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This research proposes a two-stage user-based collaborative filtering process using an artificial immune system for the prediction of student grades, along with a filter for professor ratings in the course recommendation for college students. We test for cosine similarity and Karl Pearson (KP) correlation in affinity calculations for clustering and prediction. This research uses student information and professor information datasets of Yuan Ze University from the years 2005–2009 for the purpose of testing and training. The mean average error and confusion matrix analysis form the testing parameters. A minimum professor rating was tested to check the results, and observed that the recommendation systems herein provide highly accurate results for students with higher mean grades.

The objective of this research is based on human-centered AI in education to develop a personalized hybrid course recommendation system (PHCRS) to assist students with course selection decisions from different departments. The system integrates three recommendation methods, item-based, user-based and content-based filtering, and then optimizes the weights of the parameters by using a genetic algorithm to enhance the prediction accuracy. First, we collect the course syllabi and tag each course from twelve departments for the academic years of 2015 to 2020. Next, we use the course tags, student course selection records and grades to train the recommendation model. To evaluate the prediction accuracy, we conduct an experiment on 1490 different courses selected by 5662 students from the twelve departments and then use the root-mean-squared error and the normalized discounted cumulative gain. The results show that the influence of item-based filtering on the course recommendation results is higher than that of user- and content-based filtering, and the genetic algorithm can find the optimal solution and the corresponding parameter settings. We also invite 61 undergraduate students to test our system, complete a questionnaire and provide their grades. Overall, 83.60% of students are more interested in courses at the top of the recommendation lists. The students are more autonomously motivated rather than holding extrinsic informational motivation across the hybrid recommendation method. Finally, we conclude that PHCRS can be applied to all students by tuning the optimal weights for each course selection factor for each department, providing the best course combinations for students' reference.

The main reason students drop out of online courses is often that they lose interest during learning. Moreover, it is not easy for students to choose an appropriate course before actually learning it. Course recommendation is necessary to address this problem. Most existing course recommendation methods depend on the interaction result (e.g., completion rate, grades, etc.). However, the long period required to complete a course, especially large-scale online courses in higher education, can lead to serious sparsity of interaction results. In view of this, we propose a novel course recommendation method named HGE-CRec, which utilizes context formation for heterogeneous graphs to model students and courses. HGE-CRec develops meta-path embedding simulation and meta-path weight fusion to enhance the meta-path embedding set, which can expand the learning space of the prediction model and improve the representation ability of meta-path embedding, thereby avoiding tedious manual setting of the meta-path and improving the effectiveness of the resulting recommendations. Extensive experiments show that the proposed approach has advantages over a number of existing baseline methods.

The constantly growing offering of online learning materials to students is making it more difficult to locate specific information from data pools. Personalization systems attempt to reduce this complexity through adaptive e-learning and recommendation systems. The latter are, generally, based on machine learning techniques and algorithms and there has been progress. However, challenges remain in the form of data-scarcity, cold-start, scalability, time consumption and accuracy. In this article, we provide an overview of recommendation systems in the e-learning context following four strands: Content-Based, Collaborative Filtering, Knowledge-Based and Hybrid Systems. We developed a taxonomy that accounts for components required to develop an effective recommendation system. It was found that machine learning techniques, algorithms, datasets, evaluation, valuation and output are necessary components. This paper makes a significant contribution to the field by providing a much-needed overview of the current state of research and remaining challenges.

PurposeCourse recommendations are important for improving learner satisfaction and reducing dropout rates on massive open online course (MOOC) platforms. This study aims to propose an interpretable method of analyzing students' learning behaviors and recommending MOOCs by integrating multiple data sources.Design/methodology/approachThe study proposes a deep learning method of recommending MOOCs to students based on a multi-attention mechanism comprising learning records attention, word-level review attention, sentence-level review attention and course description attention. The proposed model is validated using real-world data consisting of the learning records of 6,628 students for 1,789 courses and 65,155 reviews.FindingsThe main contribution of this study is its exploration of multiple unstructured information using the proposed multi-attention network model. It provides an interpretable strategy for analyzing students' learning behaviors and conducting personalized MOOC recommendations.Practical implicationsThe findings suggest that MOOC platforms must fully utilize the information implied in course reviews to extract personalized learning preferences.Originality/valueThis study is the first attempt to recommend MOOCs by exploring students' preferences in course reviews. The proposed multi-attention mechanism improves the interpretability of MOOC recommendations.

With the impact of COVID-19 on education, online education is booming, enabling learners to access various courses. However, due to the overload of courses and redundant information, it is challenging for users to quickly locate courses they are interested in when faced with a massive number of courses. To solve this problem, we propose a deep course recommendation model with multimodal feature extraction based on the Long- and Short-Term Memory network (LSTM) and Attention mechanism. The model uses course video, audio, and title and introduction for multimodal fusion. To build a complete learner portrait, user demographic information, explicit and implicit feedback data were added. We conducted extensive and exhaustive experiments based on real datasets, and the results show that the AUC obtained a score of 79.89%, which is significantly higher than similar algorithms and can provide users with more accurate recommendation results in course recommendation scenarios.

With the popularization development of MOOC platform, the number of online courses grows rapidly. Efficient and appropriate course recommendation can improve learning efficiency. Traditional recommendation system is applied to the closed educational environment in which the quantity of courses and users is relatively stable. Recommendation model and algorithm cannot directly be applied to MOOC platform efficiently. With the light of the characteristics of MOOC platform, MCRS proposed in this paper has made great improvement in the course recommendation model and recommendation algorithm. MCRS is based on distributed computation framework. The basic algorithm of MCRS is distributed association rules mining algorithm, which based on the improvement of Apriori algorithm. In addition, it is useful to mine the hidden courses rules in course enrollment data. Firstly, the data is pre-processed into a standard form by Hadoop. It aims to improve the efficiency of the basic algorithm. Then it mines association rules of the standard data by Spark. Consequently, course recommendation information is transferred into MySQL through Sqoop, which makes timely feedback and improves user’s courses retrieval efficiency. Finally, to validate the efficiency of MCRS, a series of experiments are carried out on Hadoop and Spark, and the results shows that MCRS is more efficient than traditional Apriori algorithm and Apriori algorithm based on Hadoop, and the MCRS is suitable for current MOOC platform.

Over the past decade, the student course recommendation process with secure certificate issuance has remained a critical research area due to the rise of e-learning and personalized learning. The recommendation system enhances the recommended educational resources to improve the students’ learning process. The previous conventional research works shared hybrid content and collaborative filtering techniques, which boosted academic performance, personalized learning, and secure certification for students. However, the existing techniques faced several difficulties in handling the syllabus updates based on evolving recommendations, complexity, and security issues related to certificate issuance. To address the challenges in the existing techniques, the research introduces the Deep Certifier-DX509 model for secure certificate issuance and student course recommendation. The proposed approach exploits the Modified Attention-Enabled Deep Long Short-Term Memory (MA-DLSTM) Model as a recommendation system to suggest the most suitable courses based on users’ prior academic performance, and integrates X509 as the Certificate generation algorithm. Specifically, the incorporation of the X509 Blockchain with Proof-of-Work (PoW) in the certificate sub-system serves as a major contribution to enhance the security with Two-step authentication and generates accurate course recommendations. Experimental results demonstrate that the proposed Deep Certifier-DX509 model shows superior performance, achieving a high Genuine User Rate (GUR) of 0.73, Memory Usage of 453.81KB, Transaction time of 1.03 s, Responsiveness of 2.39s and Throughput of 119.52bps, outperforming the other existing techniques.

This paper applies a nonlinear differential equation to the information management system of college course selection. A teaching information management system based on an approximate learning strategy is presented by using statistical linearization technology. An imprecise controller is obtained by numerical simulation of Riccati differential equations with statistical linearization. This kind of Riccati differential equation differs significantly from the ordinary one. Then the system proposes a collaborative filtering method based on nonlinear differentiation based on student feature classification. At last, this paper systematically analyzes the differences between course selection systems, business recommendations, and student attributes—the system experiments on college students' choice of a learning platform. The study found that the method was correct 34.6% of the time. This system can provide practical guidance for students to choose courses.

In this study, we built a personalized hybrid course recommendation system (PHCRS) that considers students' interests, abilities and career development. To meet students' individual needs, we adopted the five most widely used algorithms, including content-based filtering, popularity-based methods, item-based collaborative filtering, user-based collaborative filtering, and score-based methods, to build a PHCRS. First, we collected course syllabi and labeled each course (e.g., knowledge/skills taught, basic/advanced level). Next, we used course labels and students' past course selections and grades to train five recommendation models. To evaluate the accuracy of the system, we performed experiments with students in the Department of Electrical and Computer Engineering, which provides 1794 courses for 925 students and utilizes the receiver operating characteristic curve (ROC) and normalized discounted cumulative gain (NDCG) as metrics. The results showed that our proposed system can achieve accuracies of 80% for ROC and 90% for NDCG. We invited 46 participants to test our system and complete a questionnaire. Overall, 60 to 70% of participants were interested in the recommended courses, while the course recommendation lists produced by content-based filtering were in line with 67.40% of students' actual course preferences. This study also found that students were more interested in courses at the top of the recommendation lists, and more students were autonomously motivated than held extrinsic informational motivation across the five recommendation methods. These findings highlighted that the proposed course recommendation system can help students choose the courses that interest them most.