Social networks and cooperative learning in a MOOC

MOOCs are changing the educational landscape and gaining a lot of attention in scientific literature. However, the pedagogical design of these proposals has been called into question. It is precisely MOOCs’ social aspect, i.e. the interaction between course participants and the support for learning processes that has become one of the main topics of interest. This article presents the results of a research project carried out at the University of the Basque Country, which focused in cooperative learning and the intensive use of social networks in a MOOC. Significant data was compiled through Likert-type surveys, revealing that the use of both external and internal social networks in a massive open online course is a factor that is evaluated positively by students. We argue that the use of social networks as a learning strategy in a MOOC has an influence on academic performance and on the students' success rate. Furthermore, the participants’ age also has a bearing on the social networks they use, and we have found that the younger members tend to work with external networks such as Twitter or personal blogs, whereas the older students are more inclined to use forums from the Chamilo or Ning platforms.

Comment on "correlated noise in a logistic growth model"

We argue the results published by Bao-Quan Ai et al [Phys. Rev E 67, 022903 (2003)] on "correlated noise in a logistic growth model " are not correct. Their conclusion that for larger values of the correlation parameter, lambda, the cell population is peaked at x=0, which denotes the high extinction rate is also incorrect. We find the reverse behaviour corresponding to their results, that increasing lambda, promotes the stable growth of tumour cells. In particular, their results for steady-state probability, as a function of cell number, at different correlation strengths, presented in figures 1 and 2 show different behaviour than one would expect from the simple mathematical expression for the steady-state probability. Additionally, their interpretation at small values of cell number that the steady state probability increases as they increase the correlation parameter is also questionable. Another striking feature in their figures (1 and 3) is that for the same values of the parameter lambda and alpha, their simulation produces two different curves both qualitatively and quantitatively.

Towards an intelligent score following system: Handling of mistakes and jumps encountered during piano practicing

Score following has been an important area of research in AI and music since the mid 80's. Various systems were developed, but they were predominantly for providing automated accompaniment to live concert performances, dealing mostly with issues relating to pitch detection and identification of embellished melodies. They have a big potential in the area of education where student performers benefit in practice situations. Current accompaniment systems are not designed to deal with errors that may occur during practising. In this paper we present a system developed to provide accompaniment for students practising at home. First a survey of score following will be given. Then the capabilities of the system will be explained, and the results from the first experiments of the monophonic score following system will be presented.

An extended framework for evidential reasoning systems.

Use of the Dempster-Shafer (D-S) theory of evidence to deal with uncertainty in knowledge-based systems has been widely addressed. Several AI implementations have been undertaken based on the D-S theory of evidence or the extended theory. But the representation of uncertain relationships between evidence and hypothesis groups (heuristic knowledge) is still a major problem. This paper presents an approach to representing such knowledge, in which Yen’s probabilistic multi-set mappings have been extended to evidential mappings, and Shafer’s partition technique is used to get the mass function in a complex evidence space. Then, a new graphic method for describing the knowledge is introduced which is an extension of the graphic model by Lowrance et al. Finally, an extended framework for evidential reasoning systems is specified.

A comprehensive comparison between generalized incidence calculus and Dempster-Shafer theory of evidence.

Dealing with uncertainty problems in intelligent systems has attracted a lot of attention in the AI community. Quite a few techniques have been proposed. Among them, the Dempster-Shafer theory of evidence (DS theory) has been widely appreciated. In DS theory, Dempster's combination rule plays a major role. However, it has been pointed out that the application domains of the rule are rather limited and the application of the theory sometimes gives unexpected results. We have previously explored the problem with Dempster's combination rule and proposed an alternative combination mechanism in generalized incidence calculus. In this paper we give a comprehensive comparison between generalized incidence calculus and the Dempster-Shafer theory of evidence. We first prove that these two theories have the same ability in representing evidence and combining DS-independent evidence. We then show that the new approach can deal with some dependent situations while Dempster's combination rule cannot. Various examples in the paper show the ways of using generalized incidence calculus in expert systems.

Strengthening an in-service reinforcement concrete bridge with prestressed CFRP bar

Carbon fiber reinforced polymer (CFRP) bars were prestressed for the structural strengthening of 8 T-shaped reinforced concrete (RC) beams of a 21-year-old bridge in China. The ultimate bearing capacity of the existing bridge after retrofit was discussed on the basis of concrete structures theory. The flexural strengths of RC beams strengthened with CFRP bars were controlled by the failure of concrete in compression and a prestressing method was applied in the retrofit. The field construction processes of strengthening with CFRP bars—including grouting cracks, cutting groove, grouting epoxy and embedding CFRP bars, surface treating, banding with the U-type CFRP sheets, releasing external prestressed steel tendons—were introduced in detail. In order to evaluate the effectiveness of this strengthening method, field tests using vehicles as live load were applied before and after the retrofit. The test results of deflection and concrete strain of the T-shaped beams with and without strengthening show that the capacity of the repaired bridge, including the bending strength and stiffness, is enhanced. The measurements of crack width also indicate that this strengthening method can enhance the durability of bridges. Therefore, the proposed strengthening technology is feasible and effective.

Synthesis of (+)-piclavines A1 and A2

Piclavines AI and A2 have been synthesised for the first time. The route is short with the key step being the reaction of a bicyclic N-acyl iminium ion with 3-trimethysilyl-1-decene. This convergent strategy gave exclusively compounds in which the pendant decenyl group was axial, as a 6:1 mixture of E:Z-alkene diastereoisomers. Reduction of the lactam carbonyl group gave a 6:1 mixture of piclavines Al and A2, (C) 2000 Published by Elsevier Science Ltd.

Shell Model Study of Local and Global Energy Barriers in KDP

We perform a study of the energetics of KH2PO4 (KDP) by using a shell model (SM) which was constructed by adjusting the interaction parameters to ab initio calculations, and was fitted to reproduce phonons, polarization-inversion energies and structural properties. We calculate the energy profiles by performing global displacements and local distortions following the ferroelectric (FE) mode pattern in clusters of different sizes embedded in a paraelectric (PE) phase matrix. These properties are expected to be relevant to the PE-FE phase transition. The obtained SM results are compared to corresponding ab initio (AI) data. The global instabilities are found in good agreement for both KDP and DKDP. We also find qualitative good agreement in the KDP structure and even quantitative agreement in the expanded DKDP structure for the local distortions. The SM results reproduce well different trends like increasing instabilities as the cluster sizes grows, as the heavier atoms are included, and as the volume is increased, in accordance with the corresponding data from AI calculations.

The relationship between agricultural intensification and biological control: experimental tests across Europe

Agricultural intensification can affect biodiversity and related ecosystem services such as biological control, but large-scale experimental evidence is missing. We examined aphid pest populations in cereal fields under experimentally reduced densities of (1) ground-dwelling predators (-G), (2) vegetation-dwelling predators and parasitoids (-V), (3) a combination of (1) and (2) (-G-V),compared with open-fields (control), in contrasting landscapes with low vs. high levels of agricultural intensification (AI), and in five European regions. Aphid populations were 28%, 97%, and 199% higher in -G, -V, and -G -V treatments, respectively, compared to the open fields, indicating synergistic effects of both natural-enemy groups. Enhanced parasitoid : host and predator : prey ratios were related to reduced aphid population density and population growth. The relative importance of parasitoids and vegetation-dwelling predators greatly differed among European regions, and agricultural intensification affected biological control and aphid density only in some regions. This shows a changing role of species group identity in diverse enemy communities and a need to consider region-specific landscape management.

Agricultural intensification and biodiversity partitioning in European landscapes comparing plants, carabids, and birds

Effects of agricultural intensification (AI) on biodiversity are often assessed on the plot scale, although processes determining diversity also operate on larger spatial scales. Here, we analyzed the diversity of vascular plants, carabid beetles, and birds in agricultural landscapes in cereal crop fields at the field (n = 1350), farm (n = 270), and European-region (n = 9) scale. We partitioned diversity into its additive components alpha, beta, and gamma, and assessed the relative contribution of beta diversity to total species richness at each spatial scale. AI was determined using pesticide and fertilizer inputs, as well as tillage operations and categorized into low, medium, and high levels. As AI was not significantly related to landscape complexity, we could disentangle potential AI effects on local vs. landscape community homogenization. AI negatively affected the species richness of plants and birds, but not carabid beetles, at all spatial scales. Hence, local AI was closely correlated to beta diversity on larger scales up to the farm and region level, and thereby was an indicator of farm-and region-wide biodiversity losses. At the scale of farms (12.83-20.52%) and regions (68.34-80.18%), beta diversity accounted for the major part of the total species richness for all three taxa, indicating great dissimilarity in environmental conditions on larger spatial scales. For plants, relative importance of alpha diversity decreased with AI, while relative importance of beta diversity on the farm scale increased with AI for carabids and birds. Hence, and in contrast to our expectations, AI does not necessarily homogenize local communities, presumably due to the heterogeneity of farming practices. In conclusion, a more detailed understanding of AI effects on diversity patterns of various taxa and at multiple spatial scales would contribute to more efficient agri-environmental schemes in agroecosystems.