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Undergraduate Students Performance Using Educational Data Mining

Thesis Info

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Author

Mustafa, Raheela

Program

PhD

Institute

NED University of Engineering & Technology

City

Karachi

Province

Sindh

Country

Pakistan

Thesis Completing Year

2017

Thesis Completion Status

Completed

Subject

Computer Science

Language

English

Link

http://prr.hec.gov.pk/jspui/bitstream/123456789/13675/1/Raheela_Mustafa_Computer_Science_HSR_2017_NED_UET_Karachi_14.03.2017.pdf

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676727853123

Similar


The tremendous growth in electronic data of universities creates the need to have some meaningful information extracted from these large volumes of data. The advancement in data mining field makes it possible to mine educational data for improving the quality of the educational processes. This dissertation, thus, uses data mining methods to study the performance of undergraduate students. Two aspects of students’ performance have been focused on. Firstly, predicting students’ academic achievement at the end of a 4-year study programme, and secondly, studying typical progressions and combining them with prediction results. Predicting performance of students at the end of a university degree at an early stage of the degree program would help universities not only to focus more on bright students but also to initially identify students with low academic achievement and find ways to support them. The data of four academic cohorts of three faculties at NED University of Engineering & Technology, comprising 347 undergraduate students of Computer Science and Information Technology, 587 undergraduate students of Civil Engineering and 430 undergraduate students of Electronic Engineering, have been mined with different classifier models. The results show that it is possible to predict the graduation performance in final year at university using only pre-university marks and marks of first and second year courses, no socio-economic or demographic features, with a reasonable accuracy. Using only marks for students’ performance prediction and no other socio-demographic features will enable university administration to develop an educational policy that is easier to implement. This is the reason to investigate whether acceptable results can be obtained with marks only. Further, data of one cohort of students are used to predict students’ performance of the following cohort to test the generalizability and therefore the actionability of our approach. Moreover, using these classifiers, we explore how to derive courses that can serve as effective indicators for students’ performance at an early stage of the degree program for timely intervention. Indeed, once such courses are put in evidence, performance of students at the end of a course could be predicted and would allow for intervention while the indicator courses are actually taking place. A pragmatic policy is proposed to derive those indicators based on decision trees, a kind of classifiers that is explained in Chapter 2, Section 2.1.3.1. As the obtained decision trees have a lower accuracy than two other classifiers, though it is still acceptable, the goodness of the pragmatic policy needs to be further investigated. Therefore, we investigate how academic performance of students evolves over the four-year degree as a kind of triangulation. For this purpose, students of two consecutive cohorts of Computer Science and Information Technology have been clustered each year taking their final examination marks in individual courses in each of the four years. Xmeans and K-means clustering taking Euclidean distance for both algorithms have been applied. We put in evidence interesting typical progressions in particular students who have low marks all the way through their studies and students with high marks throughout their studies. The key contribution of our work is to understand the benefits of the pragmatic policy that is proposed earlier in this work. It turns that our pragmatic policy uncovers (almost) all the targeted students: students with low marks and students with high marks. Therefore, its implementation can be recommended.
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Preparation, Characterization, in Silico Modelling and Bioavailability of Nanosuspention for Poorly Water Soluble Drugs Dexibuprofen and Domperidone

The aim of this study was to prepare and evaluate the impact of polymers on fabricating stable dexibuprofen (Dexi) and domperidone nanocrystals with enhanced therapeutic potential, using a low energy, anti-solvent precipitation method (APSP). Combinations of Hydroxypropyl methyl cellulose-Polyvinyl pyrrolidone and Hydroxypropyl methyl cellulose-Eudragit were shown to be very effective in producing stable dexi-nanocrystals with particle sizes of 85.0±2.5 nm and 90±3.0 nm, and polydispersity of 0.179± 0.01, 0.182± 0.02 respectively. Produced nanocrystals of DOMP and Ethucel combination, the average particle size and polydespersity index were found to be approximately 130.00 ± 3.0 nm, 0.15 ± 0.01, moreover, the combination of hydroxyl propyl methyl cellulose and polyvinyl alcohol also found very affective to produce DOMP nanocrystals with small particle size (200.0 ±3.5nm) and PDI (0.2±0.02). The stability studies conducted for 90 days demonstrated that nanocrystals stored at 2-8°C and 25°C were more stable than those at 40°C. The maximum recovery of dexi-nanocrystals was observed from the formulations using the combination of Hydroxypropyl methylcellulose- Polyvinyl pyrrolidone and Hydroxy propyl methyl cellulose- Eudragit, which equated to98% and 94% of the nominal active drug content respectively. In case of DOMP nanosuspensions stored at 2-8 ºC and room temperature (25 ºC) exhibited higher stability compared to the samples stored at 40 ºC. Crystallinity of the processed and unprocessed dexi and DOMP particles was confirmed using Differential Scanning Calorimetry, Powdered X-ray Diffraction and Transmission Electron Microscope. FTIR studies for both Dexi and DOMP nanocrystals confirmed that the process did not affect chemical integrity of DOMP. The DOMP nanoparticles exhibited significantly enhanced dissolution rate (P<0.05) compared to the raw counterpart. The saturation solubility of the ix dexinanocrystals was substantially increased to 270.0±3.5 µg/mL compared to the raw dexi in water (51.0± 2.0μg/mL) and stabilizer solution (92.0 ± 3.0 μg/mL). Enhanced dissolution rate (P<0.05) was observed for the dexi-nanocrystals compared to the unprocessed drug substance and marketed tablets. The saturation solubility of the DOMP nanocrystals was significantly increased to 2700.0± 3.5 µg/mL compared to the raw dexi in water (952.0± 2.0μg/mL) and stabiliser solutions which include HPMC (1253.0± 3.5) and HPMC-PVA (1133.0 ± 3.5 µg/ml). DOMP nanocrystals resulted in enhanced dissolution rate (P<0.05) compared to the unprocessed drug substance. Molecular Modeling studies underpinned and substantiated the experimental studies, including the impact of polymers on nanoparticle sizes, nanocrystals recovery and stability studies of the produced dexinanocrystals. The highest potential was shown by combination ofHPMC-PVP- Dexi (-4.7) and HPMC EUD- DEXi (-4.6) . In case of DOMP, Molecular Modelling studies underpinned the molecular level understanding of the DOMP-Polymer nanocrystal interaction and substantiated the experimental studies, including the impact of polymers on nanocrystals sizes, and stability studies of the produced DOMP nanocrystals. Ethocel as single polymer showed highest binding potential (-27.26±0.24 ) for DOMP nanocrystals and HPMC-PVA was found the best dimer to effectively bind (-25.22 ± 0.79) with the nanocrystals with subsequent small particle size and high stability profile. The findings of the nociceptive assay showed that the dexinanocrystals exhibited significant analgesia, compared to the raw dexibuprofen and the control standard diclofenac sodium. The analgesic effect was, however, produced at much lower doses (5mg/kg) than that of control standard, diclofenac sodium (20mg/kg) and dexibuprofen counterparts (40 mg/kg). While theactivity results in anti-emetic assay confirmed that the nano-formulation provided significant anti-emetic effect (P< 0.05) x at a dose of 0.5 mg/kg, comparable to that of the unprocessed DOMP and the control standard Metoclopramide administered at the doses of 1.0m/kg and 30mg/kg respectively. However, DOMP nanoparticles showed the anti-emetic effect at lower doses compared to that of control standard and unprocessed DOMP. Furthermore, there was observed a substantial % increase of antiemetic potential for DOMP nanoparticles compared to the unprocessed DOMP and the standard metoclopramide.