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In this study, it was aimed to implement and evaluate an adaptive learning environment (ALE) designed according to the learner characteristics of 4th grade primary school students and integrate educational hypermedia environments with face-to-face teaching. Via a preliminary study carried out in accordance with this purpose, the variables that affect the academic achievement of the students in the primary school social studies course were analyzed, and as a result of this analysis, the design, application, and evaluation criteria of ALE were determined. In our study, which was modeled as an embedded-experimental mixed method, quantitative and qualitative data were obtained using different tools. The analysis of the data revealed that the designed learning environment had a positive effect on students' academic achievements, collaborative learning skills, and, partially, independent learning skills, and it was also effective in closing the achievement gap due to learning and cognitive styles. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence has played an important role in science teaching and learning. The purpose of this study was to fill a gap in the current review of research on AI in science education (AISE) in the early stage of education by systematically reviewing existing research in this area. This systematic review examined the trends and research foci of AI in the science of early stages of education. This review study employed a bibliometric analysis and content analysis to examine the characteristics of 76 studies on Artificial Intelligence in Science Education (AISE) indexed in Web of Science and Scopus from 2013 to 2023. The analytical tool CiteSpace was utilized for the analysis. The study aimed to provide an overview of the development level of AISE and identify major research trends, keywords, research themes, high-impact journals, institutions, countries/regions, and the impact of AISE studies. The results, based on econometric analyses, indicate that AISE has experienced increasing influence over the past decade. Cluster and timeline analyses of the retrieved keywords revealed that AI in primary and secondary science education can be categorized into 11 main themes, and the chronology of their emergence was identified. Among the most prolific journals in this field are the International Journal of Social Robotics, Educational Technology Research and Development, and others. Furthermore, the analysis identified that institutions and countries/regions located primarily in the United States have made the most significant contributions to AISE research. To explore the learning outcomes and overall impact of AI technologies on learners in primary and secondary schools, content analysis was conducted, identifying five main categories of technology applications. This study provides valuable insights into the advancements and implications of AI in science education at the primary and secondary levels. [ABSTRACT FROM AUTHOR]

Due to the effects of the COVID-19 crisis on educational institutions, schools had to close and switch to online education. Training in-service teachers to incorporate and utilize technology as part of Internet-based instructions was a challenge and pressing necessity. TPACK is an essential framework for comprehending how teachers employ technology in teaching. Despite the significance of adaptive learning environments in recent years, research has not addressed how to use these environments to improve the TPACK of in-service teachers, particularly during crises. Consequently, our objective was to design an adaptive learning environment that provides in-service math, science, and English teachers with substantial and continuing support for each TPACK component. A total of 173 in-service teachers were divided into two groups: an experimental group of 83 who used adaptive learning and a control group of 90 who used Zoom techniques. TPACK questionnaires were administered before and after the experiment. The experimental group improved TPACK more than the control group. All teachers believed that adaptive learning training helped them to build technology-integrated lesson plans. This study provides ideas and practices for developing an adaptive learning environment for the in-service teachers' TPACK development. The challenges to adaptive learning environments have been highlighted, identifying the potential for future investigations. [ABSTRACT FROM AUTHOR]

With the improvement of Information and Communication Technologies (ICTs, online learning has become a viable means for teaching and learning. Nonetheless, online learning is still facing various challenges. The challenges include lack of support and loneliness experienced by learners. Adaptive online learning is one of the means that researchers are proposing to support learners and reduce the loneliness they experience in online learning. Research in adaptive online learning has been on the rise. Though there are several review studies that have attempted to provide summaries of research and development happening in this area, there is still lack of a comprehensive and up-to-date review that looks at the aspects of adaptive online learning systems in terms of the learner characteristics being modelled, domain model, adaptation model, the various techniques used to achieve the various tasks in those models and the impact the adaptive online learning has on learning. This study therefore was initiated in order to fill this gap. The study was carried out using a systematic literature review methodology. A total of 59 articles were used in the study, drawn from six databases namely Science direct, IEEE explore, ACM, Emerald, Springer and Taylor and Francis. The results indicate that: the most used learner characteristic is learning style even though the use of learning knowledge is on the rise; there is a rise in the use of machine learning algorithms in learner modelling; learning content is the most common target for adaptation; rules is the most utilized method in the adaptation model; and most adaptive online learning have not been evaluated in terms of learning. There is therefore a need for evaluation of the developed adaptive online learning and more studies that utilize more than one learner characteristic as the basis for adaptation and those that use machine learning. [ABSTRACT FROM AUTHOR]

The ability to organize variable sensory signals into discrete categories is a fundamental process in human cognition thought to underlie many real-world learning problems. Decades of research suggests that two learning systems may support category learning and that categories with different distributional structures (rule-based, information-integration) optimally rely on different learning systems. However, it remains unclear how the same individual learns these different categories and whether the behaviors that support learning success are common or distinct across different categories. In two experiments, we investigate learning and develop a taxonomy of learning behaviors to investigate which behaviors are stable or flexible as the same individual learns rule-based and information-integration categories and which behaviors are common or distinct to learning success for these different types of categories. We found that some learning behaviors are stable in an individual across category learning tasks (learning success, strategy consistency), while others are flexibly task-modulated (learning speed, strategy, stability). Further, success in rule-based and information-integration category learning was supported by both common (faster learning speeds, higher working memory ability) and distinct factors (learning strategies, strategy consistency). Overall, these results demonstrate that even with highly similar categories and identical training tasks, individuals dynamically adjust some behaviors to fit the task and success in learning different kinds of categories is supported by both common and distinct factors. These results illustrate a need for theoretical perspectives of category learning to include nuances of behavior at the level of an individual learner. [ABSTRACT FROM AUTHOR]

Effective and engaging E-learning becomes necessary in unusual conditions such as COVID-19 pandemic, especially for the early stages of K-12 education. This paper proposes an adaptive personalized E-learning platform with a novel combination of Visual/Aural/Read, Write/Kinesthetic (VARK) presentation or gamification and exercises difficulty scaffolding through skipping/hiding/ reattempting. Cognitive, behavior and affective adaptation means are included in developing a dynamic learner model, which detects and corrects each student's learning style and cognitive level. As adaptation targets, the platform provides adaptive content presentation in two groups (VARK and gamification), adaptive exercises navigation and adaptive feedback. To achieve its goal, the platform utilizes a Deep Q-Network Reinforcement Learning (DQN-RL) and an online rule-based decision making implementation. The platform interfaces front-end dedicated website and back-end adaptation algorithms. An improvement in learning effectiveness is achieved comparing the post-test to the pre-test in a pilot experiment for grade 3 mathematics curriculum. Both groups witnessed academic performance and satisfaction level improvements, most importantly, for the students who started the experiment with a relatively low performance. VARK group witnessed a slightly more improvement and higher satisfaction level, since interactive activities and games in the kinesthetic presentation can provide engagement, while keeping other presentation styles available, when needed. [ABSTRACT FROM AUTHOR]

Intelligent tutoring systems (ITSs) have drawn researchers' attention as a means of providing personalized learning content, adaptive feedback, and instructional strategies based on students' characteristics and learning needs. Few studies, however, have explored how prospective and practicing teachers integrate ITSs into their lessons. This study examines the relationships among prospective teachers' concerns about the ITSs, their positioning of an ITSs when planning a math lesson, and their technological, pedagogical, and content knowledge (TPACK). The results indicate that the prospective teachers were intensively concerned with informational and personal aspects regarding ITSs and somewhat interested in modifying ITS-integrated teaching practices. The prospective teachers tended to assess themselves as having a high level of knowledge related to pedagogy but less knowledge about pedagogy and technology. Furthermore, the prospective teachers were more likely to position the ITSs as a servant than as a partner. When positioning the ITSs as a partner, they tended to plan their lessons based on in-the-moment student data provided by the ITS. In addition, the prospective teachers who positioned the ITS as a partner expressed more concern about modifying the ITSs and recognized that they had higher TPACK than those who positioned it as a servant. [ABSTRACT FROM AUTHOR]

A simulator-based Intelligent Tutoring System (ITS) is a computer system that is made to provide students with a learning experience that is both customizable to a student's needs (e.g., level of expertise, pace) and includes simulation, e.g., demonstrate certain domain concepts or allow problem-solving while replicating a real-life situation. ITSs offers convenient and low-cost studying. We aim to explore the recent trends and identify limitations and opportunities in recent work on STEM (Science, Technology, Engineering, and Mathematics) self-study simulator based ITSs, we conducted a systematic literature review investigating 47 papers from four different databases. The research encloses ITSs from various educational sectors, ranging from elementary, middle, secondary, tertiary, and after school training. The majority of the systems targeted tertiary education. As a result, there are many research opportunities in introducing a more generalizable approach to simulator-based ITSs which will make it easier to address many STEM-related subjects. There are also opportunities in utilizing help methods that emphasize encouragement and self-reflection. We noticed that the number of STEM-related simulator-based ITSs is relatively low. Another finding was that most simulator-based ITSs are domain-dependent, and therefore they are not reusable for other subjects. Finally, we found that the traits of feedback in simulator-based systems that result in positive learning outcomes are ones that combined immediate and delayed feedback, used procedural information in their feedback, of formative feedback type, and detailed feedback. [ABSTRACT FROM AUTHOR]

Interaction is a fundamental part of using any computer system but it is still an issue for people with special needs. In order to improve this situation, this paper describes a new device-interaction model based on adaptation rules for user models. The aim is the adaptation at the interaction level, taking into account the interaction device features in order to improve the usability through the user experience in the education sector. In the evaluation process, several students from a special education center have participated. These students have either a physical or sensory disability or autism. The results are promising enough to consider that this model will be able to help students with disabilities to interact with a computer system which will inevitably provide tremendous benefits to their academic and personal development. [ABSTRACT FROM AUTHOR]