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Moodle is a widely deployed distance learning platform that provides numerous opportunities to enhance the learning process. Moodle’s importance in maintaining the continuity of education in states of emergency and other circumstances has been particularly demonstrated in the context of the COVID-19 virus’ rapid spread. However, there is a problem with personalizing the learning and monitoring of students’ work. There is room for upgrading the system by applying data mining and different machine-learning methods. The multi-agent Observer system proposed in our paper supports students engaged in learning by monitoring their work and making suggestions based on the prediction of their final course success, using indicators of engagement and machine-learning algorithms. A novelty is that Observer collects data independently of the Moodle database, autonomously creates a training set, and learns from gathered data. Since the data are anonymized, researchers and lecturers can freely use them for purposes broader than that specified for Observer. The paper shows how the methodology, technologies, and techniques used in Observer provide an autonomous system of personalized assistance for students within Moodle platforms.

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In wireless communication systems received signal may suffer from the both multipath fading (fading) and shadowing. In this paper, ascertaining on the suitability of the Weibull distribution to model fading channel and by utilizing the fact that the gamma distribution is more convenient for manipulation than the lognormal distribution, shadowed fading channel is modeled as the Weibull-gamma. The closed-form expressions for the probability density function (PDF) and cumulative distribution function (CDF) of the received signal are derived in terms of Bessel's and Meijer's G-functions, respectively. The synergy between the Weibull-gamma and the Weibull-lognormal model is explored and established by calculating the outage probability (P out ) and average bit error probability (ABEP). This is followed by the analysis carried out for the both fixed and random number of gamma shadowed Nakagami-m cochannel interferences (CCIs) in interference-plus-noise and interference-limited environment. The proposed mathematical analysis is complimented by various numerical results.