data mining classes.
Specifically, in fall 2011, INFS 755 students had to train a predictive model to determine if a drug molecule was active or inactive. As part of this competition, the true values were hidden, and the developed models had to make a prediction and submit their results using a web service hosted on Kaggle. An automated program evaluated the results using the true values and provided a ranking of the students based on the predictive performance. To improve the model’s predictive performance, students researched and engineered innovative solutions. The spirit of competition and using extensions of techniques learned in class encouraged students to go beyond the typical classroom.
What do you do that creates a strong learning environment for your students?
My lectures focus on interactive and student-centered learning using a wide range of active learning techniques. To train myself with the best active learning strategies, I participated in a semester-long course geared towards preparation of future faculty as a student. One activity I cherish came from middle school teaching.
Jigsaw is a collaborative learning activity. The topic/class module is split into several sub-parts, and a student group is responsible for reading and understanding a sub-part (called “topic”). Students are expected to read and study the assigned topics at sufficient depth to become experts. During class, students convene in expert groups and discuss the topics amongst themselves. After strengthening their ideas about the particular topic, students are asked to gather in mixed groups so that every group has at least one expert from all the topics. Collectively, each mixed group discusses the entire class module and learning from others. The instructor can have a few minutes of summary activity at the end of the two stages. In terms of preparation, the instructor needs to guide the discussion within different groups by asking challenging questions to individual groups or to the entire class. Jigsaw works well for classes when discussion material is subjective, and the value of it in engineering disciplines is not completely known.
What’s one tip that you would offer to faculty new to teaching at Mason?
Students love to be engaged. Especially in a two-hour-plus class, it is important that student voices are heard. There are several easy exercises that I can prescribe: 1) write-pair-share activities, 2) quizzes, 3) class room assessments, and 4) jigsaws.
What’s the most challenging thing for you in your teaching, and how do you address this challenge?
The biggest challenge is getting feedback about what’s working for students’ learning during the semester. While lecturing, I elicit responses from students by pausing at several points during the lecture to ask questions about the material presented. I use the write-pair-share activity and classroom assessment techniques (CAT) like surveys and Muddiest Point. Use of CATs allows me to adapt my class through the semester for a richer learning experience.