42,395 research outputs found
The impact of personality on scholarly performance in the light of intervening job of scholarly motivation
This examination and coming about commitment are the consequence of instructive motivation intervened in the relationship between receptiveness to experience, and measures with insightful performance. Based on the alternate elements (e.g., IQ, learning moves close and biological variables) can influence academic execution that can be broken down in future examinations.Purpose: This comparative study holistically assesses the effect of personality on scholastic motivation and scholarly performance. Design/Methodology/Approach: The contribution and the relevant methodology are based on an educational motivation interceded the association between openness to experience and standards with scholarly performance. The research sample consists of students who willfully participated and they were approached to finish a personality poll (NEO-FFI), and a scholastic motivation survey (AMS-C 28, included GPA and statistic information) on a pioneering critical comparative structural model. Findings: Based on the implied arguments and yielded results, the article considers the interceding role of scholastic motivation about personality and performance. The nature of these relations, can be a point of takeoff to assist inquires about this issue. Practical implications: Based on addressing its structural purposes, the study sheds a new light on the conscientiousness that predicts both intrinsic and extrinsic motivation. Earlier investigations show that there is a relationship between openness to involvement and insights. Originality/Value: Although this study builds upon recent studies about character, motivation and different factors can affect scholastic performance that can be analyzed in future investigations as an innovative idea for the harmonization in this field.peer-reviewe
A Multi-Gene Genetic Programming Application for Predicting Students Failure at School
Several efforts to predict student failure rate (SFR) at school accurately
still remains a core problem area faced by many in the educational sector. The
procedure for forecasting SFR are rigid and most often times require data
scaling or conversion into binary form such as is the case of the logistic
model which may lead to lose of information and effect size attenuation. Also,
the high number of factors, incomplete and unbalanced dataset, and black boxing
issues as in Artificial Neural Networks and Fuzzy logic systems exposes the
need for more efficient tools. Currently the application of Genetic Programming
(GP) holds great promises and has produced tremendous positive results in
different sectors. In this regard, this study developed GPSFARPS, a software
application to provide a robust solution to the prediction of SFR using an
evolutionary algorithm known as multi-gene genetic programming. The approach is
validated by feeding a testing data set to the evolved GP models. Result
obtained from GPSFARPS simulations show its unique ability to evolve a suitable
failure rate expression with a fast convergence at 30 generations from a
maximum specified generation of 500. The multi-gene system was also able to
minimize the evolved model expression and accurately predict student failure
rate using a subset of the original expressionComment: 14 pages, 9 figures, Journal paper. arXiv admin note: text overlap
with arXiv:1403.0623 by other author
Applying Explainable Artificial Intelligence to Develop a Model for Predicting the Supply and Demand of Teachers by Region
Among various methods to improve educational conditions, efforts are being made to reduce the number of students per teacher. However, for policy decisions it is necessary to reflect multiple factors such as changes in the number of students over time and local requirements. Time-series analysis-based statistical models have been used as a method to inform policy decisions. However, the existing statistical models are linear and the accuracy of their predictions is inferior. Also, since there are both internal and external factors that influence the number of students and thus the prediction of the number of required teachers, it is necessary to develop a model that reflects this. Therefore, in this study, an artificial intelligence model based on machine learning was developed using the XGBoost technique, and feature importance, partial dependence plot, and Shap Value were used to increase the model's explanatory potential. The model showed a performance of less than 0.03 RMSE, and it was confirmed that among several factors the economically active population had the most significant effect on the number of teachers. Through this study, it was possible to examine the applicability of an artificial intelligence model with improved explanatory possibilities in predicting the number of teachers
Can AI help predict a learner’s needs? Lessons from predicting student satisfaction
The successes of using artificial intelligence (AI) in analysing large-scale data at a low cost make it an attractive tool for analysing student data to discover models that can inform decision makers in education. This article looks at the case of decision making from models of student satisfaction, using research on ten years (2008–17) of National Student Survey (NSS) results in UK higher education institutions. It reviews the issues involved in measuring student satisfaction,
shows that useful patterns exist in the data and presents issues involved in the value within the data when they are examined without deeper understanding, contrasting the outputs of analysing the data manually, and with AI. The article discusses risks of using AI and shows why, when applied in areas of education that are not clear, understood and widely agreed, AI not only carries risks to a point that can eliminate cost savings but, irrespective of legal requirement, it cannot provide algorithmic accountability
The impact of location on housing prices: applying the Artificial Neural Network Model as an analytical tool.
The location of a residential property in a city directly affects its market price. Each location represents different values in variables such as accessibility, neighbourhood, traffic, socio-economic level and proximity to green areas, among others. In addition, that location has an influence on the choice and on the offer price of each residential property. The development of artificial intelligence, allows us to use alternative tools to the traditional methods of econometric modelling. This has led us to conduct a study of the residential property market in the city of Valencia (Spain). In this study, we will attempt to explain the aspects that determine the demand for housing and the behaviour of prices in the urban space. We used an artificial neutral network as a price forecasting tool, since this system shows a considerable improvement in the accuracy of ratings over traditional models. With the help of this system, we attempted to quantify the impact on residential property prices of issues such as accessibility, level of service standards of public utilities, quality of urban planning, environmental surroundings and other locational aspects.
Digital Information Needs for Understanding Cell Divisions in the Human Body
Information needs for understanding cell divisions in the human body is important in the learning process. Although sketches, images and blocks of 3D puzzles were used for teaching and learning, unfortunately those tools are static and incapable of being manipulated. Hence, digital information is the best tool for the teaching and learning of cell divisions in the human body via software applications. A cell motion is a digital information application developed using leap motion to demonstrate cell movement in the human body. However, the factors that influence students towards adopting this application are not obvious and often ignored. The method for evaluating the factors influencing its user's acceptance is the Technology Acceptance Model (TAM) via a questionnaire distributed among medical students to gain statistically valid quantitative results through hypothesis-testing. The result indicates that digital information needs for the understanding of cell divisions in the human body are influenced by the user's Perceived Ease of Use (PEOU) and Perceived Usefulness (PU). However, the Attitude (AT) towards use did have a significant effect on PU and PEOU. Moreover, PEOU had a strong and significant influence on PU, while AT positively influenced users' behavioural intention (BI) of using digital information needs for the understanding of cell divisions in the human body
“After all, most of the ‘Myth’ has some blurred empirical foundation: determinants of University level performance of students: case study of Rural Development Course covered in 2008 with in Mekelle University"
There is a widely accepted claim which insists that students’ performance at Ethiopian universities is constrained by poor educational input provided at high school level and especially by students’ limited capacity to communicate in English. For the specific course analyzed under this paper the impact of high school performance is found to be positive but very marginal. But based on Grade point average there is positive and significant association between high school performance and university performance, especially at freshman level. But this strong association is not specifically related to math or English performance per se but to over all performance.Education attendance university class-size student performance
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