Unsupervised Learning for Understanding Student Achievement in a Distance Learning Setting

Many factors could affect the achievement of students in distance learning settings. Internal factors such as age, gender, previous education level and engagement in online learning activities can play an important role in obtaining successful learning outcomes, as well as external factors such as regions where they come from and the learning environment that they can access. Identifying the relationships between student characteristics and distance learning outcomes is a central issue in learning analytics. This paper presents a study that applies unsupervised learning for identifying how demographic characteristics of students and their engagement in online learning activities can affect their learning achievement. We utilise the K-Prototypes clustering method to identify groups of students based on demographic characteristics and interactions with online learning environments, and also investigate the learning achievement of each group. Knowing these groups of students who have successful or poor learning outcomes can aid faculty for designing online courses that adapt to different students' needs. It can also assist students in selecting online courses that are appropriate to them

Discovering student interactions with a collaborative learning forum that predict group collaboration problems

This paper investigates the role of various student interactions with a learning forum in order to ascertain the existence of different group collaboration problems. A particular focus of interest has been learning forums, since forums have become broadly adopted tools to support online group collaboration. The types of collaboration problems were drawn from previous research that identified the main student-induced collaboration problems. A data set was collected for 87 undergraduates who participated in a web-based computer science group project. It consists of two kinds of data. The first is student interaction data which were collected from a learning forum system on which the group project was undertaken. The second is the data relating to assessment of group collaboration problems, and were gathered through a questionnaire delivered to the students who participated in the group project. Multinomial logistic regression analysis has been applied for modelling the relationship between a response variable corresponding to the existence of a group collaboration problem and several predictor variables representing various student interactions with a learning forum. A set of predictive models were produced by the regression analysis, each representing a statistically significant combination of student interactions that predict the existence of one of the collaboration problems in question. The findings reveal that indicators including the number of posts that were created and replied to by individual students, and the number of times that a student viewed a discussion on a learning forum, contribute significantly in predicting the collaboration problems which were identified. The results also demonstrate that how the existence of a problem fluctuates with the alterations in the value of an indicator variable. The goodness-of-fit of the identified predictive models was measured by the Pearson chi-square test and the results of this test indicate that the models fit the sample data well. The average rate of correct classification by the models was approximately 80%, which means the regression method performs well on the sample data set. The outcomes of this research can help teachers to assess problems in web-based collaborative group work and also can be used to develop tools for automatically diagnosing group collaboration problems in web-based collaborative learning environments

How do Plants Respond to Grazing at a Molecular Level?

Grazing is a multiple-component process that includes wounding, defoliation, and saliva depositing. The molecular mechanism for how plants respond to grazing in grassland is a new topic. To address this question, we performed gene expression activities within 2 to 24 hours of grazing and proteomics analysis of rice seedling, examining hundreds of genes and proteins. Some key genes in GeneChips analysis specifically researched were β-amylase, LcSUT1, LcDREB3, and FEH gene. BSA (bovine serum albumin), an important and abundant component in saliva was used to study the saliva-plant interaction in grazing. Combined with corresponding gene and grazing research by other laboratories, this will advance our knowledge of the molecular interface of the grass-herbivore interaction

Incorporating learning styles in a computer-supported collaborative learning model

Collaborative learning enables individual learners to combine their own expertise, experience and ability to accomplish a mutual learning goal. The grouping of learners, and learning from social interactions with peer-learners, are two basic characteristics of collaborative learning. For individual learners to benefit from collaborative learning, individual learners with different characteristics must be grouped together. In this paper, we propose a computer-supported collaborative learning model which incorporates learning styles for improving collaborative learning. The proposed model is novel since it can provide overall support for collaborative learning. In addition, the way we have incorporated learning styles in the model is a new approach to constituting heterogeneous groups containing learners with dissimilar learning styles and detect learning styles through monitoring collaborative interactions

A Knowledge Framework for Information Security Modeling

The data collection process for risk assessment highly depends on the security experience of security staffs of an organization. It is difficult to have the right information security staff, who understands both the security requirements and the current security state of an organization and at the same time possesses the skill to perform risk assessment. However, a well defined knowledge model could help to describe categories of knowledge required to guide the data collection process. In this paper, a knowledge framework is introduced, which includes a knowledge model to define the data skeleton of the risk environment of an organization and security patterns about relationships between threat, entity and countermeasures; and a data integration mechanism for integrating distributed security related data into a security data repository that is specific to an organization for information security modelling

A Study of Molecular Interface of Grass-Herbivory Interaction in Grass

Grass-herbivore interaction is a complex process that involves wounding effects caused by herbivore feeding, defoliation effects due to leaf-surface loss during grazing, and the deposition of herbivore saliva onto the surface of plants (Chen et al., 2009). Wounding can stimulate plant growth but clearly differs from grazing (Mattiacci et al., 1995). Defoliation affects root development in grasses and alters the carbohydrate-metabolism pathway in rice. Saliva has been found to stimulate plant growth, enhance tiller and increase biomass. However, little is known about the molecular mechanisms of plant responses to grazing in molecular level. In our previous transcriptome studies, many genes relating with grazing were identified from sheepgrass (Li et al., 2013). In last IGC report, we proposed the concept of “molecular interface on grass-herbivore interaction” (Liu et al., 2013) to understand the interaction between plant and large herbivories on molecular level, which has significant importance on agriculture and grassland conservation. This paper will present some new results in the area

Germplasm Evaluation of an Eurasia Steppe Native Specie--Sheepgrass (\u3cem\u3eLeymus chinensis\u3c/em\u3e)

Sheepgrass (Leymus chinensis (Trin.) Tzvel) is an advantageous perennial native grass in China and other northern Eurasian countries having steppe. As an important forage grass of great value in animal husbandry, sheepgrass is well known for its abundant foliage, high palatability and high nutritive content. Sheepgrass is also valuable in grassland restoration and conservation since it is a perennial grass with a rhizome network to fix the soil and can survive well in stressful environments. Terefore, the collection, evaluation and utilization of sheepgrass are necessary for protecting grassland biodiversity, for establishing artificial pasture, restoring degraded grassland, and the development of forage industry and animal husbandry in Eurasia’s native steppe. Here, we reviewed our previous studies on the collection, evaluation of phenotypic diversity for germplasm resources, distribution and domestication of wild sheepgrass, and application of sheepgrass new varieties

Dealing with diversity in a smart-city datahub

In this paper, we present the data curation approach taken by the MK:Smart project, creating a large data repository of datasets about all aspects of the city of Milton Keynes in the UK and its citizens. The issues faced here, which we believe will become more and more common to large, data-centric smart-cities initiatives, is the one associated with the diversity of these thousands of datasets in terms of the licenses, policies and terms they are associated with them. We describe this repository of datasets, the MK Datahub, and its architecture to create data workflows from original sources to applications. We focus on the approach taken to record, in a structured, ontology-based way the components of the licenses and policies of each dataset, as well as the tools we are developing to manage such representations and to reason with them

Bovine serum albumin in saliva mediates grazing response in Leymus chinensis revealed by RNA sequencing

BACKGROUND: Sheepgrass (Leymus chinensis) is an important perennial forage grass across the Eurasian Steppe and is adaptable to various environmental conditions, but little is known about its molecular mechanism responding to grazing and BSA deposition. Because it has a large genome, RNA sequencing is expensive and impractical except for the next-generation sequencing (NGS) technology. RESULTS: In this study, NGS technology was employed to characterize de novo the transcriptome of sheepgrass after defoliation and grazing treatments and to identify differentially expressed genes (DEGs) responding to grazing and BSA deposition. We assembled more than 47 M high-quality reads into 120,426 contigs from seven sequenced libraries. Based on the assembled transcriptome, we detected 2,002 DEGs responding to BSA deposition during grazing. Enrichment analysis of Gene ontology (GO), EuKaryotic Orthologous Groups (KOG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways revealed that the effects of grazing and BSA deposition involved more apoptosis and cell oxidative changes compared to defoliation. Analysis of DNA fragments, cell oxidative factors and the lengths of leaf scars after grazing provided physiological and morphological evidence that BSA deposition during grazing alters the oxidative and apoptotic status of cells. CONCLUSIONS: This research greatly enriches sheepgrass transcriptome resources and grazing-stress-related genes, helping us to better understand the molecular mechanism of grazing in sheepgrass. The grazing-stress-related genes and pathways will be a valuable resource for further gene-phenotype studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-1126) contains supplementary material, which is available to authorized users

An underwater image enhancement model for domain adaptation

Underwater imaging has been suffering from color imbalance, low contrast, and low-light environment due to strong spectral attenuation of light in the water. Owing to its complex physical imaging mechanism, enhancing the underwater imaging quality based on the deep learning method has been well-developed recently. However, individual studies use different underwater image datasets, leading to low generalization ability in other water conditions. To solve this domain adaptation problem, this paper proposes an underwater image enhancement scheme that combines individually degraded images and publicly available datasets for domain adaptation. Firstly, an underwater dataset fitting model (UDFM) is proposed to merge the individual localized and publicly available degraded datasets into a combined degraded one. Then an underwater image enhancement model (UIEM) is developed base on the combined degraded and open available clear image pairs dataset. The experiment proves that clear images can be recovered by only collecting the degraded images at some specific sea area. Thus, by use of the scheme in this study, the domain adaptation problem could be solved with the increase of underwater images collected at various sea areas. Also, the generalization ability of the underwater image enhancement model is supposed to become more robust. The code is available at https://github.com/fanren5599/UIEM