Assessment of the Effectiveness of Distance Learning Courses and of Interactive Processes in Virtual Learning Environments

The general objectives of this study are: to analyze significant indicators for assessment of the effectiveness of Distance Learning (DL) courses; to check the extent to which such effectiveness is related to the quality of interactive processes; to examine characteristic elements of individuals’ conversations while they interact in study groups within virtual environments, with a view to contributing toward the effectiveness of DL courses; the development of a methodology for assessing the effectiveness of distance learning courses. Results confirm the importance of the context, the teacher, and learning-group variables for the effectiveness of distance courses. They also illustrate the relevance of certain speech features of students and teachers in virtual learning environments that generate conversational dynamics and contribute toward meeting the goals of DL courses.

Mobile Learning

Mobile learning, as the “portable and personal” fashion of e-learning, is intended to enhance the efficiency and effectiveness of learning in the context of handheld terminals. Most present-day learning systems run on desktop computers and are not designed for use on mobile devices such as mobile phones, smart phones, Personal Digital Assistants, etc. Mobile learning systems aim to improve the quality of learning by providing mobile learners with an easy, contextualized and ubiquitous access to knowledge. This chapter gives an overview of the current state of knowledge and research in the m-learning domain, describes issues and problems pertinent to mobile learning and offers our approach to solving these problems in the form of a mobile intelligent tutoring model we are currently developing. Given the present absence of relevant literature and referent material we think that this chapter provides developers with some new ideas.

Student Modeling in an Intelligent Tutoring System

Student modeling and cognitively diagnostic assessment are important issues that need to be addressed for the development and successful application of intelligent tutoring systems (its). Its needs the construction of complex models to represent the skills that students are using and their knowledge states, and practitioners want cognitively diagnostic information at a finer grained level. This chapter reviews our effort on modeling student’s knowledge in the ASSISTment project. Intelligent tutors have been mainly used to teach students. In the ASSISTment project, we have emphasized using the intelligent tutoring system as an assessment system that provides instructional assistance during the test. Usually it is believed that assessment get harder if students are allowed to learn during the test, as its then like try to hit a moving target. So our results are surprising that by providing tutoring to students while they are assessed we actually prove the assessment of students’ knowledge. Additionally, in this article, we present encouraging results about a fine-grained skill model with that system that is able to predict state test scores. We conclude that using intelligent tutoring systems to do assessment seems like a reasonable way of dealing with the dilemma that every minute spent testing students takes time away from instruction.

Overlay Method in Testing Knowledge by Natural Language Dialogue

Irrespective how domain knowledge is well formalized, and irrespective of applying appropriate pedagogical techniques in Intelligent tutoring system, learning and teaching process can seem complex because of unfamiliar communication with computer tutor. Using even simplest NLP techniques makes benefits in computer‘s usage, not only in e-learning domain. Natural language generation is basic technique which can make learning and teaching process much more adaptable to the student. Presenting formal knowledge by natural language sentences, as well as testing in a form of dialogue are aims to be accomplished in new Intelligent Tutoring System based on Tutor-Expert System model. In making previous system more acceptable to the student, our first step is to perform natural language generation with Croatian localization for formalized domain knowledge following by problem generation and dialog based guidance to the problem solution.

Intelligent Tutoring System for Learning Programming

Tutoring systems for programming help students understand features of target programming languages, and develop their general problem solving skills. Our system guides novices in learning syntax and semantics of programming language, problem decomposition, program design and testing. It is language independent, automatically adjusting course material to the language - subject of learning. Semantic analysis of student’s programs is based on comparison of abstract semantics trees of student’s and model-program. Model-program is coded by innovative programming language describing goals and variants of possible solutions. The notional machine defined by programming language, its verbal description of instruction actions and visualization of program execution, helps students to understand semantics of teaching language. Advancement through the course material controlled by computer teacher supports connection of new concepts to the present student’s knowledge. The system applies distinct teaching strategies to stoppers and movers.

Intelligent Tutoring System Architecture Rebuilt

In this chapter, an approach to support the development of ITS based on patterns and pattern catalogues is described. Patterns are means to facilitate inter-project communication. As such, they provide for a clear terminology in an application domain. They support the development process itself by encapsulating information at different levels (e.g. using underlying concepts and tools, adding and integrating new concepts and tools, and fundamentally changing the structure of an existing piece of software). The approach described in this chapter shows two examples of how a catalogue of patterns collected from existing ITS architectures can be used and re-used in different settings. Those settings span the field of plugin frameworks for ITS (i.e. JaBInT), a concrete “proof of concept” ITS (i.e. ChemNom and CoChemEx) using a collaboration script approach.

A Meta-Analytic Estimation of a Common Effect Size from a Series of Experiments Related To an E-Learning System Effectiveness Evaluation

As the acquisition of knowledge is often an expensive and time-consuming process, it is important to know whether it actually improves the student performance. The e-learning is a revolutionary paradigm that has lately been significantly evolving and it is closely related to the intelligent tutoring systems. Methodology for evaluating the educational influence of learning and teaching process, questions whether and in what amount, students learn effectively. Our contribution to this compulsive field of research is a meta-analysis of a series of experiments based on the same two-group methodology that reveals a more precise effect size of one particular e-learning system - eXtended Tutor-Expert System, a representative of web-based authoring shells for building intelligent tutoring systems.

A Personalized Learning System Based on Semantic Web Technologies

In this study, a personalized learning system framework that is KOULearn (Kocaeli University Learning System) has been proposed. It is based on learning object and produces a personalized learning content according to learning style and cognitive knowledge level of an individual. The proposed framework is situated in advanced learning technologies according to educational perspective and is a semantic web application according to development technologies. The proposed framework is aimed to form a personalized learning environment. Three models are included in this system: domain model, user model and adaptation model, respectively. Components of the system have been realized by ontology based knowledge modeling approach. Execution of the personalized learning content structure and learning environment of the system, related to electric education, has been examined by resonance learning module which is constituted considering personalization requirements of the system.

Using Effect Size for Group Modeling in E-Learning Systems

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.

E-Learning through Gaming

A generic theoretical framework on teaching children to negotiate is presented, founded on Piaget’s child development and Thompson and Hastie’s negotiation theories and validated through an experiment. The framework was implemented as CLIPS knowledge base, the back-end of an Intelligent Tutoring Agent (ITA). Negotiation skills were assessed through an online JAVA implementation of the board game Settlers of Catan (SoC). The CLIPS knowledge base was connected by JCLIPS to SoC. The ITA was thoroughly tested and found to be robust, with an excellent multithread handling. After installing a client, SoC can be played over Internet against other artificial or/and human players. The integrated ITA helps children to improve their negotiation skills and helps science to improve the theoretical framework, which makes it unique in its kind.