Artificial Intelligence Maturity Assessment in Leadership at Higher Education: A Case Study

There is a growing interest in artificial Intelligence (AI) as a research topic. Adapting to AI technologies has become essential for educational institutions, specifically educational leaderships in order to embrace AI trends in enhancing leadership practices. This study comes in response to the increased discussions of AI implementing in education, which has created the need for AI Maturity Model in education to help educational institutions assess their progress in AI adaptation. This paper investigated how are the leaderships at college of Arts and Social Sciences Departments in Sultan Qaboos University (SQU) in Oman, as a case study, has adapted AI technologies throughout AI Maturity lens. The study aims to assess the college AI Maturity level and the qualitative approach was used to collect the data via semi-structured interviews with the college\u27s heads and decision makers. The results showed that the leaderships at the college are updated with the AI developments, however, the academic departments are still in their early stages of using AI technologies as AI is still an emerging topic. The leaderships have good awareness of the importance of integrating AI in teaching-learning process at higher education institutions\u27 level and the effects of adapting such technologies. Through in-depth interviews and the qualitative data analysis, one key finding was that College of Arts and Social Science at SQU is establishing good infrastructure for AI revolution at higher education according to the AI 5Ds cycle as there are some significant efforts to raise the awareness of the importance and role of AI in higher education and some individual AI implementations. However, more clear plans and work are needed for the move to the next phases of applying AI technologies in H

Hydrochemistry and stable isotopes (δ 18 O and δ 2 H) tools applied to the study of karst aquifers in Southern Mediterranean basin (Teboursouk area, NW Tunisia)

Karst aquifers receive increasing attention in Mediterranean countries as they provide large supplies water used for drinkable and irrigation purposes as well as for electricity production. In Teboursouk basin, Northwestern Tunisia, characterized by a typical karst landscape, the water hosted in the carbonates aquifers provides large parts of water supply for drinkable water and agriculture purposes. Groundwater circulation in karst aquifers is characterized by short residence time and low water-rock interaction caused by high karstification processes in the study area. Ion exchange process, rock dissolution and rainfall infiltration are the principal factors of water mineralization and spatial distribution of groundwater chemistry. The present work attempted to study karstic groundwater in Teboursouk region using hydrochemistry and stable isotopes (δ18O and δ2H) tools. Karst aquifers have good water quality with low salinity levels expressed by TDS values largely below 1.5 g/l with Ca-SO4-Cl water type prevailing in the study area. The aquifers have been recharged by rainfall originating from a mixture of Atlantic and Mediterranean vapor masses

Predictors of blended learning adoption in higher education institutions in Oman: theory of planned behavior

Abstract The shift toward electronic learning due to the COVID-19 pandemic has created many opportunities to shape Oman’s learning styles. This study explores the factors that affect students’ acceptance of blended learning (BL) in higher education institutions in developing countries, focusing on Oman. The study examines the impact of demographic and social factors, attitude, subjective norms, perceived behavioral control, self-efficacy, beliefs, behavioral intention, and actual use of BL among students. The Theory of Planned Behavior (TPB) was used as a theoretical framework to understand the decision-making processes surrounding BL adoption. Hypotheses are formulated and tested using statistical analysis of survey results. The questionnaire was distributed to students from Sultan Qaboos University in Oman. The data collected were analyzed using inferential predictive modeling methods such as multiple regression analysis and Pearson correlation. The findings indicate that students have a positive attitude toward BL and are likely to choose it in the future. The study also reveals that demographic characteristics and various dimensions, such as attitude, subjective norms, perceived behavioral control, self-efficacy, beliefs, behavioral intention, and actual usage, influence students’ acceptance and utilization of BL. The results contribute to the existing literature and provide insights into the factors that affect BL adoption in developing countries

Information systems: process and practice

This book adopts a holistic interpretation of information architecture, to offer a variety of methods, tools, and techniques that may be used when designing websites and information systems that support workflows and what people require when 'managing information'

Music Students’ Perception Towards Music Distance Learning Education During COVID-19 Pandemic: Cross-Sectional Study in Jordan

During COVID-19 pandemic countries have faced various levels of COVID-19 infection rates, and millions of students are affected by changing the educational process. However, many music Schools have been faced with the challenge of dealing with a situation that necessitates emergency measures to continue the academic course in the midst of lock-downs and social distancing measures. Therefore, it is important to evaluate the effectiveness of online methods of learning and to decide their feasibility and appropriateness for music students. Thus, this research aimed to provide an analysis of Music Students’ Perception towered Music E-learning Education during COVID-19 Pandemic, to study the situation of musicians in COVID-19 and to study music Distance learning knowledge, attitudes and practices and to develop suggestions for solving the problems. A sample of (83) students from the music department in the University of Jordan completed a questionnaire. An online survey distributed The survey sought population and socio-economic information and information relating to electronic and online musical training; musical education during the COVID 19 pandemic; mental music assessments; and the skills, attitudes and practices of E-learning. Most respondents (76.2%) agreed that Distance learning is applicable in music department. While (54.2 %) of the respondents agreed Distance learning is a possible substitute for standard education. However, E-learning has actually been created as a modern way of improving the process of learning and improving learning performance

Information systems : process and practice

xiv, 274 pages ; 24 c

Handling Missing Values Based on Similarity Classifiers and Fuzzy Entropy Measures

Handling missing values (MVs) and feature selection (FS) are vital preprocessing tasks for many pattern recognition, data mining, and machine learning (ML) applications, involving classification and regression problems. The existence of MVs in data badly affects making decisions. Hence, MVs have to be taken into consideration during preprocessing tasks as a critical problem. To this end, the authors proposed a new algorithm for manipulating MVs using FS. Bayesian ridge regression (BRR) is the most beneficial type of Bayesian regression. BRR estimates a probabilistic model of the regression problem. The proposed algorithm is dubbed as cumulative Bayesian ridge with similarity and Luca’s fuzzy entropy measure (CBRSL). CBRSL reveals how the fuzzy entropy FS used for selecting the candidate feature holding MVs aids in the prediction of the MVs within the selected feature using the Bayesian Ridge technique. CBRSL can be utilized to manipulate MVs within other features in a cumulative order; the filled features are incorporated within the BRR equation in order to predict the MVs for the next selected incomplete feature. An experimental analysis was conducted on four datasets holding MVs generated from three missingness mechanisms to compare CBRSL with state-of-the-art practical imputation methods. The performance was measured in terms of R2 score (determination coefficient), RMSE (root mean square error), and MAE (mean absolute error). Experimental results indicate that the accuracy and execution times differ depending on the amount of MVs, the dataset’s size, and the mechanism type of missingness. In addition, the results show that CBRSL can manipulate MVs generated from any missingness mechanism with a competitive accuracy against the compared methods