By Mengyu Ma
Mengyu Ma, Learning Experience Designer at Center for Teaching and Learning, talked with Dr. Ming Li, Associate Professor of Electronical and Computer Engineering, about his teaching philosophy and pedagogical strategies for effectively achieving learning goals.
When I took “Linear Algebra” and “Differential Equations” courses during my undergraduate education, I had to prepare for quizzes every Monday and Wednesday evening for each course. I needed to review everything we had learned in the last week, and it always took me around 2 hours. As a third-year student, I was overwhelmed and frustrated at first. A group of freshmen from Duke Kunshan University (DKU) had the same experience when they started to explore the world of data science. Even worse, in their first 7-week academic session, they faced 4 quizzes each week.
Dr. Ming Li, associate professor of Electrical and Computer Engineering, employed “frequent quizzing” as one of his pedagogies when teaching Fall 2018 “STATS 102 Introduction to Data Science” course. To achieve his teaching philosophy of “learning by doing” and to ensure students’ automatized knowledge retrieving, Prof. Li, inspired by techniques discussed in Small Teaching by James M. Lang (2016), designed his regular class starting with a 15-minute coding quiz. Now working at the Center for Teaching and Learning (CTL) at DKU, I have changed my perspective and realized the effectiveness of frequent quizzing.
Learning by doing: a high level of retrieving
To help students build a strong foundation in data science in such a fast-paced environment, Prof. Li quizzes students every class before his lecture. The quizzes test students’ understanding and application of the concepts they have learned in the previous class, so careful in-class attention and after-class review are both required. Cognitively, humans have difficulty retrieving knowledge from long-term memory (Miller, 2011).1 Quickly and accurately drawing the knowledge we need when we need it is a challenge for memory capacity, which reflects the level of comprehension (Lang, 2016, p.35).2 However, plug-and-chug retrieving is prevented in Prof. Li’s quizzes because each question forces students to actually do something. “The result-driven quiz allows students to make use of any resources they can reach but they have to give me the expected outcome within a short time,” Prof. Li stresses. Students have successfully absorbed the knowledge if they can process and produce beautiful, error-free code. Although in-class quizzing occupies lecture time, it provides a perfect space for students to try things out.
“Mental process can become automatized”
To my surprise, students felt OK regarding the frequent quizzes. They just argued about the time limit. However, Prof. Li explained that time limit is a key point to cultivate their ability to automatized knowledge. “The process of combining mental inputs in new ways occurs in our working memory – which, unfortunately, have the limited capacity,” (Lang, 2016, p.119).3 When we need to process several cognitive tasks, automatized knowledge will free up space for us to deal with other tasks. Thus, the time-limited quiz helps students automatize their knowledge and prepare for more complicated tasks in the future. Prof. Li also mentioned that he set the time (15 minutes) based on the criteria that large companies (such as Google) use to test candidates. He equips students with the skills that lead to successful careers.
Lang (2016) pointed out that practice could enable students to automatize what they have learned,4 which matches how Prof. Li defines frequent quizzing. He regards quizzing as a combination of assessment and practice. Students have opportunities and time to perfect their after-class assignments, but in-class quizzes highlight the subtle difference among students who earn different letter grades. Generally, in the long run, once students automatize knowledge, they would be able to think more effectively and creatively as they productively process information and add on their own ideas (Lang, 2016, p.120).5 In this case, frequent quizzing enhances knowledge retention and encourages outside classroom application.
Immediate feedback has to be guaranteed
However, the most significant element of implementing frequent quizzing in class is to provide immediate feedback. They developed a platform for their course purpose using nbgrader (a tool that facilitates creating and grading assignments in the Jupyter notebook). Based on the source code, Prof. Li designed a class-wide website for students to complete assignments and quizzes. The best feature of their platform is that feedback will be provided once students submit the quiz. So, after the quizzing time, Prof. Li would briefly go over the quiz question based on the statistics generated by the system. Students would figure out their mistakes or misunderstandings right after the quiz. Immediate feedback is the prerequisite of frequent quizzing, especially for everyday tests.
After the conversations with Prof. Li and his students, I could see how powerful frequent quizzing is and how concrete the pedagogical evidence is. When I got used to the pace of two quizzes each week in my undergraduate years, I gradually realized it facilitated my learning, especially near the final week. I always had a deeper understanding of the knowledge that was quizzed, which eased my burden and allowed me to focus on other materials. Prof. Li’s students also agreed on the effectiveness of frequent quizzing. One of his students told me that it is enough for him to just redo the quizzes for final exam preparation.
If immediate feedback could be guaranteed, frequent quizzing might be helpful for some subjects, especially natural science. The pedagogy behind it equips students with hands-on skills. An in-class quiz might be more effective than a normal lecture because it directly requires students to learn by doing and automatize knowledge. Students might feel stressed at first, but when they realize that the purpose is to enhance the learning process, they will get used to it. Frequent quizzing is not only for assessment, but also for preparing students for future success in the real world.
References
- Miller, M. (2011). What College Teachers Should Know about Memory: A Perspective from Cognitive Psychology. College Teaching. 59, 117-122
- Lang, J. (2016). Small Teaching. San Francisco, CA: Jossey-Bassnbgrader
- Lang, J. (2016). Small Teaching.
- Lang, J. (2016). Small Teaching.
- Lang, J. (2016). Small Teaching.
Faculty Introduction
Ming Li
Associate professor of electrical and computer engineering, Duke Kunshan University
Dr. Ming Li’s research interests are in the areas of audio, speech, and language processing as well as multimodal behavior signal analysis and interpretation. He has published more than 130 papers and served as the member of IEEE speech and language technical committee, CCF speech language and hearing committee, CAAI artificial psychology and affective computing committee, and APSIPA speech and language processing committee.
Dr. Li was the area chair of speaker and language recognition at INTERSPEECH 2016, 2018 and 2020. Works co-authored with his colleagues have won first prize awards at Body Computing Slam Contest 2009, INTERSPEECH Computational Paralinguistic Challenge 2011, 2012 and 2019, ASRU 2019 MGB-5 challenge language identification task, INTERSPEECH 2020 fearless steps challenge speaker verification task. He received the IBM faculty award in 2016, the ISCA computer speech and language best journal paper award in 2018 and the youth achievement award of outstanding achievements of scientific research in higher education in 2020.
Li has a B.Sc. in communication engineering from Nanjing University, China; an M.Sc. in signal processing from the Institute of Acoustics, Chinese Academy of Sciences; and a Ph.D. in electrical engineering from the University of Southern California.
