of the student model and allowing further explorations of the model, through interactions and the reflection on the specifically provided at a higher level.
2.2
Technology Enhanced Learning (TEL)
The field of Technology Enhanced Learning is related to the usage of digital technologies in the practice of education. Its main focus is the alignment between the technologies applied and the different aspects of the learning experience – resources, actions, and objectives – in order to provide socio-technical innovations in education, independently from time, place and pace constraints.
Unfortunately some negative side effects are well known, like a higher rate of dropouts (Levy 2007) a feeling of loneliness, isolation and low motivation to learn (Rovai 2002).
Levy (2007) – after a more formal definition of what can be considered a dropout in eLearning field, which was previously not well defined – explored the possible reasons connected with its increase in online experiences compared to the on-campus presential ones. The main finding was that amongst the key–factors considered –namely the academic locus of control and the student’s satisfaction– only the second one showed to be a reliable indicator, whereas the locus of control seemed to play no role in the student’s dropout rate. As expected, the author found a negative correlation between the students’ satisfaction and their dropout rate.
Rovai (2002) on the other end explored the impact of learning communities in educational experiences, comparing the cases of presential ones to the eLearning ones and positively corre- lating the sense of being part of a community with a higher level of fulfilment and satisfaction. This work reached the conclusion that fostering the creation of learning communities in online courses can facilitate the dialog and decrease the psychological distances amongst the partici- pants. In the present work this point supports the idea of extending the presented information to social aspects.
Other authors have also reported that these issues could be reduced by increasing the level of engagement among students, such as in (Laurillard, Oliver, Wasson, and Ulrich 2009). They reported the capacity of the new digital media to connect innovation and practices, generating a natural sense of engagement and curiosity towards the messages encoded on that medium. In fact, the authors stressed the fact that the adoption of the new digital media can be a way to improve the students’ capabilities to express themselves, thus allowing them to enhance
the expressiveness and creativity in the educational process while scaffolding their intellectual development.
It was also found that an holistic approach demonstrates to be useful in understanding and tackling these issues (McCalla 2004). The author argued that it is fundamental to contextualise the eLearning experience, going further into the semantic web1 approach and introducing a pragmatic dimension, where the user context, intentions and objectives can be captured and used to induce a reaction in the proposed experience. The sum of these “pragmatic” layers i.e. the always existing “semantic” level, the user profile and the educational resources create the ecological approach for the design of TEL empowered systems. This holistic approach, which uses all of the defined levels as the knowledge base for the mining process, allows for a better contextualisation of the patterns that emerge inside the learners’ educational experiences and provides them with a more engaging experience.
Another possible approach is to automatically adapt the learning experience to some of the learner’s characteristics without relying on a pragmatic layer. Some of the dimensions that can drive the adaptation are personal preferences, learning goals, personal and social context or a student model i.e. a profile of the knowledge and skills acquired during the learning process.
Some experiments to create a learner model were conducted by researchers (May, George, and Prevot 2007) in the context of CMC2tools (like web discussion forums), where they created
a model for collecting the breadcrumbs3, or in other words procedures for the manipulation of
this data to obtain a profile and an approach to securely store the resulting model. Based on this collected raw data, they proposed some common analysis to produce the model which should be also visualised to guarantee optimal usage.
The learner model is also reported to be able to provide some interesting information about the student mental situation, intended as the processes and cognitive functions specifically stimulated by the current learning activity. In fact, through the abstraction process and the Semantic Web approach it becomes possible to also consider the user context and provide infor- mation better suited for supporting the learners performances in achieving their full potentiality in online tasks (Heath, Motta, and Dzbor 2005).
1for Semantic Web here is intended an approach to the information published on the Web that, through the
explicitation of the semantic of the hyper-link and the data presented, support the automatic reasoning over this data and the extraction of new knowledge by programmers.
2CMC stands for Computer Mediated Communications
3breadcrumbs, literally the small pieces of bread created when you cut it, which represent the elementary
2.2 Technology Enhanced Learning (TEL)
As stated in the previous section –centered on Information Visualization–, it is commonly accepted that choosing a graphical presentation could make the interpretation of this complex data much easier (Dror, Nadine, and Mike 2008). In this paper they proposed a methodology called VAF – Virtual Apparatus Framework – based on a novel visualisation tool Solution Trace Graph. With this approach, they were able to develop an intelligently adapted remediation system in an exploratory learning scenario.
Furthermore, opening this model to the student’s inspection is another option to increase their level of engagement, stimulating the perception of the current status (as already shown by other researcher), and to encourage reflection as learning.
Bull (1997) proposed a system, called See yourself Write, that presents a merged picture of the information about the assignments submitted and the feedback provided by the tutors and discloses the generated model for reflection on the path completed and the feedback received by learner.
This specific aspect will be presented in details in the next section, that deals with the so-called Open Learner Model approach.
While crucially important, OLM is not the only method proposed for solving the problem of creating and memorising a learner’s profile that support disclosure functionalities. Other ways to create, maintain, store, and externalise the model gave origin to different approaches, known as ”educational mash-up” (Esposito, Licchelli, and Semeraro 2004) or ”ubiquitous and decen- tralized user model” (Van Der Sluijs and Houben 2006) and (Heckmann, Schwartz, Brandherm, and Kroner 2005).
Esposito, Licchelli, and Semeraro (2004) faced the challenge of creating a student profile relying on procedures and approaches from IR (Information Retrieval): their Profile Extractor uses ML (Machine Learning) techniques to discover the preferences, needs, and interests of the learner. The source of data taken into account to extract the information are the learning performances, the communication preferences and the online behaviors adopted by students.
Instead Van Der Sluijs and Houben (2006) proposed a semantic approach to collect data from web application (based on the Semantic Web model) and to retrieve and connect data generated by the same learner inside different platform, as usually happen in the Web environment. They elaborated the GUC (Generic User model Component) as an autonomous and pluggable component for this task.
Another approach – which was lately merged into one with the one above – was ideate by Heckmann, Schwartz, Brandherm, and Kroner (2005) who proposed to rely on an ontology
developed by themselves called GUMO (Generic User Modeling Ontology) and on an extension of XML, called USERML to track, store and exchange information about learner profiles, on top of a server to store and query the ”global” profiles created.