Several challenges are associated with e-learning systems, the most significant of which is the lack of student motivation in various course activities and for various course materials. In this study, we used machine learning (ML) algorithms to identify low-engagement students in a social science course at the Open University (OU) to assess the effect of engagement on student performance. The input variables of the study included highest education level, final results, score on the assessment, and the number of clicks on virtual learning environment (VLE) activities, which included dataplus, forumng, glossary, oucollaborate, oucontent, resources, subpages, homepage, and URL during the first course assessment. The output variable was the student level of engagement in the various activities. To predict low-engagement students, we applied several ML algorithms to the dataset. Using these algorithms, trained models were first obtained; then, the accuracy and kappa values of the models were compared. The results demonstrated that the J48, decision tree, JRIP, and gradient-boosted classifiers exhibited better performance in terms of the accuracy, kappa value, and recall compared to the other tested models. Based on these findings, we developed a dashboard to facilitate instructor at the OU. These models can easily be incorporated into VLE systems to help instructors evaluate student engagement during VLE courses with regard to different activities and materials and to provide additional interventions for students in advance of their final exam. Furthermore, this study examined the relationship between student engagement and the course assessment score.

Parkinson’s disease (PD) is a movement disorder due to the loss of dopaminergic (DA) neurons in the substantia nigra. Alpha-synuclein phosphorylation and α-synuclein inclusion (Lewy body) become a main contributor, but little is known about their formation mechanism. Here we used protein expression profiling of PD to construct a model of their signalling network from drsophila to human and nominate major nodes that regulate PD development. We found in this network that LK6, a serine/threonine protein kinase, plays a key role in promoting α-synuclein Ser129 phosphorylation by identification of LK6 knockout and overexpression. In vivo test was further confirmed that LK6 indeed enhances α-synuclein phosphorylation, accelerates the death of dopaminergic neurons, reduces the climbing ability and shortens the the life span of drosophila. Further, MAP kinase-interacting kinase 2a (Mnk2a), a human homolog of LK6, also been shown to make α-synuclein phosphorylation and leads to α-synuclein inclusion formation. On the mechanism, the phosphorylation mediated by LK6 and Mnk2a is controlled through ERK signal pathway by phorbolmyristate acetate (PMA) avtivation and PD98059 inhibition. Our findings establish pivotal role of Lk6 and Mnk2a in unprecedented signalling networks, may lead to new therapies preventing α-synuclein inclusion formation and neurodegeneration.

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