The document discusses an index called the e-LYI that provides an innovative way to evaluate how well e-learning methodologies have been implemented on campus. It notes that any metric used to measure returns on investment must account for both financial and academic objectives to ensure long-term program success. References are provided relating to using new technologies like gesture-based computing and brain-computer interfaces to enhance simulation and learning.
Introduction to the Workshop "Digital Storytelling: An Effective Mechanism in...natashabukharov
Digital storytelling can enhance the learning process: this student-centered activity engages learners in social interaction, collaboration, and cooperation and it builds their awareness of language use, which becomes especially important in foreign language acquisition. This presentation introduces the workshop “Digital Storytelling: An Effective Mechanism in Foreign Language Acquisition”. During the workshop, the participants will learn what is needed for integrating the digital narration into curriculum in order to improve students’ speaking proficiency.
Digitising Education : A case for AI and IoTAshish Gupta
This slides gives a rudimentary idea of Indian education sector and proposes a high-level use case for AI and IoT devices to help resolve these issues.
M.Phil Computer Science Parallel and Distributed System ProjectsVijay Karan
List of Parallel and Distributed System IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Parallel and Distributed System for M.Phil Computer Science students.
Introduction to the Workshop "Digital Storytelling: An Effective Mechanism in...natashabukharov
Digital storytelling can enhance the learning process: this student-centered activity engages learners in social interaction, collaboration, and cooperation and it builds their awareness of language use, which becomes especially important in foreign language acquisition. This presentation introduces the workshop “Digital Storytelling: An Effective Mechanism in Foreign Language Acquisition”. During the workshop, the participants will learn what is needed for integrating the digital narration into curriculum in order to improve students’ speaking proficiency.
Digitising Education : A case for AI and IoTAshish Gupta
This slides gives a rudimentary idea of Indian education sector and proposes a high-level use case for AI and IoT devices to help resolve these issues.
M.Phil Computer Science Parallel and Distributed System ProjectsVijay Karan
List of Parallel and Distributed System IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Parallel and Distributed System for M.Phil Computer Science students.
كنيسه فى الثغر تاريخ الكنيسه الانجيليه بالابراهيميه الاسكندريه - بقلم القس ...Ibrahimia Church Ftriends
ان هذه الكنيسه منذ ولاداتها و فى كل رحله نموها هى ثمره النعمه الالهيه - ان الله هو الذى ولدها وعو الذى يقوتها و يربيها ويقدسها بعمل الروح القدس والكلمه المقدسه و فى اةقات الشده و الالم كان الله ايضا السند و المعين فهى اذا قصه الله و عمله فينا و بنا و هذه ايضا قصتك انت و فصتى انا ....و قصه كل انسان جاء به الله الى دائرة الكنيسه بصوره او باخرى ......
الكنيسه الانجيليه بالابراهيميه
IoT-based students interaction framework using attention-scoring assessment i...eraser Juan José Calderón
IoT-based students interaction framework using attention-scoring assessment in eLearning. Muhammad Farhan a,b, Sohail Jabbar a,c,d, Muhammad Aslam b, Mohammad Hammoudeh e, Mudassar Ahmad c, Shehzad Khalid f, Murad Khan g,Kijun Han d,
Accurate prediction and early identification of student at-risk of attrition are of high concern for higher
educational institutions (HEIs). It is of a great importance not only to the students but also to the
educational administrators and the institutions in the areas of improving academic quality and
efficient utilisation of the available resources for effective intervention. However, despite the different
frameworks and various models that researchers have used across institutions for predicting performance,
only negligible success has been recorded in terms of accuracy, efficiency and reduction of student
attrition. This has been attributed to the inadequate and selective use of variables for the predictive models.
AN INTEGRATED SYSTEM FRAMEWORK FOR PREDICTING STUDENTS’ ACADEMIC PERFORMANCE ...ijcsit
Accurate prediction and early identification of student at-risk of attrition are of high concern for higher educational institutions (HEIs). It is of a great importance not only to the students but also to the educational administrators and the institutions in the areas of improving academic quality and efficient utilisation of the available resources for effective intervention. However, despite the different frameworks and various models that researchers have used across institutions for predicting performance, only negligible success has been recorded in terms of accuracy, efficiency and reduction of student
attrition. This has been attributed to the inadequate and selective use of variables for the predictive models. This paper presents a multi-dimensional and an integrated system framework that involves considerable learners’ input and engagement in predicting their academic performance and intervention in HEIs. The purpose and functionality of the framework are to produce a comprehensive, unbiased and efficient way of predicting student performance that its implementation is based upon multi-sources data and database
system. It makes use of student demographic and learning management system (LMS) data from the institutional databases as well as the student psychosocial-personality (SPP) data from the survey collected from the student to predict performance. The proposed approach will be robust, generalizable, and possibly give a prediction at a higher level of accuracy that educational administrators can rely on for providing timely intervention to students.
Accurate prediction and early identification of student at-risk of attrition are of high concern for higher educational institutions (HEIs). It is of a great importance not only to the students but also to the educational administrators and the institutions in the areas of improving academic quality and efficient utilisation of the available resources for effective intervention. However, despite the different frameworks and various models that researchers have used across institutions for predicting performance, only negligible success has been recorded in terms of accuracy, efficiency and reduction of student attrition. This has been attributed to the inadequate and selective use of variables for the predictive models. This paper presents a multi-dimensional and an integrated system framework that involves considerable learners’ input and engagement in predicting their academic performance and intervention in HEIs. The purpose and functionality of the framework are to produce a comprehensive, unbiased and efficient way of predicting student performance that its implementation is based upon multi-sources data and database system. It makes use of student demographic and learning management system (LMS) data from the institutional databases as well as the student psychosocial-personality (SPP) data from the survey collected from the student to predict performance. The proposed approach will be robust, generalizable, and possibly give a prediction at a higher level of accuracy that educational administrators can rely on for providing timely intervention to students. --
THE USE OF COMPUTER-BASED LEARNING ASSESSMENT FOR PROFESSIONAL COURSES: A STR...IAEME Publication
Background/Objectives: While the increase in classroom technology, it is necessary to examine how assessment is administered through technology. The purpose of this study is to understand how students and faculty are perceived and examine the effectiveness of the computer-based assessment in professional education courses (Educational Technology) at Northern Iloilo Polytechnic State College, Iloilo, Philippines. Methods: The research design utilized in this study is mixed-method research. A computer-based assessment was utilized to assess students' performance in educational technology. This instrument was validated, and pilot tested to establish reliability. Each campus of NIPSC selected ten students of 70 as respondents during Academic Year 2016-2017. Frequency count, mean, standard deviation, and Wilcoxon signed-rank test were statistical tools used for data analyses. Findings: The study's finding showed a high score of students in the posttest ensured better performance of the students in educational technology. The increase in the posttest per performance level of the students was due to an accurate measure of what they have learned in educational technology. The majority of students users agreed that online assessment was fasters than the paper and pencil form. Also, users agreed that online assessment is contemporary and more systematic. They also stated that online assessment is consistent with the teaching style, but they are less anxious. Furthermore, according to faculty and students, ninety percent (90%) believed that computer-based assessment accurately measures what they are teaching and what they learned in school, respectively. Novelty: With the current situation that the education system is in new normal, computer-based learning is important in flexible learning. And assessment using technology is a great help to both faculty and students. Thus, state universities and colleges (SUCs) should adopt this innovation to help teaching and learning.
M-Learning Enabled Secure Exam Management Systems (MLESEMS)AM Publications
The advancement and proliferations in Information and Communication Technology (ICTs) has led to migration of learning beyond the traditional classroom Face-to- Face (F2F) to different types of learning such as Distance Flexible Learning (DFL), Electronic Learning (eLearning) and to everywhere and anytime known as Mobile Learning (M-Learning). This paper presents an attempt to exploit mobile technologies to simplify the exam management and performance assessment activities of a learning process. The work focuses on key aspects of mobile device and platform oriented design, light-weight and efficient implementation, interface usability issues related to fast and convenient question navigation, and performance assessment. A prototype system, implemented on Google Android OS, is also illustrated.
Applying adaptive learning by integrating semantic and machine learning in p...IJECEIAES
Adaptive learning is one of the most widely used data driven approach to teaching and it received an increasing attention over the last decade. It aims to meet the student’s characteristics by tailoring learning courses materials and assessment methods. In order to determine the student’s characteristics, we need to detect their learning styles according to visual, auditory or kinaesthetic (VAK) learning style. In this research, an integrated model that utilizes both semantic and machine learning clustering methods is developed in order to cluster students to detect their learning styles and recommend suitable assessment method(s) accordingly. In order to measure the effectiveness of the proposed model, a set of experiments were conducted on real dataset (Open University Learning Analytics Dataset). Experiments showed that the proposed model is able to cluster students according to their different learning activities with an accuracy that exceeds 95% and predict their relative assessment method(s) with an average accuracy equals to 93%.
7. The Yield Index “e-LYI” suggests an innovative
way to evaluate the implementation of e-
learning methodologies into campus based
environments
8.
9.
10. “Any ROI metric that institutions implement
must account for both financial and
academic objectives to ensure long-term
program success and acceptance.”
Adam Newman, Eduventures, Inc.
11. References
ConcurrentTechCorp (2012). Simulation & learning using gesture-based computing & brain computer
interfaces. Retrieved from: http://www.youtube.com/watch?v=nPyybhOntAU
Robinson, E. R.Ph., Ph.D.,Director of Distance Education, Shenandoah University School of Pharmacy
http://www.westga.edu/~distance/ojdla/fall43/robinson43.html
Kessler, S. (2011, March 23). Mobile by the numbers [Infographic] [Web log comment]. Retrieved from
http://mashable.com/2011/03/23/mobile-by-the-numbers-infogrpahic/
Kinect Education. (2011). The whole-person learner: kinecting the gaps in education. Retrieved from:
http://www.kinecteducation.com/blog/2011/12/06/the-whole-person-learner-kinecting-the-gaps-in-
education/
Quinn, C. (2012, July 30). Content Systems: Next Generation Opportunities. In Learning Solutions Magazine.
Retrieved from http://www.learningsolutionsmag.com/articles/976/