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The purpose of this paper is to present the METAL project, a French open learning analytics (LA) project for secondary school, that aims at improving the quality of teaching. The originality of METAL is that it relies on research through exploratory activities and focuses on all the aspects of a learning analytics environment.

This work introduces the different concerns of the project: collection and storage of multi-source data owned by a variety of stakeholders, selection and promotion of standards, design of an open-source LRS, conception of dashboards with their final users, trust, usability, design of explainable multi-source data-mining algorithms.

All the dimensions of METAL are presented, as well as the way they are approached: data sources, data storage, through the implementation of an LRS, design of dashboards for secondary school, based on co-design sessions data mining algorithms and experiments, in line with privacy and ethics concerns.

The issue of a global dissemination of LA at an institution level or at a broader level such as a territory or a study level is still a hot topic in the literature, and is one of the focus and originality of this paper, associated with the large spectrum of different concerns.

The goal is to assess performance prediction systems (PPS) that are used to assist at-risk learners.

The authors propose time-dependent metrics including earliness and stability. The authors investigate the relationships between the various temporal metrics and the precision metrics in order to identify the key earliness points in the prediction process. Authors propose an algorithm for computing earliness. Furthermore, the authors propose using an earliness-stability score (ESS) to investigate the relationship between the earliness of a classifier and its stability. The ESS is used to examine the trade-off between only time-dependent metrics. The aim is to compare its use to the earliness-accuracy score (EAS).

Stability and accuracy are proportional when the system's accuracy increases or decreases over time. However, when the accuracy stagnates or varies slightly, the system's stability is decreasing rather than stagnating. As a result, the use of ESS and EAS is complementary and allows for a better definition of the point of earliness in time by studying the relation-ship between earliness and accuracy on the one hand and earliness and stability on the other.

When evaluating the performance of PPS, the temporal dimension is an important factor that is overlooked by traditional measures current metrics are not well suited to assessing PPS’s ability to predict correctly at the earliest, as well as monitoring predictions stability and evolution over time. Thus, in this work, the authors propose time-dependent metrics, including earliness, stability and the trade-offs, with objective to assess PPS over time.