Vitamin D deficiency was highly prevalent in Saudi medical students. Modifiable factors such as vitamin D intake and PA could be targeted for intervention. Further studies with standardized laboratory measurements of 25(OH)D are needed to explore the role of vitamin D testing in behavioral change, which may lead to increased serum 25(OH)D levels.

The classification of learning objects (LOs) enables users to search for, access, and reuse them as needed. It makes e-learning as effective and efficient as possible. In this article the multilabel learning approach is represented for classifying and ranking multi-labelled LOs, whereas each LO might be associated with multiple labels as opposed to a single-label approach. A comprehensive overview of the common fundamental multi-label classification algorithms and metrics will be discussed. In this article, a new multi-labelled LOs dataset will be created and extracted from ARIADNE Learning Object Repository. We experimentally train four effective multi-label classifiers on the created LOs dataset and then, assess their performance based on the results of 16 evaluation metrics. The result of this article will answer the question of: what is the best multi-label classification algorithm for classifying multi-labelled LOs?

Saudi Arabia is in a tropical geographical region with a population that has access to adequate diet. There is, however, a high level of vitamin D deficiency in the Kingdom, comorbid with other disease. There is the postulation of a correlation between a healthy gut microbiota and balanced levels of serum vitamin D. This investigation looks into the effect of vitamin D supplementation on the gut flora of laboratory-bred mice as well as any possible association on body weight. BALB/C mice weighing between 34 and 35.8 g were divided into 4 groups and placed on daily doses of vitamin D of 3.75 µg (low dose), 7.5 µg (normal dose), and 15 µg (high dose). The fourth group was the control group that did not receive any supplementation with vitamin D. Body weights were monitored on weekly basis, while faecal samples from the rectum were obtained for microbial culturing and the monitoring of bacterial colony count using the Vitek 2 Compact automated system (BioMerieux, Marcy-l’Etoile, France) according to manufacturer’s guidelines. The data presented as mean ± SD, while significant differences were determined with 2-way analysis of variance in comparing differences within and between treatment groups. The different doses of vitamin D showed varying effects on the body weight and gut microbial colonies of the mice. There was a highly significant difference between the control, 15 µg (high), and 7.5 µg (normal) dose groups. This is suggestive that supplementation with vitamin D could a role in the gut microbial flora in the gut which could reflect in changes in body weight.

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With the increasing number of learning objects (LOs), the possibility of their fast and effective retrieving and storing has become a more critical issue. The classification of LOs enables users to search for, access, and reuse them in an effective and efficient way. In this article, the multi‐label learning approach is represented for classifying and ranking multi‐labeled LOs, whereas each LO might be associated with multiple labels as opposed to a single‐label approach. A comprehensive overview of the common fundamental multi‐label classification algorithms and metrics will be discussed. In this article, a new multi‐labeled LOs dataset will be created and extracted from ARIADNE Learning Object Repository. We experimentally train four effective multi‐label classifiers on the created LOs dataset and then, assess their performance based on the results of 16 evaluation metrics. The result of this article will answer the question of; what is the best multi‐label classification algorithm for classifying multi‐labeled LOs? © 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 24:651–660, 2016; View this article online at http://www.wileyonlinelibrary.com/journal/cae; DOI