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>

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 The Quality of E-Learning Based on Learning Media Using Moodle LMS on Text of Observation Reports of Grade 10th Students of Vocational School Telkom Shandy Putra Medan

2020 ◽  
Vol 2 (2) ◽  
pp. 688-699
Author(s):  
Sukma Adelina Ray ◽  
Abdurahman Adisaputera ◽  
Isda Pramuniati
Keyword(s):  
Learning Outcomes ◽  
Vocational School ◽  
Development Research ◽  
Average Value ◽  
Significant Difference ◽  
Post Test ◽  
E Learning ◽  
The Difference ◽  
Media Learning

The aims of this study to find out the quality of E-learning based on learning media using Moodle LMS on text of observation. This type of research is development research in the field of education known as Research and Development (R&D). Development research (development research) is research that is used to produce certain products and test the effectiveness of these products (Tegeh and I.M, 2013). The result of this study show that The quality of e-learning based learning media using LMS Moodle which was developed as a learning media on the observation report text material is stated to be a useful and effective contribution in improving the quality of learning outcomes of 10th grade TJA1 Vocational School Telkom Shandy Putra Medan. This is because there is a significant difference in the learning outcomes of the average value of students before using e-learning based learning media (pre-test) ie 57.0 or only about 52% are able to exceed KKM and after using e-based learning media learning using LMS Moodle (post-test) the average value of students increased to 77.0% or can be interpreted as 100% of students able to exceed the KKM. Based on these data the difference is increased by 20% or can be interpreted student learning outcomes increased by 58%.

scholarly journals KEEFEKTIFAN MODEL STUDENT TEAM ACHIEVEMENT DIVISION (STAD) DAN E-LEARNING PADA MATERI OPERASI PENJUMLAHAN DAN PENGURANGAN PECAHAN DI SEKOLAH DASAR

2018 ◽  
Vol 5 (2) ◽  
pp. 121
Author(s):  
Sri Kadarwati ◽  
Kasni Astutik ◽  
Edi Prayitno
Keyword(s):  
Learning Outcomes ◽  
Test Group ◽  
Mean Value ◽  
School Students ◽  
Student Centered ◽  
Student Centered Learning ◽  
School Year ◽  
Post Test ◽  
E Learning ◽  
Student Team

The low learning outcomes of Addition and Reduction of Fractions in class IV are partly due to: (1) a tedious learning process, (2) lack of use of ICT as a source and media of learning. To overcome this problem the research team developed student-centered learning through e-learning learning in the Student Team Achievement Division (STAD) model. This study is a true experiment using the Pre-Test Group Control Post Test Design. The population is Sambiroto 02 elementary school students as many as 75 students in the 2017/2018 school year. The E2 group with EAD learning using the STAD model reached the highest mean value of the three groups, amounting to 88.67, followed by the E1 group with STAD learning at 74.54 and the K group with expository learning at 63.10. Regression analysis showed a positive relationship between motivation and activity with learning outcomes in groups E1 and E2 so it can be concluded that the higher the student's motivation and activity score the higher the student's learning achievement in addition and subtraction operations in elementary schools.

scholarly journals Book Recommendation System using Matrix Factorization

2021 ◽  
Vol 9 (VI) ◽  
pp. 4578-4582
Author(s):  
K. Venkata Ruchitha
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Matrix Factorization ◽  
Recommendation System ◽  
Previous History ◽  
Speed Up ◽  
Area Unit ◽  
Content Based Filtering

In recent years, recommender systems became more and more common and area unit applied to a various vary of applications, thanks to development of things and its numerous varieties accessible, that leaves the users to settle on from bumper provided choices. Recommendations generally speed up searches and create it easier for users to access content that they're curious about, and conjointly surprise them with offers they'd haven't sought for. By victimisation filtering strategies for pre-processing the information, recommendations area unit provided either through collaborative filtering or through content-based Filtering. This recommender system recommends books supported the description and features. It identifies the similarity between the books supported its description. It conjointly considers the user previous history so as to advocate the identical book.

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.

Providing Personalized Services to Users in a Recommender System

2015 ◽  
Vol 10 (2) ◽  
pp. 26-48 ◽  
Author(s):  
Olukunle Oduwobi ◽  
Bolanle Adefowoke Ojokoh
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Learning Ability ◽  
Learning Material ◽  
Electronic Library ◽  
Learning Materials ◽  
Personalized Services ◽  
E Learning ◽  
Personalized Recommender System ◽  
Content Based Filtering

Instructors recommend learning materials to a class of students not minding the learning ability and reading habit of each student. Learners are finding it problematic to make a decision about which available learning materials best meet their situation and will be beneficial to their course of study. In order to address this challenge, a new e-learning material recommender system that is able to recommend quality items to learners individually is required. The aim of this work is to develop a Personalized Recommender System that switches between Content-based and Collaborative filtering techniques, with an objective to design an algorithm to recommend electronic library materials, as well as personalize recommendations to both new and existing users. Experiments were conducted with evaluations showing that the recommender system was most effective when content-based filtering and collaborative filtering were used to recommend items for new users and existing users respectively, and still achieve personalization.

Enhancing e-Learning Environment with Embedded Recommender Systems

2013 ◽  
pp. 234-253
Author(s):  
Mohamed S. El Sayed ◽  
Mona Nasr ◽  
Torky I. Sultan
Keyword(s):  
Learning Environment ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Learning Objects ◽  
E Learning ◽  
Content Based Filtering

Recommended learning objects are obtained by using a range of recommendation strategies based mainly on content based filtering and collaborative filtering approaches, each applied separately or in combination.

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.

scholarly journals The research trends in recommender systems for e-learning

2019 ◽  
Vol 14 (1) ◽  
pp. 12-27
Author(s):  
Jiemin Zhong ◽  
Haoran Xie ◽  
Fu Lee Wang
Keyword(s):  
Learning Outcomes ◽  
Recommender Systems ◽  
Learning Systems ◽  
Learning System ◽  
Future Research ◽  
Content Type ◽  
Evaluation Indicator ◽  
Recommendation Algorithms ◽  
E Learning ◽  
Key Indicators

Purpose A recommendation algorithm is typically applied to speculate on users’ preferences based on their behavioral characteristics. The purpose of this paper is to provide a systematic review of recommendation systems by collecting related journal articles from the last five years (i.e. from 2014 to 2018). This paper aims to study the correlations between recommendation technologies and e-learning systems. Design/methodology/approach The paper reviews the relevant articles using five assessment aspects. A coding scheme was put forward that includes the following: the metrics for the e-learning system, the evaluation metrics for the recommendation algorithms, the recommendation filtering technology, the phases of the recommendation process and the learning outcomes of the system. Findings The research indicates that most e-learning systems will adopt the adaptive mechanism as a primary metric, and accuracy is a vital evaluation indicator for recommendation algorithms. In existing e-learning recommender systems, the most common recommendation filtering technology is hybrid filtering. The information collection phase is an important process recognized by most studies. Finally, the learning outcomes of the recommender system can be achieved through two key indicators: affections and correlations. Originality/value The recommendation technology works effectively in closing the gap between the information producer and the information consumer. This technology could help learners find the information they are interested in as well as send them a valuable message. The opportunities and challenges of the current study are discussed; the results of this study could provide a guideline for future research.

scholarly journals Development of Learning Management System-Based Blended Learning Model using Claroline in Higher Education

2020 ◽  
Vol 14 (06) ◽  
pp. 186 ◽  
Author(s):  
Munir Tubagus ◽  
Suyitno Muslim ◽  
Suriani Suriani
Keyword(s):  
Higher Education ◽  
Blended Learning ◽  
Learning Outcomes ◽  
Management System ◽  
Learning Management System ◽  
Statistical Hypothesis ◽  
P Value ◽  
Post Test ◽  
E Learning ◽  
Learning Management

<p class="0abstract">Learning Management System (LMS) is a learning process that utilizes computer information technology equipped with internet and multimedia telecommunications facilities (graphics, audio, video) in delivering material and interaction between instructors and learners. The purpose of this study is to develop blended learning Using Claroline as a learning tool that facilitates students in learning. To achieve the objective of the study the research uses a quantitative approach to collect data using pre and post tests and questionnaires. The sample in the study were students of economic Islamic consisting of two classes, with a total of 50 students enrolled in this study. The results show that the difference in the average pre-test and post-test score was -29.43720. While the t-test that tests Ho: pre-test = post-test gives a value of t = -37.43720 with a degree of freedom of 49. While the p-value for the two-sided test of 0,000 is smaller than α = 0.05. This data approves that the statistical hypothesis Ho: pre-test = post-test is rejected, meaning that the average pre-test and post-test scores are significantly different. The findings of this study can be used to recommend effective ways of learning and teaching using e-learning that can improve student learning outcomes in higher education. The implications of this research is to encourage teachers to use e-learning technology and facilitate students with the technology in improving academic learning outcomes.</p>

Sign in / Sign up

Export Citation Format

Share Document