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 Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education.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

Negotiating (dis-)engagement in K-12 blended learning

It is well-recognised that engagement is critical for learning and school success. Engagement (and disengagement) are, however, also influenced by context. Thus, as digital technologies add complexity to the educational context, they influence classroom leadership, lesson designs and related practices, and thereby engagement. Despite being critical, engagement and disengagement are not well explored concerning these influences, with a lack of research undertaken within socially disadvantaged schools. In this qualitative study, 14 classroom observations were conducted, during five months, in twelve classes in an upper secondary school in Sweden, along with dialogues with teachers (n=12) and students (n=32). The data were analysed using thematic analysis and descriptive statistics. Identified themes include digital context, teacher leadership, engagement and disengagement. A network of relations between the (dis-)engagement compound and themes is presented. The results identified processes in which engagement shifted into disengagement and vice versa; in particular, that the intention of active learning does not automatically translate to active learning for all students, although teachers employed a higher work pace than did their students. Teacher self-efficacy and awareness of how to manage digital technologies in and outside the classroom was found to play a vital role in facilitating engagement. Understanding the (dis-)engagement compound in blended learning environments is key to inform active and visible learning for future research and supportive organisational structures

Research Evidence on the Use of Learning Analytics: Implications for Education Policy

The evidence shows that the use of learning analytics to improve and to innovate learning and teaching in Europe is still in its infancy. The high expectations have not yet been realised. Though early adopters are already taking a lead in research and development, the evidence on practice and successful implementation is still scarce. Furthermore, though the work across Europe on learning analytics is promising, it is currently fragmented. This underlines the need for a careful build-up of research and experimentation, with both practice and policies that have a unified European vision. Therefore, the study suggests that work is needed to make links between learning analytics, the beliefs and values that underpin this field, and European priority areas for education and training 2020. As a way of guiding the discussion about further development in this area, the Action List for Learning Analytics is proposed. The Action List for Learning Analytics focuses on seven areas of activity. It outlines a set of actions for educators, researchers, developers and policymakers in which learning analytics are used to drive work in Europe’s priority areas for education and training. Strategic work should take place to ensure that each area is covered, that there is no duplication of effort, that teams are working on all actions and that their work proceeds in parallel. Policy leadership and governance practices •Develop common visions of learning analytics that address strategic objectives and priorities •Develop a roadmap for learning analytics within Europe •Align learning analytics work with different sectors of education •Develop frameworks that enable the development of analytics •Assign responsibility for the development of learning analytics within Europe •Continuously work on reaching common understanding and developing new priorities Institutional leadership and governance practices •Create organisational structures to support the use of learning analytics and help educational leaders to implement these changes •Develop practices that are appropriate to different contexts •Develop and employ ethical standards, including data protection Collaboration and networking •Identify and build on work in related areas and other countries •Engage stakeholders throughout the process to create learning analytics that have useful features •Support collaboration with commercial organisations Teaching and learning practices •Develop learning analytics that makes good use of pedagogy •Align analytics with assessment practices Quality assessment and assurance practices •Develop a robust quality assurance process to ensure the validity and reliability of tools •Develop evaluation checklists for learning analytics tools Capacity building •Identify the skills required in different areas •Train and support researchers and developers to work in this field •Train and support educators to use analytics to support achievement Infrastructure •Develop technologies that enable development of analytics •Adapt and employ interoperability standard

Technology-Based Personalization: Instructional Reform in Five Public Schools

This dissertation addresses the question: How does an attempt to redesign instructional delivery using technology-based personalization affect the technical core of teaching, learning, and student outcomes? In recent years, many prominent educators, business leaders, and philanthropists have suggested that schools be redesigned to personalize students’ learning experiences using technology. However, the justification for these reforms remains largely theoretical. Empirical research on technology-based personalization is sparse, and what little research does exist focuses predominantly on macro effects rather than the specific school-level, class-level, student-level, and lesson-level mechanisms that contribute to overall student achievement. The absence of research that pushes inside the “black box” of implementation is particularly problematic given a century of failed attempts to reform the technical core of instructional delivery, with symbolic reforms typically withering in the face of institutional resistance. This study attempts to address that gap by examining the implementation of an innovative model for using technology-based personalization to deliver middle school math instruction. I draw upon theoretical tools from institutional theory, instructional improvement, and the history of educational reform to deepen our understanding of how technology-based personalization affects the role of students and teachers, the logistics of content delivery, and students’ learning outcomes. Unlike previous studies in K-12 settings, which typically use summative assessments and virtual control groups to estimate aggregate effects on student learning, this study examines the relationships among a diverse set of lesson-level variables, including instructional modality, instructional content, group size and composition, teacher characteristics, student characteristics, and learning outcomes. In doing so, this study contributes to our understanding of the on-the-ground processes and mechanisms by which technology- based personalization affects (or does not affect) student learning. Although the instructional model documented in this case study will remain anonymous, it is well known and respected among educators and philanthropists, and regarded as one of the most prominent and archetypical examples of technology-based personalization currently active in American schools. Using multiple methods, including novel applications of hierarchical linear modeling, cluster analysis, and heatmap data visualization, I explore: (a) the degree to which ground-level implementation of technology-based personalization represents an authentic departure from the traditional technology of schooling, and (b) the relationships among various elements of the model and student learning outcomes. I draw on longitudinal data from a full year of implementation in five schools, including the daily lesson assignments and assessment scores of 1,238 unique students supervised by 48 teachers. This study supports four main findings: (a) the program succeeds in altering the technical core of instruction in several fundamental ways; (b) policy and logistical constraints limit the program’s ability to reform the technical core of instruction to the degree that it aspires; (c) students who enter the program as already higher-performing are more successful on daily exit slips than students who enter the program with lower performance; and (d) the quantitative methods used in this paper represent useful and replicable tools for exploring the data produced by technology-based and personalized models

Retrieval-, Distributed-, and Interleaved Practice in the Classroom:A Systematic Review

Three of the most effective learning strategies identified are retrieval practice, distributed practice, and interleaved practice, also referred to as desirable difficulties. However, it is yet unknown to what extent these three practices foster learning in primary and secondary education classrooms (as opposed to the laboratory and/or tertiary education classrooms, where most research is conducted) and whether these strategies affect different students differently. To address these gaps, we conducted a systematic review. Initial and detailed screening of 869 documents found in a threefold search resulted in a pool of 29 journal articles published from 2006 through June 2020. Seventy-five effect sizes nested in 47 experiments nested in 29 documents were included in the review. Retrieval- and interleaved practice appeared to benefit students’ learning outcomes quite consistently; distributed practice less so. Furthermore, only cognitive Student*Task characteristics (i.e., features of the student’s cognition regarding the task, such as initial success) appeared to be significant moderators. We conclude that future research further conceptualising and operationalising initial effort is required, as is a differentiated approach to implementing desirable difficulties

Self-Directed Learning

This book on self-directed learning (SDL) is devoted to original academic scholarship within the field of education, and is the 6th volume in the North-West University (NWU) SDL book series. In this book the authors explore how self-directed learning can be considered an imperative for education in a complex modern society. Although each chapter represents independent research in the field of self-directed learning, the chapters form a coherent contribution concerning the scholarship of self-directed learning, and specifically the effect of environmental and praxis contexts on the enhancement of self-directed learning in a complex society. The publication as a whole provides diverse perspectives on the importance of self-directed learning in varied contexts. Scholars working in a wide range of fields are drawn together in this scholarly work to present a comprehensive dialogue regarding self-directed learning and how this concept functions in a complex and dynamic higher education context. This book presents a combination of theory and practice, which reflects selected conceptual dimensions of self-directed learning in society, as well as research-based findings pertaining to current topical issues relating to implementing self-directed learning in the modern world. The varied methodologies provide the reader with different and balanced perspectives, as well as varied and innovative ideas on how to conduct research in the field of self-directed learning

Artificial intelligence in education : challenges and opportunities for sustainable development

Artificial Intelligence is a booming technological domain capable of altering every aspect of our social interactions. In education, AI has begun producing new teaching and learning solutions that are now undergoing testing in different contexts. AI requires advanced infrastructures and an ecosystem of thriving innovators, but what about the urgencies of developing countries? Will they have to wait for the “luxury” of AI? Or should AI be a priority to tackle as soon as possible to reduce the digital and social divide?These are some of the questions guiding this document. In this regard, this urgent discussion should be taken up with a clear picture of what is happening and what can be done. This document gathers examples of how AI has been introduced in education worldwide, particularly in developing countries. It also sows the seeds of debates and discussions in the context of the 2019 Mobile Learning Week and beyond, as part of the multiple ways to accomplish Sustainable Development Goal 4, which targets education. The first section of this document analyses how AI can be used to improve learning outcomes. It presents examples of how AI technology can help education systems use data to improve educational equity and quality in the developing world. The section is divided into two topics that address pedagogical and system-wide solutions:i) AI to promote personalisation and better learning outcomes, exploring how AI can favour access to education, collaborative environments and intelligent tutoring systems to support teachers. We briefly introduce cases from countries such as China, Uruguay, Brazil, South Africa and Kenya as examples experimental solutions conceived from public policies, philanthropic and private organisations. ii) Data analytics in Education Management Information Systems (EMIS). Here we present opportunities for improving a state’s capacity to manage large-scale educational systems by increasing data from schools and learning, presenting cases from United Arab Emirates, Kenya, Bhutan, Kyrgyzstan and Chile.The second section “Preparing learners to thrive in an AI-saturated future” explores the different means by which governments and educational institutions are rethinking and reworking educational programmes to prepare learners for the increasing presence of AI in all aspects of human activity. Based on examples from different contexts, the section is also divided into two main parts: i) “A new curriculum for a digital and AI powered world” elaborates further on the importance of advancing in digital competency frameworks for teachers and students. Some current initiatives are presented such as the “Global Framework to Measure Digital Literacy” and “ICT Competencies and Standards from the Pedagogical Dimension”. The discussion of the curricular dimension is broadened to include new experiences for developing computational thinking in schools with examples from the European Union, United Kingdom, Estonia, Argentina, Singapore and Malaysia.ii) The second part is more focused on strengthening AI capacities through post-basic education and training. How can each country prepare the conditions for an AI-powered world? Here we present some of the most advanced cases from developed countries who are generating comprehensive plans to tackle this question, namely France, South Korea and China. We also present some cases from the technical and vocational education and training sector and some opportunities from non-formal and informal learning scenarios.The last section addresses the challenges and policy implications that should be part of the global and local conversations regarding the possibilities and risks of introducing AI in education and preparing students for an AI-powered context. Six challenges are presented: The first challenge lies in developing a comprehensive view of public policy on AI for sustainable development. The complexity of the technological conditions needed to advance in this field require the alignment of multiple factors and institutions. Public policies have to work in partnership at international and national levels to create an ecosystem of AI that serves sustainable development. The second challenge is to ensure inclusion and equity for AI in education. The least developed countries are at risk of suffering new technological, economic and social divides with the development of AI. Some main obstacles such as basic technological infrastructure must be faced to establish the basic conditions for implementing new strategies that take advantage of AI to improve learning.The third challenge is to prepare teachers for an AI-powered education while preparing AI to understand education, though this must nevertheless be a two-way road: teachers must learn new digital skills to use AI in a pedagogical and meaningful way and AI developers must learn how teachers work and create solutions that are sustainable in real-life environments. The fourth challenge is to develop quality and inclusive data systems. If we are headed towards the datafication of education, the quality of data should be our chief concern. It ́s essential to develop state capabilities to improve data collection and systematisation. AI developments should be an opportunity to increase the importance of data in educational system management.The fifth challenge is to make research on AI in education significant. While it can be reasonably expected that research on AI in education will increase in the coming years, it is nevertheless worth recalling the difficulties that the education sector has had in taking stock of educational research in a significant way both for practice and policy-making.The sixth challenge deals with ethics and transparency in data collection, use and dissemination. AI opens many ethical concerns regarding access to education system, recommendations to individual students, personal data concentration, liability, impact on work, data privacy and ownership of data feeding algorithms. AI regulation will thus require public discussion on ethics, accountability, transparency and security.The document ends with an open invitation to create new discussions around the uses, possibilities and risks of AI in education for sustainable development

The Effect of Modality on Student Achievement and Course Completion in a Developmental Mathematics Course

Students taking courses in developmental mathematics do so in one of three modalities - some take the classes face-to-face in a classroom with a professor who is physically present, others take the classes in what is known as a blended or hybrid mode in which the professor uses a combination of classroom and online time to teach the course, and another group takes the classes completely online. Increasingly, a growing number of students are taking these courses in a hybrid mode or completely online, and this phenomenon is causing educators to redesign their programs, offering more courses in these two modalities. However, some program leaders do so without any data about the achievement and course completion rates of students in the different modalities. This research 1) investigated the achievement rates of students taking an eight week developmental mathematics course, taught in three different modalities and 2) investigated the course completion rates of students taking an eight week developmental mathematics course, taught in three different modalities. Specifically, the purpose of this study was to examine the achievement and course completion rates of students enrolled in an eight week developmental mathematics course, Elementary Algebra, based on the delivery modality. The study was conducted at a large multi-campus institution located in the southeast United States as the research site. The theories used to frame the research were the Information Processing Theory and Cognitive Load Theory