Intelligent Tutoring System of Linear Programming

There is a growing technological development in intelligent teaching systems. This field has become interesting to many researchers. In this paper, we present an intelligent tutoring system for teaching mathematics that helps students understand the basics of linear programming using Linear Program Solver and Service for Solving Linear Programming Problems, through which students will be able to solve economic problems. It comes down to determining the minimum or maximum value of a linear function, which is called the objective function, according to pre-set limiting conditions expressed by linear equations and inequalities. The goal function and the limiting conditions represent a mathematical model of the observed problem. Working as a professor of mathematics in high school, I felt the need for one such work and dealing with the study of linear programming as an integral part of mathematics. There are a number of papers in this regard, but exclusively related to traditional ways of working, as stated in the introductory part of the paper. The center of work as well as the final part deals with the study of linear programming using programs that deal with this topic.

A Study on Human-Computer Interaction Mechanism in an Intelligent Tutoring System

Discussion is a common and important learning process.Involvement of a virtual agent can provide adaptive support for the discussion process.Argumen-tative knowledge construction is beneficial to learners’acquisition of knowledge,but the effectiveness of argumentative scaffolding in existing studies is not consistent.Based on an intelligent discussion system,a total of 47 undergraduate students took part in the experiment and they were assigned to three different conditions:content-related plus content-independent scaffolding condition,content-related scaffolding condition,and the control condition.Under the content-related and content-independent scaffolding condition,the computer agent provided an idea from semantically different categories(content-related scaffolding)according to the automatic categorization of the current contributions,and further inquired the participants about their attitudes and reasons(content-independent scaffolding).Under the condition of content-related scaffolding condition,the virtual agent only provided semantically different viewpoints.Under the control condition,the subjects expressed their opinion independently without the participation of the virtual agent.Findings revealed that compared with the control group,when the virtual agent provided semantically different ideas(content-related scaffolding),the discussion breadth(number of categories)was improved and the subjects felt that they had a more comprehensive understanding of the problem.Compared with the content-related scaffolding condition,when the virtual agent provided semantically different ideas and further asked about the attitudes and reasons,the subjects expressed more agreement with these views,but mentioned fewer categories during the discussion.This study suggests that the content-related scaffolding can facilitate the cognitive processing effect relevant to the topic of discussion.When the content independent scaffolding is added,it can promote the argumentative processing,but may have a negative effect on the cognitive processing related to the topic discussed.

From Big Data to Intelligent Tutoring: Exploring the Application of Big Data Technology in the Ideological and Political Education for College Counselors

This paper explores the application of big data technology in ideological and political education for college counselors.Big data-driven personalized counseling for students,social network analysis,and public opinion monitoring can optimize the efficiency of counselor work.However,there are still challenges concerning data privacy protection,technical support,and training.By scientifically evaluating and addressing these challenges,big data technology is expected to enhance the effectiveness of college counselors and improve the quality of ideological and political education.In the future,with technological advancements,big data technology will become an essential tool for higher education management.

I-Quiz:An Intelligent Assessment Tool for Non-Verbal Behaviour Detection

Electronic learning(e-learning)has become one of the widely used modes of pedagogy in higher education today due to the convenience and flexibility offered in comparison to traditional learning activities.Advancements in Information and Communication Technology have eased learner connectivity online and enabled access to an extensive range of learning materials on the World Wide Web.Post covid-19 pandemic,online learning has become the most essential and inevitable medium of learning in primary,secondary and higher education.In recent times,Massive Open Online Courses(MOOCs)have transformed the current education strategy by offering a technology-rich and flexible form of online learning.A key component to assess the learner’s progress and effectiveness of online teaching is the Multiple Choice Question(MCQ)assessment in most of the MOOC courses.Uncertainty exists on the reliability and validity of the assessment component as it raises a qualm whether the real knowledge acquisition level reflects upon the assessment score.This is due to the possibility of random and smart guesses,learners can attempt,as MCQ assessments are more vulnerable than essay type assessments.This paper presents the architecture,development,evaluation of the I-Quiz system,an intelligent assessment tool,which captures and analyses both the implicit and explicit non-verbal behaviour of learner and provides insights about the learner’s real knowledge acquisition level.The I-Quiz system uses an innovative way to analyse the learner non-verbal behaviour and trains the agent using machine learning techniques.The intelligent agent in the system evaluates and predicts the real knowledge acquisition level of learners.A total of 500 undergraduate engineering students were asked to attend an on-Screen MCQ assessment test using the I-Quiz system comprising 20 multiple choice questions related to advanced C programming.The non-verbal behaviour of the learner is recorded using a front-facing camera during the entire assessment period.The resultant dataset of non-verbal behaviour and question-answer scores is used to train the random forest classifier model to predict the real knowledge acquisition level of the learner.The trained model after hyperparameter tuning and cross validation achieved a normalized prediction accuracy of 85.68%.

Artificial intelligence innovation in education: A twenty-year data-driven historical analysis

Reflecting on twenty years of educational research,we retrieved over 400 research article on the application of artificial intelligence(AI)and deep learning(DL)techniques in teaching and learning.A computerised content analysis was conducted to examine how AI and DL research themes have evolved in major educational journals.By doing so,we seek to uncover the prominent keywords associated with AI-enabled pedagogical adaptation research in each decade,due to the discipline’s dynamism.By examining the major research themes and historical trends from 2000 to 2019,we demonstrate that,as advanced technologies in education evolve over time,some areas of research topics seem have stood the test of time,while some others have experienced peaks and valleys.More importantly,our analysis highlights the paradigm shifts and emergent trends that are gaining prominence in the field of educational research.For instance,the results suggest the decline in conventional tech-enabled instructional design research and the flourishing of student profiling models and learning analytics.Furthermore,this paper serves to raise awareness on the opportunities and challenges behind AI and DL for pedagogical adaptation and initiate a dialogue.