A study of the impact of the willingness-to-learn of CAD novice users on their competence development

The notion that ‘attitude drives behavior’ manifests itself in a variety of ways in educational and occupational settings. As applied to CAD competence development, industrial training of novice CAD users on their way to becoming competent CAD users consume a lot of corporate resources. This paper is the third paper in the line of research that attempts to answer the question having to do with what it takes to make a competent CAD user. Specifically, we examine the CAD-specific factors revolving around the trainees’ willingness-to-learn CAD.These factors are analyzed in two stages. At the start of the training, trainees’ initial attitude towards CAD is established by means of a short questionnaire. Afterwards, throughout the training, trainees’ behavior (online and offline practice) is gauged and, in turn, a relation is established to illustrate how this practice leads to the development of CAD-specific skills. For this purpose, another short questionnaire was utilized. Strong correlations were established relating the trainees’ CAD-specific behavior with the CAD-specific outcomes of learning CAD syntax.Furthermore, and in order to assess the quality of the trainees’ learning of CAD, overall competence was monitored throughout the study via performance measures that describe the time it took the trainees to build test models (speed), which reflects upon the ability to learn the syntax of the CAD tool (declarative knowledge). The sophistication of the models is also used as another measure. Correlating the trainees’ character attributes with these assessed measures, it was found that the stronger is the trainees’ will to learn CAD, the stronger is the likelihood to learn faster. Perhaps more importantly, trainees with initial favorable attitude toward CAD were shown to develop increasingly positive behavior that manifested through additional practice and other forms of visible effort.     

The effect of nonhuman’s versus human’s external regulation on children’s speech use, manifested self-regulation, and satisfaction during learning tasks

Because of several analytical and methodological critiques on the findings and contexts of children’s private speech (PS), self-regulation learning (SRL), and thinking aloud (TA), the present study was conducted to shed new light on the effect of the nonhuman’s/computer’s versus human’s/teacher’s intervention (C-Condition versus T-Condition) on young children’s speech use, SRL, and satisfaction during learning tasks. Four developmental measurements with novel criteria were used to measure: (1) speech analysis, (3) SRL as a function of task level selection, (3) SRL as a function of task precision, and (4) a friendly-chat questionnaire to measure children’s satisfaction. Two types of intervention (enacted versus verbal encouragement) were applied through computer-based learning environment and investigated by forty preschool children divided by their teachers between the two conditions equivalently. It was hypothesized that children who acted alone (C-Condition) were more PS productive, manifested higher SRL, task performance, and satisfaction. The results confirmed the hypothesis with no significant differential effect of the gender on performance, showed that the injudicious use of encouragement hindered the children’s regulation behavior, and proved that PS and TA elicitation were fully different. However, the results were not confirmed Vygotsky’s view and simultaneously not fully inline with Piaget’s view of self-regulation development.     

Throughput analysis of STS-based CDMA system with variable spreading factor in non-frequency selective Rayleigh fading channel

The throughput enhancement of space-time spreading (STS)-based code division multiple access (CDMA) system is investigated in this paper. Variable spreading factor (VSF) is utilized to improve the data throughput of the system in non-frequency selective Rayleigh fading channel. In this contribution, an analytical approach is proposed to compute a new expression for the bit error rate (BER) performance of the STS-based CDMA system against pre-despreading signal-to-noise-ratio (SNR) for different values of spreading factor. The other contribution of the paper is deriving a new analytical expression for the throughput enhancement of the VSF STS-based CDMA system in non-frequency selective Rayleigh fading channel. Simulation and analytical results demonstrate that using VSF method in STS-based CDMA system improves the throughput of the system by keeping the BER performance at a target level.     

New oscillation criteria for second order linear difference equations with positive and negative coefficients

The oscillation of second order neutral difference equations with positive and negative coefficients of the form

is investigated. We obtain many new results using the comparison between both first order and second order difference equations. An example is given to show the strength of the obtained results.     

An enhanced Web page change detection approach based on limiting similarity computations to elements of same type

This paper describes an efficient Web page detection approach based on restricting the similarity computations between two versions of a given Web page to the nodes with the same HTML tag type. Before performing the similarity computations, the HTML Web page is transformed into an XML-like structure in which a node corresponds to an open-closed HTML tag. Analytical expressions and supporting experimental results are used to quantify the improvements that are made when comparing the proposed approach to the traditional one, which computes the similarities across all nodes of both pages. It is shown that the improvements are highly dependent on the diversity of tags in the page. That is, the more diverse the page is (i.e., contains mixed content of text, images, links, etc.), the greater the improvements are, while the more uniform it is, the lesser they are.     

Identifying high perceived value practices of CMMI level 2: An empirical study

ObjectiveIn this paper, we present findings from an empirical study that was aimed at identifying the relative “perceived value” of CMMI level 2 specific practices based on the perceptions and experiences of practitioners of small and medium size companies. The objective of this study is to identify the extent to which a particular CMMI practice is used in order to develop a finer-grained framework, which encompasses the notion of perceived value within specific practices.MethodWe used face-to-face questionnaire based survey sessions as the main approach to collecting data from 46 software development practitioners from Malaysia and Vietnam. We asked practitioners to choose and rank CMMI level 2 practices against the five types of assessments (high, medium, low, zero or do not know). From this, we have proposed the notion of ‘perceived value’ associated with each practice.ResultsWe have identified three ‘requirements management’ practices as having a ‘high perceived value’. The results also reveal the similarities and differences in the perceptions of Malaysian and Vietnamese practitioners with regard to the relative values of different practices of CMMI level 2 process areas.ConclusionsSmall and medium size companies should not be seen as being “at fault” for not adopting CMMI – instead the Software Process Improvement (SPI) implementation approaches and its transition mechanisms should be improved. We argue that research into “tailoring” existing process capability maturity models may address some of the issues of small and medium size companies.     

Short-term and long-term memory analysis of learning using 2D and 3D educational contents

ABSTRACT

The effect of 2D and 3D educational content learning on memory has been studied using electroencephalography (EEG) brain signal. A hypothesis is set that the 3D materials are better than the 2D materials for learning and memory recall. To test the hypothesis, we proposed a classification system that will predict true or false recall for short-term memory (STM) and long-term memory (LTM) after learning by either 2D or 3D educational contents. For this purpose, EEG brain signals are recorded during learning and testing; the signals are then analysed in the time domain using different types of features in various frequency bands. The features are then fed into a support vector machine (SVM)-based classifier. The experimental results indicate that the learning and memory recall using 2D and 3D contents do not have significant differences for both the STM and the LTM.     

Sulphur dioxide loadings over megacity Lahore (Pakistan) and adjoining region of Indo-Gangetic Basin

This article presents spatial and temporal variations of planetary boundary layer (PBL) sulphur dioxide (SO₂) over megacity Lahore and adjoining region, a typical representative area in the Indo-Gangetic Basin (IGB) largely influenced by transported volcanic SO₂ from Africa, Middle East, and southern Europe, by using data retrieved from satellite-based Ozone Monitoring Instrument (OMI) during October 2004–September 2015. We find a positive trend of 2.4% per year (slope 0.01 ± 0.005 with y-intercept 0.35 ± 0.03 Dobson Unit (DU), correlation coefficient r = 0.55 and 2-tailed p-value at 0.1) of OMI-SO₂ column with the average value of 0.4 ± 0.05 DU. Strong seasonality of OMI-SO₂ column is observed over the region linked with local meteorology, patterns of anthropogenic emissions, crop residue burning, and vegetation cover. There exists a seasonal high value in winter 0.56 ± 0.24 DU with a peak in December 0.67 ± 0.26 DU. The seasonal lowest value is observed to be 0.29 ± 0.11 DU in wet summer with minimum value in July 0.25 ± 0.06 DU. High growth rates of OMI-SO₂ column over the study region have been observed in January, June, October, and December ranging from 5.7% to 11.6% per year. Satellite data show elevated OMI-SO₂ columns in 2007, 2008, 2011, and 2012 largely contributed by trans-boundary volcanic SO₂. A detailed analysis of volcanic SO₂ transported from Africa and Middle East (Jabal Al-Tair, Dalaffilla, and Nabro volcanoes) over the study area is presented. Air mass trajectories suggest the presence of long-range transported volcanic SO₂ at high altitude levels over Lahore and IGB region during the volcanic episodes. The SO₂ enhancements in PBL during winter season are generally due to significant vertical downdraft of high-altitude volcanic SO₂. For the first time, we present significant influence of volcanic SO₂ from southern Europe (Mt. Etna volcano) reaching over the study area. Daily mean OMI-SO₂ levels up to 21.4, 10.0, 5.6, and 2.4 DU have been noticed due to the eruptions from Dalaffilla, Mt. Etna, Nabro, and Jabal Al-Tair volcanoes, respectively.     

A new combination of artificial neural network and <Emphasis Type="Italic">K</Emphasis>-nearest neighbors models to predict blast-induced ground vibration and air-overpressure

Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R ²), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations.