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Personalized and adaptive learning has been touted to be one of the most promising emerging tools for increasing student learning and student success. Yet, the terms are neither precise nor clearly defined at this time, thus making it difficult for institutions of higher education to adopt and implement a learning approach using technology that is in its infancy and not clearly understood by those who will be utilizing it. One goal of this chapter is to define adaptive and personalized learning as it is used at this time in the hopes that as the technology evolves the promise of increased student learning can come to fruition. Adaptive learning personalizes learning by continuously evaluating each student’s performance in real time and creating an ever-changing individualized learning pathway as directed by artificial intelligence and machine learning, thus increasing learning and student satisfaction.

Based on the background of modern art education, this study understands the teaching mode, analyzes the current situation of painting teaching with low learning efficiency, points out the future development trend of art education, and solves the problem of lack of individualized art teaching demand. This paper proposes a “Individualized Painting Education Model” based on user experience and AI technology. This mode of education improves learners' painting level from three aspects. First, it provides learners with accurate learning materials immediately. Secondly, individualized guidance should be given according to learners' learning objectives. Finally, the learner's work is evaluated professionally. Based on the individualized painting education model, this study develops the “Individualized Painting Teaching System”, which consists of community module, painting creation module, painting auxiliary module and individual center. The research results show that learners can effectively improve their learning efficiency and artistic creation ability through the application of individualized painting education model, so as to enhance their interest in fine arts and painting.

In individualized learning trajectories, it could be valuable to administer small tests that focus on a specific learning outcome to determine mastery of the learning objective and to evaluate whether a student can progress to other learning objectives. For this type of application, testing time competes with direct learning time, and a large number of learning objectives could invoke a potentially large burden due to testing. Thus, it is effective to limit the number of items and to reduce testing time as much as possible. However, the number of items is directly related to the accuracy of the mastery decision and the applicability of this type of formative evaluation in practical situations. For the formative evaluation to result in valid inferences, general measurement principles are valuable as well (Bennett in Assess Educ Principles Policy Pract 18:5–25, 2011). In this chapter, we provide techniques to determine the number of items and corresponding cut scores that are necessary to decide on mastery. We apply these techniques in situations with different item characteristics and provide the outcomes for varying test situations, illustrated using a practical example.

When using an individualized learning system ALEKS to study mathematics, bilingual students can use both English-language and Spanish-language modules. When we started our study, we expected that those Spanish-language students whose knowledge of English is still not perfect would first use mostly Spanish-language modules, and that their use of English-language modules will increase as their English skills increase. Instead, what we found is that even students who are not very skilled in English use both Spanish-language and English-language modules. This raises a natural question: why, in spite of the presence of well-designed well-tested easy to Spanish-language models, the students benefit from also using English-language modules—which for them are not so easy to access (they use Google translator). In this paper, we use fuzzy logic to provide a possible theoretical explanation for this surprising behavior.

In this contribution we explore how information technology can help individualized learning in schools. Rather than tweaks, schools need a complete makeover. Analysis of the results of our test school in Amsterdam shows that freedom of choice leads to responsibility, leads to motivation, leads to higher learning outcome.

In the most learning systems, pedagogical activities are presented in a static way without considering the specifics or learner goals. However, customize the learning environment and adapt learning path to learners’ profile improve learning quality. In our proposed system, we believe that learning path’s individualization depends on collected traces’ activities in learning environment. We propose in this paper a model-oriented services to offer to each learner an individualized learning path to acquire the targeted skills. The system will be implemented as composed services norms and standards.

The MyEduDecks application is a pen-based digital flashcards program coded by several students at South Fayette High School over a development period of three years. Here we describe the results of the MyEduDecks platform when used to aid a third-grade Technology Literacy class in the South Fayette Intermediate School. The results show that the MyEduDecks application is well received by students and is an effective teaching tool for elementary instructors. Our results also show that the MyEduDecks application provides the ecosystem for an individualized learning environment.

Demands on the quality of teaching/learning materials in the sense of targeting studentsʼ individualized learning styles and needs make educators know, carefully consider, and then choose from many different digital tools in order to make their teaching more effective, appealing and engaging for students in the learning process. An online component to different paper-printed English learning materials – MyEnglishLab provides a smart solution to fulfill both teachersʼ and studentsʼ needs. The research being carried out at the Faculty of Science, University of Hradec Kralove, tests and verifies the effectiveness of blended learning using the online component MyEnglishLab in the distant form, and the tools of smartboard used in the present form of the blended learning model. In the paper we present MyEnglishLab component itself, its contribution to blended learning, then we introduce our pedagogical experiment having been going on within the Faculty of Science and students studying different scientific areas. We also present the first results coming out from testing two groups of students – one using MyEnglishLab component and the other using only paper-based materials, while both groups of students are exposed to the use of smartboard in face-to-face form.

In the last few years, the research at the University of Ostrava has been focused on the theory of adaptive e-learning and its testing in practice. Quality adaptable textbooks are essential in order for this type of education to be successful. The paper deals with one of the main parts of the stated issue – an evaluation of adaptive textbooks. Evaluation can be conducted (a) before the start of the education process, (b) immediately after the textbook has been inserted into the LMS, or (c) after the protocol of students’ behavior in the on-line mode has been analyzed. The paper describes the theoretical proposition of one type of evaluation and its verification in practice.

In this paper, the authors attempt to describe a possibility of an individual approach to learning within multicultural electronic educational space. To solve this problem the authors proposed criteria for determining learner’s cultural and cognitive profile and ways of adaptation of educational content and interface as a pre-requisite for creating “smart educational environment”. An important factor in the design of educational media environment is to focus on the personal and socio-cultural approaches to learning information typical for a selected cultural group, resulting in the possibility of constructing an individual educational trajectory. The authors propose to design a prototype of “smart educational environment” whose interface and content could be adjusted to a student’s cultural-cognitive profile.