Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn courses

Whilst a high dropout rate is a well-known problem in MOOCs, few studies take a data-driven approach to understand the reasons of such a phenomenon, and to thus be in the position to recommend and design possible adaptive solutions to alleviate it. In this study, we are particularly interested in finding a novel early detection mechanism of potential dropout, and thus be able to intervene at an as early time as possible. Additionally, unlike previous studies, we explore a light-weight approach, based on as little data as possible – since different MOOCs store different data on their users – and thus strive to create a truly generalisable method. Therefore, we focus here specifically on the generally available registration date and its relation to the course start date, via a comprehensive, larger than average, longitudinal study of several runs of all MOOC courses at the University of Warwick between 2014-1017, on the less explored European FutureLearn platform. We identify specific periods where different interventions are necessary, and propose, based on statistically significant results, specific pseudo-rules for adaptive feedback

Induction of the nicotinamide riboside kinase NAD⁺ salvage pathway in a model of sarcoplasmic reticulum dysfunction

Background Hexose-6-Phosphate Dehydrogenase (H6PD) is a generator of NADPH in the Endoplasmic/Sarcoplasmic Reticulum (ER/SR). Interaction of H6PD with 11 beta-hydroxysteroid dehydrogenase type 1 provides NADPH to support oxo-reduction of inactive to active glucocorticoids, but the wider understanding of H6PD in ER/SR NAD(P)(H) homeostasis is incomplete. Lack of H6PD results in a deteriorating skeletal myopathy, altered glucose homeostasis, ER stress and activation of the unfolded protein response. Here we further assess muscle responses to H6PD deficiency to delineate pathways that may underpin myopathy and link SR redox status to muscle wide metabolic adaptation. Methods We analysed skeletal muscle from H6PD knockout (H6PDKO), H6PD and NRK2 double knockout (DKO) and wild-type (WT) mice. H6PDKO mice were supplemented with the NAD(+) precursor nicotinamide riboside. Skeletal muscle samples were subjected to biochemical analysis including NAD(H) measurement, LC-MS based metabolomics, Western blotting, and high resolution mitochondrial respirometry. Genetic and supplement models were assessed for degree of myopathy compared to H6PDKO. Results H6PDKO skeletal muscle showed adaptations in the routes regulating nicotinamide and NAD(+) biosynthesis, with significant activation of the Nicotinamide Riboside Kinase 2 (NRK2) pathway. Associated with changes in NAD(+) biosynthesis, H6PDKO muscle had impaired mitochondrial respiratory capacity with altered mitochondrial acylcarnitine and acetyl-CoA metabolism. Boosting NAD(+) levels through the NRK2 pathway using the precursor nicotinamide riboside elevated NAD(+)/NADH but had no effect to mitigate ER stress and dysfunctional mitochondrial respiratory capacity or acetyl-CoA metabolism. Similarly, H6PDKO/NRK2 double KO mice did not display an exaggerated timing or severity of myopathy or overt change in mitochondrial metabolism despite depression of NAD(+) availability. Conclusions These findings suggest a complex metabolic response to changes in muscle SR NADP(H) redox status that result in impaired mitochondrial energy metabolism and activation of cellular NAD(+) salvage pathways. It is possible that SR can sense and signal perturbation in NAD(P)(H) that cannot be rectified in the absence of H6PD. Whether NRK2 pathway activation is a direct response to changes in SR NAD(P)(H) availability or adaptation to deficits in metabolic energy availability remains to be resolved

Forum-based Prediction of Certification in Massive Open Online Courses

Massive Open Online Courses (MOOCs) have been suffering a very level of low course certification (less than 1% of the total number of enrolled students on a given online course opt to purchase its certificate), although MOOC platforms have been offering low-cost knowledge for both learners and content providers. While MOOCs forums generated textual data (forums) have been utilized for the purpose of addressing many MOOCs key challenges like the high rate of dropout and tutor timely intervention, analysing learners’ textual interaction for the purpose of predicting certification, remains limited. Thus, this paper investigates if MOOC learner’s comments can predict their purchasing decision (certification) using a relatively large dataset of 5 MOOCs of 23 runs. Our model achieved promising accuracies, ranging between 0.71 and 0.96 across the five courses. The outcomes of this study are expected to help design future courses and predict the profitability of future runs

MOOCSent: a Sentiment Predictor for Massive Open Online Courses

One key type of Massive Open Online Course (MOOC) data is the learners’ social interaction (forum). While several studies have analysed MOOC forums to predict learning outcomes, analysing learners’ sentiments in education and, specifically, in MOOCs, remains limited. Moreover, most studies focus on one platform only. Here, we propose a cross-platform MOOCs sentiment classifier using almost 1.5 million human-annotated learners’ comments obtained from 633 MOOCs delivered via the Stanford University platform and Coursera -the largest dataset collected for sentiment analysis (SA). We explore not only various state-of-the-art SA tools, but also their confidence level distributions and evaluate their performance. Our results show that the Lexicon and Rulebased (LRB) and Convolutional Neural Network (CNN)-based sentiment tools, trained mainly on social media platforms, may not be suitable for the educational domain. We further introduce MOOCSent1, a BERT-based model for predicting MOOC learners’ sentiments from their comments, which almost doubles the accuracy of the classification results, outperforming the state-of-the-art with a 95% accuracy

Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses

Whilst a high dropout rate is a well-known problem in MOOCs, few studies take a data-driven approach to understand the reasons of such a phenomenon, and to thus be in the position to recommend and design possible adaptive solutions to alleviate it. In this study, we are particularly interested in finding a novel early detection mechanism of potential dropout, and thus be able to intervene at an as early time as possible. Additionally, unlike previous studies, we explore a light-weight approach, based on as little data as possible – since different MOOCs store different data on their users – and thus strive to create a truly generalisable method. Therefore, we focus here specifically on the generally available registration date and its relation to the course start date, via a comprehensive, larger than average, longitudinal study of several runs of all MOOC courses at the University of Warwick between 2014-1017, on the less explored European FutureLearn platform. We identify specific periods where different interventions are necessary, and propose, based on statistically significant results, specific pseudo-rules for adaptive feedback