Mobile Secure Examination System

The increasing features of Internet Technologies in all application domains have changed life styles and interactions. With the rapid development of Mobile Learning, collaborative technologies is an important for teaching, learning methods and schemes.Interaction between the students also student with the teacher is important for student to gain knowledge.In this paper, we introduce an effective queries and answers Q&A system for collaborative technologies, which can act not just like a virtual teacher, but also virtual discussion for student. With the proposed system, brings a new Questions and Answering system, student can attach their question when they want collaborate using collaborative technologies capitalize on one another?s resources and skills. Students can ask their questions to the related collaborative Group when they want to collaborate with others, asking one another for information, evaluating one another?s ideas, then each of the answer will compare with data base. These systems are based on cognitive learning theory which is a learning theory interested in how information organizes in human?s memory. ITSs are intelligent programs which know whom they will teach so computers play an important part in education and instruction aims are performed and suggested in this work. In this project described and reviewed some of ITSs in educational application and demonstrate used modules in ITSs

Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval

While there have been many proposals on making AI algorithms explainable, few have attempted to evaluate the impact of AI-generated explanations on human performance in conducting human-AI collaborative tasks. To bridge the gap, we propose a Twenty-Questions style collaborative image retrieval game, Explanation-assisted Guess Which (ExAG), as a method of evaluating the efficacy of explanations (visual evidence or textual justification) in the context of Visual Question Answering (VQA). In our proposed ExAG, a human user needs to guess a secret image picked by the VQA agent by asking natural language questions to it. We show that overall, when AI explains its answers, users succeed more often in guessing the secret image correctly. Notably, a few correct explanations can readily improve human performance when VQA answers are mostly incorrect as compared to no-explanation games. Furthermore, we also show that while explanations rated as "helpful" significantly improve human performance, "incorrect" and "unhelpful" explanations can degrade performance as compared to no-explanation games. Our experiments, therefore, demonstrate that ExAG is an effective means to evaluate the efficacy of AI-generated explanations on a human-AI collaborative task.Comment: 2019 AAAI Conference on Human Computation and Crowdsourcin

Inquiry Teaching: It is Easier than You Think!

This article is a survey of the literature on inquiry teaching. Many teachers do not participate in inquiry teaching for various reasons. The following are the main reasons: it takes too much time; students do not learn what they need for the state test; and, the teachers do not know how to grade projects and presentations. These reasons sound like rhetoric from long ago, but it is very current. In this article, research is used to show that students who participate in inquiry learning or any type of problem-based education do much better than students who do not have that opportunity. The student participants not only have better grades, but they think on a higher level, become more civic minded, and are better problem solvers. Included in the article are four models which can be used to teach inquiry science, and two lesson plans with rubrics to help grade the inquiry STS lesson. The major point being made throughout is that there is an advantage to teaching students using inquiry. The only disadvantage is not giving the students the opportunity to use inquiry and to grow

Feasibility report: Delivering case-study based learning using artificial intelligence and gaming technologies

This document describes an investigation into the technical feasibility of a game to support learning based on case studies. Information systems students using the game will conduct fact-finding interviews with virtual characters. We survey relevant technologies in computational linguistics and games. We assess the applicability of the various approaches and propose an architecture for the game based on existing techniques. We propose a phased development plan for the development of the game