scholarly journals Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

2018 ◽  
Vol 11 (1) ◽  
pp. 105 ◽  
Author(s):  
Syed Abidi ◽  
Mushtaq Hussain ◽  
Yonglin Xu ◽  
Wu Zhang
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Teaching And Learning ◽  
Performance Metrics ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Tutoring System ◽  
Learning Techniques

Incorporating substantial, sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study was to identify the confused students who had failed to master the skill(s) given by the tutors as homework using the Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study, and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models including: Naïve Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (XGBoost). We trained, validated, and tested learning algorithms, performed stratified cross-validation, and measured the performance of the models through various performance metrics, i.e., ROC (Receiver Operating Characteristic), Accuracy, Precision, Recall, F-Measure, Sensitivity, and Specificity. We found RF, GLM, XGBoost, and DL were high accuracy-achieving classifiers. However, other perceptions such as detecting unexplored features that might be related to the forecasting of outputs can also boost the accuracy of the prediction model. Through machine learning methods, we identified the group of students that were confused when attempting the homework exercise, to help foster their knowledge and talent to play a vital role in environmental development.

scholarly journals Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

2018 ◽  
Author(s):  
Syed Muhammad Raza Abidi ◽  
Mushtaq Hussain ◽  
Yonglin Xu ◽  
Wu Zhang
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Teaching And Learning ◽  
Performance Metrics ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Tutoring System ◽  
Learning Techniques

Incorporating substantial sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study is to identify the confused students who have failed to master the skill(s) given by the tutors as a homework using Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models that include: Naïve Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (XGBoost). We trained, validated and tested learning algorithms, performed stratified cross-validation and measured the performance of the models through various performance metrics i.e., ROC (Receiver Operating Characteristic), Accuracy, Precision, Recall, F-Measure, Sensitivity & Specificity. We found GLM, DT & RF are high accuracies achieving classifiers. However, other perceptions such as detection of unexplored features that might be related to the forecasting of outputs can also boost the accuracy of the prediction model. Through machine learning methods, we identified the group of students which were confused attempting the homework exercise and can help students foster their knowledge, and talent to play a vital role in environmental development.

Fake Review Detection Using Machine Learning Techniques

2020 ◽  
Vol 3 (2) ◽  
pp. 46-54
Author(s):  
Abhinandan V. ◽  
Aishwarya C. A. ◽  
Arshiya Sultana
Keyword(s):  
Machine Learning ◽  
Online Reviews ◽  
Vital Role ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Search Results ◽  
Learning Techniques ◽  
The World ◽  
Fake Reviews

Online reviews play a vital role in today's business and commerce. In the world of e-commerce, reviews are the best signs of success and failure. Businesses that have good reviews get a lot of free exposure on websites and pages that have good reviews show up at the top of the search results. Fake reviews are everywhere online. Online fake reviews are the reviews which are written by someone who has not actually used the product or the services. Because of the cut-throat competition, sellers are now willing to resort to unfair means to make their product stand out. This work introduces some supervised machine learning techniques to detect fake online reviews and also be able to block the malicious users who post such reviews.

Machine Learning Based Student Modelof an Intelligent Tutoring System

2015 ◽  
Vol 26 (4) ◽  
pp. 229-232
Author(s):  
Achi I. I ◽  
◽  
Prof. Inyiama H. C ◽  
Prof. Bakpo F. F ◽  
Agwu C. O
Keyword(s):  
Machine Learning ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Tutoring System

scholarly journals A Comprehensive Review and meta-analysis on Applications of Machine Learning Techniques in Intrusion Detection

2018 ◽  
Vol 22 ◽  
Author(s):  
Manojit Chattopadhyay ◽  
Rinku Sen ◽  
Sumeet Gupta
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Performance Metrics ◽  
Meta Analysis ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Business Organizations ◽  
Learning Techniques ◽  
Applications Of Machine Learning ◽  
Network Technologies

Securing a machine from various cyber-attacks has been of serious concern for researchers, statutory bodies such as governments, business organizations and users in both wired and wireless media. However, during the last decade, the amount of data handling by any device, particularly servers, has increased exponentially and hence the security of these devices has become a matter of utmost concern. This paper attempts to examine the challenges in the application of machine learning techniques to intrusion detection. We review different inherent issues in defining and applying the machine learning techniques to intrusion detection. We also attempt to identify the best technological solution for changing usage pattern by comparing different machine learning techniques on different datasets and summarizing their performance using various performance metrics. This paper highlights the research challenges and future trends of intrusion detection in dynamic scenarios of intrusion detection problems in diverse network technologies.

scholarly journals Smart Agriculture Monitoring System using ML

2020 ◽  
Vol 9 (4) ◽  
pp. 2404-2407
Keyword(s):  
Machine Learning ◽  
Temperature Sensor ◽  
Agricultural Sector ◽  
Cost Effective ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Agriculture Sector ◽  
Learning Techniques ◽  
Effective Manner ◽  
Cloud Computation

Agriculture plays vital role in every individual’s life. As the technology improves, agricultural sector has been improving by the needs of people. Basically, the idea here deals with monitoring of weather, temperature, soil moisture and other agriculture related aspects. The objective of this paper is to upgrade -growth probability. So by making use of Advance technologies good and efficient crop can be yield. Cloud (Firebase) is typically used to store the pre-computed data (data sets) and the data from the efficiency of agriculture sector. This idea comprises of Machine Learning techniques, Cloud Computation [5] and IoT. Here we will use machine learning techniques for predicting crop sensors and comparison between these. IoT includes NPK sensors, temperature sensor, and humidity sensor. The mechanism goes like this- initially the data from humidity, temperature sensor will be noted and NPK sensors will be placed in the soil, the values from the sensors will be sent to cloud by making use of any communication technology (ZigBee, IoT gateway devices). In cloud comparison of pre-computed data and data from sensors happens by making use of machine learning. The outcome from cloud may be stored in the server (Admin) or directly be notified to authorized person of the land in the form for notification. By taking all these parameters into consideration, we can predict the best suitable crop that can be grown and farmers will earn profit in a cost-effective manner.

Intelligent Language Tutoring System

2019 ◽  
Vol 15 (3) ◽  
pp. 60-74 ◽  
Author(s):  
Dara Tafazoli ◽  
Elena Gómez María ◽  
Cristina A. Huertas Abril
Keyword(s):  
Second Language Acquisition ◽  
Language Learning ◽  
Language Processing ◽  
Teaching And Learning ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Language Education ◽  
Computer Assisted ◽  
Tutoring System ◽  
Tutoring Systems

Intelligent computer-assisted language learning (ICALL) is a multidisciplinary area of research that combines natural language processing (NLP), intelligent tutoring system (ITS), second language acquisition (SLA), and foreign language teaching and learning (FLTL). Intelligent tutoring systems (ITS) are able to provide a personalized approach to learning by assuming the role of a real teacher/expert who adapts and steers the learning process according to the specific needs of each learner. This article reviews and discusses the issues surrounding the development and use of ITSs for language learning and teaching. First, the authors look at ICALL history: its evolution from CALL. Second, issues in ICALL research and integration will be discussed. Third, they will explain how artificial intelligence (AI) techniques are being implemented in language education as ITS and intelligent language tutoring systems (ITLS). Finally, the successful integration and development of ITLS will be explained in detail.

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