Active learning is a teaching technique that involves the students in the learning process. It is in contrast to traditional learning methods of lectures where students passively receive information without taking measures to ensure they have sufficiently understood the material. In other words, active learning involves getting students to do activities but also gets them to think about the purpose behind these activities[1]. Examples of active learning involve classroom discussions and short interactive exercises. This blog post will first further discuss active learning and its benefits and then discuss how we use active learning techniques in our curriculum.
In the seminal text “A Taxonomy of Educational Objectives”, Bloom defines the learning objectives of three different domains: knowledge (cognitive), affective (emotional), and psychomotor (physical)[2]. We can better understand the effectiveness of active learning by considering the cognitive domain, the area of mental skill acquisition rather than emotional or physical. The six objectives required for cognitive domain are [3]:
1.) Remember/Recall2.) Understand3.) Apply4.) Analyze5.) Synthesize6.) Evaluate
Effective learning in the cognitive domain is achieved by activating all these objectives. We understand that everyone learns differently; you often hear people state “I’m a visual learner” or “I’m a auditory learner”. The problem with this thinking is that it simplifies people in two categories. Additionally, it adheres to the traditional passive learning techniques that only rely on audio and visual cues. With regards to the six objectives listed above, passive learning only exercises remembering without activating the others. During a one or two hour continuous lecture, there is no time for the students to really applying and analyze the information, let alone to synthesize and evaluate. While the students may do so on their own time after lecture, the opportunity is lost to solidify the concepts when information has been most recently seen.
Critics of active learning often decry it as just another fad. However, copious research studies have refuted this claim. A review of active learning studies found support for various forms of active learning [4]. Given the various studies analyzed in the review, the author suggests introducing activities during lecture and the promotion of student engagement will improve learning outcomes.
The success of active learning has led institutions of higher learning to implement active learning principles. For example, MIT has replaced their traditional passive learning introductory physics classes with what they refer to as TEAL, Technology-Enabled Active Learning. These changes were prompted by low lecture attendance and high failure rate in the previous traditional lecture style courses. A study on TEAL performance reveals improvements in conceptual understanding, class attendance, and passing rate [5]. The study shows the failure rate dropped from 13% to 5% and lecture attendance increased from 50% to 80%, compared to a control group.
Active Learning at The Data Incubator
At The Data Incubator, we understand the importance of active learning and incorporate into our curriculum. For start, while we deliver lectures, we use an interactive code environment as our lecture notes. Students can both follow along on their computers and we encourage them to experiment with the variables to see how they affect results. Additionally, we can readily demonstrate concepts using interactive figures and plots. These plots allow students to study the effect of changing parameters. For example, we visualize the performance of a machine learning model why adjusting a hyperparameter. Students can engage with the visualization and confirm the effect we discussed during lecture. They are no longer merely remembering a fact, but are understanding and analyzing the concepts. They are actively engaged in the learning process.
We avoid long lecture formats; a typical day of training will involve multiple breaks from lecture. During these breaks, students work on small exercises that reinforce the concepts that was just discussed. Breaks from lecture are important because people have limited attention spans. Additionally, the breaks ensure students understand the earlier concepts before moving on to more advanced material. If there is no time to understand, analyze, and apply the material, students will not effectively learn new material presented.
For each teaching module, we have developed a miniproject. These miniprojects are created to involve the various learning objectives previously outlined. When conducting corporate training, we ensure students have the time to start applying the material they have just learned on a realistic problem using real data through the miniproject. Students will have the opportunity to do more then recall and understand material, but exercise higher level objectives such as synthesis and evaluation. For example, students will need to evaluate different machine learning models to determine what approach will not only be best but why.
We encourage students to work in groups; not only does this prevent a student from falling behind, but we enable students to exercise the higher level learning objectives. Peer-to-peer engagement builds confidence in the students that they are mastering the material as well as confidence of the effectiveness of active learning.
Through the promotion of student engagement and active learning, our courses have higher completion and retention rates compared to Massive Open Online Courses (MOOC). Our completion rates are around x%, compared to 5% of MOOCs that rely on passive learning. Data science is not a spectator sport and it requires engagement with the material to master it.
References
1.) Bonwell, Charles C., and James A. Eison. (1991). Active Learning: Creating Excitement in the Classroom. ASHE-ERIC Higher Education Report No. 1. Washington D.C.: The George Washington University, School of Education and Human Development.2.) Bloom, Benjamin Samuel. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. New York: David McKay Company.3.) Anderson, Lorin W.; Krathwohl, David R., eds. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Allyn and Bacon4.) Prince, Michael. (2004). Does Active Learning Work? A Review of the Research. Journal of Engineering Education. 93. 223-231.5.) Dori, Yehudit & Belcher, John. (2005). How does technology-enabled active learning affect students’ understanding of scientific concepts?. Journal of the Learning Sciences. 14(2), 243-279.
Visit our website to learn more about our offerings:
## ← Back to index