In order to be able to search, compare, gap analyse, recommend, and visualise learning objects, learning resources, or teaching assets, the metadata structure and content must be able to support pedagogically informed reasoning, inference, and machine processing over the knowledge representations. In this paper, we present the difficulties with current metadata standards in education: Dublin Core educational version and IEEELOM, using examples drawn from the areas of e-learning, institutional admissions, and learners seeking courses. The paper suggests expanded metadata components based on an e-learning system engineering model to support pedagogically informed interoperability. We illustrate some examples of the metadata relevant to competency in the nurse training domain

Gilbert, Lester

Sitthisak, Onjira

English

Southampton (e-Prints Soton)

         TENCompetence workshop, Barcelona, 2007 Pedagogically informed metadata content and structure for learning and teaching Lester Gilbert* and Onjira Sitthisak Learning Societies Lab, School of Electronics and Computing Science University of Southampton, Highfield, Southampton, SO17 1BJ, United Kingdom E-mail: {lg3, os05r}@ecs.soton.ac.uk *Corresponding author Abstract: In order to be able to search, compare, gap analyse, recommend, and visualise learning objects, learning resources, or teaching assets, the metadata structure and content must  be  able  to  support  pedagogically  informed  reasoning,  inference,  and  machine processing over the knowledge representations.  In this paper, we present the difficulties with current metadata standards in education: Dublin Core educational version and IEEE-LOM, using examples drawn from the areas of e-learning, institutional admissions, and learners  seeking  courses.    The  paper  suggests  the  preliminary  expanded  metadata components based on an e-learning system  engineering model to support pedagogically informed  interoperability.    We  illustrate  some  examples  of  the  metadata  relevant  to competency in the nurse training domain. Keywords: metadata, competency, pedagogy  1  Introduction In recent years, a variety of materials, tools, and learning environments have been created and installed in schools, universities, and organisations to support learning.  Mostly these have been created around e-learning content and collaborative learning activities like a virtual classroom [1].  Learning activities aim at maintaining or developing a learner’s competence, and there are consequent processes of seeking and interpreting evidence to decide where the learners are in their learning, where they want to go, and how they can get there.  In order to support these activities  and  objectives,  appropriate  metadata  content  and  structure  are  required  for  storing, organizing, and sharing pedagogically-related data.  A difficulty with current metadata standards  for learning objects, learning resources, or teaching assets is their lack of pedagogically-relevant content and structure. Establishing an appropriate model of metadata to support e-learning is challenging due to the wide-ranging nature of pedagogically-related data and activities, and philosophical differences of opinion amongst experts about what might be considered a relevant pedagogical approach.  We deal  with  these  difficulties  by  taking  a  general,  pedagogically-neutral  model  of  learning  and teaching, and to use the model to suggest the necessary metadata content and structure. 2  Some areas of difficulty in E-learning 2.1 E-learning E-learning remains content-focussed and assessment-oriented.  For example, a PDF document is placed into a Virtual Learning Environment (VLE) and students are later required to answer some  multiple-choice  questions  about  its  content.    While  we  may  say  that  students  need  to “know” or “understand” the document, exactly how should they be assessed?  What is the place of the document content in the curriculum?  What are the learning activities which we expect of the students as they engage in their study of the document? In addition to modelling subject matter content and assessments, this requires modelling the link between the subject matter content and the assessment of that content.  In turn, this requires modelling the intended learning outcomes to identify and integrate appropriate subject matter content within the broader teaching and learning context of unit, course, and programme. Such modelling also provides a model of learning activities. 2.2 Institutional admissions Institutional admissions  typically  require  structured personal  profiles,  where  a  prospective student identifies their current competencies and achievements.  Matching such a profile against a  course's  entry  expectations  of  pre-requisite  competencies  remains  the  time-consuming  and potentially inaccurate job of an admissions tutor, made more difficult by the often incomplete and imprecise expression of such prerequisites. A  usable  model  of  prerequisites  would  allow  the  better  expression  of  both  course requirements and students’ profiles and their correspondingly better match.  A usable  model would  also  facilitate  the  structured  accreditation  of  prior  learning,  both  experiential  and certificated, and the processing of structured e-portfolios which instantiate the resulting claimed learning. 2.3 Learners seeking courses Learners  seeking  courses  which  match  their  interests,  or  engaging  in  professional development  planning,  often  have  difficulty  discovering  appropriate  and  relevant  courses because of the exceptional variability in course description, and the inadequacy of their ability to express their interests or required development in any corresponding way.   A usable model for structuring course purpose and intent would adequately characterise a course by its prerequisites, intended learning outcomes, the competencies it expects to develop in its students, and the anticipated achievements of its successful students at each of a range of levels. 3  Metadata standards for education Generally, “metadata is information about a resource, either physical or digital” [2].  In the case of educational resources, metadata refers to information about resources used in the context of learning, education, and training [3].  Metadata helps people organize, find, and use resources effectively.  For example, metadata helps users of an educational digital library find resources in a particular subject area at a particular grade level that can be used on a particular computer.  Metadata can be used to identify multi-lingual resources or to inform a user about where and how  to  purchase  a  resource.    A  software  application  might  use  metadata  to  identify  which resources are identified with a particular unit of study.  Without metadata, managing these tasks would be difficult or impossible. There  are  two  important  accredited  metadata  standards  in  the  domain  of  education  and training  [4],  namely  Dublin  Core  (DC)  educational  version,  and  Institute  of  Electrical  and Electronic Engineers Learning Object Metadata (IEEE-LOM). In order to promote reuse of learning content, automated processes for metadata creation and search are required so that these burdens can be alleviated by machines [5].  However, it is not possible within the existing standards to represent sufficiently fine grained semantic information about learning resources in order to allow the selection of appropriate learning materials from a number of resources within some domain [6]. 4  Metadata content and structure We identify the difficulties illustrated earlier in e-learning and with current standards as a lack of  pedagogically-relevant  metadata  content  and  structure.    An  approach  to  dealing  with  this difficulty is to take a general, pedagogically-neutral model of learning and teaching and to use the model to suggest relevant metadata content and structure.  Figure 1 illustrates the E-Learning SYstems  Engineering  (ELSYE)  model  of  the  “learning  transaction”  [7],  based  upon  the “conversational” theory of Laurillard [8]. The key contributions of this model of the learning transaction are that it identifies “purpose” as an essential component of a learning and teaching situation, and it identifies the five essential components of the interaction between the teacher and learner roles as “tell”, “show”, “ask”, “response”, and “feedback” [9].  It is suggested that information about these components, and  about the transaction as a whole, should form the basis of the pedagogically-informed metadata which would be relevant to any description of content or process in a learning and teaching situation.  Figure 1  ELSYE model of the learning transaction The content and design of a learning transaction, of the “tell”, “show”, “ask”, and “feedback”, depend upon four considerations: ￿  characteristics of the learner ￿  characteristics of the media and methods being used in the learning and teaching situation ￿  characteristics of the subject matter content ￿  the intended learning outcomes.  Figure 2  Determiners of the design and content of a learning transaction Figure 2 illustrates the connection between the components of the learning transaction model and the considerations for the design and production of learning and teaching materials. In  preliminary  stage,  the  purpose,  content,  and  design  of  a  learning  transaction  may  be described by the elements illustrated in Figure 3.  These elements may be taken as the basis for the metadata which would be relevant to any learning object, learning resource, or teaching asset. In practice, metadata tagging typically needs to be undertaken from a controlled, possible extensible, vocabulary, so underpinning the metadata elements are ontologies for each category, as illustrated in Figure 4.  Figure 3  Metadata derived from the ELSYE model  Figure 4  Ontology underpinning for metadata These ontologies provide at the least the controlled vocabularies for expressing the metadata elements (possibly drawing upon the JISC Pedagogical Vocabularies Project [10]).  Interestingly, the ELSYE learning transaction model suggests that the purpose of a learning object, learning resource, or teaching asset may be expressed as a high-level statement of the competencies which it  intends  to  support.    In  turn,  a  competency  is  a  compound  statement  incorporating  the components of subject matter content, learned capability, and attitude or motivational state, to give  expression  to  the  common  statement  that  a  learner’s  “true”  understanding  of  a  domain consists of their knowledge, skills, and attitude [11].  The fourth component of a competency,  “context”, acknowledges that “understanding” is always contextual and depends upon a variety of factors which may require explicit expression if the use of a learning object, learning resource, or teaching asset in any learning and teaching situation is to be adequately characterised. To illustrate the general mechanism, we choose competencies from health care because they are amongst the most sophisticated and challenging to implement [12].  Student practitioners typically undertake a number of clinical placements during their training, and their competencies are typically assessed by geographically dispersed and time-constrained mentors and supervisors.  In this scenario, the adoption of electronic competency records and their interoperability will be enhanced via adherence to emerging standards for competency definition. We used the Royal College of Nursing (RCN) competency for developing paediatric epilepsy nurse specialist service as an example, and implemented it using XML format and the ELSYE model.  XML was chosen to provide interoperability and exchange. The example implemented is shown in Figure 5.  Figure 5  RCN competency 9  Benefits and impacts The  benefits  of  pedagogically-informed  metadata  are  expected  to  include  better  matches between  knowledge  required  and  knowledge  supplied,  between  knowledge  required  and knowledge taught, and between personal knowledge gaps and corresponding mass-individualised educational and training provision. Benefits  and  enhancements  are  also  expected  in  life-long  learning  and  personal  and professional development, since the proposed metadata structure is readily extensible to include learning and development from informal learning in hobbies, sports, and social activities. To take an example from a European Union perspective, workforce mobility and the transfer and  development  of  skills  across  member  states  and  across  organisational  sectors  would  be facilitated,  along  with  personal  and  professional  development  and  job  progression  within employers and organisations.  Employers’ requirements may be better matched with workers’ true abilities. 10 Applications Pedagogically-informed metadata would revolutionise the support for technology-enhanced learning and teaching.  We can imagine the combination of well-described content with tools and services  to  yield  configurations  of  useful  and  effective  learning  and  teaching  materials  and environments  involving  machine  processing  and  machine  reasoning  over  semantically  rich knowledge representations of pedagogic content. 11 Conclusion Awareness  in  the  sector  of  education  and  training  on  the  issue  of  learning  technology standardization is growing fast particularly in the use of metadata. However, with awareness of the importance of these issues also seems to grow some confusion and misunderstanding. We have looked at the current areas of the difficulties associated with metadata. The lack of pedagogically-relevant  content  and  structure  with  current  metadata  standards  in  education effectively  retards  the  development  of  technology-enhanced  learning.  We  propose  metadata based  on  a  simple  but  pedagogically  sound  model  of  the  learning  transaction,  and  find  rich suggestions. Such metadata can then support machine processing, flexibility and extensibility, reasoning  and  interoperability.  The  paper  also  gave  some  examples  of  competency-related metadata. We have described some related topics involving benefits, impacts and applications of the proposed metadata. References 1.  Koper,  R.  and  M.  Specht:  TenCompetence:  Lifelong  Competence  Development  and  Learning. in:Competencies in Organizational E-Learning: Concepts and Tools, M.-A. Sicilia, Editor. (2007), Idea Group. 2.  IEEE,  IEEE  Standard  for  Learning  Object  Metadata.  Available  from http://ltsc.ieee.org/wg12/index.html 3.  Duval, E.: Metadata Standards: What, Who & Why. Journal of Universal Computer Science. Vol. 7, No. 7. (2001), 591-601. 4.  Stratakis, M., V. Christophides, K. Keenoy, and A. Magkanaraki: E-Learning Standards, p.45, (2003) 5.  Al-Khalifa,  H.S.  and  H.C.  Davis:  The  Evolution  of  Metadata  from  Standards  to  Semantics  in  E-Learning Applications. in In Proceedings of Hypertext'06. (2006). Odense, Denmark. 6.  Al-Khalifa, H.S. and H.C. Davis: Replacing the Monolithic LOM: A Folksonomic Approach. in In Proceedings of The 7th IEEE International Conference on Advanced Learning Technologies (ICALT 2007). (2007). Niigata, Japan. 7.  Gilbert, L. and V. Gale: Principles of E-learning Systems Engineering. (2007): Chandos. 8.  Laurillard, D.: Rethinking University Teaching: A Conversational Framework for the Effective Use of Learning Technologies (2nd ed). (2001): Routledge Falmer. 9.  Gilbert, L., Y.W. Sim, and C. Wang: Modelling the Learning Transaction. in In Proceedings of The 5th IEEE International Conference on Advanced Learning Technologies. (2005). Kaohsiung, Taiwan. 10. Currier, S., L.M. Campbell, and H. Beetham: Pedagogical Vocabularies Review, JISC, (2005). 11. Sitthisak, O., L. Gilbert, H.C. Davis, and M. Gobbi: Adapting health care competencies to a formal competency model. in The 7th IEEE International Conference on Advanced Learning Technologies. (2007). Niigata, Japan: IEEE Computer Society Press. 12. Kunzmann, C.: Ontology-based Competence Management for Healthcare Training Planning: A Case Study. in Proceeding of the International Conference on Knowledge Management. (2006). Austria. 

Adapting health care competencies to a formal competency model.

E-Learning Standards,

Metadata Standards: What, Who & Why.

Modelling the Learning Transaction. in

Ontology-based Competence Management for Healthcare Training Planning: A Case Study.

Pedagogical Vocabularies Review,

Principles of E-learning Systems Engineering.

Replacing the Monolithic LOM: A Folksonomic Approach.

Rethinking University Teaching: A Conversational Framework for the Effective Use of Learning Technologies (2nd ed). (2001): Routledge Falmer.

TenCompetence: Lifelong Competence Development and Learning.

The Evolution of Metadata from Standards to Semantics in ELearning Applications.

Pedagogically informed metadata content and structure for learning and teaching

Pedagogically informed metadata content and structure for learning and teaching

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