Using Bayesian Networks and Machine Learning to Predict Computer Science Success

Bayesian Networks and Machine Learning techniques were evaluated and compared for predicting academic performance of Computer Science students at the University of Cape Town. Bayesian Networks performed similarly to other classification models. The causal links AQ1 inherent in Bayesian Networks allow for understanding of the contributing factors for academic success in this field. The most effective indicators of success in first-year ‘core’ courses in Computer Science included the student’s scores for Mathematics and Physics as well as their aptitude for learning and their work ethos. It was found that unsuccessful students could be identified with ≈91% accuracy. This could help to increase throughput as well as student wellbeing at university

Techno-functional properties of the selected antifungal predominant LAB isolated from fermented acorn (Quercus persica)

Improving the quality and shelf life of wheat bread using controlled novel sourdoughs is a promising biotechnology process. In the present study, after molecular identification of the selected antifungal predominant lactic acid bacteria (LAB) isolated from fermented acorn, the isolate was used as starter culture to produce controlled fermented acorn (CFA). Then the characteristics of wheat bread containing non-fermented acorn, controlled and spontaneously fermented acorn were evaluated in terms of textural properties, antioxidant capacity, in situ antifungal activity and overall acceptability in comparison with the control. Sequencing results of the PCR products led to the identification of Pediococcus acidilactici as the selected antifungal LAB isolate. The effect of controlled fermentation on production of bioactive ingredients from acorn was also verified using GC/MS analysis. Crumb texture profile analysis revealed the significant effect (P < 0.05) of acorn substitution on crumb hardness, porosity and loaf specific volume. DPPH radical scavenging activity was remarkably increased after addition of CFA. Surface growth of A. flavus on wheat bread containing CFA was also significantly lower than the other samples. Sensory acceptance of the supplemented bread didn�t show significant difference with control. Accordingly, wheat bread enrichment with CFA, not only improve its quality and shelf life but also increase its safety and techno-functionality characteristics. © 2020, Springer Science+Business Media, LLC, part of Springer Nature

Preparation and characterization of surface-modified montmorillonite by cationic surfactants for adsorption purposes

In this study, surface modification of montmorillonite with three types of cationic surfactants was investigated by adding different levels of surfactants corresponding to the CEC (cation exchange capacity) of montmorillonite; the surfactants were tetradecyl trimethylammonium bromide, cetyl trimethylammonium bromide, and didodecyl dimethylammonium bromide. Moreover, montmorillonite and modified montmorillonites were characterized by X-ray diffraction, Fourier transforms infrared spectroscopy, thermal analysis, contact angle, and zeta potential. Their surface morphologies were also determined by using the field emission scanning electron microscopy. The basal spacing of montmorillonite increased after intercalation of cationic surfactants, while the maximum basal spacing was influenced by increasing the molar mass of the surfactant. Also, for the same surfactant, maximum basal spacing enhanced when the CEC increased from 1:0 to 2:0. The results of Fourier transforms infrared spectroscopy indicated that intercalation of surfactants between montmorillonite layers leads to changes in functional groups of modified montmorillonite. To summarize, we successfully modified montmorillonite, making it a potential nanoadsorbent that could be used for the adsorption of valuable compounds such as phenolic compounds from wastewaters and byproducts of food industries

Students' performance prediction model using meta-classifier approach

Students’ performance is vitally important at all stages of education, particularly for Higher Education Institutions. One of the most important issues is to improve the performance and quality of students enrolled. The initial symptom of at-risks’ students need to be observed and earlier preventive measures are required to be carried out so as to determine the cause of students’ dropout rate. Hence, the purpose of this research is to identify factors influencing students’ performance using educational data mining techniques. In order to achieve this, data from different sources is employed into a single platform for pre-processing and modelling. The design of the study is divided into 6 different phases (data collection, data integration, data pre-processing such as cleaning, normalization, and transformation, feature selection, patterns extraction and model optimization as well as evaluation. The datasets were collected from a students’ information system and e-learning system from a public university in Malaysia, while sample data from the Faculty of Engineering were used accordingly. This study also employed the use of academic, demographical, economical and behaviour e-learning features, in which 8 different group models were developed using 3 base-classifiers; Decision Tree, Artificial Neural Network and Support Vector Machine, and 5 multi-classifiers; Random Forest, Bagging, AdaBoost, Stacking and Majority Vote classifier. Finally, the highest accuracy of the classifier model was optimized. At the end, new Students’ Performance Prediction Model was developed. The result proves that combination demographics with behaviour using a meta-classifier model with optimized hyper parameter produced better accuracy to predict students’ performance

Mining student information system records to predict students’ academic performance

Educational Data Mining (EDM) is an emerging field that is concerned with mining and exploring the useful patterns in educational data. The main objective of this study is to predict the students’ academic performance based on a new dataset extracted from a student information system. The dataset was extracted from a private university in the United Arab of Emirates (UAE). The dataset includes 34 attributes and 56,000 records related to students’ information. The empirical results indicated that the Random Forest (RF) algorithm was the most appropriate data mining technique used to predict the students’ academic performance. It is also revealed that the most important attributes that have a direct effect on the students’ academic performance are belonged to four main categories, namely students’ demographics, student previous performance information, course and instructor information, and student general information. The evidence from this study would assist the higher educational institutions by allowing the instructors and students to identify the weaknesses and factors affecting the students’ performance, and act as an early warning system for predicting the students’ failures and low academic performance