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The need for high-quality remote learning experiences has been illustrated by the COVID-19 pandemic. As such, there is a need to explore instructional videos that go beyond expert exposition as the main pedagogical approach. An emerging body of research has begun to investigate instructional videos that feature dialogue. However, this body of research has focused primarily on whether such videos are effective. In contrast, the purpose of our study is to investigate the dialogic learning processes involved as students viewing dialogic videos develop mathematical meaning. We employed a Bakhtinian perspective to analyse the learning of a pair of Grade 9 students who engaged with dialogic instructional videos. The results focus on ventriloquation as a learning process. 

Ultra-miniaturized microendoscopes are vital for numerous biomedical applications. Such minimally invasive imagers allow for navigation into hard-to-reach regions and observation of deep brain activity in freely moving animals. Conventional solutions use distal microlenses. However, as lenses become smaller and less invasive, they develop greater aberrations and restricted fields of view. In addition, most of the imagers capable of variable focusing require mechanical actuation of the lens, increasing the distal complexity and weight. Here, we demonstrate a distal lens-free approach to microendoscopy enabled by computational image recovery. Our approach is entirely actuation free and uses a single pseudorandom spatial mask at the distal end of a multicore fiber. Experimentally, this lensless approach increases the space-bandwidth product, i.e., field of view divided by resolution, by threefold over a best-case lens- based system. In addition, the microendoscope demonstrates color resolved imaging and refocusing to 11 distinct depth planes from a single camera frame without any actuated parts.