This blog explores practical ways to enhance student engagement and learning by transforming lecture recordings into comprehensive learning resources using AI tools. In light of declining lecture attendance and poor preparation habits among university students, the traditional teaching methods of lectures, tutorials, and assigned readings need adaptation. AI applications, such as ChatGPT 4.0-Omni, Google's NotebookLM, and Anthropic’s Claude 3.5 Sonnet, offer innovative solutions by generating concise lecture summaries, visual mind maps, quizzes, and even podcast-style audio content.
Background
At most universities, lectures and tutorials are the primary methods of teaching. Lectures convey core concepts and explain the fundamental ideas and logical relationships behind key topics. Conversely, tutorials provide interactive, small group learning environments where students can deepen their understanding through discussions, problem-solving, and personalized feedback. Assigned readings complement both formats, allowing students to explore the material in greater depth and reinforce their learning. Consequently, students primarily acquire knowledge through lectures, tutorials, and assigned readings.
However, we are witnessing changes in student behaviours that can significantly impact their learning through these three main sources. First, while the availability of lecture recordings is beneficial for students with personal, work, or health challenges, it has led to a notable decline in lecture attendance, with a report indicating attendance rates as low as 26% by mid-semester. As a result, many students choose to watch recordings at their convenience, which may cause them to miss out on the immediate engagement and social learning opportunities that live lectures offer. In contrast, tutorials appear to be less affected, with the same report showing an average attendance rate of 84%. Second, it is reported that up to 80% of university students are not completing their assigned readings, further hindering their understanding and performance.
To address this situation, numerous discussions and suggestions have emerged, including the adoption of a flipped classroom approach, which aims to transform lectures into a more “tutorial-like” learning experience. Implementing this model will require significant effort and resources to effectively redesign the delivery method. In this approach, students are expected to complete their pre-class preparation to acquire a sufficient basic understanding before participating in in-class activities. This preparation enables them to actively engage with the knowledge and skills during class, ultimately maximizing their learning.
However, given students’ frequent neglect of assigned readings, requiring them to be well-prepared for in-class activities can be challenging. Additionally, we recognize that students’ lives are becoming increasingly busy, and changing their existing behaviours may require substantial effort, if it’s even possible. This raises the important question: are there simpler ways we can enhance student learning, considering their infrequent attendance at lectures and their tendency to overlook assigned readings?
Generative AI applications are rapidly advancing in their capabilities. Notably, the launch of ChatGPT 4.0-Omni in May 2024 marks a significant milestone, as it can now engage in omnichannel communication through video, audio, and text with users. In September 2023, Google released its new AI notebook, called NotebookLM, built on its Gemini Pro generative AI model. This tool offers unique capabilities, such as source-grounding and automatic podcast creation, enhancing how users interact with and utilize information. Additionally, Anthropic’s Claude 3.5 Sonnet has shown excellent performance in content generation, particularly excelling at creating maps and diagrams.
In light of these developments, we have explored the possibility of transforming lecture recordings into an engaging and comprehensive set of learning resources. This approach aims to enrich the learning experience for students, allowing them to engage with the lecture recordings in a variety of formats and choose their preferred way of learning.
A quick summary of the lectures
Our goal is to first provide a summary of each lecture. A recorded lecture typically lasts for two hours, so being able to summarize the key topics covered in one page would be highly beneficial for students. This summary would provide them with a quick, overall understanding of the lecture, enabling them to grasp essential topics before watching the entire lecture. Generative AI tools like ChatGPT are particularly effective at summarizing content and providing concise explanations. These tools can be utilized to distil a recorded lecture into an easily digestible summary, making it simpler for students to identify and focus on key discussions.
Many recorded lectures come with downloadable transcripts. By uploading these transcripts to an AI tool like ChatGPT, we can easily generate a summary of the lecture. In a trial with one of our subjects, ChatGPT produced a concise summary that included a list of seven key topics covered in the lecture. A sample extract of the summary is shown below.
In ChatGPT 4.0-Omni, using the prompt: “Give me a summary of the lecture”:
Response:
A mind map to illustrate key concepts and their relationships
A lecture typically introduces several key concepts and demonstrates how they are related. A primary learning goal for students is to understand these key concepts and articulate the connections among them. In How Learning Works: Eight Research-Based Principles for Smart Teaching, authors Marsha Lovett and colleagues present eight key principles that are grounded in research on how students learn. One key principle is to teach students how to effectively organize knowledge. This principle highlights that helping students create a coherent framework for understanding concepts can improve their ability to integrate new information with their existing knowledge. By encouraging students to develop mental models or visual representations of the relationships among concepts, educators can promote deeper learning and enhance information retention.
Therefore, our second goal is to identify a list of key concepts related to a topic, such as problem analysis, from a lecture and create a visual representation of their relationships using a mind map diagram or a concept graph. This time, we use Google’s NotebookLM to compile the key concepts and their relationships associated with problem analysis, resulting in the following response.
In NotebookLM, using the prompt: “Based on the lecture, identify all the key concepts associated with problem analysis and explain their relationships”:
Response:
As shown in the response from NotebookLM above, it provides source references—labelled with numbers—that link explanations to the corresponding sections of the lecture transcript. This feature is useful for verifying the accuracy of the explanations given by the AI tool.
Since Claude 3.5 Sonnet excels in diagram creation, we leverage it to create a mind map or a concept graph.
In Claude 3.5 Sonnet, using the prompt: “Create a detailed graph using the key concepts and their relationships. Use different colours to group related concepts, and label the connections with descriptive names to clarify the relationships between them”:
Response:
A quiz for testing students’ understanding
Testing is crucial for students to develop and refine their understanding. It helps them identify gaps in their knowledge and recognize misunderstandings. When students encounter challenging questions or answer incorrectly, it encourages them to revisit and reassess those concepts. Therefore, providing opportunities for students to practice new skills and receive constructive feedback is a key principle highlighted in Marsha Lovett’s book.
In saying that, there are challenges as outlined here with quiz assessments that one needs to factor into the equation.
NotebookLM includes a built-in feature that can generate a “Study Guide” with revision quiz questions based on uploaded documents, which can be accessed in the “Notebook Guide” section. We use this feature to generate quiz questions with answers based on the lecture.
In NotebookLM, using the “Study Guide” creation feature:
Response:
Students can then use these quiz questions to assess their understanding and revisit the lecture materials if needed.
How about learning from a Podcast?
Also noted in Marsha Lovett’s book, motivation plays a significant role in learning. Educators should cultivate a learning environment that fosters students’ intrinsic motivation by emphasizing the relevance of the material, providing choices, and setting achievable goals. In addition to offering recorded lectures, summary notes, and mind maps or concept graphs, NotebookLM also enables the creation of engaging podcast audio content. These podcasts transform traditional dialectic lectures into lively conversations between two hosts, summarizing the content into approximately 10 to 20 minutes while incorporating humour, making the material more accessible and enjoyable for students. This innovative approach not only enhances engagement but also encourages active listening and retention of key concepts.
Hence, we use NotebookLM to create a podcast based on the lecture. This feature is also accessed in the “Notebook Guide” section.
In NotebookLM, using the “Audio Overview” creation feature:
(This is a 4 mins extract of the podcast.)
In this brief blog post, we have explored various methods to transform our lecture recordings into diverse learning materials that engage students and enhance their learning experience. Generative AI tools facilitate this transformation, enabling the creation of new learning materials to be fully automated or to take less than an hour of work. There are certainly more possibilities beyond those we describe here. With its human-like intelligence across various tasks, generative AI has great potential to serve as a smart teaching assistant, helping to develop more engaging learning materials in multiple formats, including text, audio, graphics, chatbots, and video. I believe this is an exciting area for exploration and research.
Please feel free to reach out and share any exciting findings with me at winn.chow1@unimelb.edu.au.
Winn Wing-Yiu Chow,
Senior Lecturer, School of Computing and Information Systems,
The University of Melbourne
24 November 2024
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