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based domain. According to a well known clue of the framework, distinguish is an action whose closest related cognitive process is Analyze.

By means of this association, our original sentence can be expressed in the framework's vocabulary as Analyze × ConceptualKnowledge. In terms of complexity, we might infer that for accomplishing such an objective more than just memorizing a plain list of facts is involved: because knowledge contemplates several concepts and their relationships. In addition, analytical thinking skills are also required which are more complex than remembering. Hence, a particular technique to help learning of that kind of knowledge and skill should be used, consequently.

We have used freely a simple symbolic notation in this example just to suggest that a formalization of the procedure is, at least in principle, quite possible and technically interesting, especially when we are trying to find requirements for a tool. So, we bring to attention that the semantic based inference procedure could be automated to some extent to support the methodology. But naturally the essential question remains:
How can CS1 exercise concepts be adequately aligned with to Bloom's Taxonomy of verbs, nouns and action rules? What are the right concepts?

Such a question, and posed this way, is too abstract, indeed. Hence, we want to consider and propose some ideas that we hope, serves to refine it and decompose it into more concrete questions and related issues as well as to identify paths to cope with them.

We summarize the general principles fundamenting Bloom's Taxonomy, expressed according to our particular understanding and objectives:

1. Dimensions of learning objectives are knowledge and processes. They are domainindependent .

2. Those dimensions can be (linearly) arranged according to categories representing increasingly levels of cognitive complexity 3. In order to reach one particular category, the inferior category must have been reached first 4. Domain-specific cognitive dimensions can always be semantically related with domainindependent cognitive dimensions. Thus, domain-specific learning objectives satisfy the same principles of cognitive complexity.