Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

A Smart Collaborative Educational Game with Learning Analytics to Support English Vocabulary Teaching

Learning Analytics (LA) approaches have proved to be able to enhance learning process and learning performance. However, little is known about applying these approaches for second language acquisition using educational games. Therefore, this study applied LA approaches to design a smart collaborative educational game, to enhance primary school children learning English vocabularies. Specifically, the game provided dashboards to the teachers about their students in a real-time manner. A pilot experiment was conducted in a public primary school where the students’ data from experimental and control groups, namely learning and motivation test scores, interview and observation, were collected and analyzed. The obtained results showed that the experimental group (who used the smart game with LA) had significantly higher motivation and performance for learning English vocabularies than the control group (who used the smart game without LA). The findings of this study can help researchers and practitioners incorporate LA in their educational games to help students enhance language acquisition

Creating smarter teaching and training environments: innovative set-up for collaborative hybrid learning

This paper brings together previous work from a number of research projects and teaching initiatives in an effort to introduce good practice in setting up supportive environments for collaborative learning. The paper discusses prior use of social media in learning support, the role of dashboards for learning analytics in Global Software Development training, the use of optical head-mounted displays for feedback and the use of NodeXl visualization in managing distributed teams. The scope of the paper is to provide a structured approach in organizing the creation of smarter teaching and training environments and explore ways to coordinate learning scenarios with the use of various techniques. The paper also discusses challenges from integrating multiple innovative features in educational contexts. Finally the paper attempts to investigate the use of smart laboratories in establishing additional learning support and gather primary data from blended and hybrid learning pilot studies

Learning Education: An ‘Educational Big Data’ approach for monitoring, steering and assessment of the process of continuous improvement of education

Changing regulations, pedagogy and didactics worldwide, have ensured that the educational system has changed severely. But the entrance of Web 2.0 and other technologies had a significant impact on the way we educate and assess our education too. The Web 2.0 applications also increase the cooperation between stakeholders in education and has led to the phenomenon ‘Learning Education’. Learning Education is a term we use for the phenomenon where educational stakeholders (i.e. teachers, students, policy-makers, partners etc.) can learn from each other in order to ultimately improve education. The developments within the Interactive Internet (Web 2.0) enabled the development of innovative and sophisticated strategies for monitoring, steering and assessing the ‘learning of education’. These developments give teachers possibilities to enhance their education with digital applications, but also to monitor, steer and assess their own behavior. This process can be done with multiple sources, for example questionnaires, interviews, panel research, but also the more innovative sources like big social data and network interactions. In this article we use the term ‘educational big data’ for these sources and use it for monitoring, steering and assessing the developments within education, according to the Plan, Do, Check, Act principle (PDCA). We specifically analyze the Check-phase and describe it with the Learning Education Check Framework (LECF). We operationalize the LECF with a Learning Education Check System (LECS), which is capable to guide itself and change those directions as well in response to changing ways and trends in education and their practices. The system supports the data-driven decision making process within the learning education processes. So, in this article we work on the LECF and propose and describe a paper-based concept of the – by educational big data driven – LECS. Besides that, we show the possibilities, reliability and validity for measuring the ‘Educational Big Data’ within an educational setting

Creating smarter teaching and training environments: innovative set-up for collaborative hybrid learning

This paper brings together previous work from a number of research projects and teaching initiatives in an effort to introduce good practice in setting up supportive environments for collaborative learning. The paper discusses prior use of social media in learning support, the role of dashboards for learning analytics in Global Software Development training, the use of optical head-mounted displays for feedback and the use of NodeXl visualization in managing distributed teams. The scope of the paper is to provide a structured approach in organizing the creation of smarter teaching and training environments and explore ways to coordinate learning scenarios with the use of various techniques. The paper also discusses challenges from integrating multiple innovative features in educational contexts. Finally the paper attempts to investigate the use of smart laboratories in establishing additional learning support and gather primary data from blended and hybrid learning pilot studies

Towards highly informative learning analytics

Among various trending topics that can be investigated in the field of educational technology, there is a clear and high demand for using artificial intelligence (AI) and educational data to improve the whole learning and teaching cycle. This spans from collecting and estimating the prior knowledge of learners for a certain subject to the actual learning process and its assessment. AI in education cuts across almost all educational technology disciplines and is key to many other technological innovations for educational institutions. The use of data to inform decision-making in education and training is not new, but the scope and scale of its potential impact on teaching and learning have silently increased by orders of magnitude over the last few years. The release of ChatGPT was another driver to finally make everyone aware of the potential effects of AI technology in the digital education system of today. We are now at a stage where data can be automatically harvested at previously unimagined levels of granularity and variety. Analysis of these data with AI has the potential to provide evidence-based insights into learners’ abilities and patterns of behaviour that, in turn, can provide crucial action points to guide curriculum and course design, personalised assistance, generate assessments, and the development of new educational offerings. AI in education has many connected research communities like Artificial Intelligence in Education (AIED), Educational Data Mining (EDM), or Learning Analytics (LA). LA is the term that is used for research, studies, and applications that try to understand and support the behaviour of learners based on large sets of collected data

A Systematic Literature Review of Empirical Studies on Learning Analytics in Educational Games

Learning analytics (LA) in educational games is considered an emerging practice due to its potential of enhancing the learning process. Growing research on formative assessment has shed light on the ways in which students' meaningful and in-situ learning experiences can be supported through educational games. To understand learners' playful experiences during gameplay, researchers have applied LA, which focuses on understanding students' in-game behaviour trajectories and personal learning needs during play. However, there is a lack of studies exploring how further research on LA in educational games can be conducted. Only a few analyses have discussed how LA has been designed, integrated, and implemented in educational games. Accordingly, this systematic literature review examined how LA in educational games has evolved. The study findings suggest that: (1) there is an increasing need to consider factors such as student modelling, iterative game design and personalisation when designing and implementing LA through educational games; and (2) the use of LA creates several challenges from technical, data management and ethical perspectives. In addition to outlining these findings, this article offers important notes for practitioners, and discusses the implications of the study’s results