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The introduction of computers into education raises questions such as: are computers in education effective? If they are, in what sense? What are the most effective strategies for using computers in education? How should teachers be encouraged to use them?To answer these questions a large-scale experiment (Project Comptown) was carried out in Israel, to test ways and means under real rather than laboratory conditions. This project is a research-oriented educational intervention, applying massive computerisation of schools and their 'close environment' to two localities in Israel (Arad and Ashkelon). Our starting point was the premise that computerisation of education is an inevitable process. Consequently, turning the computer into a 'cultural tool' in schools becomes a major challenge, aiming to narrow the gap between 'school culture' and 'real-world culture'. The main objectives for Project Comptown are: [l] To create a computer culture in schools: [ 2 ] To use the computer's potential for innovative teaching and learning, both inside and outside schools. To achieve these, we identified a number of principles which we considered pre-conditions for an 'appropriate' computer strategy in schools.

Maintaining a meta‐population structure significantly contributes to species viability and is often the basis for defining the difference between a naturally patchy and a fragmented landscape. However, a heterogeneous landscape may be patchy for habitat generalists and fragmented for specialists, preventing the formation of meta‐population structures in habitat specialists. We examined this hypothesis on the generalist Lichtenstein's short‐fingered gecko Stenodactylus sthenodactylus and the endangered specialist Middle Eastern short‐fingered gecko S. doriae, two species of the genus Stenodactylus, inhabiting the southern Arava Valley in Israel. We compared the genetic structure of the populations of these two geckos by amplified fragment length polymorphism analysis, expecting to find decreased gene diversity within the small populations that fail to form a meta‐population structure. Indeed, we found that among populations, the habitat specialist S. doriae had a low level of gene flow, whereas the habitat generalist S. sthenodactylus had a relatively high level of gene flow. However, unexpectedly, the most isolated population of the specialist S. doriae, located in the Samar dune (a small patch of 2.3 km2), exhibited the highest level of gene diversity of all the populations studied (expected heterozygosity = 0.4286). Moreover, the results showed that the Samar population is genetically unique when compared with its neighboring populations. Gene flow between two populations located to the north and to the south bypass the Samar population. The generalist S. sthenodactylus, in contrast, did not exhibit an exceptional level of gene diversity. The origin of the exceptional diversity and genetic uniqueness of the Samar population of S. doriae may be associated with traits that make this gecko highly adaptive to this specific landscape unit. It also emphasizes the need to establish special conservation efforts for the protection of high‐quality habitats that provide adequate conditions for a source population of specialist species.

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This article proposes a taxonomy to aid decision makers in selecting computer software that is consistent with the their values and preferences for instruction. It builds on two interrelated arguments: 1) the nature of instruction and the use of information technology derive from a conceptual framework that is embedded in an explicit or implicit belief about the nature of human development and learning, and 2) due to the value nature of instruction, the instructional beliefs embedded in software should be congruent with the decision maker's beliefs underlying instruction. The taxonomy consists of three components: characteristics of patterns of instruction, properties of software, and the congruence between them. These components are interrelated in a mapping sentence [1] that maps characteristics of instruction onto properties of software. The taxonomy refers to two types of educational decision makers. Believers, whose decisions are predetermined by a belief commitment and orchestrators, whose decisions are heuristically taken. Decisions for believers are straight forward; decisions for orchestrators are more complicated and depend on contextual factors represented in the mapping sentence. Links between research findings and the taxonomy are presented and illustrate the taxonomy's use and its utility in predicting real world decisions.