In recent years, there has been a great interest in integrating crowdsourced API documents that are often dispersed across multiple places. Because of the complexity of natural language, however, automatically synthesized documents often fall short on quality and completeness compared to those authored by human experts. We develop a complementary 'human-as-sensors' approach to document integration that generates API FAQs based on users' help-seeking behavior and history. We investigated the benefits and limitations of this approach in the context of programming education. This paper describes a prototype system called COFAQ and a controlled experiment with 18 novice programmers. The study confirms that the generated FAQs effectively fosters knowledge transfer between the programmers and significantly reduce the need for repeated search. It also discovers several difficulties novice programmers encountered when seeking API help as well as the strategies they used to seek and utilize API knowledge. © 2019 IEEE.