Between Tradition and Innovation in ICT and Teaching

Between Tradition and Innovation in ICT and Teaching

Antonio Cartelli (University of Cassino and Southern Lazio, Italy)
Copyright: © 2009 |Pages: 7
DOI: 10.4018/978-1-60566-198-8.ch026
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Abstract

During the past few decades, the expanded use of PCs and the Internet introduced many changes in human activities and cooperated in the transformation process leading from the industrial society to the knowledge society. Among other things, the above instruments played a special role in education, and two main phases can be easily recognized: the former one where computing and ICT were mostly used to enhance individuals’ learning features (i.e., teachers mainly had the role of educational worker: planning, controlling and evaluating students’ learning processes); the latter one, more recent and centered on ICT use, where teachers had to adopt situated and collaborative learning strategies, build communities of learners (CoLs), organize students’ work for enhancing problem finding and solving, while helping the development of their ZPDs (zones of proximal development, meaning individuals’ cognitive areas marked by the distance between the subject’s knowledge/experience in a given field and the same knowledge/experience in the best skilled individuals in the community). The above transformation modified not only teachers’ functions, but also the whole school environment and the students’ role within it. The same ICT will help teachers and professors in finding solutions to learning problems by giving them new instruments for the analysis and continuous monitoring of students’ learning processes.
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Background

As already stated, computers entered very early into educational processes, often under the influence of pedagogical and psychological theories. As regards the influence of IT on individual teaching-learning processes, one of the most relevant contributions in defining the ways computers could be used in education came from Taylor (1980), who proposed three metaphors for them: tutor, tool, and tutee. The first one refers to the computer support to teachers’ work, the second one to instruments or tools autonomously used by students, and the third one to computer programming skills students must have to let problems be solved by computers. Galliani and others (1999) extended these metaphors while considering the great deal of software tools devoted to education and developed with the time. Tutor appellation describes how computer systems support or substitute (in the specific situation of auto-instruction) teachers and tutors in their work. Computer-assisted instruction (CAI), computer-assisted education (CAE), and computer-assisted learning (CAL) software are examples of the above systems. The former ones, CAI and CAE, implement into the topics to be taught the structure of the software the designer makes up (i.e., they force the user to follow a well-defined learning route within them); good examples for this kind of software are: 1) tools for theorems’ demonstration or physical phenomena emulation, and 2) surveying/testing software made by questions with pre-built multiple answers or yes/no answers. CAL software, with respect to the other tools, gives more importance to learning than to teaching; that is, users can now freely move within different scenarios and can decide by themselves what to do, or can browse in a personal way the context the software proposes. Good examples for these software packages are educational games, edutainment (acronym for education-entertainment) tools, simulation systems (often used for training), and many multimedia or hypermedia tools.

A further extension of tutor metaphor comes from the results of artificial intelligence application in education and especially: intelligent computer-assisted instruction (ICAI) systems and intelligent tutoring systems (ITSs). In these systems, with respect to CAI and CAL tools, there is no pre-determined teaching route or strategy, but there are three independent modules interacting among themselves: an expert (i.e., a knowledge basis on a very specific domain), a pupil (implementing the knowledge representation of a student interacting with the system), and a teacher (implementing the teacher behavior rules of everyday teaching and determining the didactic strategies to be adopted during the student-system dialogue).

With respect to the tool metaphor, its extended version now includes (together with the software students can use to produce information, i.e., editors or at most word processors) office automation suites and special tools for the analysis of a large amount of data and for browsing specific contexts (usually provided with authoring, co-authoring functions).

Finally, the extension of the tutee metaphor is mainly represented from tools for the creation of special developmental environments, such as the ones Papert created with LOGO.

Key Terms in this Chapter

E-Learning Platform: An information system that schools, universities, and institutions can use for teaching (only online or supporting traditional teaching) which can have the following features (all together or individually): a) be a content management system (CMS), guaranteeing the access to didactic materials for the students; b) be a learning management system (LMS), where the use of learning objects makes easier the learning of a given topic; c) be a computer-supported collaborative learning system (CSCLS), which makes easier the use of collaborative and situated teaching/learning strategies; and d) build a virtual community of students, tutors, and professors using knowledge management (KM) strategies.

Learning Style: The personal way individuals think and learn. Also, if each individual develops a preferred set of approaches to learning, many authors suggest a well-defined set of learning models. Research seems to agree on the following elements: a) the adoption of special teaching strategies can make easier learning for students or not, depending on their learning styles; b) learning styles can evolve with individuals; and c) individuals’ learning styles can be modified by special learning environments.

Mental Scheme: The set of concepts, and dependencies among them, that individuals carry out for facing the problem of interpreting phenomena, without any reference to scientific knowledge or disciplinary paradigms.

Web Technologies: The set of all instruments that allows people to use the Web and its protocols for improving communication and acquiring information. These are based on hardware, which are mostly networks of computers, and software resources, which are mostly Web servers using the HTTP protocol for communicating, interfaced with RDBMS (relational data base management systems).

Online Action Research: Uses the Internet for extending the features of traditional action research and its cyclical structure, based for many authors on five different phases: diagnosing, action-planning, action-taking, evaluating, and specifying learning. The Internet allows the continuous monitoring of action-research events and gives the researcher a further instrument to study phenomena and intervene on them.

Portfolio: The report collecting documents, scores, interviews, and so forth, and demonstrating the students’ skills, achievements, learning, and competencies, with respect to: a) previously defined areas of skill, b) specific learning outcomes from these areas, c) appropriate learning strategies that have to be developed by the student, and d) performance indicators.

Misconceptions (Preconceptions): Wrong ideas people manifest while explaining phenomena, with respect to scientific paradigms (i.e., people’s ideas are evaluated with respect to scientific ones). The term preconception is adopted when the wrong idea appears before people meet curricular disciplines. The term misconception is used to mark the students’ mistakes in phenomena interpretation.

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