Recent advancements in machine learning have started to put their mark on educational technology. Technology is evolving fast and, as people adopt it, schools and universities must also keep up (nearly 70% of primary and secondary schools in the UK are now using tablets for various purposes). As these numbers are likely going to follow the same increasing trend, it is imperative for schools to adapt and benefit from the advantages offered by technology: real-time processing of data, availability of different resources through connectivity, efficiency, and many others. To this end, this work contributes to the growth of educational technology by developing several algorithms and models that are meant to ease several tasks for the instructors, engage students in deep discussions and ultimately, increase their learning gains. First, a novel, fine-grained knowledge representation is introduced that splits phrases into their constituent propositions that are both meaningful and minimal. An automated extraction algorithm of the propositions is also introduced. Compared with other fine-grained representations, the extraction model does not require any human labor after it is trained, while the results show considerable improvement over two meaningful baselines. Second, a proposition alignment model is created that relies on even finer-grained units of text while also outperforming several alternative systems. Third, a detailed machine learning based analysis of students' unrestricted natural language responses to questions asked in classrooms is made by leveraging the proposition extraction algorithm to make computational predictions of textual assessment. Two computational approaches are introduced that use and compare manually engineered machine learning features with word embeddings input into a two-hidden layers neural network. Both methods achieve notable improvements over two alternative approaches, a recent short answer grading system and DiSAN – a recent, pre-trained, light-weight neural network that obtained state-of-the-art performance on multiple NLP tasks and corpora. Fourth, a clustering algorithm is introduced in order to bring structure to the feedback offered to instructors in classrooms. The algorithm organizes student responses based on three important aspects: propositional importance classifications, computational textual understanding of student understanding and algorithm similarity metrics between student responses. Moreover, a dynamic cluster selection algorithm is designed to decide which are the best groups of responses resulting from the cluster hierarchy. The algorithm achieves a performance that is 86.3% of the performance achieved by humans on the same task and dataset. Fifth, a deep neural network is built to predict, for each cluster, an engagement response that is meant to help generate insightful classroom discussion. This is the first ever computational model to predict how engaging student responses will be in classroom discussion. Its performance reaches 86.8% of the performance obtained by humans on the same task and dataset. Moreover, I also demonstrate the effectiveness of a dynamic algorithm that can self-improve with minimal help from the teachers, in order to reduce its relative error by up to 32%.