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

Multi-Layered Chinese Citizenship: Policy Analysis on the Educational Rights of Internal Immigrants’ Children

Internal immigration in China has experienced a huge increase since the economic reform was launched in the late 1970s. While contemporary Hukou (household registration) system has contributed to stop the immigrants from sharing the local welfare benefits, especially the public education resources. This research focuses on the children of all immigrants, more precisely, on the non-local hukou children. Combining approaches of structured, focused comparative study with document analysis and multiple case study, this research investigated the citizenship status of non-local hukou Chinese children in terms of educational rights in three study periods: compulsory education, high school study and higher education. Relevant public policy documents from the central government and three city governments have been examined carefully with a focus on who is excluded institutionally from the educational rights. It is found that the educational rights of non-local hukou children are better fulfilled in the compulsory education period, compared with the other two. Multi-layered citizenship status exists not only among urban and rural hukou citizens, local and non-local hukou citizens, but also among the non-local hukou children in terms of educational rights. Non-local hukou children’s right to high school study and higher education depends on the socioeconomic status of their parents, instead of being the Chinese citizen themselves. Equal citizenship has not yet been the core value of policy making in China

Cyclin B1/CDK1-regulated mitochondrial bioenergetics in cell cycle progression and tumor resistance.

A mammalian cell houses two genomes located separately in the nucleus and mitochondria. During evolution, communications and adaptations between these two genomes occur extensively to achieve and sustain homeostasis for cellular functions and regeneration. Mitochondria provide the major cellular energy and contribute to gene regulation in the nucleus, whereas more than 98% of mitochondrial proteins are encoded by the nuclear genome. Such two-way signaling traffic presents an orchestrated dynamic between energy metabolism and consumption in cells. Recent reports have elucidated the way how mitochondrial bioenergetics synchronizes with the energy consumption for cell cycle progression mediated by cyclin B1/CDK1 as the communicator. This review is to recapitulate cyclin B1/CDK1 mediated mitochondrial activities in cell cycle progression and stress response as well as its potential link to reprogram energy metabolism in tumor adaptive resistance. Cyclin B1/CDK1-mediated mitochondrial bioenergetics is applied as an example to show how mitochondria could timely sense the cellular fuel demand and then coordinate ATP output. Such nucleus-mitochondria oscillation may play key roles in the flexible bioenergetics required for tumor cell survival and compromising the efficacy of anti-cancer therapy. Further deciphering the cyclin B1/CDK1-controlled mitochondrial metabolism may invent effect targets to treat resistant cancers

A comparison study of digital sinusoidal fringe generation technique: defocusing binary patterns VS focusing sinusoidal patterns

With the recent advancements in digital technology, three-dimensional (3-D) shape measurement has played an increasingly important role in fields including manufacturing, homeland security, medical sciences, and entertainment. Over the past decades, numerous 3-D shape measurement techniques have been developed. Among these existing techniques, fringe analysis based on phase-shifting sinusoidal structured patterns stands out because of its numerous advantages. However, there are still some major challenges of the existing digital fringe projection system for accurate 3-D shape measurement and for future speed improvement. They are: (1) projector nonlinearity problem, (2) synchronization problem, and (3) exposure time limitation problem. There are currently two approaches to generate sinusoidal fringe patterns with a digital-light-processing (DLP) projector: defocusing binary patterns (DBP) and focusing sinusoidal patterns (FSP). The focus of this dissertation research is to compare these methods for high-quality 3-D shape measurement. We developed a system based on a digital fringe projection and phase-shifting technique to perform various comparison tests. The system utilizes a DLP projector to project computer generated fringe patterns onto the object and a charged-coupled-device (CCD) camera to acquire the fringe images. Conventionally, sinusoidal fringe patterns are usually supplied to a focused projector, and the DBP method is used to properly defocus the projector to generate sinusoidal patterns from binary structured patterns. We compare the performance of the new DBP approach against the traditional FSP method by analyzing the phase errors introduced by following factors: (1) defocusing degree, (2) exposure time, (3) synchronization, and (4) projector nonlinear gamma. The traditional FSP involves some practical issues for high-quality measurement. Our experiment found it is possible to generate ideal sinusoidal fringe patterns by the DBP method, and when the projector is defocused to a certain degree, the phase error induced by the DBP method is very close to that produced by the FSP approach. With the DBP method, 3-D reconstruction was shown to be feasible. Short exposure time is especially needed when measuring fast motion. For the FSP method, the minimum exposure time of the camera is limited by the projector\u27s fringe projection rate, and the phase error is very large when a very short exposure time is needed. The experimental results show that the phase error does not change very much when the exposure time alters, and if a very short exposure time is needed, the DBP method clearly outperforms the FSP method for 3-D shape measurement. It also provides a potential way to develop fast 3-D shape measurement technique. For the DLP projector, if it is supplied with sinusoidal fringe patterns, the synchronization between the projector and the camera is critical. When the projector is not synchronized with the camera, the phase error for the DBP method is much smaller than that for the FSP method when the exposure time is not multiples of projection cycle. By implementing the DBP method in our system, we could achieve 3-D reconstruction without synchronization between the projector and the camera. Projector gamma correction, which is usually a time-consuming procedure, is mandatory for the FSP method. In this research, we found no projector gamma correction is needed for the DBP method. Our experimental results demonstrated it can achieve high-quality 3-D reconstruction by the DBP method without projector nonlinearity calibration. Compared with the FSP method, the possible shortcomings of the DBP method are: (1) seemingly sinusoidal fringe patterns are still composed of high-frequency harmonics, which results in measurement error, and (2) the depth range of high-contrast fringe patterns is small. Even with these drawbacks, this new technique still has the potential to replace the conventional fringe generation technique

Process planning for the subtractive rapid manufacturing of heterogeneous materials: Applications for automated bone implant manufacturing

This research presents a subtractive rapid manufacturing process for heterogeneous materials, in particular for custom shaped bone implants. Natural bone implants are widely used in the treatment of severe fractures or in tumor removal. In order for the human body to accept the bone implant material and heal properly, it is essential that the bone implant should be both mechanically and biologically compatible. Currently, the challenge of having correctly shaped natural bone implants created from an appropriate material is met through hand-shaping done by a surgeon. CNC-RP is a rapid machining method and software that can realize a fully automated Subtractive Rapid Prototyping (RP) process, using a 3-axis milling machine with a 4th axis indexer for multiple setup orientations. It is capable of creating accurate bone implants from different clinically relevant material including natural bone. However, there are major challenges that need to be overcome in order to implement automated shape machining of natural bones. They are summarized as follows: (1) Unlike homogeneous source materials for which a part can be machined from any arbitrary location within the original stock, for the case of donor bones, the site and orientation of implant harvest need to consider the nature of the heterogeneous internal bony architecture. (2) For the engineered materials, the source machining stock is in the convenient form of geometrically regular shapes such as cylinders or rectangular blocks and the entities of sacrificial supports can connect the part to the remaining stock material. However, irregularly-shaped bones and the heterogeneity of bone make the design of a fixture system for machining much more complicated. In this dissertation, two major areas of research are presented to overcome these challenges and enable automated process planning for a new rapid manufacturing technique for natural bone implants. Firstly, a new method for representing heterogeneous materials using nested STL shells is proposed. The nested shells model is called the Matryoshka mode, based in particular on the density distribution of human bone. The Matryoshka model is generated via an iterative process of thresholding the Hounsfield Unit (HU) data from a computed tomography (CT) scan, thereby delineating regions of progressively increasing bone density. Then a harvesting algorithm is developed to determine a suitable location to generate the bone implant from within the donor bone is presented. In this harvesting algorithm, a density score and similarity score are calculated to evaluate the overall effectiveness of that harvest site. In the second research area, an automated fixturing system is proposed for securing the bone implant during the machining process. The proposed method uses a variant of sacrificial supports (stainless surgical screws) to drill into appropriate locations and orientations through the free-form shaped donor bone, terminating at proper locations inside the solid part model of the implant. This automated fixturing system has been applied to machine several bone implants from surrogate bones to 3D printed Matryoshka models. Finally, the algorithms that are developed for setup planning are implemented in a CAD/CAM software add-on called CNC-RPbio . The results of this research could lead to a clinically relevant rapid machining process for custom shaped bone implants, which could create unique implants at the touch of a button. The implication of such high accuracy implants is that patients could benefit from more accurate reconstructions of trauma sites, with better fixation stability; leading to potentially shorter surgeries, less revisions, shorter recovery times and less likelihood of post-traumatic osteoarthritis, to name a few

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