Master of Philosophy (M.Phil.)http://dl.lib.uom.lk/handle/123/187262023-06-05T23:53:18Z2023-06-05T23:53:18ZA Statistical model to identify the influence of mathematics on students' performance in engineering programsNanayakkara, KADSAhttp://dl.lib.uom.lk/handle/123/139512022-10-12T02:47:49ZA Statistical model to identify the influence of mathematics on students' performance in engineering programs
Nanayakkara, KADSA
Mathematics plays a major role in higher education as it is particularly essential to develop the analytical thinking of students in a wide range of disciplines, especially, in engineering sciences. Therefore, exploring the student academic performance has been a crucial aspect of the educational research recently. In this study, the impact of mathematics in Level 1 and Level 2 on student engineering performance in Level 2 was investigated for seven engineering disciplines at the Faculty of Engineering, University of Moratuwa, Sri Lanka under two scenarios: (i) effect of mathematics in Level 1 and Level 2 simultaneously and (ii) effect of mathematics in Level 1 and Level 2 separately by using unadjusted and adjusted Canonical Correlation Analysis (CCA). A theoretical model underlying relationship between two measurements, mathematics performance and engineering performance was developed based on literature review. The Structural Equation Modeling based on Partial Least Squares (PLS-SEM) technique was used to validate the conceptual model and proposed an index to measure the mathematical influence on student engineering performance. The first canonical variate of engineering was found to be the best proxy indicator for the engineering performance. The impact of mathematics in semester 2 is significantly higher compared with the impact of mathematics in semester 1 on engineering performance in Level 2. The mathematics in Level 1 and Level 2 jointly influenced on the engineering performance in Level 2 irrespective of the engineering disciplines and the level of impact of mathematics varies among engineering disciplines. The individual effect of mathematics in Level 2 is significantly higher compared to the individual effect of mathematics in Level 1 on engineering performance in Level 2. The mathematics in Level 1 is still important in affecting students’ engineering performance in Level 2 as there is a significant effect indirectly. The results obtained in this study can be utilized in curriculum development in mathematics modules.
Effectiveness of recursive estimation of time series analysis and forecastingCooray, TMJAhttp://dl.lib.uom.lk/handle/123/12352023-01-17T03:42:44Z0006-01-01T00:00:00ZEffectiveness of recursive estimation of time series analysis and forecasting
Cooray, TMJA
This study is about practical forecasting and analysis of time series, to investigate the effectiveness of recursive estimation of time series analysis and forecasting performance for real data sets. It addresses the question of how to analyze time series data, identify structure, explain observed behavior, modeling those structure and how to use insight gained from the analysis to make informed forecasts. For the purpose of the study total production of paddy and total demand of electricity in Sri Lanka were used. Those values were obtained from the Annual Bulletin, published. by the Central Bank of Sri Lanka. The thesis is organised into two parts. The first part is a course of methods and theory. Time series modelling concepts are described with 'abstract' definitions related to actual time series to give empirical meaning and facilitate understanding. Formal algorithms are developed and methods are applied to analyze data. Two detailed case studies are presented, illustrating the practicalities that arise in time series analysis forecasting. The second part is a course of applied time series analysis and forecasting. It shows how to build the models and perform the analyses shownin the first part using the our own software called "Space" and another downdable software called the "BATS" application program The first few chapters are concerned with sing theoretical aspects of en-bloc time series models such as the seasonal decomposition method exponential smoothing method, Winter's seasonal method, and the ARIMA methodology to describe the' behaviour of the data series. Even though fairly general, these model do not account for the uncertainties due to the specific choice of trend / seasonal! level. The main drawbacks in this study are its lack of accessing model uncertainties, when choosing the recursive estimation of time series models based on the Kalman filter. Therefore we used an approach -that incorporates all uncertainties involved in the time series modelling simultaneously. Dynamic state space models provided an excellent basis for constructing and forecasting models for a number of reasons. In particular recursive estimation of time series based on the use of discounting techniques proved to be extremely useful in practice. Many practitioners have a natural feel for the discounting concept, and furthermore when one discounting factor has been specified, the standard technique may be utilised. in addition to that the Kalman filter based on state space form and Bayesian models can be used to analyse the incomplete data set using EM algorithms. The last two chapters were devoted for empirical evaluation of data series in order to investigate the effectiveness of recursive estimation of time series. According to the forecast performance of recursive time series models are much more accurate than the en-bloc models. This means that the mean percentage error (MAP E) recursive estimation oftime series model is relatively small (nearly 0.5%) so that this method gives higher degrees of accuracy. The recursive estimation of time series models can play an important role of time series modelling. However, these procedures are based on the predictor-corrector type algorithms. Hence without identifying the appropriate structure the variation of parameters could be implemented in contrast to "en-bloc" procedure s which could be used only after assuming the specific type of parameter variation;
0006-01-01T00:00:00ZRationale and validity of field selection procedure in technical and engineering educationWickramasinghe, SChttp://dl.lib.uom.lk/handle/123/11372023-01-17T03:42:13Z0006-01-01T00:00:00ZRationale and validity of field selection procedure in technical and engineering education
Wickramasinghe, SC
Selection of fields in graduate and technician courses in engineering is a problem, specially found in Sri Lanka
There is probably no research work reported on issues and strategies to overcome this problem
Issues of field selection in Sri Lanka for engineering courses of degree and technician levels are discussed in this thesis
Statistical data available and collected for the work are analysed and evaluated
Recommendations are made for modalities of field selection for category mentioned
The entry criteria, methods used and performance will be studied by relating the input and output data. Analysis will be done taking into account the restrictions imposed by delays in results, district quota etc. and their effect on the individuals and society as a whole. Personality factors of the students such as home background, financial problems and temperament have not been considered. In addition, the standard of each institution, the quality of teaching, training and methods of evaluation were not taken into account. Even though these factors bear an effect on the outcome, they are not easily measurable in quantitative terms and as such they have been left out.;
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