
## Background 4

We go through the main material we use in this work pointing to its relevance with respect to our goals, especially looking for requirements concerning e-Learning.  Our intention is that this report could serve as a source for further research. In the following, we focus on programming-in-the-small for learning purposes, additional forms of expressing programs
( [39 ]) are briefly mentioned but really not considered here.

## 4.1 Cs1 View Under Acm/Ieee

We take a very brief look at the ACM/IEEE Computing Curricula 2001 (CC2001 for short;
[1]). It serves here simply as a particular sample model for realizing what a CS1 course might thematically cover and we employ it as a well-known and widely referred standard where some relevant aspects deserve special attention. Given the scope of this work we just account for two of them in order to identify potential research opportunities for e-Learning.

## Multiple Profiles And Expected Performance 4.1.1

We first recall that CC2001 considers 5 disciplines related to computing, namely Computer Science (CS), Computer Engineering (CE), Information Systems (IS), Information Technology (IT) and Software Engineering (SE).

Such a division turns out relevant in our context because CC2001 conceives different profiles with respect to expected performance on programming capabilities, even though C2001 assumes a common core for the first year and values programming as an essential s kill.

To see that, let us take a look at Table 1 which is an small excerpt from Table 3.3 of [1] just showing the portion named 'Algorithms'. We notice that columns CS and IS
are mostly opposed to each other, according to this model.  Moreover, a uniform and interesting difference between CS and SE is clearly expressed, too.  Although these are models of expectation on competences from a curricular point of view, they actually may coincide with students expectations and professional aspirations.

In fact, due to organizational aspects in some CS Departments, it happens that students from different computing disciplines attend the same course and thus get exposed to the s ame programme at the first-year level 3 . It is interesting to remember that known studies like [35] showed a bimodal 4 distribution of the student performance in programming evidencing the lack of normality on assimilation. Thus, first year course implementations may nno to cope with a similar situation, which means to pay careful attention to assessment and grading systems, and consequently to exercise models, as a consequence (as done in
[33, 32] cognitively sustained on Bloom's Taxonomy).

We identify another opportunity to employ e-Learning techniques to help students remain more uniform as a group. Using appropriate exercises in those areas where cognitive 3 In our personal experience, SE, IS and CS. In student proportions that agree with data [ 26 ], in our