Constraints on the general solutions of Einstein cosmological equations by Hubble parameter times cosmic age: a historical perspective

A version of this paper is in Arxiv.org: arXiv:1306.0238In a historical perspective, compact solutions of Einstein’s equations, including the cosmological constant and the curvature terms, are obtained, starting from two recent observational estimates of the Hubble’s parameter (H0) and the “age” of the universe (t0). Cosmological implications for ΛCDM (Λ Cold Dark Matter), KOFL (k Open FriedmanLemaitre), plus two mixed solutions are investigated, under the constraints imposed by the relatively narrow current uncertainties. Quantitative results obtained for the KOFL case seem to be compatible with matter density and the highest observed red-shifts from distant galaxies, while those obtained for the ΛCDM may be more difficult to reconcile

Is the Multiverse Hypothesis capable of explaining the Fine Tuning of Nature Laws and Constants? The Case of Cellular Automata

The objective of this paper is analyzing to which extent the multiverse hypothesis provides a real explanation of the peculiarities of the laws and constants in our universe. First we argue in favor of the thesis that all multiverses except Tegmark's > are too small to explain the fine tuning, so that they merely shift the problem up one level. But the > is surely too large. To prove this assessment, we have performed a number of experiments with cellular automata of complex behavior, which can be considered as universes in the mathematical multiverse. The analogy between what happens in some automata (in particular Conway's >) and the real world is very strong. But if the results of our experiments can be extrapolated to our universe, we should expect to inhabit -- in the context of the multiverse -- a world in which at least some of the laws and constants of nature should show a certain time dependence. Actually, the probability of our existence in a world such as ours would be mathematically equal to zero. In consequence, the results presented in this paper can be considered as an inkling that the hypothesis of the multiverse, whatever its type, does not offer an adequate explanation for the peculiarities of the physical laws in our world. A slightly reduced version of this paper has been published in the Journal for General Philosophy of Science, Springer, March 2013, DOI: 10.1007/s10838-013-9215-7.Comment: 30 pages, 16 figures, 5 tables. Slightly reduced version published in Journal for General Philosophy of Scienc

A novel algorithm for dynamic student profile adaptation based on learning styles

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.E-learning recommendation systems are used to enhance student performance and knowledge by providing tailor- made services based on the students’ preferences and learning styles, which are typically stored in student profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the students’ changing behaviour. In this paper, we introduce new algorithms that are designed to track student learning behaviour patterns, capture their learning styles, and maintain dynamic student profiles within a recommendation system (RS). This paper also proposes a new method to extract features that characterise student behaviour to identify students’ learning styles with respect to the Felder-Silverman learning style model (FSLSM). In order to test the efficiency of the proposed algorithm, we present a series of experiments that use a dataset of real students to demonstrate how our proposed algorithm can effectively model a dynamic student profile and adapt to different student learning behaviour. The results revealed that the students could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method

ULEARN: Personalised Learner’s Profile Based On Dynamic Learning Style Questionnaire

The file attached to this record is the author's final peer reviewed version.E-Learning recommender system effectiveness re- lies upon their ability to recommend appropriate learning con- tents according to the learner learning style and preferences. An effective approach to handle the learner preferences is to build an efficient learner profile in order to gain adaptation and individualisation of the learning environment. It is usually necessary to know learning style and preferences of the learner on a domain before adapting the learning process and course content. This study focuses on identifying the learning styles of students in order to adapt the learning process and course content. ULEARN is an adaptive recommender learning system designed to provide learners with personalised learning environment such as course learning objects that match their adaptive profile. This paper presents the algorithm used in ULEARN to reduce dynamically the number of questions in Felder-Silverman learning style ques- tionnaire used to initialise the adaptive learner profile. Firstly, the questionnaire is restructured into four groups, one for each learning style dimension; and a study is carried out to determine the order in which questions will be asked in each dimension. Then an algorithm is built upon this ranking of questions to calculate dynamically the initial learning style of the user as they go through the questionnaire