Tuesday 15 March 2016

iLab Project at MIT

iLab Project at MIT: iLab is dedicated to the proposition that online laboratories – real laboratories accessed through the Internet – can enrich science and engineering education by greatly expanding the range of experiments that students are exposed to in the course of their education.
 Unlike conventional laboratories, iLabs can be shared across a university or across the world.
The iLabs vision is to share expensive equipment and educational materials associated with lab experiments as broadly as possible within higher education and beyond.
iLab teams have created remote laboratories at Massachusetts Institute of Technology (MIT in microelectronics, chemical engineering, polymer crystallization, structural engineering, and signal processing as case studies for understanding the complex requirements of operating remote lab experiments and scaling their use to large groups of students at MIT and around the world.
Based on the experiences of the different iLab development teams The iLabs Project is developing a suite of software tools that makes it efficient to bring online and manage complex laboratory experiments. The iLabs Shared Architecture has the following design goals:
  • Minimize development and management effort for users and providers of remote labs
  • Provide a common set of services and development tools.
  • Scale to large numbers of users worldwide

  • Allow multiple universities with diverse network infrastructures to share access

iLabs harness the Internet and enable students to use real instruments via remote online laboratories. Conducting experiments motivates students; it also causes them to learn more effectively. Experiments allow a student to compare reality with simulations, collaborate with each other, and follow their curiosity. Yet, significant expense, space and safety considerations prevent many engineering classes from including lab components. By providing online access to remote laboratories, MIT is delivering the educational benefits of hands-on experimentation both to our own students and to students anywhere, at any time.

  • The iLabs Project is developing a suite of software tools that makes it easier to bring online and manage complex laboratory experiments. The iLab Shared Architecture (ISA) is a robust, scalable, open-source infrastructure built on web service that has been developed to provide a unifying software framework that can support access to a wide variety of online laboratories. Users and the online laboratories can be globally distributed across an arbitrary number of locations linked only by the Internet. Users access these remote laboratories through single sign-on and a simple standard administrative interface. 

webinar

Short for Web-based seminar, a webinar is a presentation, lecture, workshop or seminar that is transmitted over the Web using video conferencing software. A key feature of a Webinar is its interactive elements: the ability to give, receive and discuss information in real-time.
Using Webinar software participants can share audio, documents and applications with webinar attendees. This is useful when the webinar host is conducting an lecture or information session. While the presenter is speaking they can share desktop applications and documents.
Contrast with Webcast, in which the data transmission is one way and does not allow interaction between the presenter and the audience.

Sunday 13 March 2016

TPACK

Download your TPACK(Techno Pedagogical Content Knowledge) Study Material It is in pdf(Portable Document Format)Download and take print out for your EDU10.13 Unit 2 Reference

Chem Collective Virtual Lab


Chem Collective Virtual Lab
The Chem Virtual Lab is an online simulation of a chemistry lab. It is designed to help students link chemical computations with authentic laboratory chemistry. The lab allows students to select from hundreds of standard reagents (aqueous) and manipulate them in a manner resembling a real lab. 

GOALS

As a project in the National Science Digital Library (NSDL), the ChemCollective's goals are to support a community of instructors interested in improving chemistry education through interactive and engaging online activities.
Paper-and pencil homework typically emphasizes applying formulas— a process that can become routine and disconnected from the reality and fun of doing chemistry. In contrast, simulation-based exercises offer new ways to promote learning and motivation. Interactive exercises can allow students to explore and reinforce fundamental concepts in contexts that are increasingly complex, realistic, and engaging. Our goal is to create flexible, interactive learning environments where college and high school students can approach chemistry more like practicing scientists.

BRIEF HISTORY

The Chemistry Collective began with the IrYdium Project's Virtual Lab in 2000, which provides a flexible simulation so that instructors may use it for a great variety of student activities. The project evolved to create scenario-based learning activities designed to provide interactive, engaging materials that link chemistry concepts to the real world.
The project leader is Dr. David Yaron, Associate Professor of Chemistry at Carnegie Mellon. Many of the original activities on this site were developed by a group at Carnegie Mellon, including Yaron, experienced software engineers, undergraduate programmers, educational consultants, and technical writers.
Many Virtual Lab activities were also designed by contributors from other universities, including a number from Robert Belford, Jordi Cuadros and Sophia Nussbaum. The Virtual Lab was recognized in 2003 withMERLOT's Classic Award in chemistry and Editor's Choice for exemplary software across all disciplines. In 2010, the ChemCollective won the Science Prize for Online Resources in Education (SPORE) award.

Intelligent tutoring system(ITS)

Intelligent tutoring system(ITS)


An intelligent tutoring system (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without intervention from a human teacher. ITSs have the common goal of enabling learning in a meaningful and effective manner by using a variety of computing technologies. There are many examples of ITSs being used in both formal education and professional settings in which they have demonstrated their capabilities and limitations. There is a close relationship between intelligent tutoring, cognitive learning theories and design; and there is ongoing research to improve the effectiveness of ITS. An ITS aims to solve the problem of over-dependency of students over teachers for quality education. It aims to provide access to high quality education to each and every student, thus reforming the entire education system.

Intelligent Tutoring Systems,

Intelligent Tutoring Systems, Tutoring Module,Expert Module

—Intelligent Tutoring Systems are systems which pro-vide direct customized instruction to students. An IntelligentTutoring System consists of four modules. This research concen-trates on two of the modules, namely, the Tutoring Module andthe Expert Module, analyzes tutoring systems which contributeto the design of these modules. The study identifies the issueswhich have not been addressed in previous contributions.
 Index Terms
—Intelligent Tutoring Systems, Tutoring Module,Expert Module
I. INTRODUCTION
An intelligent tutoring system (ITS) is any computer systemthat provides direct customized instruction or feedback tostudents, i.e. without the intervention of human beings, whilstperforming a task. ITS typically consist of four modules [5]:the Expert Module, which comprises of facts and rules of theparticular domain to be conveyed to the student; the TutoringModule, which designs and regulates instructional interactionswith the students; the Student Module, which is a dynamicrepresentation of the students current state of knowledge; andthe User Interface, which controls interaction between thestudent and the system. The design of an ITS can focus onvarious issues, including the tutoring decisions which takeplace in the tutoring module and the facts and rules representedin the expert module. The following study discusses each of these modules as well as tutoring systems which address thedesign of these modules, as well as identifies the issues whichhave not been addressed in previous design of these modules.The following figure illustrated the structure of an ITS.
II. TUTORINGMODULE
The tutoring module is the instructional module that designsand regulates instructional interactions with the students. It isclosely linked to the student model, using knowledge aboutthe student, and its own tu torial goal structure to decidewhich pedagogic activities will be presented: hints to over-come impasses in performance, advice, support, explanations,different practice tasks, tests to confirm hypotheses in thestudents model, etc. Assessment is a very important functionof the tutoring module. The function of the tutoring module isessentially to perform continuous assessment of the student,and thereby interact with the expert module to prescribe furtheraction.Decisions on further instruction are taken in the Tutoring Mod-ule. The decisions are taken based on various characteristics.Some tutoring systems take decisions based on the studentsperformance and teaching history [1], while others decidebased on students emotions, known as Affective Modeling [2].Learning habits and learning targets are also taken into account[3].Example-tracing tutors make use of a ”behaviour graph” inthe tutoring module, which is a directed, acyclic graph thatrepresents multiple ways of solving the problem [4]. Basedon the method chosen by the student, decisions are taken.The categories of Intelligent Tutors - Curriculum Sequencingand Tutoring Strategies identified by Tom Murray also addressthe Tutoring Module. Systems which belong to the categoryof Curriculum Sequencing consists of rules, constraints andstrategies for sequencing courses. Sequencing and rules arenot necessarily decided by the author, and can be decideddynamically. Systems which belong to the category of TutoringSystems incorporate micro-level tutoring strategies, sophisti-cated sets of instructional primitives, and multiple tutoringstrategies. Granularity of hints and explantions is small. Tu-toring rules are decided by the author. Domain knowledge of systems belonging to both categories are typically shallow.COCA is also an intelligent tutoring system [7] which placesimportance on both authoring of the domain as well as how thematerial should be taught. In its Tutoring Module, COCA alsoattempts to simulate the decisions a teacher might make duringthe tutorial process, such as a decision point about the natureof the student’s next activity (instruction or assessment),