Adaptive Course Generation in Semantic Web Context

International audiencePersonalized support for learners becomes very important in the context of increasing re-use of resources from heterogeneous and distributed learning repositories. This paper presents OrPAF, an Adaptive Educational Hypermedia (AEHS) and web-based System which integrates semantic web models and technologies like ontologies, semantic annotations and learning standards in order to achieve interoperability with e-learning systems. The key feature of OrPAF is the construction of adaptive hypermedia courses: both the course structure and the course content are dynamically generated and adapted to learners. On the one hand, a learning ontology is proposed to describe, at meta-level, abstract characteristics of an e-learning system. This learning ontology is instantiated to construct learning models: domain model, learner model and pedagogical model. On the other hand, semantic annotations and a semantic relevance measure are proposed to improve the LOM metadata associated to learning web resources in order to reuse and share them

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Towards a competency model for adaptive assessment to support lifelong learning

Adaptive assessment provides efficient and personalised routes to establishing the proficiencies of learners. We can envisage a future in which learners are able to maintain and expose their competency profile to multiple services, throughout their life, which will use the competency information in the model to personalise assessment. Current competency standards tend to over simplify the representation of competency and the knowledge domain. This paper presents a competency model for evaluating learned capability by considering achieved competencies to support adaptive assessment for lifelong learning. This model provides a multidimensional view of competencies and provides for interoperability between systems as the learner progresses through life. The proposed competency model is being developed and implemented in the JISC-funded Placement Learning and Assessment Toolkit (mPLAT) project at the University of Southampton. This project which takes a Service-Oriented approach will contribute to the JISC community by adding mobile assessment tools to the E-framework

Psychometrics in Practice at RCEC

A broad range of topics is dealt with in this volume: from combining the psychometric generalizability and item response theories to the ideas for an integrated formative use of data-driven decision making, assessment for learning and diagnostic testing. A number of chapters pay attention to computerized (adaptive) and classification testing. Other chapters treat the quality of testing in a general sense, but for topics like maintaining standards or the testing of writing ability, the quality of testing is dealt with more specifically.\ud All authors are connected to RCEC as researchers. They present one of their current research topics and provide some insight into the focus of RCEC. The selection of the topics and the editing intends that the book should be of special interest to educational researchers, psychometricians and practitioners in educational assessment

Managing evolution and change in web-based teaching and learning environments

The state of the art in information technology and educational technologies is evolving constantly. Courses taught are subject to constant change from organisational and subject-specific reasons. Evolution and change affect educators and developers of computer-based teaching and learning environments alike – both often being unprepared to respond effectively. A large number of educational systems are designed and developed without change and evolution in mind. We will present our approach to the design and maintenance of these systems in rapidly evolving environments and illustrate the consequences of evolution and change for these systems and for the educators and developers responsible for their implementation and deployment. We discuss various factors of change, illustrated by a Web-based virtual course, with the objective of raising an awareness of this issue of evolution and change in computer-supported teaching and learning environments. This discussion leads towards the establishment of a development and management framework for teaching and learning systems

The Road Ahead for State Assessments

The adoption of the Common Core State Standards offers an opportunity to make significant improvements to the large-scale statewide student assessments that exist today, and the two US DOE-funded assessment consortia -- the Partnership for the Assessment of Readiness for College and Careers (PARCC) and the SMARTER Balanced Assessment Consortium (SBAC) -- are making big strides forward. But to take full advantage of this opportunity the states must focus squarely on making assessments both fair and accurate.A new report commissioned by the Rennie Center for Education Research & Policy and Policy Analysis for California Education (PACE), The Road Ahead for State Assessments, offers a blueprint for strengthening assessment policy, pointing out how new technologies are opening up new possibilities for fairer, more accurate evaluations of what students know and are able to do. Not all of the promises can yet be delivered, but the report provides a clear set of assessment-policy recommendations. The Road Ahead for State Assessments includes three papers on assessment policy.The first, by Mark Reckase of Michigan State University, provides an overview of computer adaptive assessment. Computer adaptive assessment is an established technology that offers detailed information on where students are on a learning continuum rather than a summary judgment about whether or not they have reached an arbitrary standard of "proficiency" or "readiness." Computer adaptivity will support the fair and accurate assessment of English learners (ELs) and lead to a serious engagement with the multiple dimensions of "readiness" for college and careers.The second and third papers give specific attention to two areas in which we know that current assessments are inadequate: assessments in science and assessments for English learners.In science, paper-and-pencil, multiple choice tests provide only weak and superficial information about students' knowledge and skills -- most specifically about their abilities to think scientifically and actually do science. In their paper, Chris Dede and Jody Clarke-Midura of Harvard University illustrate the potential for richer, more authentic assessments of students' scientific understanding with a case study of a virtual performance assessment now under development at Harvard. With regard to English learners, administering tests in English to students who are learning the language, or to speakers of non-standard dialects, inevitably confounds students' content knowledge with their fluency in Standard English, to the detriment of many students. In his paper, Robert Linquanti of WestEd reviews key problems in the assessment of ELs, and identifies the essential features of an assessment system equipped to provide fair and accurate measures of their academic performance.The report's contributors offer deeply informed recommendations for assessment policy, but three are especially urgent.Build a system that ensures continued development and increased reliance on computer adaptive testing. Computer adaptive assessment provides the essential foundation for a system that can produce fair and accurate measurement of English learners' knowledge and of all students' knowledge and skills in science and other subjects. Developing computer adaptive assessments is a necessary intermediate step toward a system that makes assessment more authentic by tightly linking its tasks and instructional activities and ultimately embedding assessment in instruction. It is vital for both consortia to keep these goals in mind, even in light of current technological and resource constraints.Integrate the development of new assessments with assessments of English language proficiency (ELP). The next generation of ELP assessments should take into consideration an English learners' specific level of proficiency in English. They will need to be based on ELP standards that sufficiently specify the target academic language competencies that English learners need to progress in and gain mastery of the Common Core Standards. One of the report's authors, Robert Linquanti, states: "Acknowledging and overcoming the challenges involved in fairly and accurately assessing ELs is integral and not peripheral to the task of developing an assessment system that serves all students well. Treating the assessment of ELs as a separate problem -- or, worse yet, as one that can be left for later -- calls into question the basic legitimacy of assessment systems that drive high-stakes decisions about students, teachers, and schools." Include virtual performance assessments as part of comprehensive state assessment systems. Virtual performance assessments have considerable promise for measuring students' inquiry and problem-solving skills in science and in other subject areas, because authentic assessment can be closely tied to or even embedded in instruction. The simulation of authentic practices in settings similar to the real world opens the way to assessment of students' deeper learning and their mastery of 21st century skills across the curriculum. We are just setting out on the road toward assessments that ensure fair and accurate measurement of performance for all students, and support for sustained improvements in teaching and learning. Developing assessments that realize these goals will take time, resources and long-term policy commitment. PARCC and SBAC are taking the essential first steps down a long road, and new technologies have begun to illuminate what's possible. This report seeks to keep policymakers' attention focused on the road ahead, to ensure that the choices they make now move us further toward the goal of college and career success for all students. This publication was released at an event on May 16, 2011

Analytics and complexity: learning and leading for the future

There is growing interest in the application of learning analytics to manage, inform and improve learning and teaching within higher education. In particular, learning analytics is seen as enabling data-driven decision making as universities are seeking to respond a range of significant challenges that are reshaping the higher education landscape. Experience over four years with a project exploring the use of learning analytics to improve learning and teaching at a particular university has, however, revealed a much more complex reality that potentially limits the value of some analytics-based strategies. This paper uses this experience with over 80,000 students across three learning management systems, combined with literature from complex adaptive systems and learning analytics to identify the source and nature of these limitations along with a suggested path forward

Maximizing Competency Education and Blended Learning: Insights from Experts

In May 2014, CompetencyWorks brought together twenty-three technical assistance providers to examine their catalytic role in implementing next generation learning models, share each other's knowledge and expertise about blended learning and competency education, and discuss next steps to move the field forward with a focus on equity and quality. Our strategy maintains that by building the knowledge and networks of technical assistance providers, these groups can play an even more catalytic role in advancing the field. The objective of the convening was to help educate and level set the understanding of competency education and its design elements, as well as to build knowledge about using blended learning modalities within competency-based environments. This paper attempts to draw together the wide-ranging conversations from the convening to provide background knowledge for educators to understand what it will take to transform from traditional to personalized, competency-based systems that take full advantage of blended learning

A semantic web services-based infrastructure for context-adaptive process support

Current technologies aimed at supporting processes whether it is a business or learning process - primarily follow a metadata- and data-centric paradigm. Whereas process metadata is usually based on a specific standard specification - such as the Business Process Modeling Notation (BPMN) or the IMS Learning Design Standard - the allocation of resources is done manually at design-time, and the used data is often specific to one process context only. These facts limit the reusability of process models across different standards and contexts. To overcome these issues, we introduce an innovative Semantic Web Service-based framework aimed at changing the current paradigm to a context-adaptive service-oriented approach. Following the idea of layered semantic abstractions, our approach supports the development of abstract semantic process model - reusable across different contexts and standards - that enables a dynamic adaptation to specific actor needs and objectives. To illustrate the application of our framework and establish its feasibility, we describe a prototypical application in the E-Learning domain