This paper aims to better understand the development of students’ metacognitive learning processes when participating actively in a CSCL system called KnowCat. To this end, a longitudinal case study was designed, in which 18 university students took part in a 12-month (two semesters) learning project. The students followed an instructional process, using specific features of the KnowCat design to support and improve their interaction processes, especially peer-learning processes. Our research involved both supervising the students’ collaborative learning processes throughout the learning project and focusing our analysis on the qualitative evolution of their interaction processes and of their metacognitive learning processes. The results of the current research suggest that the pedagogical use of the KnowCat system may favour and improve the development of the students’ metacognitive learning processes. In addition, the implications of the design of CSCL networks and related pedagogical issues are discussed.

The Social Network Analysis (SNA) techniques allow modelling and analysing the interaction among individuals based on their attributes and relationships. This approach has been used by several researchers in order to measure the social processes in collaborative learning experiences. But oftentimes such measures were calculated at the final state of experiences, what may be hardly representative of students' behaviours during the learning processes. Therefore, a temporal dimension in SNA metrics may extend and improve the understanding about students' interactions in a collaborative scenario. In this respect, this paper presents a systematic review about SNA metrics used for analysing CSCL scenarios and proposes to trace the behaviour of such metrics during experiences through the inclusion of a temporal dimension. In order to expose this approach, a real collaborative learning experience, supported by a platform called SMLearning System, was analysed. We found that social relationships among students tend to be symmetric, i.e., there was a proportional distribution of efforts and contributions of students, which is an expected condition in a collaborative scenario. Such observations are based on the temporal behaviour of the reciprocity metric and the correlation between in- and out- degree centrality metrics measured in time.

This paper studies how the integration of group awareness tools in the knowledge management system called KnowCat (Knowledge Catalyser), which promotes collaborative knowledge construction, may both foster the students' perception about the meaningfulness of visualization of group awareness information and promote better collaborative processes as well as enhance better task performance. Forty‐seven university students participated in a research study, where one group of 23 students used KnowCat without the awareness console (non‐awareness group); the other 24 students used KnowCat with the awareness console (awareness group). Both groups used KnowCat during one semester. Data analysis revealed that the awareness group means were higher than those of the non‐awareness group in terms of participation, cognitive and metacognitive learning activities, and task performance. Moreover, students revealed that knowing what, where and how much their classmates were contributing acted as positive feedback by encouraging participation and orienting their own behaviour and their contribution to the collaborative work. In this paper, we claim that the visualization group awareness information in KnowCat, a utility allowing students to visualize and track what, where, how much and how often other participants contributed to the KnowCat knowledge area, had a positive impact on the students' collaborative behaviour.

Meta-modelling is one of the pillars of model-driven engineering (MDE), where it is used for language engineering and domain modelling. Even though the current trend is the use of two-level meta-modelling frameworks, several researchers have pointed out limitations of this scheme for some scenarios and suggested a meta-modelling approach with an arbitrary number of meta-levels in order to obtain more flexible and simpler system descriptions. Unfortunately, such multi-level meta-modelling systems are still in their infancy, lacking, for example, integration with model manipulation languages, a characterization of different possibilities for instantiation and inheritance and primitives for interconnecting multi-level languages in a flexible way. In the paper, we propose a number of extensions to multi-level (also called deep) meta-modelling, on the basis of the needs raised by its use for practical MDE. In particular, we discuss on the issues related to code generation from deep languages, the benefits of allowing inheritance at every meta-level and patterns and techniques for a fine-grain control of the meta-level of elements. Finally, we provide primitives to control the impedance mismatch when connecting models at different meta-levels.