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

Learn Smarter, Not Harder – Exploring the Development of Learning Analytics Use Cases to Create Tailor-Made Online Learning Experiences

Our world is significantly shaped by digitalization, fostering new opportunities for technology-mediated learning. Therefore, massive amounts of knowledge become available online. However, concurrently these formats entail less interaction and guidance from lecturers. Thus, learners need to be supported by intelligent learning tools that provide suitable knowledge in a tailored way. In this context, the use of learning analytics in its multifaceted forms is essential. Existing literature shows a proliferation of learning analytics use cases without a systematic structure. Based on a structured literature review of 42 papers we organized existing literature contributions systematically and derived four use cases: learning dashboards, individualized content, tutoring systems, and adaptable learning process based on personality. Our use cases will serve as a basis for a targeted scientific discourse and are valuable orientation for the development of future learning analytics use cases to give rise to the new form of Learning Experience Platforms

Digitally enhanced assessment in virtual learning environments

One of the main challenges in teaching and learning activities is the assessment: it allows teachers and learners to improve the future activities on the basis of the previous ones. It allows a deep analysis and understanding of the whole learning process. This is particularly difficult in virtual learning environments where a general overview is not always available. In the latest years, Learning Analytics are becoming the most popular methods to analyze the data collected in the learning environments in order to support teachers and learners in the complex process of learning. If they are properly integrated in learning activities, indeed, they can supply useful information to adapt the activities on the basis of student’s needs. In this context, the paper presents a solution for the digitally enhanced assessment. Two different Learning Dashboards have been designed in order to represent the most interesting Learning Analytics aiming at providing teachers and learners with easy understandable view of learning data in virtual learning environments

eduGraph: A Dashboard for Personalised Feedback in Massive Open Online Courses

Learning Analytics is concerned with the design and implementation of tools and processes for collecting, analysing, and communicating information about teaching and learning. It is enabled by data, but not driven by it, rather it tries to empower human judgements by presenting meaningful facts. This thesis explores the data generated in Open edX courses to understand how it can be analysed and used to impact learners' motivation in online courses. It is carried out using Design Science, a research methodology aiming to produce artefacts that can improve the interaction with problems. In this thesis I present the eduGraph dashboard, a dashboard that uses Learning Analytics to present meaningful insights about learners' learning process in Massive Open Online Courses (MOOCs). Results indicate that learners perceive the dashboard as useful and effective at motivating them to take part in online courses, and that it enables them to keep track of their progress in the courses. I posit that the biggest problem facing Learning Analytics today are the lack of accessible data, and that it is possible for reasearchers to create more accurate learner models by using Learning Anaytics theories and methods in combination with the iterative and technical process of Information Systems development.Masteroppgave i informasjonsvitenskapINFO390MASV-INF