Academic journal article Educational Technology & Society

A Study about Placement Support Using Semantic Similarity

Academic journal article Educational Technology & Society

A Study about Placement Support Using Semantic Similarity

Article excerpt


In open and networked learning environments (Koper, Rusman, & Sloep, 2005) it is an important problem to support learners to find appropriate learning opportunities that fit to their competence level and prior learning. While traditional technology-enhanced learning environments are currently facing an innovation phase due to the widespread use of social and mobile media most of the time the assessment systems lag far behind the innovation processes. We see the development of assessment systems for open and networked learning environments as one of the grand challenges for technology-enhanced learning that makes it necessary to develop and evaluate alternative methods to approximate the prior knowledge of learners and to construct individual learning paths through their learning network (Janssen et al., 2010). Traditionally this problem has been addresses by intelligent tutoring systems with methods from adaptive hypermedia research like scalar models, overlay models, perturbation models or genetic models (Brusilovsky & Millan, 2007). However, it is rather a closed world in which these models operate; systems can only take account of what they represent/know about the learner in the current learning environment. All "system experiences" are lost after changing the learning environment and cannot be reused in another system. This problem has been recognized as the "open corpus" problem (Brusilovsky & Henze, 2007). While these models have their value in traditional e-learning processes in learning networks they are not applicable at all. Alternative bottom-up approaches are needed to offer personalized learning paths that do not need extensive sets of metadata to reason about prior knowledge of learners. Traditionally, higher education institutions in several European countries support the accreditation of prior learning (APL) (Merrifield et al., 2000). A typical APL procedure consists of four main phases (Van Bruggen et al., 2004):

* In a profiling phase the institution collects information about the learner's needs and personal background.

* In the second phase learners collect and present evidence about their qualifications and experience. This evidence should support a claim for credit for the new qualification they are seeking.

* In the assessment phase the evidence submitted by the learner is analysed and reviewed conforming to the local assessment standards. The result of this phase of the procedure is an answer to the question of whether the student should be granted recognition of their prior learning.

* In the accreditation phase the results are verified by the department responsible for awarding the credit or recognizing the outcome of the assessment.

The procedures of APL are costly and time-consuming because they involve domain experts to assess the contents of the portfolios submitted by the students. There are two different approaches to accreditation in higher education institutions. On the one hand there is a generalized accreditation procedure based for example on certificates from vocational education which are expected to be equivalent to local certificates. On the other hand there is an individual accreditation procedure that also takes into account prior learning from non-formal and informal contexts. This second type of accreditation is seeking technological support models (Joosten-ten Brinke et al., 2008). These support models can range from a form of pre-advising the experts about which documents are relevant for the target course or study programme or it could help students to only fill their portfolios with material that is relevant to possible exemption decisions. At the same time these models have the potential to contribute to a future research agenda for technology-enhanced assessment in open and networked learning environments.

The basic assumption of our research is that prior knowledge of learners can be approximated by the content of the learner portfolio and therefore overlap between the documents in the portfolio and the courses of the plan/curriculum can act as a proxy to give exemptions and provide a personalized curriculum. …

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