Hawaii International Conference on System Sciences

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.

Cognitive Learning Environment and Classroom Analytics (CLECA): A Method Based on Dynamic Data Mining Techniques

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

http://www.ijarcs.info/index.php/Ijarcs/article/download/3067/3050

Checkpoint – An Online Descriptive Answers Grading Tool

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 

Multimodal Peripheral Alert to Improve Teaching-Learning for Blended Classroom

ML-based intelligent real-time feedback system for blended classroom

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.

Intelligent Classroom System for Qualitative Analysis of Students' Conceptual Understanding

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 especially where the class size is large. In this paper, we put forth the design, analysis and implementation details along with some experimental outputs 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.

/pdf/autonomous-educational-testing-system-using-unsupervised-24zv24w4l2.pdf

Jannat Talwar

Papers

Intelligent Classroom System for Qualitative Analysis of Students' Conceptual Understanding

Autonomous Educational Testing System Using Unsupervised Feature Learning.