Amrit Thapa

Presently, I am a senior lecturer in the International Educational Development program in the Graduate School of Education at University of Pennsylvania. This is my sixth year at Penn, where I have been teaching graduate-level courses such as Economics of Education in Developing countries, Principles of Monitoring and Evaluation (M & E) of International Education Development (quantitative course), Advanced topics in M & E (quantitative course), and Global Perspectives on School Climate. I have been fortunate to get varied teaching experience in my teaching career so far. I have taught at the undergraduate and graduate levels, small and big size classes, and in both developed and developing countries. I feel good to function in this profession. In all my academic positions, I have tremendously enjoyed teaching and the interactions with students from all over the world and from diverse professional and backgrounds. In this article, I would like to share some of my observations, challenges and personal experiences regarding teaching methods course for an average student in a class with high variance of statistical backgrounds. 

As the name of the program suggests, students in the IEDP program come from multiple countries and varied racial/ethnic and cultural backgrounds. I find that so fascinating. At the same time, there is a fair degree of deviation of statistical knowledge/skills among students in a  typical program like this. As an instructor of Methods/Quantitative course, this could be challenging to create a fair and conducive learning environment for all. One of the challenges in such settings is to teach the course in a non-intimidating manner, and at the same time maintaining the quality of the content, which is integral to students’ learning. There are many variables that need to be identified and solved, and it is not always as straightforward as a logic model. 

First, one observational point about students, both cross-sectionally and longitudinally, is that statistics is not usually the favorite subject for a range of students. In social science graduate programs (except for a few disciplines), students usually are required to take a minimum of one or two of these courses. They might not be always taking the course due to a love for the subject. Rather, many students have so-called “fear” of the subject. Given the nature of the subject, this is a valid concern. To negate this seemingly skewed perspective towards the subject, I have  found that the tone of the class is immensely important. Or else, there is high probability that instructor would find oneself as an outlier 😊 So, I try my best to keep the classroom climate normal, friendly, welcoming and respectful. Sometimes I do that with a quick ice-breaker activity. This could be related to topic of discussion, or something exogenous. Yes, qualitative ideas can be mixed into a quantitative course. 

Second, the mode of the class is also important. Being aware that all students have different ways of learning and understanding the subject matter, I would use variety of mediums for instruction such as intuitive explanations, relevant videos, figures and graphs, mathematical expositions, and plenty of examples to explain concepts. In the same token, even with regards to students’ evaluations, I like to diversify the student assessments across different areas, including problem sets, tests, class participation, presentations, and writing assignments. This not only gives the instructor a balanced view of students’ learning, but also helps the students to correlate their understandings with what has been taught in the class. 

It is the central tendency of our human brain to diverge away to other things if something is not interesting. So, whenever possible, I try to take the opportunity to include “fun” aspects in the course. This is important particularly for courses such as quantitative methods, which have the propensity to lose students’ attention quickly. In my classes, I like to include variation in learning methods. This convergence strategy to direct their attention back to the subject matter could range from activities like quizzes, group discussions, panel discussions, video clips and even debates. Whenever these “fun aspects” are randomly distributed in the course, I have found students’ participatory rates going high. This is a reliable strategy. Likewise, I have found that even for virtual/online sessions, including a variety of components such as videos, interactive diagrams, and fun cartoons has a greater likelihood of engaging students compared to just lecturing. 

Third, the interaction effect is an essential ingredient. I have found that making the classroom as interactive as possible is one component that has power. Although lectures and readings are essential aspects of my teaching, I give high critical value to classroom discussions and students’ participation. In this regard, I have found a few trials helpful. For example, asking students to solve a problem in the class in small samples is a good experiment. Likewise, organizing multiple mini quiz-like sessions is helpful as well. 

Fourth, I believe that each student must understand the concepts taught in the classroom and apply that reasoning in everyday life. In other words, the concept of lab has a much greater confidence interval than just learning statistical softwares. I have discovered a few strategies to realize these objectives. I have discovered that while talking about class content, sharing with students varied examples is enriching. At times, it is also logical to ask students for examples of what they have been thinking or doing as part of their assignments in other courses. That way, they relate to the content, and this could help them with their learning curve. Likewise, assignments play a huge role in helping students learn the topics. The choice and format of the assignments seem important. What I have learned is that giving them the opportunity to play and explore real life data is helpful. Similarly, challenging them to replicate a statistical analysis using data of their own exploration is an important piece of the equation. 

Fifth, helping students to calm down their anxiety relating to statistics seems to work well, particularly with those set of students who self-select by thinking: “can I also do it?” We all are aware, thanks to all the recent research on various disciplines like science, psychology, and education, that stress is a huge inhibitor of learning and success. The intervention need not be complicated. For example, in simple ways, one could carefully craft each week’s lecture depending on how students are taking in and monitoring their learning. In addition, giving personal attention to students and talking about their anxieties about the course and sharing easy to digest resources for the course is particularly helpful. Moreover, affability goes a long way. It is helpful to make sure that students don’t feel biased and are comfortable approaching the instructor with questions both in the classroom and outside individually. Morals sometimes can turn out to be more helpful than models. 

To motivate students to be in the line of best fit in respect to their learning could be daunting. Students, at times, are dependent on the instructor, and at times look so independent. There are numerous identification problems, and it is not always linear as we sometimes assume. But, one simple identification strategy to motivate students is to constantly remind students of the higher benefits of the skills that they are acquiring as compared to the costs they incur in the learning process. They need this reassurance, and this is truly effective. For example, I have noticed that they find it fascinating to hear in what kind of analysis they could anticipate in their internships, as part of their jobs, and so on. Sometimes sharing your own journey and work (if needed, in a simplified way) can be instrumental in inspiring them. This will also help them to avoid regressing to where they were in their learning scale. And it helps to overcome their learning discontinuity.  

In summation, I am reminded of a line from my own teacher, “There are three kinds of teachers: one who complains, one who explains, and one who inspires.” My hope is to be in the third category. But I should confess that the path is challenging, and there is no one treatment to demystify the methods. Furthermore, there are many unobservables that we cannot control in a particular setting. Each course is different, each session is new, and each student is unique. We are also nested in a multi-level hierarchical manner amidst disciplines, programs and schools. Therefore, I am fully aware that the points that I have shared here might not have high external validity and should be interpreted with caution. But, these could be helpful data points and can be tested for their impact and robustness. I firmly believe that I am a continuous learner, and I feel fortunate to have the opportunity to learn many things in day-to-day interactions with students and colleagues in the University. I find it truly fulfilling when I can use my knowledge and my research to share with students, and in a way that will contribute to this field and welfare of society in a significant manner. 

Amrit Thapa is a senior lecturer in GSE-IEDP.

This essay continues the series that began in the fall of 1994 as the joint creation of the College of Arts and Sciences, the Center for Teaching and Learning and the Lindback Society for Distinguished Teaching. 

See https://almanac.upenn.edu/talk-about-teaching-and-learning-archive for previous essays.