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Proceedings ArticleDOI

Intelligent Classroom System for Qualitative Analysis of Students' Conceptual Understanding

TL;DR: The design, analysis and implementation details along with some preliminary results to build a system that integrates all the above mentioned tasks with minimal teacher involvement that not only automates the traditional classroom scenario but also overcomes its inherent shortcomings and fallacies are put forth.
Abstract: With the increase of ubiquitous data all over the internet, intelligent classroom systems that integrate traditional learning techniques with modern e-learning tools have become quite popular and necessary today. Although a substantial amount of work has been done in the field of e-learning, specifically in automation of objective question and answer evaluation, personalized learning, adaptive evaluation systems, the field of qualitative analysis of a student's subjective paragraph answers remains unexplored to a large extent. The traditional board, chalk, talk based classroom scenario involves a teacher setting question papers based on the concepts taught, checks the answers written by students manually and thus evaluates the students' performance. However, setting question papers remains a time consuming process with the teacher having to bother about question quality, level of difficulty and redundancy. In addition the process of manually correcting students' answers is a cumbersome and tedious task. In this paper, we put forth the design, analysis and implementation details along with some preliminary results to build a system that integrates all the above mentioned tasks with minimal teacher involvement that not only automates the traditional classroom scenario but also overcomes its inherent shortcomings and fallacies.
Citations
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Book ChapterDOI
01 Feb 2021
TL;DR: In this article, the authors investigate different dynamic data mining methods that can be deployed into a modern classroom to assist both the teaching and learning atmosphere based on the past and present data.
Abstract: With the advent of modern data analytics tools, understanding the bits and pieces of any environment with the abundance of relevant data has become a reality. Traditional post event analyses are evolving toward on-line and real-time processes. Along with versatile algorithms are being proposed to address the data types suitable for dynamic environments. This research would investigate different dynamic data mining methods that can be deployed into a modern classroom to assist both the teaching and learning atmosphere based on the past and present data. Time series data regarding student’s attentiveness, academic history, content of the topic, demography of the classroom and human sentiment analysis would be fed into an algorithm suitable for dynamic operations to make the learning ambience smarter, resulting in better information being available to educators to take most appropriate measures while teaching a topic. The research objective is to propose an algorithm that can later be implemented with proper hardware set-up.

6 citations

Journal ArticleDOI
TL;DR: This application would simulate human thinking for assessing descriptive answers using natural language processing and would first parse the answer that is given as an input to it, and then check how similar the given answer is to the ideal answer whose keywords will be provided by the teachers.
Abstract: We live in an age of technology. Everything is automated. Even in the field of education, the use of technology is increasing largely. However, even today exams where theoretical questions need to be answered are taking place manually. This is because little work progress has been made in the field of grading theoretical answers written by students during examinations. Hence, we plan on creating an application that will help in evaluating answers. We call this application “Checkpoint”. It is a natural language processing based descriptive answer checking and grading application. This application would simulate human thinking for assessing descriptive answers using natural language processing. NLP involves natural language understanding that is, enabling computers to derive meaning from human or natural language input, and produce the desired output. This application would first parse the answer that is given as an input to it. Taking into consideration for the presence of synonyms it will check how similar the given answer is to the ideal answer whose keywords will be provided by the teachers. Depending on the similarity, it will grade the answers. Keywords: Exams, paper checking, assessment, online examination, grading, theoretical answers analysis

2 citations

Book ChapterDOI
06 Nov 2022
TL;DR: In this paper , the authors present an in-class multimodal alert method for teachers and students to address the challenges in a blended classroom, which utilizes performance prediction and classification of students for real-time alert.
Abstract: AbstractDesigning peripheral warnings and notifications to support teaching-learning is progressively increasing. However, existing work usually fails to effectively integrate real-time alerts and tackle poor students in a blended classroom. We present an in-class multimodal alert method for teachers and students to address the challenges. The system utilizes performance prediction and classification of students for real-time alert. The classification of students based on course performance helped in optimizing the number of alerts. The peripheral device selection aided in preventing the disruption in the lecture follow. Moreover, alert content delivery timing (start, during, and end of the class session) is used to reduce alert fatigue. We reported the design and the initial study results. The results show that 25 teachers and students reacted positively to the system design, technology, and features.KeywordsPeripheral alertReal-time feedbackE-learning toolBlended class

1 citations

References
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Book ChapterDOI
21 Apr 1998
TL;DR: This paper explores the use of Support Vector Machines for learning text classifiers from examples and analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task.
Abstract: This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of different learning tasks. Furthermore they are fully automatic, eliminating the need for manual parameter tuning.

8,658 citations

Journal ArticleDOI
01 Jun 2002
TL;DR: A taxonomy for characterizing Web data extraction fools is proposed, a survey of major web data extraction tools described in the literature is briefly surveyed, and a qualitative analysis of them is provided.
Abstract: In the last few years, several works in the literature have addressed the problem of data extraction from Web pages. The importance of this problem derives from the fact that, once extracted, the data can be handled in a way similar to instances of a traditional database. The approaches proposed in the literature to address the problem of Web data extraction use techniques borrowed from areas such as natural language processing, languages and grammars, machine learning, information retrieval, databases, and ontologies. As a consequence, they present very distinct features and capabilities which make a direct comparison difficult to be done. In this paper, we propose a taxonomy for characterizing Web data extraction fools, briefly survey major Web data extraction tools described in the literature, and provide a qualitative analysis of them. Hopefully, this work will stimulate other studies aimed at a more comprehensive analysis of data extraction approaches and tools for Web data.

760 citations


"Intelligent Classroom System for Qu..." refers background in this paper

  • ...Laender [1], presented a survey that covers a rigorous taxonomy to classify and analyze web data mining....

    [...]

Proceedings ArticleDOI
06 Jan 2003
TL;DR: This paper compares artificial neural network and support vector machine algorithms for use as text classifiers of news items and identifies a reduction in feature set that provides improved results.
Abstract: Text categorization is the process of sorting text documents into one or more predefined categories or classes of similar documents. Differences in the results of such categorization arise from the feature set chosen to base the association of a given document with a given category. Advocates of text categorization recognize that the sorting of text documents into categories of like documents reduces the overhead required for fast retrieval of such documents and provides smaller domains in which the users may explore similar documents. In this paper we are interested in examining whether automatic classification of news texts can be improved by prefiltering the vocabulary to reduce the feature set used in the computations. First we compare artificial neural network and support vector machine algorithms for use as text classifiers of news items. Secondly, we identify a reduction in feature set that provides improved results.

156 citations

01 Jan 2001
TL;DR: The small display form factor of these portable devices greatly diminishes the rate at which these sites can be browsed, showing the requirement of efficient algorithms to extract the content of web pages and build a faithful reproduction of the original pages with the important content intact.
Abstract: In recent times, the way people access information from the web has undergone a transformation. The demand for information to be accessible from anywhere, anytime, has resulted in the introduction of Personal Digital Assistants (PDAs) and cellular phones that are able to browse the web and can be used to find information using wireless connections. However, the small display form factor of these portable devices greatly diminishes the rate at which these sites can be browsed. This shows the requirement of efficient algorithms to extract the content of web pages and build a faithful reproduction of the original pages with the important content intact.

93 citations


"Intelligent Classroom System for Qu..." refers methods in this paper

  • ...We could either use Regular expression matching or HTML Document Object Model (DOM) parsing [3] to extract the questions....

    [...]

Book ChapterDOI
TL;DR: This work surveys a variety of information extraction techniques that enable information agents to automatically gather information from heterogeneous sources and delivers the results to the users.
Abstract: Information agents are emerging as an important approach to building next- generation value-added information services. An information agent is a distributed system that receives a goal through its user interface, gathers information relevant to this goal from a variety of sources, processes this content as appropriate,and delivers the results to the users. We focus on the second stage in this generic architecture. We survey a variety of information extraction techniques that enable information agents to automatically gather information from heterogeneous sources.

40 citations


"Intelligent Classroom System for Qu..." refers methods in this paper

  • ...Kushmerick [2] tracked a profile of finitestate approaches to the Web Data Mining problem....

    [...]

  • ...Data mining techniques derived from Natural Language Processing and Hidden Markov Models have also been discussed in the past [2]....

    [...]