The current study aims to examine the level of problems faced by university students in distance learning, in addition to identify the differences in these problems in terms of the availability of internet services, gender, college, GPA, interactions, academic cohort, and family economic status. The study sample consisted of (3172) students (57.3% females). The researchers developed a questionnaire with (32) items to measure distance learning problems in four areas: Psychological (9 items), academic (10 items), technological (7 items), and study environment (6 items). The responses are scored on a (5) point Likert Scale ranging from 1 (strongly disagree) to 5 (strongly agree). Means, standard deviations, and Multivariate Analysis of Variance (MANOVA) were used to analyze the data. The findings showed that students faced high levels of psychological and academic problems and medium levels of technological and study environmental problems. The findings also indicated statistically significant differences in the levels of all problems based on the availability of internet services. In addition, the sample in scientific colleges manifested higher levels of academic problems, and females showed higher levels of study environmental problems. Statistically significant differences also appeared in all types of problems based on study cohort and family economic status.
Problems Facing University Students in Distance Learning
distance learning
problems
university students
Publication Date
Sat Dec 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
Comparison Between Ordinary Methods (LS,IV) and Robust Methods (2SWLS,LTS,RA) to estimate the Parameters of ARX(1,1,1) Model for Electric Loads
أنموذج الانحدار الذاتي بوجود متغير خارجي (ARX)
الكشف عن القيم الشاذة
طريقة المربعات الصغرى الاعتيادية (OLS)
طريقة المتغيرات المساعدة ذات الخطوات الاربعة (4SIV)
طريقة المربعات الصغرى الموزونة ذات المرحلتين (2SWLS)
طريقة المربعات الصغرى المشذبة (LTS)
مقدرات التباينات الذاتية للبواقي(RA).
(ARX)
The Outliers Detection
Ordinary Least Squares Method
Four-Step Instrumental Variables Method
Two-Stage Weighted Least Squares Method
Least Trimmed Squares Method
Residual Auto-covariances Estimators.
Abstract:
The models of time series often suffer from the problem of the existence of outliers that accompany the data collection process for many reasons, their existence may have a significant impact on the estimation of the parameters of the studied model. Access to highly efficient estimators is one of the most important stages of statistical analysis, And it is therefore important to choose the appropriate methods to obtain good estimators. The aim of this research is to compare the ordinary estimators and the robust estimators of the estimation of the parameters of
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