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Computer labs are commonly used in computing education to help students reinforce the knowledge obtained in classrooms and to gain hands-on experience on specific learning subjects. While traditional computer labs are based on physical computer centers on campus, more and more virtual computer lab systems (see, e.g., [1, 2, 3, 4]) have been developed that allow students to carry out labs on virtualized resources remotely through the internet. Virtual computer labs make it possible for students to use their own computers at home, instead of relying on computer centers on campus to work on lab assignments. However, they also make it difficult for students to collaborate, due to the fact that students work remotely and there is a lack of support of sharing and collaboration. This is in contrast to traditional computer labs where students naturally feel the presence of their peers in a physical lab room and can easily work together and help each other if needed. Funded by NSF’s Division of Undergraduate Education, this project develops a collaborative virtual computer lab (CVCL) environment to support collaborative learning in virtual computer labs. The CVCL environment leverages existing open source collaboration tools and desktop sharing technologies and adds new functions unique to virtual computer labs to make it easy for students to collaborate while working on computer labs remotely. It also implements several collaborative lab models to support different forms of collaboration in both formal and informal settings. We have developed the main functions of the CVCL environment and begun to use it in classes in the Computer Science (CS) department at Georgia State University. While the original project focuses on computer labs in its traditional sense, the issue of lack of collaboration applies to much broader learning settings where students work on tasks or assignments on computers, with or without being associated with a lab environment. Due to the high mobility of students in modern campuses and the fact that many learning activities are carried out over the Internet, computer-based learning increasingly happen in students’ personal spaces (e.g., homes, apartments), as opposed to public learning spaces (e.g., laboratories, libraries). In these personal spaces, it is difficult for students to get help from classmates or teaching assistants (TAs) when encountering problems. As a result, collaborative learning is difficult and rare. This is especially true for urban universities such as Georgia State University where a significant portion of students are part-time students and/or commute. To address this issue, we intend to broaden the concept of “virtual computer lab” to include general computer based learning happening in “virtual space,” which is any location where people can meet using networked digital devices [5]. Virtual space is recognized as an increasingly important part of “learning spaces” and asks for support from both the technology aspect and learning theory aspect [5]. Collaborative learning environments that support remote collaboration in virtual computer labs would fill an important need in this broader trend. 

In response to our paper on the evolutionary history of the Chinese flora, Qian suggests that certain features of the divergence time estimation employed might have led to biased conclusions in Lu et al (2018). Here, we consider Qian's specific criticisms, explore the extent of uncertainty in the data and demonstrate that (i) no systematic bias toward dates that are too young or too old is detected in Lu et al.; (ii) constraint of the crown age of angiosperms does not bias the generic ages estimated by Lu et al.; and (iii) ages derived from the Chinese regional phylogeny do not bias the conclusions reported by Lu et al. All these analyses confirm that the conclusions reported previously are robust. We argue that, like many large‐scale biodiversity analyses, sources of noise in divergence time estimation are to be expected, but these should not be confused with bias.