Medha Sagar

Abstract—Mathematical Word Problems (MWPs) are verbal
formulations of real-world scenarios representing an abstract
mathematical idea. They aid us in demonstrating the relevance
of mathematics in extrapolating everyday tasks. Set Theory is a
field of mathematics that is used to discern the nature of sets and
the relations between them. Set-theory operations are binary in
nature. They are used to depict the type of association between
two sets. In the current scenario, the… Show more

Abstract—Mathematical Word Problems (MWPs) are verbal
formulations of real-world scenarios representing an abstract
mathematical idea. They aid us in demonstrating the relevance
of mathematics in extrapolating everyday tasks. Set Theory is a
field of mathematics that is used to discern the nature of sets and
the relations between them. Set-theory operations are binary in
nature. They are used to depict the type of association between
two sets. In the current scenario, the difference in the levels
of abstraction of deriving regular expressions and interpreting
natural language poses a challenging task. In this paper, we
present a novel approach to solve set-theory based word problems
automatically using the semantics of the language. Our system
analyzes set-theory questions obtained from various sources of
informal text that are crowd sourced such as online forums, social
media and competitive examination portals and computes the
result by discerning the language of the problem. Our algorithm
mimics the paradigm through which humans attempt to solve

set-theory MWPs. We discretized our approach into two sub-
tasks, information extraction and problem solving, implementing

separate evaluation for each stage and subsequently ensuring that
the error propagation from one phase to the next is curbed and
efficiency for each stage can be individually determined. Our
system uses language semantics to identify the set entities in
a word problem, and subsequently maps these entities in an
expression that embodies the problem. In the problem solving
phase, we obtain the final result of the word problem by inferring
the relation that is to be determined and implementing the
corresponding set- theory function to compute the solution. We
corroborated our result for the two phases individually using
supervised learning. In the information extraction phase, our
system exhibited an accuracy of 83.5% and its performance in the
problem solving phase is 60%, Show less

Computer Programming as a process that embodies the creation of an executable computer program for a given computational problem by analyzing the task and developing an algorithm that computes the desired result. Due to its complex and diverse nature, programming requires a certain level of expertise in analysis of algorithms, data structures, mathematics, formal logic as well as related tasks such as testing and debugging. Due to increasing awareness of need for programming, there exists… Show more

Computer Programming as a process that embodies the creation of an executable computer program for a given computational problem by analyzing the task and developing an algorithm that computes the desired result. Due to its complex and diverse nature, programming requires a certain level of expertise in analysis of algorithms, data structures, mathematics, formal logic as well as related tasks such as testing and debugging. Due to increasing awareness of need for programming, there exists numerous competitive programming websites where students can practice and solve problems. The aim of our work is to assess the performance of students on such platforms. This work shall not only help the learners to self-assess themselves, but it will also aid the educators to evaluate the progress of their students. To meet this objective, the data was collected from two different competitive programming environments, namely, HackerEarth-a globally accessible competitive programming website and our university's in-house programming portal, a university-based programming environment. We used supervised learning to predict the performance of students for both the datasets. The accuracy obtained for the HackerEarth dataset is 80%, while the accuracy for the University dataset was computed to be 91%. Apart from predicting the performance, rigorous analyses were done unearth hidden trends responsible for a learners programming acumen. Show less