AutoScor: An Automated System for Essay Questions Scoring

The automated scoring or evaluation for written student responses have been, and are still a highly interesting topic for both education and natural language processing, NLP, researchers alike. With the obvious motivation of the difficulties teachers face when marking or correcting open essay questions; the development of automatic scoring methods have recently received much attention. In this paper, we developed and compared number of NLP techniques that accomplish this task. The baseline for this study is based on a vector space model, VSM. Where after normalisation, the baseline-system represents each essay by a vector, and subsequently calculates its score using the cosine similarity between it and the vector of the model answer. This baseline is then compared with the improved model, which takes the document structure into account. To evaluate our system, we used real essays that submitted for computer science course. Each essay was independently scored by two teachers, which we used as our gold standard. The systems’ scoring was then compared to both teachers. A high emphasis was added to the evaluation when the two human assessors are in agreement. The systems’ results show a high and promising performance

Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future

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

Failure in the classroom : a study of differentiated expectations for children in two urban elementary schools.

This dissertation analyzes key factors which influence the negative expectations that classroom teachers hold for certain students whom they perceive to be low achievers and whom they refer for placement in special education. An examination of bias and differentiated expectations in classrooms is conducted in two urban elementary schools and is based on the hypothesis that the children who fail are most often those who differ from their teachers in social class, ethnicity, socioeconomic level, learning style and behaviors, and gender. The first target school is identified as having a low rate (16%) of referral to special education, while the other school is identified as having a high rate (32%) of referral to special education. Twelve regular education teachers from each school complete questionnaires about their experiences with and attitudes towards low-achieving students. Each identifies successful and unsuccessful students and then describes him/herself as a learner. Student records are examined also to determine commonalties in the traits of those students referred to special education for remedial or compensatory instruction. Finally, comparisons of school philosophy and school climate are made between the two target schools. The findings of this study indicate that the misperceptions and life experiences of classroom teachers are important influences on the negative stereotyping and lowered expectations directed toward certain students. In the target schools, veteran as well as less experienced male and female teachers respond more favorably to students who resemble themselves ethnically, socioeconomically, and in learning style, and less favorably to those students who differ. Statistical significance is found when matching the learning characteristics selected by teachers to describe their successful students with learning characteristics they select when describing their own traits as learners. Of the students identified as unsuccessful and referred to special education, a disproportionate number are minority and male and are described by teachers as having problems with motivation, temperament, and need for direction. These and other reasons for referrals cited by teachers in both schools are similar and reflect poorly understood bias toward ethnic, racial, and linguistic minority children as well as misinformation about the developmental differences between boys and girls

Wastewater treatment improvement through an intelligent integrated supervisory system

This paper shows the result of years of work by a cooperative research group including chemical engineers, environmental scientists and computer scientists. This research has been focused on the development and implementation of new techniques for the optimisation of complex process management, mainly related to wastewater treatment plants (WWTP). The experience obtained indicates that the best approach is a Supervisory System that combines and integrates classical control of WWTP (automatic controller for maintaining a fixed dissolved oxygen level in the aeration tank, use of mathematical models to describe the process...) with the application of knowledge-based systems (mainly expert systems and case-based systems). The first part is an introduction to wastewater treatment processes and an explanation of the complexity of the management and control of such complex processes. The next section illustrates the architecture of the supervisory system and the work carried out to develop and build the expert system, the casebased system and the simulation model for implementation in a real plant (the Granollers WWTP). Finally, some results of the field validation phase of the Supervisory System when dealing with real situations in the plant are described.Aquest article mostra el resultat de la col·laboració portada a terme durant els darrers anys entre grups d'enginyeria química, enginyeria ambiental i intel·ligència artificial. El treball se centra en el desenvolupament de tècniques per a la millora i supervisió de processos complexos, especialment del tractament biològic d'aigües residuals. L'experiència demostra que la millor opció requereix desenvolupar un sistema supervisor que combini i integri tècniques de control clàssic (controlador automàtic del nivell d'oxigen dissolt en el reactor biològic, ús de models descriptius del procés, etc.) amb sistemes basats en el coneixement (concretament sistemes experts i sistemes basats en casos). El present article descriu la complexitat de la gestió del procés de tractament de les aigües residuals, l'arquitectura integrada que es proposa i el desenvolupament i la construcció de cadascun dels mòduls d'aquesta proposta per a la implementació real a l'estació depuradora d'aigües residuals de Granollers. Finalment, es detallen alguns resultats del procés de validació del seu funcionament enfront de situacions quotidianes de la planta