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The text underscores the growing importance of digital education higher institutions and the need to adopt a 'human-centered' approach to maximize learners' potential. This approach focuses on the needs of both learners and educators, recognizing their unique perspectives. The shift to online learning presents challenges, requiring educators to design virtual courses that replicate in-person dynamics. Sustaining engagement and meaningful relationships in virtual classes is crucial for the well-being of both students and instructors. The text emphasizes the significance of strategies such as office hours, online group work, and overall interaction to create a classroom-like atmosphere and foster connections between students and instructors. These measures contribute to a more responsive online learning environment that addresses individual student needs. Humanizing online learning involves recognizing students as individuals with unique needs, moving beyond mere device interaction. The text discusses challenges in asynchronous online environments, emphasizing the importance of synchronous interactions to combat feelings of disconnection. Various strategies are suggested to humanize online learning, including the use of technology to enhance availability and engagement, personalized feedback, and the incorporation of diverse learning activities. Instructor-student relationships are highlighted as crucial in online learning, impacting students' perceptions and satisfaction. [ABSTRACT FROM AUTHOR]

The purpose of this study was to derive a learner model from technological education for AI convergence individualized learning. The learning model devised in this study reflects the characteristics of technological problem-solving as it considers all areas of cognition, definition, and psychodynamics. Among the dimensions of recognition, judgment, creativity, execution, attitude, and interaction of technological problem-solving learning styles, the dimensions that acted mainly according to the content of the problem and the learning type could be selected and constructed as a model. In this study, a creativity-execution model was devised in consideration of the 'adaptationinnovation type' of the creativity level and the 'reflection-action type' of the execution level. In addition, an interactive-attitude model was devised in which the 'independent-cooperative type' in the interaction dimension and the 'avoidance-participation type' in the attitude dimension act dynamically. Furthermore, a three-factor learner model was developed considering the dimensions of cognitive judgment, creativity, and execution. Also, a five-factor learner model for technological problem-solving was devised and presented, with all five dimensions of cognition-judgment, creativity, execution, interaction, and attitude of the technological problem-solving learning style as factors. The learner characteristics defined through the model design suggested the direction of AI convergence education considering individual characteristics of learners and provided basic data for it. In the future, it is suggested that specific individualized teaching and learning development research be conducted in AI convergence education, considering the characteristics of learners according to the learning style of technological problem-solving. [ABSTRACT FROM AUTHOR]

The current health situation has posed a significant challenge for students and teachers of all educational stages, as they have had to adapt the teaching-learning process to the health restrictions imposed during the COVID-19 pandemic. The aim of this study is to analyze the motivation of university students in education sciences in relation to their gender, religious tendencies, and social skills. A total of 130 university students enrolled in the University of Granada at the Ceuta and Melilla campuses (M = 23.38; SD = 6.95) participated in this research in an ex post facto quantitative study with a descriptive, correlational, and cross-sectional design. The students responded to the adaptation into Spanish of the Situational Motivation Scale of Guay et al. (2000). Among the results, it can be highlighted that the most intrinsically motivated population is female and Catholic. Regarding social skills, sociability, communication, and assertiveness are the skills that most influence student's degree of motivation. The data drawn from this study demonstrate the usefulness of analyzing students' motivation to provide them with more individualized teaching, as well as the need to work on students' social skills. [ABSTRACT FROM AUTHOR]

In recent years, in the field of education, there has been a clear progressive trend toward precision education. As a rapidly evolving AI technique, machine learning is viewed as an important means to realize it. In this paper, we systematically review 40 empirical studies regarding machine-learning-based precision education. The results showed that the majority of studies focused on the prediction of learning performance or dropouts, and were carried out in online or blended learning environments among university students majoring in computer science or STEM, whereas the data sources were divergent. The commonly used machine learning algorithms, evaluation methods, and validation approaches are presented. The emerging issues and future directions are discussed accordingly. [ABSTRACT FROM AUTHOR]

This paper discusses the unprecedented challenges and possible directions in which the field of educational assessment is going after the outbreak of COVID‐19. Though the pandemic leads to a lot of pressure related to instruction, learning, and assessment, it also provides opportunities that are likely to require changes to the current theories and practices as well as the assumptions that are no longer justified. It is hoped that the challenges will motivate our field and the instructional experts to work closely together with the learning and instruction process so that assessment will be better integrated and can provide data‐driven insights related to the sequence and content of instruction based on the diagnosis of students' strengths and weaknesses from assessment data. We expect that the integration of assessment in learning and instruction will definitely exert significant positive impact on learning outcomes. We believe the field will become aware of the assessment issues that teaching at a distance presents and will come up with innovative solutions to the many emerging problems when the world of instruction and learning undergoes significant changes from social distancing. This paper provides comments, and predictions about what the challenges and directions in educational assessment will be. [ABSTRACT FROM AUTHOR]

Past research on situated expectancy-value theory has regularly provided evidence of different motivational patterns indicating that not only can students be characterized by different levels of motivation (e.g., low vs. high), but also by divergent profiles (e.g., high success expectancies, low task values). This person-oriented two-wave study (a) identified and compared the motivational patterns of secondary school students from different learning environments (i.e., Student-Centered Learning vs. Teacher-Directed Learning), (b) analyzed the stability of and changes to these patterns during a school year, and (c) examined whether achievement-related choices and performance predicted the pattern changes. Using data from German secondary school students (T1: N = 1153; M = 13.97 years, SD = 1.37; 49% girls) multigroup latent transition analysis revealed four different motivational patterns, including a (a) High Motivational pattern, (b) Medium Motivational pattern, (c) Low Motivational pattern, and (d) Highly Confident/Hardly Interested pattern. The distribution of these patterns differed significantly between students from Student-Centered-Learning and Teacher-Directed-Learning environments. Approximately 47% of students in Teacher-Directed Learning were in the low motivational class whereas the Student-Centered Learning environment exhibited approximately half of that number. The extremely stable nature of these classes highlights the strong relevance of the educational context for student motivation and supports situated expectancy-value theory. [ABSTRACT FROM AUTHOR]

Organic learning engages undergraduates in discovering new knowledge based on prior learning through variety of guided activities that stimulate inquiry-based learning and critical thinking in the research process. Some activities include searching, browsing, accessing, gathering, evaluating, assessing, reflecting, organizing, linking, and synthesizing. Learning how to access information by using a variety of search strategies as well as delivery platforms such as Google, discovery, individual databases, and the internet can be overwhelming and challenging. When students discover how to search and access desired sources through a variety of explicitly designed information literacy instructions with clearly defined learning outcomes, they take ownership of developing their learning and research skills. Through organic learning, they experience lightbulb moments, asking questions, discussing topics, and then searching again for more information. Organic learning unconsciously involves students in self-education, engaging them in the research process without pressuring them to practice redundant, rote exercises. Often undergraduates encounter difficulty in conceptualizing the research process as complex and multi-faceted. The authors argue that organic learning strategies to activate prior learning that builds advanced searching skills and increase new scholarly knowledge. [ABSTRACT FROM AUTHOR]

Kernel Fisher discriminant analysis (KFDA) is a very popular learning method for the purpose of classification. In this paper, we propose a novel learning algorithm to improve KFDA and make it very suitable for dealing with the large-scale and high-dimensional data sets. The proposed algorithm is termed individualized KFDA (IKFDA). IKFDA is based on individualized learning , i.e., a strategy to learn and classify the individual test samples one by one. Our approach seeks to find the appropriate training subset, referred to as learning area , for each individual test sample, and then employ the learning area to construct the KFDA model for the test sample. For each individual test sample, IKFDA exploits some types of similarity measures to determine a learning area that consists of the training samples that are most similar to the test sample. Compared with the traditional learning algorithms that often exploit the whole training set to construct the learning models without considering the distribution property of the test samples, IKFDA can adaptively learn the individual test samples. It is a powerful tool to deal with the real-world complicated data sets that are often very large-scale and high-dimensional, and are usually drawn from the different distributions. Extensive experiments show that the proposed algorithm can obtain good classification results. [ABSTRACT FROM AUTHOR]

The purpose of this article is to introduce and develop an approach educational research oriented, in order to integrate assisted instruction in assisted didactics design, based on a quality-quantity paradigm. In this context, the analysis focuses on a methodological approach, permanently reframed by the conceptual, analytical and theoretical updated frameworks. This manner reflects the versioning process of the hardware and software development, and highlights the educational technology concept integrated in a classical teaching-learning activity. [ABSTRACT FROM AUTHOR]

Purpose. This special volume is dedicated to eight updated and expanded communications selected from 33 refereed papers presented at the inaugural international conference on Technology-based Learning with Disability (LWD-07) which took place on July 19–20, 2007 at Wright State University in Dayton, Ohio. Methods. Approximately 140 researchers and practitioners attended LWD-07 representing a cross-section of K-12 and higher education, pure and applied research, disability-related industry and rehabilitation agencies with common interests in facilitating educational attainment for people with all types of disabilities through use of technology. Results. The communications selected for publication are representative of the breadth of interest at the nexus of disability, assistive technology, and the pedagogy of individualized learning. Conclusions. Access to education is a key component for quality of life and rehabilitation of any individual with a disability. [ABSTRACT FROM AUTHOR]

Individualized learning is a fundamental tenet of the Carnegie Foundation's new recommendations for physician training. A primary goal of Mount Sinai School of Medicine's new curriculum is to train self-directed physicians who have mastered lifelong learning skills. The Individual Scholarly Project and Independent Research Experience (INSPIRE) was created to enable fourth-year students to conduct mentored, independent scholarly projects to develop critical thinking skills and intellectual independence. Four students were accepted into the 2008 12-week pilot. Two did clinical research, 1 conducted medical education research, and 1 continued a basic science project. INSPIRE featured weekly sessions in which students shared their progress, heard about the careers of physician-scientists, and participated in presentation skills workshops. Mentors gave feedback using a form based on the program goals. Anonymous evaluations showed that all participants believed their goals were successfully fulfilled, that the likelihood of including research in their careers increased, and that they felt more skilled at writing abstracts and presenting their work orally. Students agreed that INSPIRE was a valuable opportunity for acquiring in-depth knowledge on a topic and building research and presentation skills. Helping students shape an individualized scholarly experience may help to produce doctors who are self-directed, are personally fulfilled, are able to serve society with diverse expertise, and have the tools to become leaders in their fields. [ABSTRACT FROM AUTHOR]

In the context of learning classification knowledge by inquiry examples, how to help students perform induction learning more efficiently and meaningfully with adaptive instruction strategies is an important research issue. This paper presents a machine-learning approach to developing computer-assisted learning supports for inductive learning tasks by providing individualized inquiry examples. A learning process based on the knowledge refinement strategy is first proposed to model the learning task of induction by inquiry examples. Several machine-learning techniques are developed and evaluated to support various instruction/learning activities such as evaluating student learning outcomes, providing appropriate examples for students to study, and giving individualized hints to motivate and facilitate learning during the induction learning process. Integrated with the intelligent learning supports, a web-based system, named ALBIX (Active Learning By Inquiry Examples), was implemented so that students can actively construct, verify and refine their classification knowledge in an interactive manner. Finally, the learning supports presented in this paper are shown to be effective in their design purposes through a set of simulation tests. A small-scaled prototype testing also showed that teachers and students might be interested in such kind of active learning strategy. [ABSTRACT FROM AUTHOR]

The article offers information on individualized learning in medical education programs.