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In order to understand web-based application user behavior, web usage mining applies unsupervised learning techniques to discover hidden patterns from web data that captures user browsing on web sites. For this purpose, web session clustering has been among the most popular approaches to group users with similar browsing patterns that reflect their common interest. An adequate web session clustering implementation significantly depends on the measure that is used to evaluate the similarity of sessions. An efficient approach to evaluate session similarity is sequence alignment, which is known as the task of determining the similarity of elements between sequences. In this paper, we review and compare sequence alignment-based measures for web sessions, and also discuss sequence similarity measures that are not alignment-based. This review also provides a perspective of sequence similarity measures that manipulate web sessions in usage clustering process.

Engaging students in peer assessment is an innovative assessment process which has a positive impact on students learning experience. However, the adoption of peer assessment can be slow and uncomfortably experienced by students. Moreover, peer assessment can be prone to several biases. In this paper, we argue that the analysis of peer assessment interactions and phenomena can benefit from the social network analysis domain. We applied a graphlet-based method to a dataset collected during in-class courses integrating a peer assessment platform. This allowed for the interpretation of networking structures shaping the peer assessment interactions, leading for the description of consequent peer assessment roles and their temporal dynamics. Results showed that students develop a positive tendency towards adopting the peer assessment process, and engage gradually with well-balanced roles, even though, initially they choose mostly to be assessed by teachers and more likely by peers they know. This study contributes to research insights into peer assessment learning analytics, and motivates future work to scaffold peer learning in similar contexts.

In our attempt to support Object-Oriented Programming (OOP) learning for beginners, we designed a novel microworld called PrOgO. It is based on a three-dimensional (3D) constructive game metaphor for describing OOP basic concepts and their implementation. In this paper, we describe a study about the use of PrOgO by beginners to investigate their behaviours when interacting with the programming microworld. The study is based on the collection, analysis and reporting of data about players (playing analytics). The data analysis allows the identification and the characterisation of different behaviours. From an educational perspective, the expected behaviour has been confirmed for a limited number of students. This enabled us to conclude that the design of the game needs to be improved. In addition, behaviours triggered by most students might have other educational values, which could be confirmed by other similar studies.

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