Design approaches in technology enhanced learning

Design is a critical to the successful development of any interactive learning environment (ILE). Moreover, in technology enhanced learning (TEL), the design process requires input from many diverse areas of expertise. As such, anyone undertaking tool development is required to directly address the design challenge from multiple perspectives. We provide a motivation and rationale for design approaches for learning technologies that draws upon Simon's seminal proposition of Design Science (Simon, 1969). We then review the application of Design Experiments (Brown, 1992) and Design Patterns (Alexander et al., 1977) and argue that a patterns approach has the potential to address many of the critical challenges faced by learning technologists

AI as a Methodology for Supporting Educational Praxis and Teacher Metacognition

Evidence-based practice (EBP) is of critical importance in education where emphasis is placed on the need to equip educators with an ability to independently generate and reflect on evidence of their practices in situ – a process also known as praxis. This paper examines existing research related to teachers’ metacognitive skills and, using two exemplar projects, it discusses the utility and relevance of AI methods of knowledge representation and knowledge elicitation as methodologies for supporting EBP. Research related to technology-enhanced communities of practice as a means for teachers to share and compare their knowledge with others is also examined. Suggestions for the key considerations in supporting teachers’ metacognition in praxis are made based on the review of literature and discussion of the specific projects, with the aim to highlight potential future research directions for AIEd. A proposal is made that a crucial part of AIEd’s future resides in its curating the role of AI as a methodology for supporting teacher training and continuous professional development, especially as relates to their developing metacognitive skills in relation to their practices

OntoAIMS: ontological approach to courseware authoring

In this paper we discuss how current ontology concepts can be beneficial for more flexible and semantic rich description of the authoring process and for the provision of authoring support of Intelligent Educational Systems (IES) with respect to the three main authoring modules: domain editing, course composition and resource management. We take a semantic perspective on the knowledge representation within such systems and explore the interoperability between the various ontological structures for domain, instructional and resource modeling and the modeling of the entire authoring process. We build upon our research on Authoring Task Ontology and exemplify it within OntoAIMS system. We present authoring scenarios and show their mapping with authoring task ontology. Further we discuss the OntoAIMS framework for management of electronic learning objects (resources) and their usage in the automatic generation of course templates for the authors. Finally, we describe our architecture, based on the ontological specification of the authoring process

Using Ontological Engineering to Overcome AI-ED Problems: Contribution, Impact and Perspectives

This article reflects on the ontology engineering methodology discussed by the paper entitled BUsing Ontological Engineering to Overcome AI-ED Problems published in this journal in 2000.We discuss the achievements obtained in the last 10 years, the impact of our work as well as recent trends and perspectives in ontology engineering for AIED

Knowledge Representation for Potential Field of Study Recognition

Knowledge Representation is a part of Artificial Intelligence that focuses on the formalism design. The knowledge about a specific domain is expressed epistemologically and computationally. One of the main reasons for this is that knowledge must be represented so as to easily identify the structure and characteristics of classes and the relationship among them. This paper will focus on the systematic investigation of ontology's formula that is presented by Description logics. We believe that Description logics be able to sketch, define, integrate and maintain the ontology

An Integrated Approach for Automatic\ud Aggregation of Learning Knowledge Objects

This paper presents the Knowledge Puzzle, an ontology-based platform designed to facilitate domain\ud knowledge acquisition from textual documents for knowledge-based systems. First, the\ud Knowledge Puzzle Platform performs an automatic generation of a domain ontology from documents’\ud content through natural language processing and machine learning technologies. Second,\ud it employs a new content model, the Knowledge Puzzle Content Model, which aims to model\ud learning material from annotated content. Annotations are performed semi-automatically based\ud on IBM’s Unstructured Information Management Architecture and are stored in an Organizational\ud memory (OM) as knowledge fragments. The organizational memory is used as a knowledge\ud base for a training environment (an Intelligent Tutoring System or an e-Learning environment).\ud The main objective of these annotations is to enable the automatic aggregation of Learning\ud Knowledge Objects (LKOs) guided by instructional strategies, which are provided through\ud SWRL rules. Finally, a methodology is proposed to generate SCORM-compliant learning objects\ud from these LKOs

Escaping the Trap of too Precise Topic Queries

At the very center of digital mathematics libraries lie controlled vocabularies which qualify the {\it topic} of the documents. These topics are used when submitting a document to a digital mathematics library and to perform searches in a library. The latter are refined by the use of these topics as they allow a precise classification of the mathematics area this document addresses. However, there is a major risk that users employ too precise topics to specify their queries: they may be employing a topic that is only "close-by" but missing to match the right resource. We call this the {\it topic trap}. Indeed, since 2009, this issue has appeared frequently on the i2geo.net platform. Other mathematics portals experience the same phenomenon. An approach to solve this issue is to introduce tolerance in the way queries are understood by the user. In particular, the approach of including fuzzy matches but this introduces noise which may prevent the user of understanding the function of the search engine. In this paper, we propose a way to escape the topic trap by employing the navigation between related topics and the count of search results for each topic. This supports the user in that search for close-by topics is a click away from a previous search. This approach was realized with the i2geo search engine and is described in detail where the relation of being {\it related} is computed by employing textual analysis of the definitions of the concepts fetched from the Wikipedia encyclopedia.Comment: 12 pages, Conference on Intelligent Computer Mathematics 2013 Bath, U

Using Artificial Intelligence to Circumvent the Teacher Shortage in Special Education: A Phenomenological Investigation

The purpose of this hermeneutic phenomenological research study was to understand district technology leaders’ receptivity to employing artificial co-teachers, based on their lived experiences with Artificial Intelligence (AI). Facing a problematic teacher shortage in special education, the Jade County School District was not readily employing available AI technologies such as IBM’s WATSON and MIT Media Lab’s TEGA, to aide in filling the instructional voids caused by special education teacher attrition. Veblen’s theory of technological determinism provided the necessary framework for this study, which focused on how district technology leaders described their willingness or apprehension to employ autonomous machines to independently instruct students with disabilities in the classroom. This research study was carried out in a large public-school district with a high number of special education teacher vacancies. Purposeful sampling was used to recruit 11 district-level technology leaders who were responsible for developing and sharing a vision for how new technology could be employed to support the needs of students. The principal researcher applied hermeneutic phenomenology to interpret data from photo-elicitations, audio-recorded focus groups, and individual interviews