New Empirical Data Findings for Student Experiences of E-Learning analytics Recommender Systems and their Impact on System Adoption

2019 ◽  
Vol 10 (2) ◽  
pp. 54-63
Author(s):  
Hadeel Alharbi ◽  
Kamaljeet Sandhu
Keyword(s):  
Saudi Arabia ◽  
Recommender System ◽  
Learning Analytics ◽  
Student Experiences ◽  
Survey Questionnaire ◽  
System A ◽  
Saudi Arabian Students ◽  
E Learning ◽  
Support Students

This article examines Saudi Arabian students' experiences of using an e-learning analytics recommender system during their study and the extent to which their experiences were predictors of their adoption and post-adoption of the system. A sample of 353 students from various universities in Saudi Arabia completed a survey questionnaire for data collection. Results showed that user experience is a significant predictors of student adoption and post-adoption of an e-learning recommender system. Based on these findings, this study concluded that universities must support students to develop their awareness of, and skills in using an e-learning recommender system to support students' long-term acceptance and use of the system.

Robust Data Findings of E-Learning Analytics Recommender Systems and Their Impact on System Adoption for Student Experiences

2018 ◽  
Vol 8 (3) ◽  
pp. 1-12 ◽  
Author(s):  
Hadeel Alharbi ◽  
Kamaljeet Sandhu
Keyword(s):  
Saudi Arabia ◽  
Recommender Systems ◽  
Recommender System ◽  
Learning Analytics ◽  
Cultural Practices ◽  
Student Experiences ◽  
Educational Equality ◽  
E Learning ◽  
Gender Based ◽  
Support Students

E-learning recommender systems have an import role in Saudi Arabia to facilitate the education empowerment of women. The understanding of the key factors that affect adoption is critical to achieving educational equality in outcomes in countries with gender-based cultural practices. Therefore, this study examined Saudi Arabian students' experiences of using an e-learning analytics recommender system during their study and the extent to which their experiences were predictors of their adoption and post-adoption of the system. A sample of 353 students from various universities in Saudi Arabia completed a survey questionnaire for data collection. Results showed that user experience is a significant predictor of student adoption and post-adoption of an e-learning recommender system. This study determined that adoption is significantly linked to the ability to effectively navigate and utilise the e-learning systems. Therefore, based on these findings, this study concluded that universities must support students to develop their awareness of, and skills in using an e-learning recommender system to support students' long-term acceptance and use of the system.

Factors Influencing the Acceptance, Use, and Continued Use of the E-Learning Recommender Systems

2018 ◽  
Vol 10 (1) ◽  
pp. 31-50
Author(s):  
Hadeel Alharbi ◽  
Kamaljeet Sandhu
Keyword(s):  
Saudi Arabia ◽  
Recommender Systems ◽  
Total Sample ◽  
Descriptive Statistics ◽  
Survey Questionnaire ◽  
Quality Factors ◽  
E Learning ◽  
Continued Use ◽  
Main Instrument ◽  
Structured Questionnaire

The purpose of this article is to report the descriptive statistics for the responses obtained from the survey of Saudi Arabia students about their experience of using e-learning recommender system during their study. This article utilizes a survey questionnaire as the main instrument for data collection. Hence, a self-completion, well-structured questionnaire was developed based on previous literature and was then distributed to a random sample and participation was completely voluntary. A total sample of 353 university students from various universities in Saudi Arabia participated in this article. Results showed that user experience and service quality factors are significant predictors of students' adoption and post-adoption of e-learning recommender systems.

scholarly journals Academic Staff Perceptions on the E-Learning Recommender System: A Case of Saudi Arabia

2017 ◽  
Vol 8 (2) ◽  
pp. 1
Author(s):  
Hadeel Alharbi ◽  
Kamaljeet Sandhu
Keyword(s):  
Saudi Arabia ◽  
Recommender System ◽  
Qualitative Data ◽  
Academic Staff ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Staff Perceptions ◽  
Case Study Methodology ◽  
E Learning

This paper explores the academic staff perceptions on the factors affecting the acceptance and continuance usage of e-learning recommender system in Saudi Arabia on the basis of a qualitative data that were collected using the case study methodology. In this research, the case study design was selected for the qualitative methodology and semi-structured interviews were employed as the data collection method for the case study. The case study is based in a university implementing an e-learning recommender system in Saudi Arabia. We conducted interviews with five management staff and thus qualitative data were collected. Data analysis was performed and NVIVO 10th version software was also utilised. Data were coded and themes were then generated. Findings indicate several factors that affect an e-learning recommender system adoption that include user experience, service quality, perceived usefulness and perceived ease of use. Various suggestions were offered in this study and we also propose practical implications according to the identified insufficiencies.

Problems of Distance Education

2018 ◽  
Vol 7 (1) ◽  
pp. 14-23 ◽  
Author(s):  
Rostislav Fojtík
Keyword(s):  
Distance Education ◽  
Distance Learning ◽  
Computer Science ◽  
Full Time ◽  
Advantages And Disadvantages ◽  
E Learning ◽  
Educational Requirements

Abstract Distance learning and e-learning have significantly developed in recent years. It is also due to changing educational requirements, especially for adults. The article aims to show the advantages and disadvantages of distance learning. Examples of the 20-year use of the distance learning form of computer science describe the difficulties associated with the implementation and implementation of this form of teaching. The results of students in the full-time and distance form of teaching in the bachelor’s study of computer science are compared. Long-term findings show that distant students have significantly lower scores in the first years of study than full-time bachelor students. In the following years of study, the differences diminish, and students’ results are comparable. The article describes the possibilities of improving the quality of distance learning.

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

2018 ◽  
Vol 2 (4) ◽  
pp. 271 ◽  
Author(s):  
Outmane Bourkoukou ◽  
Essaid El Bachari
Keyword(s):  
Learning Style ◽  
Recommender System ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Research Field ◽  
Learning System ◽  
K Nearest Neighbors ◽  
E Learning ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

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