In curriculum learning, teaching involves cooperative selection of sequences of data via plans to facilitate efficient and effective learning. One-off cooperative selection of data has been mathematically formalized as entropy-regularized optimal transport and the limiting behavior of myopic sequential interactions has been analyzed, both yielding theoretical and practical guarantees. We recast sequential cooperation with curriculum planning in a reinforcement learning framework and analyze performance mathematically and by simulation. We prove that infinite length plans are equivalent to not planning under certain assumptions on the method of planning, and isolate instances where monotonicity and hence convergence in the limit hold, as well as cases where it does not. We also demonstrate through simulations that argmax data selection is the same across planning horizons and demonstrate problem-dependent sensitivity of learning to the teacher's planning horizon. Thus, we find that planning ahead yields efficiency at the cost of effectiveness.

Questions are prevalent in everyday speech and they are often used to teach. When learners receive explicit cues that the intent of a question is pedagogical, they draw inferences that lead to superior learning. Although the ability to infer pedagogical intent is critical, very little is known about the mechanisms that support the inference that any particular statement is pedagogical or not. We tested the hypothesis that the prosody of speech marks the intent of pedagogical and information-seeking questions. In Studies 1 and 2, 256 naïve participants rated 100 pedagogical and information-seeking questions, spoken in child- or adult-directed speech. We found that naïve listeners can accurately infer pedagogical intent on the basis of prosody alone. In Study 3, we begin charting the acoustic features that differentiate pedagogical from information-seeking questions. These findings provide a window into the mechanisms that allow learners to infer pedagogical intent in otherwise ambiguous situations.

Researchers have debated whether instructional-based teach-ing or exploration-based active learning is better for decadeswith unsatisfying results. A main obstacle is the difficulty inprecisely controlling and characterizing the pedagogical meth-ods used and the learning conditions in empirical studies. Toaddress this, we leveraged existing computational models ofteaching and active learning to formalize the methods and thelearning process. We compared the two pedagogical methodsin a concept-learning framework and investigated their effec-tiveness under various scenarios. Our results show that whenthe learner and teacher are conceptually aligned, teaching isat least as effective as, and often much more effective than ac-tive learning, but when the alignment is broken, active learningcan yield moderate improvement over teaching. We concludeby discussing our results’ implications for the debate and theprospects of bringing computational models to bear on com-plex real-world problems that are resistant to simple experi-mental investigation.