User:DataNomadX/Evaluate an Article
 | Evaluate an article
Complete your article evaluation below. Here are the key aspects to consider: Lead sectionA good lead section defines the topic and provides a concise overview. A reader who just wants to identify the topic can read the first sentence. A reader who wants a very brief overview of the most important things about it can read the first paragraph. A reader who wants a quick overview can read the whole lead section.
ContentA good Wikipedia article should cover all the important aspects of a topic, without putting too much weight on one part while neglecting another.
Tone and BalanceWikipedia articles should be written from a neutral point of view; if there are substantial differences of interpretation or controversies among published, reliable sources, those views should be described as fairly as possible.
Sources and ReferencesA Wikipedia article should be based on the best sources available for the topic at hand. When possible, this means academic and peer-reviewed publications or scholarly books.
Organization and writing qualityThe writing should be clear and professional, the content should be organized sensibly into sections.
Images and Media
Talk page discussionThe article's talk page — and any discussions among other Wikipedia editors that have been taking place there — can be a useful window into the state of an article, and might help you focus on important aspects that you didn't think of.
Overall impressions
Examples of good feedbackA good article evaluation can take a number of forms. The most essential things are to clearly identify the biggest shortcomings, and provide specific guidance on how the article can be improved. |
Which article are you evaluating?
[edit]Why you have chosen this article to evaluate?
[edit]I chose the Q-learning article because it represents a core algorithm in reinforcement learning which is a fundamental topic in modern artificial intelligence that is highly relevant to our course. Despite its importance, the article rated as C-Class that exhibits several areas for improvement, such as incomplete explanations of key concepts (e.g., convergence and action selection), a lack of up-to-date citations, and insufficient details on algorithm variants like Deep Q-learning. My preliminary impression is that while the article lays a decent foundation, it would greatly benefit from targeted revisions to clarify and expand upon these aspects.
Evaluate the article
[edit]I have evaluated the Q-learning article, available at Q-learning, because it represents a core algorithm in reinforcement learning which is one of the fundamental topics in modern AI and central to my course. I chose this article as it is crucial for understanding key principles, yet, as indicated by its C-Class rating and discussions on its talk page, it still has several areas needing improvement. My preliminary impression is that while the article provides a decent introduction, it lacks depth in several important areas. For instance, the article makes technical claims, such as asserting that “it has been proven that for any finite MDP,” without providing adequate citations or further explanation. Additionally, the discussion on convergence is vague; it mentions that “the convergence proof was presented later by Watkins and Dayan” but fails to explain what exactly converges, under what conditions, or why this is significant. Similarly, essential details regarding action selection during the learning process are missing, leaving a gap in understanding how the algorithm operates in practice. The talk page further highlights concerns about outdated citations and dead links, which undermine the article’s reliability. Moreover, while the article includes useful diagrams and flowcharts, the technical language used may be overwhelming for non-expert readers. A more accessible lead that outlines the article’s major sections and simplifies the technical jargon would be beneficial. Overall, although the Q-learning article lays a solid foundation, it would be greatly enhanced by updating references with more current research, providing clearer explanations of key concepts, and reorganizing content to improve flow and readability, making it a more comprehensive and valuable resource for both specialists and newcomers alike.