Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Educational data mining and learning analytics for 21st century higher education: A review and synthesis

Several challenges are associated with e-learning systems, the most significant of which is the lack of student motivation in various course activities and for various course materials. In this study, we used machine learning (ML) algorithms to identify low-engagement students in a social science course at the Open University (OU) to assess the effect of engagement on student performance. The input variables of the study included highest education level, final results, score on the assessment, and the number of clicks on virtual learning environment (VLE) activities, which included dataplus, forumng, glossary, oucollaborate, oucontent, resources, subpages, homepage, and URL during the first course assessment. The output variable was the student level of engagement in the various activities. To predict low-engagement students, we applied several ML algorithms to the dataset. Using these algorithms, trained models were first obtained; then, the accuracy and kappa values of the models were compared. The results demonstrated that the J48, decision tree, JRIP, and gradient-boosted classifiers exhibited better performance in terms of the accuracy, kappa value, and recall compared to the other tested models. Based on these findings, we developed a dashboard to facilitate instructor at the OU. These models can easily be incorporated into VLE systems to help instructors evaluate student engagement during VLE courses with regard to different activities and materials and to provide additional interventions for students in advance of their final exam. Furthermore, this study examined the relationship between student engagement and the course assessment score.

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Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

Personalized learning full-path recommendation model based on LSTM neural networks

The student’s performance prediction is an important research topic because it can help teachers prevent students from dropping out before final exams and identify students that need additional assistance. The objective of this study is to predict the difficulties that students will encounter in a subsequent digital design course session. We analyzed the data logged by a technology-enhanced learning (TEL) system called digital electronics education and design suite (DEEDS) using machine learning algorithms. The machine learning algorithms included an artificial neural networks (ANNs), support vector machines (SVMs), logistic regression, Naive bayes classifiers and decision trees. The DEEDS system allows students to solve digital design exercises with different levels of difficulty while logging input data. The input variables of the current study were average time, total number of activities, average idle time, average number of keystrokes and total related activity for each exercise during individual sessions in the digital design course; the output variables were the student(s) grades for each session. We then trained machine learning algorithms on the data from the previous session and tested the algorithms on the data from the upcoming session. We performed k-fold cross-validation and computed the receiver operating characteristic and root mean square error metrics to evaluate the models’ performances. The results show that ANNs and SVMs achieve higher accuracy than do other algorithms. ANNs and SVMs can easily be integrated into the TEL system; thus, we would expect instructors to report improved student’s performance during the subsequent session.

Using machine learning to predict student difficulties from learning session data

Early prediction of school dropout is a serious problem in education, but it is not an easy issue to resolve. On the one hand, there are many factors that can influence student retention. On the other hand, the traditional classification approach used to solve this problem normally has to be implemented at the end of the course to gather maximum information in order to achieve the highest accuracy. In this paper, we propose a methodology and a specific classification algorithm to discover comprehensible prediction models of student dropout as soon as possible. We used data gathered from 419 high schools students in Mexico. We carried out several experiments to predict dropout at different steps of the course, to select the best indicators of dropout and to compare our proposed algorithm versus some classical and imbalanced well-known classification algorithms. Results show that our algorithm was capable of predicting student dropout within the first 4-6weeks of the course and trustworthy enough to be used in an early warning system.

Early dropout prediction using data mining: a case study with high school students

E-Health is referred to the application of Information and Communication Technologies (ICT) to the wide range of issues affecting the health care field. For example this may include the process starting with the registration and the diagnosis, to patients' follow-up as well as the organizational management related to each of these activities. From a citizen' viewpoint, e-Health provides considerable advantages related to information personalization which in some cases can include alternative diagnoses. From a health professional's viewpoint, e-Health refers to the actual improved access to relevant information, which, for example, can be directly associated with major medical journals and associations, electronic prescribing and overall accessibility to patients' personalized health information by ease of access to Medical Records (MR). However, current solutions do not provide real time access to such MRs especially in cases of emergency such as accidents. Therefore, in this paper, we present a new solution for e-Health, named KAU-Health; KAU-Health is a new system based on cloud computing which allows hospital users to interact in real time, and obtain critical information, which is crucial for immediate decisions making.

KAU e-health mobile system

The teaching unit is a way to plan the teaching/ learning process about a content item that becomes the focus of a specific educative process, bringing consistency and significance This way of organizing knowledge and experience should consider the diversity of elements that contextualize the process (level of student development, social background, family and market, Project Curriculum, available resources), to regulate the practice of the contents, to select the basic objectives intended to achieve, the methodological guidelines with which they work and finally the teaching/learning experiences necessary to perfect the process In this research work we present a system that allow the organization of a set of teaching and learning activities and meets, at its highest level of detail all the elements that compose the curriculum: setting goals and content, design and development of activities and evaluation, organization of space and time, and providing the necessary resources

Cataloging teaching units: Resources, evaluation and collaboration

Based on the rapid development of technology-enhanced learning, this paper describes the conceptual process developed for the CSchool educational system, which aims to support administration of the educational process by applying Distributed User Interfaces (DUIs) to cloud services. CSchool encourages students, teachers and parents to use new technologies in the classroom. This implementation happens within the entire educational environment, taking advantage of easy-to-use distributed interfaces to facilitate learning-oriented interaction and collaboration between its users. CSchool is part of the “Escuela 2.0” (School 2.0) project designed to support and engage a large number of Spanish schools; it responds to the need to create and facilitate collaborative learning within the educational process.

Distributed User Interfaces in a Cloud Educational System

In this article we will try to use the services of Facebook, in a controlled environment, so that teachers can carry out their teaching. From the educational point of view, Facebook provides a new starting point, collaborative work, from plugins development tailored to the needs of teachers in schools. And this requires to take into account the hierarchical structure and the distribution of groups and students in the center, and, to have a system to monitor the work of the students for assessment and grading. With the work proposed here, we do not intend to create a social network, conceived as "Facebook", but including a plugin that allows the use of "Facebook" like a Learning Management System (LMS). These features would make the social network "Facebook", offer collaborative services for members of the educational community, and compete with other applications, like Google Apps Education. Moreover, being a LMS in a collaborative and education environment, facilitate the management, rating and monitoring of student activities. The justification for this system comes from the high number of students and teachers who already have an account on the social network, and they are already used to their patterns of communication and interaction. This adaptation may allow greater use of Information and Communication Technologies (ICT) and government resources that have been allocated to the project Escuela 2.0.

/pdf/using-facebook-for-collaborative-academic-activities-in-czjuetbxq9.pdf

Habib M. Fardoun

Papers

Early dropout prediction using data mining: a case study with high school students

KAU e-health mobile system

Cataloging teaching units: Resources, evaluation and collaboration

Distributed User Interfaces in a Cloud Educational System

Using facebook for collaborative academic activities in education