Recommender System for E-Learning through Content and Profile Based Approach

The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

“…To overcome the new user problem, proposed system integrates the studentś LinkedIn profile features to represent students in the vector space. Moreover, the research conducted in [130] applied query expansion techniques to generate revised query from the keywords in the studentś query. Then the revised query will be matched with the existing course contents available in the repository.…”

“…Table 1 illustrates the summary of CBRS-based course RS systems identified in the systematic review in terms of drawbacks or comments. Venugopalan et al [130] Query expansion is used to increase number of features before matching them with existing course contents, ranking the number of learning objects (closest to the userś query) returned by the revised query to control the output/ scalability.…”

“…It can be seen that ontologies are also incorporated into CFRS-based hybrid course RS to improve final recommendation. Many studies on CBRS incorporated several techniques to improve the quality of the final recommendation [57,129,130]. In [57] used LinkedIn profile features to represent students in vector space and as a solution to new user problem.…”

“…In [57] used LinkedIn profile features to represent students in vector space and as a solution to new user problem. N-gram query expansion techniques is used [130] to deduce the studentś academic interests from the key words they entered into the system before matching it with the existing course contents available in the system. Then they ranked the expanded queries based on the contents in the repository to find the best match.…”

Learning analytics and collaborative groups of learners in distance education: a systematic mapping study

“…The benefits categorized as intelligent services are: learning management system (Lavoie and Proulx, 2019), semantic recommendation using ontology (Sharma and Ahuja, 2016); hybrid recommendation based on student profile (Kapembe and Quenum, 2019); deep reinforcement learning structure (Huang et al, 2019); decision-making system (El Fouki et al, 2017), content-based recommendation system (Venugopalan et al, 2016), domain-specific language (Balderas et al, 2013), WAVE architecture (Manhães et al, 2014), intelligent teaching assistant system (Wang et al, 2019), profile analysis system (El Moustamid et al, 2017), algorithm based on the technique of optimizing ant colonies (Kozierkiewicz-Hetmańska and Zyśk, 2013), prototype indicators (Florian et al, 2011), online learning systems based on big data technologies (Dahdouh et al, 2018), agentbased recommendation system, Java2D technology-based e-learning system (Hamada, 2012), ID based recommendation system (Zakrzewska, 2012;Anaya et al, 2013), capture system (Lagman and Mansul, 2017), custom model (Chanaa and Faddouli, 2018), ontology model (Joy et al, 2019), evaluation tool (Dimopoulos et al, 2013), adaptive recommendation method (Chen et al, 2020), Kernel Context Recommendender System algorithm (Iqbal et al, 2019), distributed course recommendation systems (Dahdouh et al, 2019), custom user interface (Kolekar et al, 2018), recommendation system techniques for educational data mining (Thai-Nghe et al, 2010), individualized artificial intelligence tutor and LBA model (Kim and Kim, 2020) based on a system called SBAN (Zaoudi and Belhadaoui, 2020). The application of methods and techniques of data analysis provide student grade prediction, behavior pattern detection, academic progress forecasting, modeling, course dropout risk prediction, also providing student performance feedback to teachers.…”