Clustering Students Based on Gamification User Types and Learning Styles

The aim of this study is clustering students according to their gamification user types and learning styles with the purpose of providing instructors with a new perspective of grouping students in case of clustering which cannot be done by hand when there are multiple scales in data. The data used consists of 251 students who were enrolled at a Turkish state university. When grouping students, K-means algorithm has been utilized as clustering algorithm. As for determining the gamification user types and learning styles of students, Gamification User Type Hexad Scale and Grasha-Riechmann Student Learning Style Scale have been used respectively. Silhouette coefficient is utilized as clustering quality measure. After fitting the algorithm in several ways, highest Silhouette coefficient obtained was 0.12 meaning that results are neutral but not satisfactory. All the statistical operations and data visualizations were made using Python programming language.Comment: 9 pages, 3 figure

Персоналізація навчання з використанням адаптивних технологій та доповненої реальності

The research is aimed at developing the recommendations for educators on using adaptive technologies and augmented reality in personalized learning implementation. The latest educational technologies related to learning personalization and the adaptation of its content to the individual needs of students and group work are considered. The current state of research is described, the trends of development are determined. Due to a detailed analysis of scientific works, a retrospective of the development of adaptive and, in particular, cloud-oriented systems is shown. The preconditions of their appearance and development, the main scientific ideas that contributed to this are analyzed. The analysis showed that the scientists point to four possible types of semantic interaction of augmented reality and adaptive technologies. The adaptive cloud-based educational systems design is considered as the promising trend of research. It was determined that adaptability can be manifested in one or a combination of several aspects: content, evaluation and consistency. The cloud technology is taken as a platform for integrating adaptive learning with augmented reality as the effective modern tools to personalize learning. The prospects of the adaptive cloud-based systems design in the context of teachers training are evaluated. The essence and place of assistive technologies in adaptive learning systems design are defined. It is shown that augmented reality can be successfully applied in inclusive education. The ways of combining adaptive systems and augmented reality tools to support the process of teachers training are considered. The recommendations on the use of adaptive cloud-based systems in teacher education are given.Дослідження спрямоване на розробку рекомендацій для освітян щодо використання адаптивних технологій та доповненої реальності в реалізації персоналізованого навчання. Розглянуто новітні освітні технології, пов’язані з персоналізацією навчання та адаптацією його змісту до індивідуальних потреб студентів та групової роботи. Описано сучасний стан досліджень, визначено тенденції розвитку. Завдяки детальному аналізу наукових робіт показано ретроспективу розвитку адаптивних та, зокрема, хмарних систем. Проаналізовано передумови їх появи та розвитку, основні наукові ідеї, що сприяли цьому. Аналіз показав, що вчені вказують на чотири можливі типи семантичної взаємодії доповненої реальності та адаптивних технологій. Перспективним напрямком досліджень вважається адаптивне проектування хмарних освітніх систем. Було визначено, що адаптивність може проявлятися в одному або в поєднанні кількох аспектів: змісту, оцінки та послідовності. Хмарна технологія взята як платформа для інтеграції адаптивного навчання з доповненою реальністю як ефективних сучасних інструментів для персоналізації навчання. Оцінюються перспективи адаптивного проектування хмарних систем у контексті навчання вчителів. Визначено сутність та місце допоміжних технологій у проектуванні адаптивних систем навчання. Показано, що доповнену реальність можна успішно застосовувати в інклюзивній освіті. Розглянуто шляхи поєднання адаптивних систем та інструментів доповненої реальності для підтримки процесу підготовки вчителів. Дано рекомендації щодо використання адаптивних хмарних систем у навчанні вчителів

Exploring the role of experience API in supporting new trends in Educational Technology: A literature review

Despite its growth in the area of educational technology, Experience API (xAPI) continues to be under-used as a solution across the different platforms in institutions and organizations. There is a lack of any detailed summary in the literature about the potential and the limitation of using xAPI in conjunction with learning platforms and technologies. This thesis examines the role of xAPI in promoting, shaping and supporting learning in organizational contexts. This discussion is developed by using cases reported in the literature and new cases from contemporary educational technologies. The thesis illustrates the role the standard plays within current major trends in digital learning and within the context of a broader ecosystem of learning platforms and technologies. It provides a useful and thorough account of xAPI and its potential to an audience of individuals responsible for implementing xAPI within organizations. xAPI provides to some extent a promise of improved impact to Performance Evaluation and Evaluating training Effectiveness. However, xAPI lacks concrete cases and examples to support its utilization in the fields of Learning Analytics, Performance Management, Predictive Learning and Workforce Planning. Keywords: Experience API (xAPI), Learning Management Systems, Learning Record Store, Learning Analytics, Microlearning, Evaluation Effectiveness, Predictive Learning, Adaptive Learning, Workforce Planning

Examining Interventions and Cognitive Load Factors in Online Learning Experiences

Since the beginning of the development of massive open online courses (MOOCs), these and other online learning environments have been considered as potential partial solutions to some persistent problems in higher education. These learning environments, while they have great educational value, have not been as effective as they could be, because they have largely been built with little or no foundation in the cognitive processes (e.g., the conversion of items from short-term to long-term memory) required for effective and efficient online learning. Many innovative online learning approaches are in development, such as personalized learning (learning experiences tailored to address particular information that students need) using adaptive learning systems (machine learning techniques used by computers to recommend materials). However, these approaches would also benefit from being grounded in cognitive theory to better reveal how learning occurs in these systems. Furthermore, crucial features of interventions in online learning, such as supplementary elements designed to fill in gaps or reinforce knowledge, have not been thoroughly examined in conjunction with the insights of cognitive theory and the concept of desirable difficulty (i.e., the notion that the addition of difficulty to a task can improve learning and increase retention). In this exploratory work, I experimentally examine five different types of interventions and their effects on undergraduate engineering students’ learning gains and experience. This study presents quantitative research along with detailed qualitative thematic analysis. Its objective is to provide critical insights into how to better design online learning environments and how we can create more effective interventions that promote students’ online learning gains. The research questions for this work are: (1) What factors in online learning environments affect learning gains (i.e., measured difference between post- and pre-test scores) for undergraduate engineering students?; (2) What factors in online learning environments affect the learning experience for undergraduate engineering students, and, specifically, what factors produce desirable difficulty?; and (3) What factors in online learning affect undergraduate engineering students’ self-reported memory? The experimental results, examined within the framework of cognitive theory, showed quantitatively that levels of frustration with interventions were correlated with learning gains while qualitative analysis results revealed instances that both confirmed and contradicted aspects of the quantitative results. A number of practical design guidelines emerged from the analysis: for example, in specific circumstances, one type of intervention is likely to be more effective than another, or that particular sorts of additional difficulties should be avoided. These recommendations may provide researchers with a better understanding of how to challenge students in more efficient and productive ways in online learning environments.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162900/1/seokjook_1.pd