We present the design and use of myDALITE, a web-based learning platform that provides students with asynchronous peer instruction that can be used in-and out-of-class. Supporting active learning pedagogy, myDALITE engages students in 1) written explanation (rationales for their answers); 2) comparison of these rationales to those of peers; and 3) reflection on the quality of the rationales. Furthermore, myDALITE assignments provide teachers with the opportunity to see what their students are thinking, before and/or after classroom instruction.
The task of argument quality ranking, which identifies the quality of free text arguments, remains, to this day, a challenge. While most state-of-the-art initiatives use point-wise ranking methods and predict an absolute quality score for each argument, we instead focus on learning how to order them by their relative convincingness, experimenting with several learning-to-rank methods for argument quality. We leverage BERT's powerful ability in building a representation of an argument, paired with learning-to-rank approaches (point-wise, pairwise, list-wise) to rank arguments according to their measure of convincingness. We also demonstrate how an ensemble of models trained with different ranking losses often improves the performance at identifying the most convincing arguments of a list. Finally, we compare BERT coupled with learning-to-rank methods to state-of-the-art approaches on all major argument quality datasets available for the ranking task, demonstrating how a learning-to-rank approach generally performs better at outlining the topmost convincing arguments.
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