Authors:
Amal Ben Soussia
;
Chahrazed Labba
;
Azim Roussanaly
and
Anne Boyer
Affiliation:
Université de Lorraine, LORIA, France
Keyword(s):
Earliness, Stability, Indicators, Learning Analytics, Machine Learning, k-12 Learners.
Abstract:
The high failure rate is a major concern in distance online education. In recent years, Performance Prediction Systems (PPS) based on different analytical methods have been proposed to predict at-risk of failure learners. One of the main studied characteristics of these systems is its ability to provide accurate early predictions. However, these systems are usually assessed using a set of evaluation measures (e.g. accuracy, precision) that do not reflect the precocity, continuity and evolution of the predictions over time. In this paper, we propose to enrich the existing indicators with time-dependent ones including earliness and stability. Further, we use the Harmonic Mean to illustrate the trade-off between the predictions earliness and the accuracy. In order to validate the relevance of our indicators, we used them to compare four different PPS for predicting at-risk of failure learners. These systems are applied on real data of K-12 learners enrolled in an online physics-chemistr
y module.
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