Educational Recommender System based on Learner’s Annotative Activity

In this paper, we propose learners course recommender system of the E-Dirassa platform. This system, which distinguishes between new and active learners, adopts three complementary recommendation approaches: content-based recommendation as well as course-based collaborative filtering for active learners, and static profile-based collaborative filtering for new learners. Let us also note the use of multi-criteria decision aid (MDA) techniques for the choice of the textual similarity measure to be used in the first approach of content-based recommendation.

Section: Course-based Collaborative Filteringmentioning

confidence: 99%

Combined E-Learning Course Recommender System

Talaghzi

Bellafkih

Bennane

et al. 2023

“…In recent years, educational recommender systems (ERS) have attracted significant attention as a solution for learners. ERS is crucial in helping learners find educational resources relevant to their material and context, and it can also help students to select some adequate courses [15]. Some studies have also shown that recommender systems greatly help and support students' eLearning [3] [25].…”

Section: Machine Learning and Feature Selection 21 Role Of Machine Le...mentioning

confidence: 99%

Selecting the Best K Features for Predicting Student Participation in Generic Competency Development Activities in Higher Education

Wong,

So,

Tsang

et al. 2023

Generic competency (GC) is an essential but often overlooked aspect of developing students in higher education. While there is much research about using technologies to develop discipline- specific skills for students, the use of technologies in GC development is insufficient. In particular, more research is needed on using technologies to predict student participation in GC development activities (GCDAs). Machine learning (ML) can use student characteristics, known as features, to predict their involvement in GCDAs. However, too many features will slow down the prediction process and reduce the ability to pinpoint the best features for prediction. This study explored an effective way to identify the minimal number of features essential for predicting student participation in GCDAs. The findings help educators develop recommendation systems to help students select the most beneficial GCDA for their holistic development. We collected 98 features from 9570 students from a community college. Then, we applied the Principal Component Analysis and SelectKBest algorithms to reduce the number of features from 98 to 8. Finally, we compared the accuracy of predictions using KNN and ANN based on the all-feature dataset with those based on the reduced-feature dataset. The results showed that the reduced-feature dataset maintained good prediction accuracy and enabled the educator to recommend the GCDAs to students. The findings could drive further research and development in applying machine learning technologies to enhance the recommendations for GCDAs for higher-education students.

“…Besides, due to the diversity in the background; various students have various learning demands [2]. Accordingly, it is essential to improve recommender systems to assist learners in choosing courses, resources, or learning materials in E-learning [21]. E-learning has earned popularity due to the COVID-19 pandemic.…”

Section: Advantages Of E-learning During Pandemic Situationmentioning

confidence: 99%

E-Learning Recommendation System for Big Data Based on Cloud Computing

Rahhali

Oughdir

Jedidi³

2021

In educational institutions, E-learning has been known as a successful technology for enhancing performance, concentration, and thus providing higher academic success. Nevertheless, the conventional system for executing research work and selecting courses is a time-consuming and unexciting practice, that not only directly impacts the students ’ academic achievement but also impacts the learning experience of students. In addition to that, there is an enormous number of various kinds of data in the E-Learning domain both structured and unstructured, and the academic establishments attempt to manage and understand big complicated data sets. To fix this problem, this paper proposes a model of an E-learning recommendation system that will suggest and encourage the learner in choosing the courses according to their needs. This system used big data tools such as Hadoop and Spark to enhance data collection, storage, analysis, processing, optimization, and visualization, furthermore based on cloud computing infrastructure and especially Google cloud services.