Remote practical activities have been demonstrated to be efficient when learners come to acquire inquiry skills. In computer science education, virtualization technologies are gaining popularity as this technological advance enables instructors to implement realistic practical learning activities, and learners to engage in authentic and problem-based learning. However, virtualization solutions have not been designed especially for education and do not address any pedagogical concern. Since several large-scale studies showed that instructional supports during practical activities are almost as important as technical features, this article investigates the following research question: how the scaffolding around the lab increases students' engagement in remote practical learning of computer science? To answer this question, we introduce the Lab4CE environment, a remote laboratory for computer education which adopts a distributed, modular and flexible architecture to integrate a set of scaffolding tools and services intended for instructors and learners. An exploratory study conducted with 139 undergraduate students enrolled in the first year of a computer science degree suggests a positive effect of the framework on learners' engagement when they come to practice system administration, and reveals a significant positive correlation between students' activity within the system and students' learning achievement.

The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

This study analyzes students' behaviors in a remote laboratory environment in order to identify new factors of prediction of academic success. It investigates relations between learners' activities during practical sessions, and their performance at the final assessment test. Based on learning analytics applied to data collected from an experimentation conducted with our remote lab dedicated to computer education, we discover recurrent sequential patterns of actions that lead us to the definition of learning strategies as indicators of higher level of abstraction. Results show that some of the strategies are correlated to learners' performance. For instance, the construction of a complex action step by step, or the reflection before submitting an action, are two strategies applied more often by learners of a higher level of performance than by other students. While our proposals are domain-independent and can thus apply to other learning contexts, the results of this study led us to instrument for both students and instructors new visualization and guiding tools in our remote lab environment.

This paper addresses learning analytics for evaluation of learners performance in remote laboratories. The objectives we identified to provide self-and social awareness while learners are practicing in their virtual learning environment are threefold: (1) the definition of a performance metric that requires no assessment tests, (2) the tracking of data to infer that metric in real time, and (3) the visualization of the performance metric without impacting learners' cognitive load. To support these needs, we propose (i) a metric related to the technical rightness of instructions carried out by learners, (ii) a generic learning analytics framework featuring an enriching engine able to infer indicators, and (iii) two distinct visualization tools. These proposals have been implemented in Lab4CE, our remote laboratory for computer education, and experimented in an authentic learning context. This experimentation showed that most students have significantly used both visualization tools, and that their usage decreased while the overall learners performance increased.

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While learning management systems have spread for the last decades, many teachers still struggle to fully operate a LMS within their teaching, beyond its role of a simple resources repository. Teachers need to engage themselves as learners of their own environment, to improve their techno-pedagogical skills. To this end, we propose a behavioral model based on teaching analytics to provide teachers with self and social awareness of their own practices on the LMS. The present article focuses on the model we designed on the basis of (i) a qualitative analysis from interviews we had with several instructional designers and (ii) a quantitative analysis we conducted on teachers' LMS activities. We used this model to define three teaching analytics indicators and build a peer recommendation system in order to encourage teachers to improve their skills in the LMS.

In this paper we design a set of awareness and reflection tools aiming at engaging learners in the deep learning process during a practical activity carried out through a virtual and remote laboratory. These tools include: (i) a social awareness tool revealing to learners their current and general levels of performance, but also enabling the comparison between their own and their peers' performance; (ii) a reflection-onaction tool, implemented as timelines, allowing learners to deeply analyze both their own completed work and the tasks achieved by peers; (iii) a reflection-inaction tool acting as a live video player to let users easily see what others are doing. An experimentation involving 80 students was conducted in an authentic learning setting about operating system administration; the participants evaluated the system only slightly higher than traditional computational environments when it comes to leverage reflection and critical thinking, even if they evaluated the system as good in terms of usability.