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.

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.

Metis

2012 ◽  
pp. 24-42 ◽  
Author(s):  
Jody S. Underwood
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Content Area ◽  
Learning Activities ◽  
Digital Learning ◽  
Design Phase ◽  
Learning Contexts ◽  
Good Advice ◽  
E Learning ◽  
Ready To Learn

Recommender systems in e-learning contexts typically try to “intelligently” recommend actions to a learner based on the actions of previous learners. One of the limitations of such systems is that a lot of data is needed in order to recommend meaningful activities. This chapter describes one approach for addressing this limitation in a framework that uses a structured map of mathematics concepts and processes to power a recommender system that will recommend to students digital learning activities for which they are ready. This recommender system is called Metis, for the Greek goddess of good advice, and is currently in the design phase. Metis takes seriously the idea that to build on the knowledge, skills, and abilities (KSAs) that a student has, it is essential to identify those KSAs. Trying to build on KSAs that a student does not have is misguided. Metis recommends activities linked to KSAs that students are ready to learn, and more standard recommender algorithms further refine the list of recommended activities. Taking this approach has the potential to make activities more engaging, which can lead learners to greater interest in the content area.

scholarly journals Measuring learner's performance in e-learning recommender systems

2010 ◽  
Vol 26 (6) ◽  
Author(s):  
Khairil Imran Ghauth ◽  
Nor Aniza Abdullah
Keyword(s):  
Learning Outcomes ◽  
Recommender Systems ◽  
Recommender System ◽  
Knowledge Gain ◽  
Online Systems ◽  
Post Test ◽  
Score Gain ◽  
E Learning ◽  
Student’S Performance ◽  
Content Based Filtering

<span>A recommender system is a piece of software that helps users to identify the most interesting and relevant learning items from a large number of items. Recommender systems may be based on collaborative filtering (by user ratings), content-based filtering (by keywords), and hybrid filtering (by both collaborative and content-based filtering). Recommender systems have been a useful tool to recommend items in many online systems, including e-learning. However, not much research has been done to measure the learning outcomes of the learners when they use e-learning with a recommender system. Instead, most of the researchers were focusing on the accuracy of the recommender system in predicting the recommendation rather than the knowledge gain by the learners. This research aims to compare the learning outcomes of the learners when they use several types of e-learning recommender systems. Based on the comparison made, we propose a new e-learning recommender system framework that uses content-based filtering and good learners' ratings to recommend learning materials, and in turn is able to increase the student's performance. The results show that students who used the proposed e-learning recommender system produced a significantly better result in the post-test. The results also show that the proposed e-learning recommender system has the highest percentage of score gain from pre-test to post-test.</span>

scholarly journals Crowdsourced Recommender System

2018 ◽  
Author(s):  
Mario Mallia Milanes ◽  
Matthew Montebello
Keyword(s):  
Web 2.0 ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Relevant Information ◽  
Learning Systems ◽  
Learning Material ◽  
Timely Manner ◽  
Online Learners ◽  
E Learning

The use of artificially intelligent techniques to overcome specific shortcomings within e-learning systems is a well-researched area that keeps on evolving in an attempt to optimise such resourceful practices. The lack of personalization and the sentiment of isolation coupled with a feeling of being treated like all others, tends to discourage and push learners away from courses that are very well prepared academically and excellently projected intellectually. The use of recommender systems to deliver relevant information in a timely manner that is specifically differentiated to a unique learner is once more being investigated to alievate the e-learning issue of being impersonal.  The application of such a technique also assists the learner by reducing information overload and providing learning material that can be shared, criticized and reviewed at one’s own pace. In this paper we propose the use of a fully automated recommender system based on recent AI developments together with Web 2.0 applications and socially networked technologies. We argue that such technologies have provided the extra capabilities that were required to deliver a realistic and practical interfacing medium to assist online learners and take recommender systems to the next level.

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.

New Discoveries for User Acceptance of E-Learning Analytics Recommender Systems in Saudi Arabia

2019 ◽  
Vol 10 (1) ◽  
pp. 31-42 ◽  
Author(s):  
Hadeel Alharbi ◽  
Kamaljeet Sandhu
Keyword(s):  
Technology Acceptance ◽  
Learning Outcomes ◽  
Recommender Systems ◽  
Technology Acceptance Model ◽  
Learning Analytics ◽  
Saudi Arabian ◽  
Ministry Of Education ◽  
Electronic Learning ◽  
E Learning ◽  
Acceptance Model

This article adopts e-learning analytics principles to provide a new model to explain the acceptance behaviour of recommender systems adoption with e-learning in the Saudi Arabian context and reflects the increasing focus of the Saudi Arabian Ministry of Education on delivering online educational services. This focus has come at the necessity to improve overall access to the education system, and higher education and has been driven with evidence of improving learning outcomes with electronic learning (e-learning) information and instructional technology with the use of e-learning analytics recommender systems. This review utilises the technology acceptance model as a theoretical framework to generate a set of interlocked hypotheses that go to explaining student behaviours towards technological acceptance and continued usage intention of recommender systems.

A Multifactorial Analysis of the Acceptance of Recommender System for Saudi Universities the Literature Revisited

2017 ◽  
pp. 90-105
Author(s):  
Hadeel Alharbi ◽  
Kamaljeet Sandhu
Keyword(s):  
Student Learning ◽  
Recommender Systems ◽  
Recommender System ◽  
Educational Outcomes ◽  
New Technologies ◽  
Learning Experience ◽  
Multifactorial Analysis ◽  
Hands On ◽  
E Learning ◽  
Enhance Student Learning

Technology for learning is increasingly about enhancing users' interactions with the technology to improve learning outcomes. Of particular importance however to improving educational outcomes is the need to complement the technological advancements with advances in the educational practices of teachers to broaden the uptake of new technologies for learning. Recommender Systems are personalised services that aim to predict a learner's interest in some services or items such as courses, grades, references, links, etc. available in e-learning applications and to provide appropriate recommendations. Such systems can potentially enhance student learning by providing students with a more hands on, interactive and tailored learning experience.

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