Assessment of Employee Using Simple Multi-Attribute Technique Exploiting Rank (SMARTER) and Behaviorally Anchor Rating Scale (BARS) Method

Lecturers' active role as the spearhead of higher education has an essential role in improving higher education quality and sustainability. Therefore, assessing work behaviour is needed to measure how lecturers participate in achieving the vision and mission, quality improvement, and service guarantee to students and complementary documentation. This condition became the basis of research. They are implementing decision support systems with Simple Multi-Attribute Rating Technique Exploiting Ranges (SMARTER) and Graphic Rating Scale (GRS) to measure a lecturer's behaviour by using multiple criteria. With the SMARTER method and  Behaviorally Anchor Rating Scale (BARS). By applying the impermeable BARS method, the work behaviour assessment process results in ease and accuracy that is more in line with the employees' behaviour being assessed. With the SMARTER approach, an assessment of employee work behaviour is produced, with 90% of alternatives used. The results are Good

Embracing the future: embedding digital repositories in the University of London

Digital repositories can help Higher Education Institutions (HEIs) to develop coherent and coordinated approaches to capture, identify, store and retrieve intellectual assets such as datasets, course material and research papers. With the advances of technology, an increasing number of Higher Education Institutions are implementing digital repositories. The leadership of these institutions, however, has been concerned about the awareness of and commitment to repositories, and their sustainability in the future. This study informs a consortium of thirteen London institutions with an assessment of current awareness and attitudes of stakeholders regarding digital repositories in three case study institutions. The report identifies drivers for, and barriers to, the embedding of digital repositories in institutional strategy. The findings therefore should be of use to decision-makers involved in the development of digital repositories. Our approach was entirely based on consultations with specific groups of stakeholders in three institutions through interviews with specific individuals. The research in this report was prepared for the SHERPA-LEAP Consortium and conducted by RAND Europe

Sentiment Analysis of Teachers Using Social Information in Educational Platform Environments

© 2020 World Scientific Publishing Company. Electronic version of an article published as International Journal on Artificial Intelligence Tools, Vol. 29, No. 02, 2040004 (2020): https://doi.org/10.1142/S0218213020400047.Learners’ opinions constitute an important source of information that can be useful to teachers and educational instructors in order to improve learning procedures and training activities. By analyzing learners’ actions and extracting data related to their learning behavior, educators can specify proper learning approaches to stimulate learners’ interest and contribute to constructive monitoring of learning progress during the course or to improve future courses. Learners-generated content and their feedback and comments can provide indicative information about the educational procedures that they attended and the training activities that they participated in. Educational systems must possess mechanisms to analyze learners’ comments and automatically specify their opinions and attitude towards the courses and the learning activities that are offered to them. This paper describes a Greek language sentiment analysis system that analyzes texts written in Greek language and generates feature vectors which together with classification algorithms give us the opportunity to classify Greek texts based on the personal opinion and the degree of satisfaction expressed. The sentiment analysis module has been integrated into the hybrid educational systems of the Greek school network that offers life-long learning courses. The module offers a wide range of possibilities to lecturers, policymakers and educational institutes that participate in the training procedure and offers life-long learning courses, to understand how their learners perceive learning activities and specify what aspects of the learning activities they liked and disliked. The experimental study show quite interesting results regarding the performance of the sentiment analysis methodology and the specification of users’ opinions and satisfaction. The feature analysis demonstrates interesting findings regarding the characteristics that provide indicative information for opinion analysis and embeddings combined with deep learning approaches yield satisfactory results.Peer reviewe

A novel approach of multimedia instruction applications in engineering education

Effective use of educational technology depends on knowledge of why and how to utilize technology to solve teaching and learning problems. The present study first conducts a systematic literature review of the limited studies undertaken on multimedia instruction applications for engineering education to critique the current status of knowledge in this area. The conventional qualitative content analysis method was employed for data analysis. The results highlighted the incompatibility of three basic educational elements i.e. engineering curriculum, educational resources and engineering students’ learning characteristics all of which posed major challenges in teaching and learning engineering courses. Multimedia instruction enhances engineering students’ understanding of engineering concepts, procedures, problems and solutions through direct visualization. Furthermore, it could indirectly assist students in achieving higher order learning levels and skills through enhancing or supporting educational resources and increasing students’ motivation. Mobile multimedia instruction and a student-generated multimedia learning approach to improve engineering education are suggested for future research

Classification Of Gender Using Global Level Features In Fingerprint For Malaysian Population

A new approach of algorithm based on the Mark Acree’s theory, focusing on fingerprint global extracted features is proposed and implemented for enhancing gender classification method. This proposed method can automatically execute the ridge calculation process from the 25mm2 fingerprint and enhance the forensic gender classification process. In this study, a relationship between fingerprint global features and a gender of person in Malaysian population is also explored, enhanced and improved by exploiting another five additional fingerprint features. A sample of 3000 fingerprints from 300 respondents of random selection are carefully taken before any relationship can be determined. For the classification part, five extracted features of the fingerprint are used which are Ridge Density (RD), Mean Ridge Count (RC), Ridge Thickness to Valley Thickness Ratio (RTVTR), White Lines Count (WLC) and Mean Pattern Types (PT). Two classification approaches which are the descriptive statistical and data mining are used in order to examine the classification of the gender by using the five extracted features. For data mining classification part, there are four popular machine learning classifiers used which are Bayesian Net.work (Bayes Net.), Multilayer Perceptron Neural Network (MLPNN), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). These four classifiers are used in the data mining task with five test cases each in order to find the accuracies of the gender classification. The accuracy of the results from the proposed method is compared to the Acree Method is shown in terms of relative error. For statistical approach using Ridge Density (RD), the relative error is 3.7% for male respondent and 4.1% for female respondent. Meanwhile, the overall performance of the result from the proposed method achieved more than 90% classification rate for all the classifiers. SVM emerges as the best classifier for all the different cases in order to classify the gender using the results from the proposed method

A novel approach of multimedia instruction applications in engineering education

Effective use of educational technology depends on knowledge of why and how to utilize technology to solve teaching and learning problems. The present study first conducts a systematic literature review of the limited studies undertaken on multimedia instruction applications for engineering education to critique the current status of knowledge in this area. The conventional qualitative content analysis method was employed for data analysis. The results highlighted the incompatibility of three basic educational elements i.e. engineering curriculum, educational resources and engineering students’ learning characteristics all of which posed major challenges in teaching and learning engineering courses. Multimedia instruction enhances engineering students’ understanding of engineering concepts, procedures, problems and solutions through direct visualization. Furthermore, it could indirectly assist students in achieving higher order learning levels and skills through enhancing or supporting educational resources and increasing students’ motivation. Mobile multimedia instruction and a student-generated multimedia learning approach to improve engineering education are suggested for future research. © 2005-2016 JATIT & LLS. All rights reserve