Innovating Pedagogy 2015: Open University Innovation Report 4

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This fourth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Center for Technology in Learning at SRI International. We proposed a long list of new educational terms, theories, and practices. We then pared these down to ten that have the potential to provoke major shifts in educational practice, particularly in post-school education. Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education. These are summarised below in an approximate order of immediacy and timescale to widespread implementation

Exploratory Analysis in Learning Analytics

This article summarizes the methods, observations, challenges and implications for exploratory analysis drawn from two learning analytics research projects. The cases include an analysis of a games-based virtual performance assessment and an analysis of data from 52,000 students over a 5-year period at a large Australian university. The complex datasets were analyzed and iteratively modeled with a variety of computationally intensive methods to provide the most effective outcomes for learning assessment, performance management and learner tracking. The article presents the research contexts, the tools and methods used in the exploratory phases of analysis, the major findings and the implications for learning analytics research methods

The student-produced electronic portfolio in craft education

The authors studied primary school students’ experiences of using an electronic portfolio in their craft education over four years. A stimulated recall interview was applied to collect user experiences and qualitative content analysis to analyse the collected data. The results indicate that the electronic portfolio was experienced as a multipurpose tool to support learning. It makes the learning process visible and in that way helps focus on and improves the quality of learning. © ISLS.Peer reviewe

Towards a System of Guidance, Assistance and Learning Analytics Based on Multi Agent System Applied on Serious Games

With the revolution that the education field has known concerning the methods of learning and especially the integration of new technology, several new tools have appeared to replace the tools already existing, and among them there are serious games, serious games as new tool dedicated to education have occupied an important place, and replaced other tools often used in the learning process. But in the order that serious games reach the intended objectives and help instructors to achieve their perspectives considered, they must be equipped with a guidance and assistance system that will assist the learners during the progression in the sequence of the video game, and in addition, they must be equipped with a system of learning analytics that will help instructors to improve the learning process and teaching methods according to the learning outcomes and feedbacks of their learners. In this perspective of research and development we will establish in this paper a new system of assistance, guidance and learning analytics based on a multi agent system that will work in tandem with a web-based serious game

STUDY OF SPORTS TEACHERS STUDENTS' SKILLS FOR SELF-ASSESSMENT OF THEIR PROFESSIONAL QUALITIES

?n essential characteristic of the teacher’s profession is the constant process of self-improvement, which is impossible without existence of a specific personal position and criteria of the sport pedagogue. One of the trends for self-improvement is his personal and professional qualities. They are important because of their specific role – as a means of influence on the trained students in the educational process. Self-evaluation of the level of their growth is a regulator for the sport teachers conduct and activity because it determines the genuine orientation for the level of his qualities, the satisfaction or dissatisfaction

Foundations of dynamic learning analytics: Using university student data to increase retention

With digitisation and the rise of e-learning have come a range of computational tools and approaches that have allowed educators to better support the learners' experience in schools, colleges and universities. The move away from traditional paper-based course materials, registration, admissions and support services to the mobile, always-on and always accessible data has driven demand for information and generated new forms of data observable through consumption behaviours. These changes have led to a plethora of data sets that store learning content and track user behaviours. Most recently, new data analytics approaches are creating new ways of understanding trends and behaviours in students that can be used to improve learning design, strengthen student retention, provide early warning signals concerning individual students and help to personalise the learner's experience. This paper proposes a foundational learning analytics model (LAM) for higher education that focuses on the dynamic interaction of stakeholders with their data supported by visual analytics, such as self-organising maps, to generate conversations, shared inquiry and solution-seeking. The model can be applied for other educational institutions interested in using learning analytics processes to support personalised learning and support services. Further work is testing its efficacy in increasing student retention rates

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers

Student Modeling and Analysis in Adaptive Instructional Systems

There is a growing interest in developing and implementing adaptive instructional systems to improve, automate, and personalize student education. A necessary part of any such adaptive instructional system is a student model used to predict or analyze learner behavior and inform adaptation. To help inform researchers in this area, this paper presents a state-of-the-art review of 11 years of research (2010-2021) in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models. We mainly emphasize increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems. In addition, we discuss challenges inherent in real-world multimodal modeling, such as uncontrolled data collection environments leading to noisy data and data sync issues. Finally, we reinforce our findings and conclusions through an industry case study of an adaptive instructional system. In our study, we verify that adding multiple data modalities increases our model prediction accuracy from 53.3% to 69%. At the same time, the challenges encountered with our real-world case study, including uncontrolled data collection environment with inevitably noisy data, calls for synchronization and noise control strategies for data quality and usability

Design-activity-sequence: A case study and polyphonic analysis of learning in a digital design thinking workshop

In this case study, we report on the outcomes of a one-day workshop on design thinking attended by participants from the Computer-Supported Collaborative Learning conference in Philadelphia in 2017. We highlight the interactions between the workshop design, structured as a design thinking process around the design of a digital environment for design thinking, and the diverse backgrounds and interests of its participants. Data from in-workshop reflections and post-workshop interviews were analyzed using a novel set of analytical approaches, a combination the facilitators made by possible by welcoming participants as coresearchers

Power to the Teachers:An Exploratory Review on Artificial Intelligence in Education

This exploratory review attempted to gather evidence from the literature by shedding light on the emerging phenomenon of conceptualising the impact of artificial intelligence in education. The review utilised the PRISMA framework to review the analysis and synthesis process encompassing the search, screening, coding, and data analysis strategy of 141 items included in the corpus. Key findings extracted from the review incorporate a taxonomy of artificial intelligence applications with associated teaching and learning practice and a framework for helping teachers to develop and self-reflect on the skills and capabilities envisioned for employing artificial intelligence in education. Implications for ethical use and a set of propositions for enacting teaching and learning using artificial intelligence are demarcated. The findings of this review contribute to developing a better understanding of how artificial intelligence may enhance teachers’ roles as catalysts in designing, visualising, and orchestrating AI-enabled teaching and learning, and this will, in turn, help to proliferate AI-systems that render computational representations based on meaningful data-driven inferences of the pedagogy, domain, and learner models