No abstract

In 2015, 35 million learners participated online in 4,200 MOOCs organised by over 500 universities. Learning designers orchestrate MOOC content to engage learners at scale and retain interest by carefully mixing videos, lectures, readings, quizzes, and discussions. Universally, far fewer people actually participate in MOOCs than originally sign up with a steady attrition as courses progress. Studies have correlated social engagement to completion rates. The FutureLearn MOOC platform specifically provides opportunities to share opinions and to reflect by posting comments, replying, or following discussion threads. This paper investigates learners' social behaviours in MOOCs and the impact of engagement on course completion. A preliminary study suggested that dropout rates will be lower when learners engage in repeated and frequent social interactions. We subsequently reviewed the literature of prediction models and applied social network analysis techniques to characterise participants' online interactions examining implications for participant achievements. We analysed discussions in an eight week FutureLearn MOOC, with 9855 enrolled learners. Findings indicate that if learners starts following some , the probability of their finishing the course is increased; if learners also interact with those they follow, they are highly likely to complete, both important factors to add to the prediction of completion model.

No abstract

1. To investigate the distribution profile of functional inhibitory non-adrenergic noncholinergic (i-NANC) nerves and the contribution of NO to the NANC relaxation in the cat, we studied the effects of NW-nitro-L-arginine methyl ester (L-NAME) on NANC relaxation elicited by electrical field stimulation (EFS) in the trachea, bronchus and bronchiole. 2. EFS applied to the tracheal smooth muscle during contraction induced by 5-HT (10-5 M) in the presence of atropine (10-6 M) and guanethidine (10-6 M) elicited a monophasic NANC relaxation. By contrast, NANC relaxation elicited in the peripheral airway was biphasic, comprising an initial fast followed by a second slow component and L-NAME (10-5 M) selectively abolished the first component without affecting the second one. In the trachea, L-NAME (10-5 M) completely suppressed the monophasic NANC relaxation when single or short repetitive stimuli (< 5) with 1 ms pulse duration were applied. However, at higher repetitive stimuli (> 10) with 1 or 4 ms pulse duration, suppression of NANC relaxation was incomplete. 3. In the small bronchi obtained from L-NAME-pretreated cats, EFS applied during contraction induced by 5-HT (10-5 M) elicited only the slow component of NANC relaxation which is sensitive to tetrodotoxin. 4. In the peripheral airway, a newly synthesized VIP antagonist (10-6 M) or o-chymotrypsin (1 U ml-') considerably attenuated the amplitude of L-NAME -insensitive relaxation.5. Single or repetitive EFS consistently evoked excitatory junction potentials (EJPs) in the central and peripheral airways. When tissues were exposed to atropine (10-6 M) and guanethidine (10-6 M), single or repetitive EFS did not alter the resting membrane potential. 6. These results indicate that at least two neurotransmitters, possibly NO or NO-containing compounds and VIP, are involved in i-NANC neurotransmission and the distribution profile of the two components differs in the central and peripheral airway of the cat.The tracheobronchial smooth muscle is innervated by nerve fibres from cranial parasympathetic outflow and sympathetic trunks (Smith & Taylor, 1971). Recent studies revealed that the cranial parasympathetic nervous system in the airway contains non-adrenergic non-cholinergic (NANC) inhibitory nerves in addition to the well-documented cholinergic excitatory nerve fibres. Furthermore, activation of C-fibre afferent (sensory) nerves induces a number of airway responses including smooth muscle contraction, mucus secretion and plasma exudation through NANC excitatory transmitter (Barnes, 1991

The enormity of the amount of learning materials in e-learning has led to the difficulty of locating suitable learning materials for a particular learning topic, creating the need for recommendation tools within a learning context. In this paper, we aim to address this need by proposing a novel e-learning recommender system framework that is based on two conceptual foundations-peer learning and social learning theories that encourage students to cooperate and learn among themselves. Our proposed framework works on the idea of recommending learning materials with a similar content and indicating the quality of learning materials based on good learners' ratings. A comprehensive set of experiments were conducted to measure the system accuracy and its impact on learner's performance. The obtained results show that the proposed e-learning recommender system has a significant improvement in the post-test of about 12.16% with the effect size of 0.6 and 13.11% with the effect size of 0.53 when compared to the e-learning with a content-based recommender system and the e-learning without a recommender system, respectively. Furthermore, the proposed recommender system performed better in terms of having a small rating deviation and a higher precision as compared to e-learning with a contentbased recommender system.