Introducing the Degree of Knowledge Gain (DKG) Metric: Enhancing Learning Through Online Searches
In today's digital age, online search engines have become indispensable tools for education and knowledge acquisition. They provide immediate access to a vast expanse of information, fostering continuous learning by enabling users to refine their queries and explore related concepts. However, evaluating the effectiveness of web searches in fostering true knowledge acquisition presents a unique challenge. Are users merely memorizing facts, or are they engaging in deep learning characterized by comprehensive understanding and integration of knowledge?
My doctoral dissertation, "Quantifying Knowledge Gain in Online Searches: The DKG Metric", addresses this critical need by introducing a novel metric designed to measure the extent of knowledge gained during web searches.
The Challenge of Measuring Knowledge Acquisition
Web searches offer quick access to information, but they don't inherently ensure deep learning. Traditional methods of evaluating knowledge gain, such as pre- and post-tests, open-ended exercises, and self-assessments, often fall short in capturing the complexities of digital literacy, cognitive overload, and diverse search strategies. These methods can also be intrusive, disrupting the natural search process.
To overcome these challenges, I developed the Degree of Knowledge Gain (DKG) metric. This model focuses on quantifying the amount of new, relevant information assimilated into a user's existing knowledge base during online searches. The DKG metric aims to provide a more nuanced understanding of how individuals engage with digital information and learn from it.
How the DKG Metric Works
The DKG metric is introduced through a detailed formalization process that examines the inherent entropy in the search process. This analysis covers how searchers collect information, interact with various sources, and the effort needed to reach certain detection probabilities.
Two experiments were conducted to validate the DKG metric:
Indicators of Learning Transfer: Using the Online Information Searching Strategy (OISS) and a taxonomy of query reformulation, this experiment employed the Think-Aloud Protocol (TAP) and interviews to identify strategies that facilitate knowledge acquisition during online searches.
Validation within Web Search Contexts: This experiment focused specifically on how the DKG metric complements traditional methods of assessing knowledge acquisition. It highlighted the importance of implicit knowledge measures, such as discovering complex information and understanding query complexity, content nuances, and key term identification.
Practical Applications and Implications
The DKG metric offers practical applications for both educators and search engine developers:
For Educators: Insights from the DKG metric can help design or curate online resources that are more effective in fostering learning. This could involve creating content that encourages critical thinking and presents information comprehensively.
For Search Engine Developers: The DKG metric can guide the refinement of algorithms to prioritize content with high potential for knowledge gain. This may involve ranking resources that not only match keywords but also contribute to deeper understanding and learning.
"Quantifying Knowledge Gain in Online Searches: The DKG Metric" underscores the need for innovative metrics that reflect the complexities of knowledge acquisition in the digital age. By introducing the DKG metric, this work offers a practical model to enhance the educational utility of the web, with significant implications for educational practice and technology development.
If you want to know more about it, you can download the manuscript directly from the Federal University of the State of Rio de Janeiro's repository here.