A Cognitive Diagnostic Module Based on the Repair Theory for a Personalized User Experience in E-Learning Software

Path-Based Recommender System for Learning Activities Using Knowledge Graphs

Recommender systems can offer a fertile ground in e-learning software, since they can assist users by presenting them with learning material in which they can be more interested, based on their preferences. To this end, in this paper, we present a new method for a knowledge-graph-based, path-based recommender system for learning activities. The suggested approach makes better learning activity recommendations by using connections between people and/or products. By pre-defining meta-paths or automatically mining connective patterns, our method uses the student-learning activity graph to find path-level commonalities for learning activities. The path-based approach can provide an explanation for the result as well. Our methodology is used in an intelligent tutoring system with Java programming as the domain being taught. The system keeps track of user behavior and can recommend learning activities to students using a knowledge-graph-based recommender system. Numerous metadata, such as kind, complexity, and number of questions, are used to describe each activity. The system has been evaluated with promising results that highlight the effectiveness of the path-based recommendations for learning activities, while preserving the pedagogical affordance.

Teacher Training Effectiveness in Self-Regulation in Virtual Environments

Higher education in the 21st century faces the challenge of changing the way in which knowledge is conveyed and how teachers and students interact in the teaching-learning process. The current pandemic caused by SARS-CoV-2 has hastened the need to face up to this challenge and has furthered the need to approach the issue from the perspective of digitalisation. To achieve this, it is necessary to design training programmes geared towards teaching staff and which address both the use of technology and instructional design aimed at promoting the development of self-regulated learning (SRL) and automatic feedback systems. In this study, work was carried out with 23 teachers (8 inexperienced and 15 experienced teachers) in a training programme conducted through Moodle. The aims were: (1) to test whether there were any significant differences between the behaviour patterns of new teachers compared to experienced teachers, (2) to determine whether clusters of behaviour patterns corresponded to the type of teacher and (3) to ascertain whether the level of teacher satisfaction with the training activity in digital teaching will depend on the type of teacher. A quantitative as well as a qualitative design was applied. Differences were found in the behaviour patterns in the training activities for the development of rubrics and use of learning analytics systems in virtual learning environments. It was also found that the type of teacher did not correspond exactly to the behaviour cluster in the learning platform. In addition, no significant differences were found in the level of satisfaction between the two kinds of teacher. The main contribution this study makes is to provide a detailed description of the training stage as well as the materials required for its repetition. Further analytical studies are required on teacher perception of training programmes in digital teaching in order to provide personalised training proposals that lead to an effective use of teaching in digital environments.

Agent-Based Recommendation in E-Learning Environment Using Knowledge Discovery and Machine Learning Approaches

E-learning is a popular area in terms of learning from social media websites in various terms and contents for every group of people in this world with different knowledge backgrounds and jobs. E-learning sites help users such as students, business workers, instructors, and those searching for different educational institutions. Excluding the benefits of this system, there are various challenges that the users face in online platforms. One of the important challenges is the true information and right content based on these resources, search results and quality. This research proposes virtual and intelligent agent-based recommendation, which requires users’ profile information and preferences to recommend the proper content and search results based on their search history. We applied Natural Language Processing (NLP) techniques and semantic analysis approaches for the recommendation of course selection to e-learners and tutors. Moreover, machine learning performance analysis applied to improve the user rating results in the e-learning environment. The system automatically learns and analyzes the learner characteristics and processes the learning style through the clustering strategy. Compared with the recent state-of-the-art in this field, the proposed system and the simulation results show the minimizing number of metric errors compared to other works. The achievements of the presented approach are providing a comfortable platform to the user for course selection and recommendations. Similarly, we avoid recommending the same contents and courses. We analyze the user preferences and improving the recommendation system performance to provide highly related content based on the user profile situation. The prediction accuracy of the proposed system is 98% compared to hybrid filtering, self organization systems and ensemble modeling.