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Information and communication technologies (ICTs) are an integral part of our environment, and their uses vary across generations and among individuals. Today’s student population is made up of “digital natives” who have grown up under the ubiquitous influence of digital technologies, and for whom the use of ICT is common and whose daily activities are structured around media use. The aim of this study is to examine the impact of ICT use and digital skills on students’ academic performance and to explore the digital divide in France. Data were collected through face-to-face questionnaires administered to 1323 students enrolled in three French universities. Principal component analysis, a non-hierarchical k-means clustering approach and multilevel ordered logistic regression were used for data analysis and provide four main findings: first, poor investment in ICT affects students’ results; second, the ICT training offered by universities has little impact on students’ results; third, student performance improves with the innovative and collaborative use of ICTs; fourth, the acquisition of digital skills increases students’ academic performance. The results show that the digital divide still exists, and this raises questions about the effectiveness of education policies in France. They suggest also that organizational change in universities is essential to enable an exploitation of ICT.

Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students’ knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students’ performance. In this study, almost 70 papers were analyzed to show different modern techniques widely applied for predicting students’ performance, together with the objectives they must reach in this field. These techniques and methods, which pertain to the area of Artificial Intelligence, are mainly Machine Learning, Collaborative Filtering, Recommender Systems, and Artificial Neural Networks, among others.

What are students learning? Throughout the world, governments striving to improve educational quality are turning to national assessments to provide this much-needed information in key curriculum areas. The capacity for carrying out national assessments has grown remarkably in recent years, but it has not been matched by widespread use of their findings. This book seeks to maximize an appreciation for the value of such data and to assist countries in exploiting the knowledge that national assessments yield. [It] identifies the main factors affecting the use of national assessment findings. These include the political context in which an assessment is carried out, the nature of the assessment (census based or sample based), the assignment of accountability for the results, and the quality of assessment instruments. The book describes the type of information that the main report of a national assessment should contain, as well as other means of communicating findings to technical and nontechnical audiences. It outlines general considerations in translating national assessment results into policy and action, and examines specific procedures for using the data in policy making, educational management, teaching, and promoting public awareness. The topics addressed in this volume should be of interest to policy makers, educators, researchers, and development practitioners. (DIPF/Orig.).

This study examines the impact of gender and age differences on the performance of students from different Hungarian universities and colleges in online learning during the third wave of COVID-19. The survey responses were assessed using Partial Least Squares estimation technique. The research model attempts to understand the influence of environmental and situational variables (i.e., compatibility, accessibility, perception of online self-efficacy, mobility) on performance and satisfaction with online education. Apart from mobility, other indicators have significant impact on respondents’ performance. However, moderating effect of age and gender almost do not influence the performance of surveyed Hungarian students. The results demonstrate that gender impacts the compatibility → performance pathway. The age of respondents has no effect on relationships between environmental and situational variables and performance.

Chemical tests (qualitative analysis) on functional groups may improve students’ understanding of basic concepts about the structure of organic compounds and their reactivity. However, upper-secondary school students have difficulties in learning organic qualitative analysis. This research has studied whether the gender of students and school-type affect development of experimental reasoning on organic qualitative analysis. From three school-types, 50.2% males and 49.8% females were sampled through a multistage sampling procedure and participated in a cross-sectional survey. Data from 263 students were collected with the aid of diagnostic test on knowledge of organic qualitative analysis. A two-way between-groups ANOVA and independent-samples t-test were used to analyse the data. It was found no interaction effect of gender and school-type on students’ development of experimental reasoning on organic qualitative analysis.

Educational institutions are creating a considerable amount of data regarding students, faculty and related organs. This data is an essential asset for academic institutions as it has valuable insights, knowledge and intelligence for the policymakers. Students are the fundamental entities and primary source of data creation in any educational environment. The educational institutions need to distinguish students who are weak in their studies and require special attention and monitoring to improve their learning behaviours, future academic performances and factors that can affect their interpretation. This paper adopted a hybrid classification model using Decision tree and support vector machine (SVM) algorithms to predict students’ academic performance. We statistically analyzed and identified factors that can affect students’ academic performance. The dataset used is collected from Bachelor students of the City University of Science and Information Technology (CUSIT). The experimental results revealed 71.79%, 74.04% and 78.85% for decision tree, and 69.87%, 74.04% and 71.15% accuracy for SVM models respectively for different splits. The study identified seven different factors that can directly affect the students’ performance associated with educational institutions and social networks. Factors like “time spent on social networks,” “type of games playing on mobiles,” and “time spent on playing mobile games” significantly affect students’ performance.

Modern approaches in education technology, which make use of advanced resources such as electronic books, infographics, and mobile applications, are progressing to improve education quality and learning levels, especially during the spread of the coronavirus, which resulted in the closure of schools, universities, and all educational facilities. To adapt to new developments, students’ performance must be tracked in order to closely monitor all unfavorable barriers that may affect their academic progress. Educational data mining (EDM) is one of the most popular methods for predicting a student’s performance. It helps monitoring and improving students’ results. Therefore, in the current study, a model has been developed so that students can be informed about the results of the computer networks course in the middle of the second semester and 11 machine algorithms (out of five classes). A questionnaire was used to determine the effectiveness of using infographics for teaching a computer networks course, as the results proved the effectiveness of infographics as a technique for teaching computer networks. The Moodle (Modular Object-Oriented Dynamic Learning Environment) educational platform was used to present the course because of its distinctive characteristics that allow interaction between the student and the teacher, especially during the COVID-19 pandemic. In addition, the different methods of classification in data mining were used to determine the best practices used to predict students’ performance using the weka program, where the results proved the effectiveness of the true positive direction of functions, multilayer perceptron, random forest trees, random tree and supplied test set, f-measure algorithms are the best ways to categories.

Graduate students are unaware of their final qualification for a course. Even though there were many models available, few works with feature selection and prediction with no control over the number of features to be used. As a result of the lack of an improved performance forecasting system, students are only qualified on the second or third attempt. A warning system in place could help the students reduce their arrear count. All students undertaking higher education should obtain the qualification at their desired level of education without delay to transit to their careers on time. Therefore, there should be a predictive system for students to warn during the course work period and guide them to qualify in a first attempt itself. Although so many factors were present that affected the qualifying score, here proposed a feature selection technique that selects a minimal number of well-playing features. Also proposed a model Supervised Learning Approach to unfold Student's Academic Future Progression through Supervised Learning Approach for Student's Academic Future Progression (SLASAFP) algorithm that recommends the best fitting machine learning algorithm based on the features dynamically. It has proven with comparable predictive accuracy.

In the world of student education is an important component where the role of students is as someone who is psychologically ready to receive lessons or other input from the school. However, each student has different performance and development, therefore it is important to do monitoring so that student performance will always be monitored by the school for improving student quality maintenance. Also, in the process of valuing education for students needs to be done by giving an appreciation in the form of giving gifts or just giving words and motivation so that students can perform better in learning and participating in other activities at school. In terms of selecting students with good performance or those who have a very declining development using the school method not only assess students by one criterion but with several criteria to produce a decision that can be accepted by many people. Performance Students must also be monitored by the school or the related rights. In this paper, the student performance prediction was assessed with 5 criteria components and the result shows there are 10 very satisfy students, 10 satisfying students, 10 well students, and 10 Enough students from sample 40 students.

Now a day’s higher education is become more competitive. Students are the main pole of today’s education. All educational institutions are focusing on the quality improvement and change in the traditional evaluation methods. Due to the high competition among Private Universities and existing National Universities the scenario of evaluation methods has important role so that the students shall be kept on track as an active learner through their modified methods of evaluation. Consequently, the evaluation of students through traditional methods has limitations as it is based on the crisp boundaries. The students having a very small difference of marks can be placed into different grades. Also the students who have missed the chance of appearing for one of the subject head may be fail due to absolute method of grading. A soft computing model for students’ evaluation of student in educational institute using subject wise and other activities performance is developed in this paper. To consider uncertainties occur during the semester fuzzy logic technique is applied.