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Database Systems A Practical Approach To Design Implementation And Management

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.

https://dl.acm.org/doi/pdf/10.1145/3293881.3295783

Predicting academic performance: a systematic literature review

The prediction of student academic performance has drawn considerable attention in education. However, although the learning outcomes are believed to improve learning and teaching, prognosticating the attainment of student outcomes remains underexplored. A decade of research work conducted between 2010 and November 2020 was surveyed to present a fundamental understanding of the intelligent techniques used for the prediction of student performance, where academic success is strictly measured using student learning outcomes. The electronic bibliographic databases searched include ACM, IEEE Xplore, Google Scholar, Science Direct, Scopus, Springer, and Web of Science. Eventually, we synthesized and analyzed a total of 62 relevant papers with a focus on three perspectives, (1) the forms in which the learning outcomes are predicted, (2) the predictive analytics models developed to forecast student learning, and (3) the dominant factors impacting student outcomes. The best practices for conducting systematic literature reviews, e.g., PICO and PRISMA, were applied to synthesize and report the main results. The attainment of learning outcomes was measured mainly as performance class standings (i.e., ranks) and achievement scores (i.e., grades). Regression and supervised machine learning models were frequently employed to classify student performance. Finally, student online learning activities, term assessment grades, and student academic emotions were the most evident predictors of learning outcomes. We conclude the survey by highlighting some major research challenges and suggesting a summary of significant recommendations to motivate future works in this field.

Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

Student performance modelling is one of the challenging and popular research topics in educational data mining (EDM). Multiple factors influence the performance in non-linear ways; thus making this field more attractive to the researchers. The widespread availability of e ducational datasets further catalyse this interestingness, especially in online learning. Although several EDM surveys are available in the literature, we could find only a few specific surveys on student performance analysis and prediction. These specific surveys are limited in nature and primarily focus on studies that try to identify possible predictor or model student performance. However, the previous works do not address the temporal aspect of prediction. Moreover, we could not find any such specific survey which focuses only on classroom-based education. In this paper, we present a systematic review of EDM studies on student performance in classroom learning. It focuses on identifying the predictors, methods used for such identification, time and aim of prediction. It is significantly the first systematic survey of EDM studies that consider only classroom learning and focuses on the temporal aspect as well. This paper presents a review of 140 studies in this area. The meta-analysis indicates that the researchers achieve significant prediction efficiency during the tenure of the course. However, performance prediction before course commencement needs special attention.

Student performance analysis and prediction in classroom learning: A review of educational data mining studies

Ionic liquid and surfactant functionalized ZnO nanoadsorbent for Recyclable Proficient Adsorption of toxic dyes from waste water

One of the most challenging tasks in the education sector in India is to predict student's academic performance due to a huge volume of student data. In the Indian context, we don't have any existing system by which analyzing and monitoring can be done to check the progress and performance of the student mostly in Higher education system. Every institution has their own criteria for analyzing the performance of the students. The reason for this happing is due to the lack of study on existing prediction techniques and hence to find the best prediction methodology for predicting the student academics progress and performance. Another important reason is the lack in investigating the suitable factors which affect the academic performance and achievement of the student in particular course. So to deeply understand the problem, a detail literature survey on predicting student’s performance using data mining techniques is proposed. The main objective of this article is to provide a great knowledge and understanding of different data mining techniques which have been used to predict the student progress and performance and hence how these prediction techniques help to find the most important student attribute for prediction. Actually, we want to improve the performance of the student in academic by using best data mining techniques. At last, it could also provide some benefits for faculties, students, educators and management of the institution.

/pdf/literature-survey-on-student-s-performance-prediction-in-2y1jwi4i1w.pdf

Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques

Educational Data Mining (EDM) is one of the crucial application areas of data mining which helps in predicting educational dropout and hence provides timely help to students. In Indian context, predicting educational dropouts is a major problem. By implementing EDM, we can predict the learning habits of the student. At present EDM has not been introduced at higher education level. Due to this we cannot recognize the genuine problems of students during their education. The objective of this analysis is to find the existing gaps in predicting educational dropout and find the missing attributes if any, which my further contribute for better prediction. After that we try to find the best attributes and DM techniques which are frequently used for dropout prediction. Based on the combination of missing attribute and best attribute of student data thus far, a new algorithm can be tested which may overcome the shortcomings of previous work done.

/pdf/literature-survey-on-educational-dropout-prediction-c3hx69zf24.pdf

Literature Survey on Educational Dropout Prediction

Accurate knowledge of the alignment of conduction and valence bands of layers at the heterojunction and warrant knowledge of the band offsets at the interface is essential for Zn1−x Sb x O/ZnO based quantum well device designing and modeling. Under this scenario, valence band offsets of Zn1−x Sb x O/ZnO heterostructures grown by the pulsed laser deposition technique was measured by photoelectron spectroscopy and consequently, the conduction band offset was calculated by UV-visible spectroscopy. The change in band alignment has been observed with the dopant (Sb) concentration. Ratios of conduction band offset to valence band offset were estimated to be 1.67 and 0.04 for x = 0.03 and 0.06, respectively, for Sb doped films. A Type-II band alignment was observed at the Zn0.97Sb0.03O/ZnO interface, whereas the Type-I band alignment took place at the Zn0.94Sb0.06O/ZnO interface.

Band offset measurements in Zn1−x Sb x O/ZnO hetero-junctions

Reduction in point defects of sol–gel derived ZnO thin films with oxygen ambient

Crack free, uniform and high quality ultrathin films (∼ 2–3 nm) of zinc oxide were achieved by cost effective and simplest wet chemical sol-gel route. The films were deposited at different spinning speed (1500 to 6000 rpm) on the silicon substrates. The average thickness per layer for each sample was measured by using surface profiler. The thickness of the film is reduced linearly with the spinning speed but the uniformity of the films was degraded for deposition at higher RPM (≤ 3500). The uniformity of deposited layers was observed by Photoluminescence mapping and it was found that the cracks density enhanced for higher spinning. In AFM images for the deposited films the grains were appeared with outlying distribution and the average grain size was increased with spinning speed. The films possessed high uniformity with lowest thickness (∼ 5 nm) at 3000 rpm. Further reduction in thickness was achieved by decreasing the concentration of zinc in the prepared sol. It was observed that the films were still smooth but the thickness extensively reduced up to 2 nm. The effect of substrate size on the quality of thin film also observed. To study this effect the films were deposited on 1 × 1, 2 × 2, 3 × 3 and 5 × 5 cm2 substrates at 3000 rpm. It was observed by PL mapping that for substrates with large area the density of cracks was enhanced.

Amanpal Singh

Papers

Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques

Literature Survey on Educational Dropout Prediction

Band offset measurements in Zn1−x Sb x O/ZnO hetero-junctions

Reduction in point defects of sol–gel derived ZnO thin films with oxygen ambient

Deposition of ZnO ultrathin films by sol-gel route