DeepEval: An Integrated Framework for the Evaluation of Student Responses in Dialogue Based Intelligent Tutoring Systems

The automatic assessment of student answers is one of the critical components of an Intelligent Tutoring System (ITS) because accurate assessment of student input is needed in order to provide effective feedback that leads to learning. But this is a very challenging task because it requires natural language understanding capabilities. The process requires various components, concepts identification, co-reference resolution, ellipsis handling etc. As part of this thesis, we thoroughly analyzed a set of student responses obtained from an experiment with the intelligent tutoring system DeepTutor in which college students interacted with the tutor to solve conceptual physics problems, designed an automatic answer assessment framework (DeepEval), and evaluated the framework after implementing several important components. To evaluate our system, we annotated 618 responses from 41 students for correctness. Our system performs better as compared to the typical similarity calculation method. We also discuss various issues in automatic answer evaluation

Measuring Semantic Textual Similarity and Automatic Answer Assessment in Dialogue Based Tutoring Systems

This dissertation presents methods and resources proposed to improve onmeasuring semantic textual similarity and their applications in student responseunderstanding in dialogue based Intelligent Tutoring Systems. In order to predict the extent of similarity between given pair of sentences,we have proposed machine learning models using dozens of features, such as thescores calculated using optimal multi-level alignment, vector based compositionalsemantics, and machine translation evaluation methods. Furthermore, we haveproposed models towards adding an interpretation layer on top of similaritymeasurement systems. Our models on predicting and interpreting the semanticsimilarity have been the top performing systems in SemEval (a premier venue for thesemantic evaluation) for the last three years. The correlations between our models\u27predictions and the human judgments were above 0.80 for several datasets while ourmodels being very robust than many other top performing systems. Moreover, wehave proposed Bayesian. We have also proposed a novel Neural Network based word representationmapping approach which allows us to map the vector based representation of a wordfound in one model to the another model where the word representation is missing,effectively pooling together the vocabularies and corresponding representationsacross models. Our experiments show that the model coverage increased by few toseveral times depending on which model\u27s vocabulary is taken as a reference. Also,the transformed representations were well correlated to the native target modelvectors showing that the mapped representations can be used with condence tosubstitute the missing word representations in the target model. models to adapt similarity models across domains. Furthermore, we have proposed methods to improve open-ended answersassessment in dialogue based tutoring systems which is very challenging because ofthe variations in student answers which often are not self contained and need thecontextual information (e.g., dialogue history) in order to better assess theircorrectness. In that, we have proposed Probabilistic Soft Logic (PSL) modelsaugmenting semantic similarity information with other knowledge. To detect intra- and inter-sentential negation scope and focus in tutorialdialogs, we have developed Conditional Random Fields (CRF) models. The resultsindicate that our approach is very effective in detecting negation scope and focus intutorial dialogue context and can be further developed to augment the naturallanguage understanding systems. Additionally, we created resources (datasets, models, and tools) for fosteringresearch in semantic similarity and student response understanding inconversational tutoring systems

Effect of scion varieties and wrapping materials on success of tongue grafting in Kiwifruit (Actinidia deliciosa) in Dolakha, Nepal

This study was conducted at Boch, Bhimeshwor-8, Dolakha, Nepal from January to May, 2019 to study the effect of scion variety and wrapping materials on growth performance of kiwi seedling rootstock. The field experiment was carried out in Factorial Randomized Complete Block Design using four replications. The treatments consisted of four scion varieties (Monty, Bruno, Hayward, Allison) grafted onto one year old kiwi seedling (Actinidia deliciosa) and two types of wrapping material (Grafting tape and Polyethylene plastic). The measured traits included sprout length, diameter, number of leaves, and number of sprouted bud per graft, graft success, mortality and survival percentage of grafts. The success rate of kiwi grafting was significantly affected by the scion variety and the wrapping materials. Allison variety showed the minimum days (61.72 days) to first sprouting and the maximum length of sprouts, diameter, number of leaves and number of sprouted bud per graft at the final observation. Monty variety showed the lowest growth performance. The maximum graft success (96.87%) and survival percentage of grafts (93.75%) was observed in Allison variety statistically at par with Bruno and Hayward and the lowest graft success(73.44%) and survivability(64.21%) was observed in Monty due to high mortality of the sprouted grafts. Grafting tape was superior to polyethylene plastic in terms of days to first sprouting (64.08 days), number of sprouted buds per grafts, number of leaves, graft success (92.18%) and survival of the grafts (87.01%) at the final observation. Interactive effect was found non-significant. In a nutshell, Allison is the best scion variety for grafting under the climatic condition of Dolakha and the grafting tape was the suitable tying material

Draft bills and research reports on: reducing judicial corruption and child labor in Nepal

These two draft bills and accompanying research report comprise the work of two teams of Nepali officials from Nepal's Ministry of Law and Justice who prepared them in the context of the Boston University School of Law Program on Legislative Drafting for Democratic Social Change. They attended that Program as part of a larger Ministry of Law and Justice Program, funded by the United Nations Development Program (UNDP), to strengthen Nepal's legal framework and the Rule of Law. Using the bills and reports as case studies, the four officials aimed to learn legislative theory, methodology and techniques. The Ministry had assigned them, on their return to Nepal, to play a significant role in institutionalizing an on-going learning process to strengthen Nepali drafters' capacity to prepare the effectively implementable legislation necessary to ensure good governance and development