Our anchors were stories rather than lectures and were designed to be explored by students and teachers”. The anchor characteristics emphasized here and focused on in the present contribution are “active construction”, authenticity

(“realistic contexts”) and a narrative, motivating embedding (“story character”). A particular strength of Anchored Instruction and its BIBF 1120 supplier characteristics is the fact that its idea of situatedness combines fostering of both cognition and motivation: appropriate anchor problems can create meaningful contexts, where motivational and cognitive activation should go hand in hand. 2 Indeed, the benefits of AI were shown in more than a dozen of studies, well summarized in the meta-analysis of Blumschein (2003). A weighted average of explained variance 〈r2〉≈0.14 was found 3 (corresponding to an effect size on the boundary from medium to large, see Cohen, 1988), with values up to r2=0.66 ( Bottge et al., 2002) for solving contextualized problems (a main purpose

AI was invented for). Note, that AI thus offers considerable support for the theoretical expectation (explained in the preceding subsection on “Cognition and Learning”), that story contexts can foster meaningful learning. Moreover, it does so by using the “embedding” form of story contexts (mentioned above), where students are supposed to work and learn with various problems related to the embedded Selleck Forskolin science content. AI has thus both sound theoretical and empirical support, and the NSP approach was strongly inspired by it. Concerning however a broader implementation of its idea, and their further development in classroom practice, there are

some difficulties put forward in particular by both educational researchers and teachers interested in classroom innovation. A first Amylase difficulty with multimedia anchors is the considerable amount of time (and money) necessary for their development, usually far beyond the budgets available in schools. 4 Moreover, in most cases the necessary technological know-how cannot be assumed to be already present, which for broad classroom implementation requires even more unrealistic expenses for training. Two more difficulties teachers are particularly worried about, is the small flexibility of multimedia anchors with respect to curricular and instructional features, and the large extent to which a change of the teaching script is required by AI. A given classroom situation is defined by topics to be covered, length, complexity (and other features) appropriate for the particular class being taught and the like, all of which cannot be easily changed or adapted in videodiscs or other multimedia software (or only at the expense of the large investment of resources as already mentioned). Moreover, the very far-reaching change of the teaching script required by AI is very often not feasible (or desirable) for a given teacher in a given teaching situation.