An intelligent tutoring system for teaching and learning Hoare logic

2002 ◽  
Author(s):  
K. Goshi ◽  
P. Wray ◽  
Yong Sun
Keyword(s):  
Teaching And Learning ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Hoare Logic ◽  
Tutoring System

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.

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.

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.

Web-Based Music Intelligent Tutoring Systems

2011 ◽  
pp. 231-248
Author(s):  
Somnuk Phon-Amnuaisuk ◽  
Chee Keh Siong
Keyword(s):  
Teaching And Learning ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Learning Technologies ◽  
Tutoring System ◽  
Web Based ◽  
Tutoring Systems ◽  
Pedagogical Aspect ◽  
Teaching Music ◽  
Inference Techniques

Our work bridges two interesting topics: the research in the area of web-based applications and the area of learning technologies. We give an overall picture of the current development in web-based music intelligent tutoring system (WMITS). The term WMITS is coined by us to describe the two main areas in our focus. In this chapter, we address the following issues: (i) the pedagogical aspect of teaching and learning music, (ii) the background of music intelligent tutoring system and (iii) our WMITS system for teaching music theories. A web-based environment offers strengths in terms of accessibility and self-paced learning. However, the environment has a great drawback in terms of inter-activities between the users and the system. Our design addresses this issue by developing a specialised client tool. The client tool provides an interactive environment for score editing which is crucial for learning music theories. The system incorporates three major inference techniques (i.e. Deductive, Inductive, and Bayesian Inference) in dealing with music theories and uncertain knowledge such as students’ understanding.

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