A Proposed Course Recommender Model based on Collaborative Filtering for Course Registration

Salehudin, Norazuwa Binti; Kahtan, Hasan; Abdulgabber, Mansoor Abdullateef; Al-bashiri, Hael

doi:10.14569/ijacsa.2019.0101122

Cited by 4 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One system has been proposed based on collaborative filtering where courses are suggested based on student performance in previous semesters. This is accomplished by taking the grades of passed courses and the average grades when a course is taken multiple times or when it is passed in the first place in order to gain new experience or to enhance the GPA [12][13][14] [15]. Some systems are also integrated with students' social media accounts in order to allow students to see how many of their friends on those social networks are taking a course in which he or she is interested [16].…”

Section: Course Registrationmentioning

confidence: 99%

Online Course Registration and Advisory Systems Based on Students’ Personal and Social Constraints

Faraj¹,

Ali²

2021

KJAR

View full text Add to dashboard Cite

Komar University of Science and Technology (KUST) is among very few universities in Iraq that follows the prerequisite system which is very common in the tertiary level institutions around the world. In such kinds of systems, the prerequisites represent a one or set of courses a student must have completed in order to be able to enrol on a specific course. Although these kinds of systems give total freedom to students on when and how they are going to take courses, deciding which course to take that serves in the best interest of the student is very challenging and sometimes time consuming.This paper presents a web based system developed and implemented by KUST facilitating course registration for the students and administrative staff such that the best possible schedule and course/section combination is presented to the students considering their personal constraints and course requirements. To assist students to make proper decisions the system provides them with collected and analyzed historical data about each offered course such as passing rate of the course, the number of selected friends who registered for and marked the course as favorite and how many courses the selected course is prerequisite of. The collected data is gathered from the currently in place database containing course registration data since spring 2016 which is believed to be adequate to produce accurate results.The system also has the role of academic advisor, which plays an important role in the students’ academic life. Since traditional advising requires extra effort, times and sometimes the suggestion that comes from an academic advisor might not be in the best interest of students due to the lack of information about the student’s academic background or not having sufficient time advising a large number of students in a short window of registration period.

show abstract

Section: Course Registrationmentioning

confidence: 99%

Online Course Registration and Advisory Systems Based on Students’ Personal and Social Constraints

Faraj¹,

Ali²

2021

KJAR

View full text Add to dashboard Cite

show abstract

“…The IoT is already attracting a slew of academic and business interests. Smarter and smaller devices are introduced in a variety of IoT domains every day, including monitoring management, smart houses, smart vehicles, smart farming, smart tourism, e-health, and e-learning, to mention a few [2][3][4][5][6][7][8][9]. e-Learning refers to using electronics and the Internet in addition to software applications in the learning process and thus making it more effective and dynamic [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Investigating Internet of Things Impact on e-Learning System: An Overview

Elneel¹,

Kahtan²,

Fakharudin³

et al. 2023

IJACSA

View full text Add to dashboard Cite

Learning systems have reached their peak with the revolution of smart technologies. In the past few years, the Internet of Things (IoT) has become one of the most advanced and popular technologies, affecting many different areas. Using IoT in an e-learning system is a fantastic technology that improves the e-learning system and makes it more inventive and cutting-edge. The key challenge addressed in this study is the acceptance of IoT usage in e-learning systems as well as how to improve it so that it can be utilized properly. This research concentrates on how IoT can benefit e-learning systems and how it might benefit users of e-learning systems. A comprehensive literature review was conducted to get acquainted with the important research related to IoT technology and e-learning systems through online research databases and reliable scientific journals. The first research finding is that e-learning systems need such modern techniques as IoT to enable interconnection, increase reliability, and enhance the enjoyment of the educational process. The second result is that research related to the development of new technologies like the IoT has a significant impact on enhancing the performance of new systems and bringing about positive change. This study highlights the value of IoT, particularly in e-learning systems. It aids in the development of new strategies that will improve the efficacy of e-learning systems and stimulate researchers to develop advanced technology.

show abstract

“…However, the amount of information on the Internet increases tremendously every day, thus complicating the decision-making process of Internet users. To assist the users in dealing with information overload, researchers have developed a recommender system (RS) that can provide a list of preferable items among the huge amount of items available by predicting users' preferred items [2][3][4][5][6][7]. State-of-the-art RSs can be grouped into content-based, collaborative filtering (CF) or combined (i.e.…”

Section: Introductionmentioning

confidence: 99%

Memory-based Collaborative Filtering: Impacting of Common Items on the Quality of Recommendation

Al-bashiri¹,

Kahtan²,

Abdulgabber³

et al. 2019

IJACSA

Self Cite

View full text Add to dashboard Cite

In this study, the impact of the common items between a pair of users on the accuracy of memory-based collaborative filtering (CF) is investigated. Although CF systems are a widely used recommender system, data sparsity remains an issue. As a result, the similarity weight between a pair of users with few ratings is almost a fake relationship. In this work, the similarity weight of the traditional similarity methods is determined using exponential functions with various thresholds. These thresholds are used to specify the size of the common items amongst the users. Exponential functions can devalue the similarity weight between a pair of users who has few common items and increase the similarity weight for users who have sufficient co-rated items. Therefore, the pair of users with sufficient co-rated items obtains a stronger relationship than those with few common items. Thus, the significance of this paper is to succinctly test the impacting of common items on the quality of recommendation that creates an understanding for the researchers by discussing the findings presented in this study. The MovieLens datasets are used as benchmark datasets to measure the effect of the ratio of common items on the accuracy. The result verifies the considerable impact exerted by the factor of common items.

show abstract

A Proposed Course Recommender Model based on Collaborative Filtering for Course Registration

Cited by 4 publications

References 12 publications

Online Course Registration and Advisory Systems Based on Students’ Personal and Social Constraints

Online Course Registration and Advisory Systems Based on Students’ Personal and Social Constraints

Investigating Internet of Things Impact on e-Learning System: An Overview

Memory-based Collaborative Filtering: Impacting of Common Items on the Quality of Recommendation

Contact Info

Product

Resources

About