scholarly journals The Effect of Distance Teaching on Academic Achievement in the Online Course of Introduction to Computers Science

2020 ◽  
Vol 1 (2) ◽  
pp. p23
Author(s):  
Ying-Sheng Kuo

For distance teaching, if the teacher can obtain the predicted scores that students may obtain in the final exam at this time, the students whose predicted scores do not meet the standard can be found. Teachers can strengthen the teaching of this type of students in the teaching process, which will greatly improve the overall teaching efficiency of teachers. The target of this research is the students of the course of the “Introduction to Computers Science” at Open University of Kaohsiung. This course is a distance teaching based on online teaching, and the questionnaire and final test are also completed on line. A total of 95 students filled out the questionnaire online, accounting for 77.24% of the electives. This study aims to assess the measurement of students’ pre-learning computer experience, software operation ability, learning motivation, and computer attitude, and analyze their relationship with the performance of subsequent distance teaching on the course of the “Introduction to Computers Science”, and find a multiple regression model to predict student performance. It was found that a multiple linear regression model combining 7 independent variables was statistically significantly related to the final test scores.

2021 ◽  
Vol 12 ◽  
Author(s):  
Zijing Ran ◽  
Xiaomei Xue ◽  
Lin Han ◽  
Robert Terkeltaub ◽  
Tony R. Merriman ◽  
...  

ObjectiveTo clarify the relationship between serum urate (SU) decrease and visceral fat area (VFA) reduction in patients with gout.MethodsWe retrospectively analyzed 237 male gout patients who had two sets of body composition and metabolic measurements within 6 months. Subjects included had all been treated with urate-lowering therapy (ULT) (febuxostat 20–80 mg/day or benzbromarone 25–50 mg/day, validated by the medical record). All patients were from the specialty gout clinic of The Affiliated Hospital of Qingdao University. The multiple linear regression model evaluated the relationship between change in SU [ΔSU, (baseline SU) – (final visit SU)] and change in VFA [ΔVFA, (baseline VFA) – (final visit VFA)].ResultsULT resulted in a mean (standard deviation) decrease in SU level (464.22 ± 110.21 μmol/L at baseline, 360.93 ± 91.66 μmol/L at the final visit, p <0.001) accompanied by a decrease in median (interquartile range) VFA [97.30 (81.15–118.55) at baseline, 90.90 (75.85–110.05) at the final visit, p < 0.001]. By multiple regression model, ΔSU was identified to be a significant determinant variable of decrease in VFA (beta, 0.302; p = 0.001).ConclusionsThe decrease in SU level is positively associated with reduced VFA. This finding provides a rationale for clinical trials to affirm whether ULT promotes loss of visceral fat in patients with gout.


2018 ◽  
Author(s):  
Muhammad Syukri

The purpose of this study is to determine the effect of locally-generated revenue (PAD), balancing funds and Foreign Direct Investment (FDI) to Progress Regions Level districts and cities in South Sulawesi Province either simultaneously or partially. Type of research used is applied research with quantitative data. The data used obtained from the Statistics Central Institution (BPS), which covers 24 districts and cities of South Sulawesi Province in 2016 in the form of thousands of rupiah. The research procedure is (1) Descriptive Analysis. (2) Establish multiple regression model. (3) Partial test and simultaneous test, and (4) determine the coefficient determination. Based on the simultaneous test of multiple linear regression model that PAD (X1), Balance Funds (X2) and FDI (X3) have significant effect to regional progress level (Y). And partial model testing, only PAD (X1) and fund balancing (X2) which significantly influence to regional progress level (Y). Meanwhile, FDI (X3) has no significant effect on regional progress level (Y). Therefore, the cultivation of FDI should be targeted and can provide direct benefits for the community in improving their welfare that will impact on improving regional progress. The suggestions needed in the development of further research that is required addition of other variables that can affect the level of regional progress.


Author(s):  
Benjamin Doglas ◽  
Richard Kimwaga ◽  
Aloyce Mayo

Abstract Moringa Oleifera (MO) is a highly effective conditioner in the dewatering of Fecal sludge (FS). However, the model for the prediction of its optimal dose has not yet been documented. This article presents the results of the developed model for the prediction of MO optimal doses. The developed model was based on assessing the FS parameters and MO stock solution. The FS samples were obtained from a mixture of a pit latrine and septic tank and were analyzed at the water quality laboratory of the University of Dar es Salaam. The multiple linear regression model was used to establish a relationship between MO optimal dose as a function of FS characteristics (pH, Electrical Conductivity, Total Solids and Total Suspended Solids) and concentration of MO stock solution. The results indicated that the main contributing factors which determine the MO optimal dose were the concentration of MO stock solution, followed by pH of FS. The model results showed a good agreement between the predicted and observed MO optimal dose with a coefficient of determination of R2 = 0.72 and 0.9 for calibration and validation respectively. Therefore, the model can be adapted to determine the MO optimal dose without running the Jar-test experiment.


Author(s):  
Gunawardena Egodawatte

This paper discusses the development of a multiple regression model to predict the final examination marks of students in an undergraduate business statistics course. The marks of a sample of 366 students in the Winter 2017 semester were used to fit the regression model. The final model contained three predictor variables namely two test marks and the homework assignment mark. The marks of another 194 students from Winter 2018 were used to validate the model. The model validation showed that it can be used for future cohorts of students for prediction. The two main objectives of the study were to use the model as a teaching tool in class and to use the model to predict final examination marks of future students.


2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
R. M. Yuvaraj

Land surface temperature (LST) is a key factor in numerous areas such as climate change, land use/land cover in the urban areas, and heat balance and is also a significant participant in the creation of climate models. Landsat data has given numerous possibilities to understand the land processes by means of remote sensing. The present study has been performed to identify the LST of the study region using Landsat 8 OLI/TIRS satellite images for two time periods in order to compare the data. The study also attempted to identify and predict the role and importance of NDVI, NDBI, and the slope of the region on LST. The study concludes that the maximum and minimum temperatures of 40.44 C and 20.78 C were recorded during the November month whereas the maximum and minimum LST for month March has increased to 42.44 C and 24.57 C respectively. The result indicates that LST is inversely proportional to NDVI (−6.369) and slope (−0.077) whereas LST is directly proportional to NDBI (+14.74). Multiple linear regression model has been applied to calculate the extents of NDVI, NDBI, and slope on the LST. It concludes that the increase in vegetation and slope would result in slight decrease in temperature whereas the increase in built-up will result in a huge increase in temperature.


Author(s):  
Drinold Aluda Mbete

Objectives: The study aims to develop a Bayesian multiple regression model with informative inverse gamma prior and t the model to malaria symptom dataset.Place and Duration of Study: The study was carried out in Masinde Muliro University of Science and Technology (MMUST). The study used 300 malaria related symptom dataset obtained from Health service records of different patients (students) between the time period of 1st January, 2015 to 20th December, 2015.Methodology: Multiple linear regression model with Bayesian parameter estimation is used. The Normal prior distribution for θ parameter and inverse gamma prior distribution for the σ2 parameter is derived. Gibbs sampler and Metropolis Hasting algorithm is used with Markov Chain Monte Carlo (MCMC) method to produce an iteration of about 102,491 with Burn-in of 2500 and thinning of 10 that resulting to eective sample size of 90000.Results: The results shows that all the estimated posterior predictive p-values are between 0.05 and 0.95 indicating an adequate t for the individual observation of the data in the model. The results also reveals that the data values and the average distance between the data values and the mean tend to be close to each other and the estimated coeffcient of θ′s approximately 95%draws fall within each of the corresponding highest posterior density intervals. Conclusion: Though the Least Squares method is sucient for estimating the coeffcients of the regression parameters, the Bayesian estimates recorded comparatively very small standard errors making the Bayesian method more robust in analysing symptom dataset.


2021 ◽  
Vol 4 ◽  
pp. 1-6
Author(s):  
Gian Pietro Zaccomer ◽  
Maurizia Sigura

Abstract. The currently IT tools provides increased opportunities to organize professional and recreational activities by interactive maps easily accessible for users. An experiment was conducted to understand if the great potential offered by new technologies match with the ability to produce good quality spatial data by users. The goal was to assess whether their knowledge of GIS affected the quality of their mapping activity by online map-based survey with maps as part of on line questionnaire. The attention was paid to university students as target of potential users of these tools, considering different skills acquired during their studies: from theoretical courses of geography, to theoretical and practical courses (dedicated labs) on GIS. The experiment involved more than 200 students of the University of Udine during the academic year 2019–2020. In this framework, a further study was developed investigating factors playing a role in students’ ability to complete the proposed exercises. The analysis was based on a multiple regression model which assumes the number of exercises completed as a dependent variable, and the student profile characteristics (gender, type of student, knowledge of GIS, and other IT skills) as independent variables. The estimated models pointed out both students’ willingness and deep knowledge of GIS as main factors effected the students’ ability to complete the proposed exercises. Fewer effects were associated to their gender and residence.


2008 ◽  
Vol 21 (22) ◽  
pp. 5764-5776 ◽  
Author(s):  
Hi Ku Cho ◽  
Jhoon Kim ◽  
Yeonjin Jung ◽  
Yun Gon Lee ◽  
Bang Yong Lee

Abstract Effects of cloud, air temperature, and specific humidity on downward longwave irradiance and their long-term variabilities are examined by analyzing the measurements made at the King Sejong Station in the Antarctic Peninsula during the period of 1996–2006. It has been shown that the downward longwave irradiance (DLR) is significantly correlated with three variables: air temperature, specific humidity, and cloudiness. Based on the relationship of the three variables with DLR, a multiple linear regression model has been developed in order to evaluate the relative contribution of each of the variables to the variation of DLR. The three variables together explained 75% of all the variance in daily mean DLR. The respective contribution from specific humidity and cloudiness to the variation of DLR was 46% and 23%; thus most of the DLR variability can be explained by the variations in the two variables. The annual mean of longwave cloud forcing shows 52 W m−2 with no remarkable seasonal cycle. It is also noted that the overcast cloud effect gives an increase by 65 W m−2 with respect to clear-sky flux throughout the year. It is suggested that the multiple regression model can be used to estimate the radiative forcings of variables influencing the DLR variability. A highly significant decrease in DLR with an average of −1.52 W m−2 yr−1 (−0.54% yr−1) is found in an analysis from the time series of the deseasonalized monthly mean values. Accordingly, the atmospheric flux emissivity, air temperature, and specific humidity have also decreased in their time series, while the cloudiness has increased insignificantly. Consequently, it may be concluded that the recent decrease in DLR is mainly attributed to the net cooling effect due to the decrease in air temperature and specific humidity, which overwhelm the slight warming effect in cloudiness. Analysis of mean monthly trends for individual months shows that, as for the annual data, the variations in DLR are mostly associated with those of air temperature, specific humidity, and cloudiness.


Author(s):  
Jiancai Deng ◽  
Fang Chen ◽  
Weiping Hu ◽  
Xin Lu ◽  
Bin Xu ◽  
...  

Chlorophyll a (Chl-a) is an important indicator of algal biomass in aquatic ecosystems. In this study, monthly monitoring data for Chl-a concentration were collected between 2005 and 2015 at four stations in Meiliang Bay, a eutrophic bay in Lake Taihu, China. The spatiotemporal distribution of Chl-a in the bay was investigated, and a statistical model to relate the Chl-a concentration to key driving variables was also developed. The monthly Chl-a concentration in Meiliang Bay changed from 2.6 to 330.0 μg/L, and the monthly mean Chl-a concentration over 11 years was found to be higher at sampling site 1, the northernmost site near Liangxihe River, than at the three other sampling sites. The annual mean Chl-a concentration fluctuated greatly over time and exhibited an upward trend at all sites except sampling site 3 in the middle of Meiliang Bay. The Chl-a concentration was positively correlated with total phosphorus (TP; r = 0.57, p < 0.01), dissolved organic matter (DOM; r = 0.73, p < 0.01), pH (r = 0.44, p < 0.01), and water temperature (WT; r = 0.37, p < 0.01), and negatively correlated with nitrate (NO3−-N; r = −0.28, p < 0.01), dissolved oxygen (DO; r = −0.12, p < 0.01), and Secchi depth (ln(SD); r = −0.11, p < 0.05). A multiple linear regression model integrating the interactive effects of TP, DOM, WT, and pH on Chl-a concentrations was established (R = 0.80, F = 230.7, p < 0.01) and was found to adequately simulate the spatiotemporal dynamics of the Chl-a concentrations in other regions of Lake Taihu. This model provides lake managers with an alternative for the control of eutrophication and the suppression of aggregations of phytoplankton biomass at the water surface.


10.18060/184 ◽  
2006 ◽  
Vol 7 (2) ◽  
Author(s):  
David Westhuis ◽  
Philip M. Ouellette ◽  
Corey L. Pfahler

Research comparing courses taught exclusively in traditional face-to-face settings versus courses taught entirely online have shown similar levels of student satisfaction. This article reports findings from a comparative study of student achievement in research skills from classes using two different instructional formats. One group used a classroom-based instructional format and the other group used an online web-based instructional format. Findings indicate that there were no statistically significant differences between the two class formats for eight out of eleven outcome student performance activities and ten out of 13 pedagogical strategies. There were large effect size differences based on class format on four of the student performance activities and for student satisfaction with six of the pedagogical methods. When statistically significant differences were found, it was determined that student performance on learning activities and satisfaction with pedagogical methods were higher for the students in the traditional class. The findings support the conclusions of several studies concerning the effectiveness of online teaching. Limitations and implications for further studies are also suggested.


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