In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.

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A Review of Content-Based and Context-Based Recommendation Systems

Learning time of student is precious, over-practice of target knowledge component wastes student’s time, however, under-practice may mean the student may not grasp target knowledge component properly. To any student, it is helpful if intelligent tutoring system can determine how many practice opportunities needed for mastery of knowledge component. In this paper, to improve student’s learning efficiency, a method of predicted learning curve based on grey models is proposed to determine the counts of practice op-portunity for mastery of knowledge component. The experimental results show that the predicted value on error rate of practice opportunity over knowledge component with the proposed method is much closer to the value of real learning curve than the predicted learning curve produced by learning factors analysis. It implies the proposed prediction method is potential to present reasonable practices for personalized tutoring.

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Evaluation of Student Performance with Predicted Learning Curve Based on Grey Models for Personalized Tutoring

A theoretical review of research and publications on digitalization of the educational space, including digital didactics, is presented. The authors consider the theoretical aspects of the definition of “digital didactics” in the light of recent domestic and foreign studies. The novelty of this study lies in the characterization of the main trends in the problem of digital didactics, highlighting several problems that modern Russian teachers face in the process of digital learning. The authors draw attention to the need to consider the basics of digital didactics in the context of meaning formation. More than 55 publications of domestic and foreign researchers were studied. The issues of defining the concept of digital didactics are considered, the General characteristics of digital didactics at the present stage, the most important problems and prospects for its study are given. This article will allow researchers working in the field of digital didactics to get acquainted with the most well-known concepts of digital learning today.

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Fundamentals of Digital Didactics: Specifics and Development at the Present Stage

Smart wearables are becoming an irreplaceable part of daily living by supporting their users to maintain or adopt healthier lifestyles and monitor their current status. While the trend is increasing, little has been accomplished in the field of personalized solutions. In the present study, two models derived from distinct conceptual themes were developed, and the performance was evaluated utilizing a wearable prototype in the form of smart glasses. A statistical and a reinforcement learning approach were adopted to construct a personalization layer in terms of a predefined system reaction upon specific user behavior. The settings of the present study involve the user behavior derived from Artificial Intelligence (AI) based human activity recognition, among others, and the system reaction being a supportive Augmented Reality (AR) based functionality. Each approach yielding different benefits and drawbacks, imminently leads to a comparative analysis based on the efficiency offered by assessing the inference, update, and trend handling time. Both models are built upon the user's previous data, resulting in a data driven approach that is entirely different for each user and tailored to the user preferences. The results derived from the comparative analysis indicate that both approaches offer the personalization seeked, with the reinforcement learning approach to adapt faster.

AI Methods for Personalized Suggestions on Smart Glasses Based on Human Activity Recognition*

This study proposed a strategy of addressing the ‘‘one size fits all’’ approach in education. The researchers design and implement a system called HELPNAYAN that selects the most optimal learning objects and path based on the students’ preferences.

HELPNAYAN: An Adaptive Learning System Utilizing Bayesian Network and Felder-Silverman Learning Style Model to Improve Grade 10 Students’ Learning in Physics

Study in Literatures shows that tracing knowledge state of student is corner stone of intelligent tutoring system for personalized learning. In this paper, an academic question recommender based on Bayesian network is developed for personalizing practice question sequence with tracing mastery level of student on knowledge components. This question recommender is discussed with theoretical analysis, and designed and implemented in software engineering way. It provides instructor with tools for building knowledge component network and setting question of course. It also makes student personalize practice questions of course. This question recommender is planned to deploy in real learning context for the future validation of how well such question recommendation improves performance and saves practice time for student.

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Qingsheng Zhang

Papers

Develop Academic Question Recommender Based on Bayesian Network for Personalizing Student’s Practice