Teaching with Annotations

Prabhu Natarajan
Department of Computer Science, School of Computing (SoC)

Prabhu shares his experience of using annotations in his online lecture videos to facilitate students’ understanding of the module content.

lecture with annotation
Photo courtesy of Prabhu NATARAJAN
Natarajan, P. (2022, July 26). Teaching with annotations. Teaching Connections. https://blog.nus.edu.sg/teachingconnections/2022/07/26/teaching-with-annotations/

Online lecture videos are powerful teaching resources for e-learning, massive open online courses (MOOCs) and within blended learning environments. The lecture slides are usually scaffolded with graphical content like pictures, tables, animation, equations, and so on. However, the most important thing to have is the handwritten annotations that help to connect all the content in the slides (Lai et al., 2011). These annotations provide instructional support to engage students while they are watching those videos. They also teach students about the “process” rather than static ideas. In this article, we will use Cognitive Load Theory (Sweller, 2011) to explain how teacher’s annotation on slides can help to maximise the student’s learning.

Figure 1. A sample annotation that has been used in teaching Operating System concepts.
Figure 1. A sample annotation that has been used in teaching Operating System concepts.

Cognitive Load Theory was originally researched by educational psychologist John Sweller in 1980s (Sweller, 2011). This theory explains how the human brain processes and stores information that we learn daily. This theory suggests that there are two types of memory: (a) working memory and (b) long-term memory. Working memory is where we hold and process new information whenever we are exposed to new topics. This is smaller in capacity and hence if the information is not transferred to long-term memory, the information can be easily forgotten. On the other hand, the long-term memory is where we store all the information that we have learned over the years. This is larger in capacity and hence we are able to remember many things over the years. Mapping information from our working memory to long-term memory is how we learn new things.

According to this theory, when students are presented with too many pieces of new information at once, the brain suffers cognitive overload. Given that we have very limited capacity in our working memory, overloading this memory can result in poor learning, since the brain can no longer process all the new information being presented all at once. Often when we teach using PowerPoint slides, all the content would be presented at once. This can be challenging for students to grasp all the information in the slide in addition to processing auditory information from the teacher. This can cause cognitive overload for the students, making it harder for them to transfer their learning from working memory to long-term memory.

As teachers, we have to maximise the amount of new information a student can move from their working memory to long-term memory. One way to achieve this is to guide the students step by step, using handwritten annotations during the lecture. For example, in Figures 2 and 3 (see below), there are two different lecture videos that summarise the concept of “linear regression” in Machine Learning. Figure 2 summarises the topic using text and equations, whereas Figure 3 provides a summary using table, figure, equations, algorithms and most importantly, the handwritten annotations that walk students through the entire process step-by-step. The different color coding, labels and arrows also helps the teacher to organise and present the content clearly.

Figure 2. Video sequence of a slide with only text/equations.

Figure 3. Video sequence of a slide with table, figure, and equations that are connected by handwritten annotations.

These simple annotations that the teacher writes on the slide (Figure 3) will be beneficial for students in helping them better understand the lecture content and simplify their learning process. I used this approach while preparing the online lecture videos for the module IT1244 “AI Technology & Impact”. As a result, students found the online lecture videos more engaging, digestible and easy to understand. This is evident from the student feedback (see Figure 4).

Figure 4. Student feedback (for IT1244 during Semester 2, AY2021/22) that reflects how the lecture videos with annotations are easy to understand.
Figure 4. Student feedback (for IT1244 during Semester 2, AY2021/22) that reflects how the lecture videos with annotations are easy to understand.

The key lessons I learned from this teaching experience are as follows:

  • Teaching a complex topic is a skill and while doing so, we should present the content such that students can understand the concepts with little or no effort.
  • We should not put too much strain on their working memory which hinders the learning process and their interest in the subject.
  • Handwritten annotations help to connect the content in the slides⸻the pictures, tables, animation, and equations⸻to enhance students’ understanding of the module’s concepts.

In a future post, I will discuss the tools and hardware that I used to annotate my online  lecture videos. If you are interested to find out more or potentially adopt some of these techniques, please feel free to contact me.



Prabhu NATARAJAN is a Lecturer from the Department of Computer Science at School of Computing. He teaches Artificial Intelligence for non-CS students, Computer Organization and Operating System concepts. He is passionate about teaching and likes to teach complex concepts in the most simple and easy way. He has a strong interest in AI Education and cognitive psychology to enhance the student learning experience. Before joining NUS, he was teaching in DigiPen Institute of Technology, Singapore.

Prabhu can be reached at prabhu.n@nus.edu.sg


Lai Y.-S., Tsai H.-H., & Yu P.-T. (2011). Integrating annotations into a dual-slide PowerPoint presentation for classroom learning. Journal of Educational Technology & Society, 14(2), 43–57. Retrieved from http://www.jstor.org/stable/jeductechsoci.14.2.43.

Sweller, J. (2011). Cognitive load theory. In J. P. Mestre & B. H. Ross (Eds.), The psychology of learning and motivation: Cognition in education (pp. 37–76). Elsevier Academic Press. https://doi.org/10.1016/B978-0-12-387691-1.00002-8

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