The pedagogy of computer programming using cognitive development through an e-learning object

Motivated by the needs of a pedagogy focusing on minimizing the learning difficulties in program semantics knowledge and logical reasoning, this research project develops a cognitive development-based pedagogy for introductory programming to support students in organizing and constructing knowledge to learn computer programming. A pedagogy is described as a practice and learning theory that defines the teaching and learning. Regarding the practice of this pedagogy, it uses a cognitive learning tool, called e-learning object, to support the scaffolding. With regard to the theory, this pedagogy is developed based on Vygotsky’s Zone of Proximal Development and Piaget’s theory for cognitive development. In particular the scaffolding of this pedagogy includes three major learning processes. The first two learning processes focus on supporting students constructing knowledge on program semantics and conceptually map this knowledge to the coding process. The last learning process extends the learning to self-practice by demanding students to complete a set of exercises independently. All of these learning processes are supported by using the e-learning object, which is the major cognitive learning tool used in this pedagogy to support cognitive development. It is called e-learning object as it is designed by organizing a group of learning objects, in which each of them is to deliver the concepts of a specific unit topic of program control. Together with the course materials, these learning objects are accessed through the college’s ‘Blackboard System’. In addition to the major objective of improving students’ learning performance, this cognitive development-based pedagogy also extends from this objective to find out whether the positive learning outcome connects to cognitive development, and also whether this pedagogy can be embraced by teachers for use in their teaching processes. With these objectives, six research questions are defined in two stages of study. Research questions Q1 and Q2 are used to study students’ learning outcomes in year 1 and 2, and research questions Q3 to Q5 are used to find out whether students’ learning outcomes are connected to cognitive development. Research question Q6 focuses on whether this pedagogy matches teachers’ knowledge of using it, based on their knowledge of applying technology-based pedagogy. The research methodology of this project is the triangulation design where quantitative data are enriched by the collection of qualitative data. This mixture of quantitative and qualitative data collection in different research questions enables this study to interpret the values of this cognitive development-based pedagogy with different views from students and teachers. The research methods mainly include the quasi-experimental method, survey method and the rating scale anchoring method. With these methods, data are collected by using pre-test and post-test papers, questionnaires, and a checklist of rating scale anchoring mental specifications. They are analysed by two-tailed t-test, descriptive method with mean analysis and the one- way repeated measure ANOVA. These research and data analysis methods have been proven effective and used widely, in educational research projects. This research project makes four major contributions: (i) the e-learning object used in this pedagogy can be used to improve students’ learning performance in computer programming; (ii) evidence that a pedagogy focusing on cognitive development can be used to improve students’ learning performance without being limited by programming languages; (iii) development of a cognitive development- based pedagogy for wide use in introductory programming without being limited by teachers’ knowledge and programming languages; and (iv) learning with this cognitive development-based pedagogy builds up students’ problem-solving skills and applies them to different subject areas. With these achieved goals, this project therefore provides a conceptual and operational model for a pedagogical approach to Computer Science teachers design and use in their teaching process

Dependence of heat transport on the strength and shear rate of prescribed circulating flows

We study numerically the dependence of heat transport on the maximum velocity and shear rate of physical circulating flows, which are prescribed to have the key characteristics of the large-scale mean flow observed in turbulent convection. When the side-boundary thermal layer is thinner than the viscous boundary layer, the Nusselt number (Nu), which measures the heat transport, scales with the normalized shear rate to an exponent 1/3. On the other hand, when the side-boundary thermal layer is thicker, the dependence of Nu on the Peclet number, which measures the maximum velocity, or the normalized shear rate when the viscous boundary layer thickness is fixed, is generally not a power law. Scaling behavior is obtained only in an asymptotic regime. The relevance of our results to the problem of heat transport in turbulent convection is also discussed.Comment: 7 pages, 7 figures, submitted to European Physical Journal

A Hybrid Quantum Encoding Algorithm of Vector Quantization for Image Compression

Many classical encoding algorithms of Vector Quantization (VQ) of image compression that can obtain global optimal solution have computational complexity O(N). A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45sqrt(N) times approximately. In this paper, a hybrid quantum VQ encoding algorithm between classical method and quantum algorithm is presented. The number of its operations is less than sqrt(N) for most images, and it is more efficient than the pure quantum algorithm. Key Words: Vector Quantization, Grover's Algorithm, Image Compression, Quantum AlgorithmComment: Modify on June 21. 10pages, 3 figure

Cash flow forecast for South African firms

This paper applies models in the extant literature that have been used to forecast operating cash flows to predict the cash flows of South African firms listed on the Johannesburg Stock Exchange. Out-of-sample performance is examined for each model and compared between them. The reported results show that some accrual terms, i.e. depreciation and changes in inventory do not enhance cash flow prediction for the average South African firm in contrast to the reported results of studies in USA and Australia. Inclusion of more explanatory variables does not necessarily improve the models, according to the out-of-sample results. The paper proposes the application of moving average model in panel data, and vector regressive model for multi-period-ahead prediction of cash flows for South Africa firms

Towards Long-endurance Flight: Design and Implementation of a Variable-pitch Gasoline-engine Quadrotor

Majority of today's fixed-pitch, electric-power quadrotors have short flight endurance ($<$ 1 hour) which greatly limits their applications. This paper presents a design methodology for the construction of a long-endurance quadrotor using variable-pitch rotors and a gasoline-engine. The methodology consists of three aspects. Firstly, the rotor blades and gasoline engine are selected as a pair, so that sufficient lift can be comfortably provided by the engine. Secondly, drivetrain and airframe are designed. Major challenges include airframe vibration minimization and power transmission from one engine to four rotors while keeping alternate rotors contra-rotating. Lastly, a PD controller is tuned to facilitate preliminary flight tests. The methodology has been verified by the construction and successful flight of our gasoline quadrotor prototype, which is designed to have a flight time of 2 to 3 hours and a maximum take-off weight of 10 kg.Comment: 6 page

A machine learning study to identify spinodal clumping in high energy nuclear collisions

The coordinate and momentum space configurations of the net baryon number in heavy ion collisions that undergo spinodal decomposition, due to a first-order phase transition, are investigated using state-of-the-art machine-learning methods. Coordinate space clumping, which appears in the spinodal decomposition, leaves strong characteristic imprints on the spatial net density distribution in nearly every event which can be detected by modern machine learning techniques. On the other hand, the corresponding features in the momentum distributions cannot clearly be detected, by the same machine learning methods, in individual events. Only a small subset of events can be systematically differ- entiated if only the momentum space information is available. This is due to the strong similarity of the two event classes, with and without spinodal decomposition. In such sce- narios, conventional event-averaged observables like the baryon number cumulants signal a spinodal non-equilibrium phase transition. Indeed the third-order cumulant, the skewness, does exhibit a peak at the beam energy (Elab = 3–4 A GeV), where the transient hot and dense system created in the heavy ion collision reaches the first-order phase transition

The systematics of some Jamaican excavating sponges (Porifera)

Thirteen species of coral-dwelling sponges are reported from the reefs of Discovery Bay on the north coast of Jamaica. Seven of these are new species: Cliona delitrix, C. peponaca, C. langae, C. laticavicola, C. aprica (family Clionidae); Alectona jamaicensis (family uncertain); and Siphonodictyon brevitubulatum (family Adociidae). Two other species, Cliona schmidti and C. janitrix, are new records for the West Indies. A shallow-water and a deep-water form of two new species are described. Transplant studies indicate that these forms, distinguished by differences in the dimensions of the spicules and papillae, the degree of papillary fusion and, to a slight degree, spicule morphology, are largely habitat-induced. It is suggested that differences in silica concentration with depth may affect spicule sizes