Using Effect Size for Group Modeling in E-Learning Systems

Using Effect Size for Group Modeling in E-Learning Systems

Divna Krpan (University of Split, Croatia), Suzana Tomaš (University of Split, Croatia) and Roko Vladušic (University of Split, Croatia)
DOI: 10.4018/978-1-61692-008-1.ch012
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There is great need for collaboration in education and e-learning systems which imply the necessity for group modeling. Since Bloom’s experiment, which produced effect size of 2-sigma, there were many attempts to repeat those results with intelligent tutoring systems. Our experiments show effectiveness of xTEx-Sys in measure of effect size. The goal of our research and development is to get as close as possible to effect size of 2-sigma. There is greater need for collaboration in e-learning systems and there are some indications that collaboration could increase effectiveness. Since collaboration is closely coupled with groups, directions for future development and exploration of e-learning systems lay in field of group modeling. Group modeling also implies creation of stereotype models.
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There is a gap between knowledge and skills that students acquire in school and those required in business. To bridge that gap, additional learning is required. Development of industry and business imply lifelong learning and teaching process. Since traditional instruction is expensive and dependant on specific restraints, for example, time, place and location, it is necessary to search for easier solutions. E-learning could be the answer. With increasing availability of the Internet, high speed and bandwidth, and rapid development of computer technology, it is more convenient to deliver content to students at distance.

Some authors define e-learning as an interactive learning in which learning content is available online and provides automatic feedback to the student’s learning activities, but online communication between participants (students and teachers, or between students) doesn't have to be included, so the main focus is on learning content (Paulsen, 2002).

The e-learning includes different ways of delivering educational content: via Internet (LAN and WAN), audio and video tape, CD-ROM, interactive TV, satellite broadcasting. It covers applications and processes such as computer-based learning, virtual classrooms, Web-based learning and digital collaboration.

E-learning can be delivered in two ways: synchronously and asynchronously (IsoDynamic, 2006). Synchronous e-learning (Erudium, 2001) takes place in virtual classroom. It is based on a simultaneous access of instructors and all other participants to content, in real time; it may include Web-based videoconferencing, audio conferencing and online chat. In synchronous mode, e-learning has no space constraints, but time constraints are present because learning is done at precise appointments between instructors and students. Asynchronous e-learning may take place at any time, and is self paced, it refers to learning methods in which student and teacher interact with each other one at a time, in turn. Asynchronous e-learning is probably more interesting because of its lower cost of development and reusable components and it eliminates all time and location related constraints. In asynchronous e-learning teacher and student are not active or online at same time (examples of activities are: e-mail, forums, newsgroups, etc.) (Erudium, 2001).

Advances in Internet access speed and availability of personal computing platforms have increased opportunities for use of e-learning technologies (Collier, 2002). The technology infrastructure must support users and network and provide open environment to support interoperability between components, and also security to protect distributed users and content.

A special class of asynchronous e-learning systems are Intelligent Tutoring Systems (ITSs) that represent an advanced learning and teaching environment adaptable to individual student’s characteristics (Stankov, et al., 2008). The ITSs are intended to emulate one teacher interacting with one student more directly (IsoDynamic, 2001). They model how a teacher would teach in the class and also keep track of student’s performance (Jain, 2008). The intelligent tutoring system (ITS) can be defined as a learning technology that dynamically adapts learning content to needs and preferences of the student and learning (Erudium, 2001).

Rickel (1989) presents the following components of the ITS: (i) learning scenario, (ii) domain knowledge representation, (iii) student modeling, (iv) pedagogical knowledge, and (v) user interface. Those components are sometimes different, according to different authors. For example, Shute and Psotka (1995) point out that the ITS must have: (i) knowledge of the domain (expert model), (ii) knowledge of the learner (student model), and (iii) knowledge of the teaching strategies (tutor).

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