Educational Recommender Systems Research Articles

The Educational Recommendation System with Artificial Intelligence Chatbot: A Case Study in Thailand

The educational recommendation system with an artificial intelligence (AI) chatbot is a system designed to operate on smartphones and is intended for use at the College of Industrial Technology in Thailand. Compatible with both iOS and Android operating systems, the educational recommendation system in this research is capable of providing educational recommendations and valuable information about the engineering degree program. It includes information about the Electronics Engineering Technology Department, relevant bodies of knowledge that can enhance awareness and public relations, and a proactive introduction to information. The main objectives of this study are to develop the capacity to analyze educational issues and provide useful and accurate information for educational recommendations, as well as to explore the possibilities for enhancing this system. The results of the study address the three research questions and demonstrate that the process and structure of the educational recommendation system with an AI chatbot can be utilized to create educational recommendation tools in higher education. These tools are accessible anywhere and at any time through small mobile devices. The system is also compatible with popular social media applications and is capable of analyzing user questions precisely and accurately to find the optimal answers that can meet the user’s needs. However, there are still gaps in the research that need to be investigated in the future. These studies should include exploring a broader range of applications among the general population and educational settings to assess the effectiveness of the educational recommendation system with an AI chatbot. Additionally, they should examine the skills that impact the utilization of educational technology in various contexts.

Dynamic educational recommender system based on Improved LSTM neural network

Nowadays, virtual learning environments have become widespread to avoid time and space constraints and share high-quality learning resources. As a result of human–computer interaction, student behaviors are recorded instantly. This work aims to design an educational recommendation system according to the individual's interests in educational resources. This system is evaluated based on clicking or downloading the source with the help of the user so that the appropriate resources can be suggested to users. In online tutorials, in addition to the problem of choosing the right source, we face the challenge of being aware of diversity in users' preferences and tastes, especially their short-term interests in the near future, at the beginning of a session. We assume that the user's interests consist of two parts: (1) the user's long-term interests, which include the user's constant interests based on the history of the user's dynamic activities, and (2) the user's short-term interests, which indicate the user's current interests. Due to the use of Bilstm networks and their gradual learning feature, the proposed model supports learners' behavioral changes. An average accuracy of 0.9978 and a Loss of 0.0051 offer more appropriate recommendations than similar works.

Investigating the Use of Deep Learning and Implicit Feedback in K12 Educational Recommender Systems

Recommender systems are increasingly being used in university or online education. However, recommender systems still have not found major usage in K12 education. This may be because of unique challenges that recommender systems face when used by a young and diverse population. Regardless, recommender systems for K12 education could provide many benefits for students and teachers, such as the simplification of personalized learning. Some of the issues with K12 educational recommender systems may be solved by the use of deep learning and implicit feedback. As such, we investigated the use of deep recommendation algorithms and implicit feedback for K12 educational recommender systems. To do this, we compared metrics for highly cited traditional and deep recommendation algorithms trained on explicit and implicit data. We found that recommendation algorithms using deep learning as a group do not differ in performance compared to traditional recommendation algorithms. We also found that the use of implicit feedback led to higher performance than using explicit feedback. The best performing algorithm used both deep learning and implicit feedback. We conclude that the deep learning can be a benefit for K12 recommender systems, particularly when the ordered sequence of items is carefully accounted for. We also conclude that researchers and developers must carefully consider which feedback contributes the most information for learning.

ConceptGCN: Knowledge concept recommendation in MOOCs based on knowledge graph convolutional networks and SBERT

Massive Open Online Courses (MOOCs) have gained popularity in the technology-enhanced learning (TEL) domain. To enhance the learning experience in MOOCs, educational recommender systems (ERSs) can play a crucial role by suggesting courses or learning materials that align with students' knowledge states. Thereby, understanding a student's learning needs and predicting knowledge concepts that the student might be interested in are important to provide effective recommendations. Inspired by the superior ability of knowledge graphs (KGs) in modeling the heterogeneous data in MOOCs and Graph Neural Networks (GNNs) in learning on graph-structured data, few works focusing on GNN-based recommendation of knowledge concepts in MOOCs have emerged recently. However, existing approaches in this domain have limitations mainly related to complexity, semantics, and transparency. To address these limitations, in this paper we propose ConceptGCN, an end-to-end framework that combines KGs, Graph Convolutional Networks (GCNs), and pre-trained transformer language model encoders (SBERT) to provide personalized and transparent recommendations of knowledge concepts in the MOOC platform CourseMapper. We conducted extensive offline experiments and an online user study (N=31), demonstrating the benefits of the ConceptGCN-based recommendation approach, in terms of several important user-centric aspects including accuracy, novelty, diversity, usefulness, overall satisfaction, use intentions, and reading intention. In particular, our results indicate that, if SBERT is used for the initial embeddings of items in the KG, a self-connection operation and a semantic similarity-based score function in the aggregation operation of GCN are not necessarily needed.

ALBERT-Based Personalized Educational Recommender System: Enhancing Students’ Learning Outcomes in Online Learning

Online learners must navigate vast educational resources to find materials that meet their needs. This study introduces an ALBERT-based personalized educational recommender system to improve student learning. ALBERT (A Lite BERT), an optimized variant of the BERT algorithm, captures contextualized word representations and understands the semantic meaning of learning resources, student profiles, and interactions. This study evaluates the ALBERT-based recommender system’s personalized learning recommendations. To assess learning outcomes, a diverse group of students from different educational domains is evaluated. Before and after the recommender system, academic performance, knowledge retention, and engagement are assessed. User satisfaction surveys assess recommendation quality, relevance, and user experience. The recommender system uses ALBERT’s model optimization to improve recommendation accuracy, learner engagement, and personalized learning. The evaluation shows the ALBERT-based personalized recommender system improves online learning outcomes. System-generated recommendations boost student engagement, knowledge retention, and academic performance. User satisfaction surveys show that the ALBERT-based system meets learners’ needs by providing relevant and high-quality recommendations. This research shows how advanced deep learning algorithms like ALBERT can improve personalized online learning. ALBERT’s optimized training and inference speeds up the recommender system’s scalability. This empowers learners to access tailored and high-quality educational resources, maximizing their learning outcomes and potential in online learning.

English

Recommender systems in education aim to help students make good decisions about the direction of their learning. The design of such systems in conventional research has treated the decision making process of students as a black box and assumes the best recommendations to be those that accurately predict student choices. Such an approach overlooks potentially valuable use cases for supporting optimal decision making, especially in self-directed learning contexts which present such challenges as identifying all available options, accurately evaluating the options against selection criteria, and selecting the best choice. This qualitative study aims to understand the areas where students struggle in the context of planning an open-ended project in order to inform the design of educational recommender systems. Data from interviews with 7 students at an international engineering school in Japan are analyzed to examine choice behaviors, influences on choice, and difficulty to choose in a self-directed learning context. The results illustrate considerations for designing educational recommender systems that can support the divergent thinking and convergent thinking demands of decision making. We provide case-based examples where the use of different recommender metrics, such as novelty and diversity, may provide value to users with different approaches to the decision-making process.   Key words: Decision making, self-regulated learning, educational recommender systems.

A Systematic Literature Review on the Implications of Educational Recommender System in Teaching Learning Environment

A Systematic Literature Review on the Implications of Educational Recommender System in Teaching Learning Environment

Developing a Personalized E-Learning and MOOC Recommender System in IoT-Enabled Smart Education

Developing a Personalized E-Learning and MOOC Recommender System in IoT-Enabled Smart Education

Requirements Elicitation Based on Psycho-Pedagogical Theatre for Context-Sensitive Affective Educational Recommender Systems

Requirements Elicitation Based on Psycho-Pedagogical Theatre for Context-Sensitive Affective Educational Recommender Systems

Research on educational resource recommendation system based on MRLG Rec

Abstract Internet information capacity is expanding and developing at a high speed every day. The huge amount of information data involves many problems, such as resources could be scattered, overlapped and confused and thus difficult for users to timely and accurately capture information suitable for them, especially in the field of education. Through the MRLG Rec algorithm, the following processes can be carried out: deep processing, characterisation of data to generate unique individual portraits of user information and to store education resource databases at the same time and analysis of recommendation system to provide users with more accurate high-quality education resources, thus forming a two-way unblocked and efficient information transmission closed-loop, which help learners find teaching resources of the time. It also has screening channels of high-quality educational resources. Through the analysis of experimental data, it is concluded that the algorithm improves the user viscosity of human-computer interaction in the teaching resource recommendation system.

A Goal-driven Personalized Educational Content Recommendation System for Self-Learning

A Goal-driven Personalized Educational Content Recommendation System for Self-Learning

How do Students Perceive Artificial Intelligence in YouTube Educational Videos Selection? A Case Study of Al Ain City

This research focused on the students' perceptions of the role of Artificial Intelligence in the educational content recommendation system by using the SEM- Artificial Neural Network approach. Data obtained from structured surveys indicated that Artificial Intelligence plays a significant role in the educational content recommendation process. Most relationships proposed in the conceptual model remained significant, with p values as strong as p> 0.000. Further, the IPMA and ANN also revealed in-depth results. Thus, this research concludes that the video recommendation system is helping young students to search for and find the educational content they want. Besides, this system also provides them with a searching facility with fewer efforts, adding more value to YouTube as one of the most prominent digital platforms today.

A Categorization of Workplace Learning Goals for Multi-Stakeholder Recommender Systems: A Systematic Review

Educational recommender systems offer benefits for workplace learning by tailoring the selection of learning activities to the individual’s learning goals. However, existing systems focus on the learner as the primary stakeholder of learning processes and do not consider the organization’s perspective. We conducted a systematic review to develop a categorization of workplace learning goals for multi-stakeholder recommender systems. Out of an initial set of 13,198 publications exported from databases, a final sample of 34 key publications was identified, according to predefined inclusion criteria. Content analysis and reflective exchange were deployed to synthesize workplace learning goals investigated in the key publications. We identified five categories of workplace learning goals that can be arranged along a dimension from intrinsic (goals set exclusively by the learner) to external (goals set exclusively by the organization). Our categorization provides a common language for multi-stakeholder recommender systems incorporating both the learner’s and the organization’s perspectives.

A systematic literature review on educational recommender systems for teaching and learning: research trends, limitations and opportunities.

Recommender systems have become one of the main tools for personalized content filtering in the educational domain. Those who support teaching and learning activities, particularly, have gained increasing attention in the past years. This growing interest has motivated the emergence of new approaches and models in the field, in spite of it, there is a gap in literature about the current trends on how recommendations have been produced, how recommenders have been evaluated as well as what are the research limitations and opportunities for advancement in the field. In this regard, this paper reports the main findings of a systematic literature review covering these four dimensions. The study is based on the analysis of a set of primary studies (N = 16 out of 756, published from 2015 to 2020) included according to defined criteria. Results indicate that the hybrid approach has been the leading strategy for recommendation production. Concerning the purpose of the evaluation, the recommenders were evaluated mainly regarding the quality of accuracy and a reduced number of studies were found that investigated their pedagogical effectiveness. This evidence points to a potential research opportunity for the development of multidimensional evaluation frameworks that effectively support the verification of the impact of recommendations on the teaching and learning process. Also, we identify and discuss main limitations to clarify current difficulties that demand attention for future research.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10639-022-11341-9.

A Recommendation Model for College English Digital Teaching Resources Using Collaborative Filtering and Few-Shot Learning Technology

This study designs and implements a digital English instructional resource management recommendation system based on collaborative filtering technology based on CF research and the construction of digital English instructional resources. This study designs the system with B/S structure combined with hierarchical design architecture, plans the overall design goal, architecture design, compilation structure, and key technologies, and designs and implements the system's core modules on the basis of fully analysing the functional and nonfunctional requirements of personalized educational resource recommendation system. Furthermore, the traditional CF has been improved to address scalability, data sparseness, and user cold start in the recommendation process. The evaluation results show that this algorithm has a recall rate of 96.37% and a system resource recommendation accuracy rate of 95.31%, both of which are higher than the traditional method's 6.37%. This algorithm can overcome the drawbacks of traditional algorithms, improve recommendation accuracy, and efficiently provide high-quality English teaching resources. Educators and students will be more likely to find high-quality digital English instructional resources if they use the instructional resource system proposed in this study.

The Design of Personalized Education Resource Recommendation System under Big Data.

With the advent of the Internet and the era of big data, education is increasingly dependent on data resources to support product and business innovation, and the lack of data resources has severely limited the areas involved. As a general information filtering method, personalized recommendation systems analyze the historical interaction data between users and items to build user interest models in an environment of “information overload”, allowing users to discover and recommend information that interests them. However, the explosive growth of information in the network makes users wander in the sea of information, and it is increasingly difficult to find the information they really need, i.e., information overload. This has given rise to personalized recommendation systems, which currently have more mature applications in industries such as e-commerce, music services, and movie services. To this end, this paper studies and implements a customized educational resource recommendation system that can handle big data. The results show that the values of different similarity calculations all fluctuate with the gradual increase of the number of nearest neighbors, and the algorithm in this paper is maximum at the number of neighbors around 60; then, it is inferred that applying the calculation method to the recommendation algorithm will improve the recommendation accuracy. Therefore, education uses the concept of big data to process the huge amount of education data and find some correlations and laws in education, so as to realize “teaching according to the material, teaching according to the material”.

Application of Conditional Random Field Model Based on Machine Learning in Online and Offline Integrated Educational Resource Recommendation

It is of great significance to mine the learning resources that learners are interested in from massive data and recommend appropriate educational resources to them according to the characteristics of students. To improve the accuracy of educational resource recommendation, this paper proposes an educational resource recommendation system based on a graph attention network and conditional random field fusion model. It builds all comments for each student and educational resource into a comment graph. Through the graph's topological structure to capture the network and the dependency between words in the commentary text, the adjacency information of each node is aggregated by the graph attention network based on connection relation. After the graph attention network layer, the conditional random field inference layer is added. The label sequence with the highest probability is output by the dependent random field inference layer, which is taken as the final recommendation result of the model. Experimental results show that the proposed algorithm has better performance in accuracy and diversity than the traditional recommendation algorithm.

ABiNE-CRS: course recommender system in online education using attributed bipartite network embedding

ABiNE-CRS: course recommender system in online education using attributed bipartite network embedding

The Construction of Accurate Recommendation Model of Learning Resources of Knowledge Graph under Deep Learning

With the rapid development of science and technology and the continuous progress of teaching, it is now flooded with rich learning resources. Massive learning resources provide learners with a good learning foundation. At the same time, learners want to be precise from many learning resources. Second, it becomes more and more difficult to quickly obtain the learning resources you want. Therefore, it is very important to accurately and quickly recommend learning resources to learners. During the last two decades, a large number of different types of recommendation systems were adopted that present the users with contents of their choice, such as videos, products, and educational content recommendation systems. The knowledge graph has been fully applied in this process. The application of deep learning in the recommendation systems has further enhanced their performance. This article proposes a learning resource accurate recommendation model based on the knowledge graph under deep learning. We build a recommendation system based on deep learning that is comprised of a learner knowledge representation (KR) model and a learning resource KR model. Information such as learner’s basic information, learning resource information, and other data is used by the recommendation engine to calculate the target learner’s score based on the learner KR and the learning resource KR and generate a recommendation list for the target learner. We use mean absolute error (MAE) as the evaluation indicator. The experimental results show that the proposed recommendation system achieves better results as compared to the traditional systems.

A Recommender System Method for Children’s Education Using Mobile Technology

In the last decade, we have seen attention and scientific research in using technology, especially the mobile technology, in education. The critical effects of technology in child education are increasing motivation, the self-confidence of children and their group cooperation. The purpose of this research is to provide a child educational recommender system using mobile technology, which includes decision making and real-time monitoring of the educational status of each child, personalization of educational materials based on the educational status of each child, recommendation of educational materials based on the extent of their dissemination in the children’s educational network, etc. In this research, the type of research based on the objective is applied research. The statistical population includes children between the ages of 8 and 10 years. The child’s educational recommender system includes input, processing and output. The mobile technology was used to track each child’s educational activity and educational status and turn them into input keywords of the recommender system. In the processing section, various matching functions are provided to match each child’s detected educational status with the educational documents. Finally, in the output section, educational documents adapted to each child’s needs are e-mailed to the mobile phone of the child’s parents. Because children cannot recognize and express their educational needs, in the proposed recommender system, all phases, from the recognition of children’s educational activity and diagnosis of the educational status of each child to the presentation of educational documents related to the diagnosed educational status, have been done automatically. The educational recommender system is simulated using NetLogo and R. Also, the implementation of the educational recommender system has been done on 36 children. According to the results, the educational recommender system was able to increase children’s learning more than twice.