This paper investigates the use of conversational agents to scaffold on-line collaborative learning discussions through an approach called Academically Productive Talk (APT). In contrast to past work on dynamic support for collaborative learning, where agents were used to elevate conceptual depth by leading students through directed lines of reasoning (Kumar & Rosé, "IEEE Transactions on Learning Technologies", 4(1), 2011), this APT-based approach uses generic prompts that encourage students to articulate and elaborate their own lines of reasoning, and to challenge and extend the reasoning of their teammates. This paper integrates findings from a series of studies across content domains (biology, chemistry, engineering design), grade levels (high school, undergraduate), and facilitation strategies. APT based strategies are contrasted with simply offering positive feedback when the students themselves employ APT facilitation moves in their interactions with one another, an intervention we term Positive Feedback for APT engagement. The pattern of results demonstrates that APT based support for collaborative learning can significantly increase learning, but that the effect of specific APT facilitation strategies is context specific. It appears the effectiveness of each strategy depends upon factors such as the difficulty of the material (in terms of being new conceptual material versus review) and the skill level of the learner (urban public high school vs. selective private university). In contrast, Feedback for APT engagement does not positively impact learning. In addition to an analysis based on learning gains, an automated conversation analysis technique is presented that effectively predicts which strategies are successfully operating in specific contexts. Implications for design of more agile forms of dynamic support for collaborative learning are discussed.