One of the earliest attempts to provide visualisation tools to identify risky students and devise ways of supporting their learning has been done by Mazza and Dimitrova (Mazza and Dimitrova 2007). CourseVis is a visualisation tool that helps instructors to early identify problems students may have.
There are two main independent directions of research on open learner models. One direction focuses on visualising the model to support students’ self-reflection and planning. The other one encourages students to participate in the modeling process, such as engaging students through the negotiation or collaboration on the construction of the model (Mitrovic and Martin 2007). Representations of the student model vary from displaying high-level summaries of the information included in the model (such as skill meters1) to complex concept maps or Bayesian
Networks2.
A range of benefits have been reported from the opening of the student models to the learners which range from the increased learner’s awareness on the knowledge development process to the elicitation of the difficulties encountered (Mitrovic and Martin 2007). Furthermore, the disclosure seems to have a positive impact on the students’ engagement, motivation, and knowledge reflection (Bull 2004) and (Zapata-Rivera and Greer 2004).
Dimitrova, Self, and Brna (2001) explored interactive open student modeling by engaging students to negotiate with the system during the modeling process.
Chen, Chou, Deng, and Chan (2007) investigated active open student models in order to motivate them to improve their academic performance.
Brusilovsky, Sosnovsky, and Shcherbinina (2004) embedded, inside one of their adaptive link annotation systems known as QuizGuide, an open learning model in the engine and demon- strated that this arrangement can remarkably increase the student’s motivation to work with non-mandatory educational contents.
1a skill meter is generally a very compact indicator of the mastery level on concepts or skills achieved by
learners
2Bayesian Networks is an approach to build an explanation of hidden variables by observing the external
2.3 Open Learner Model (OLM)
To support social learning, a common approach is to show learners average values of the group model e.g. the average knowledge status of the group in a given topic. These models fall into the category of group based student models. Both individual and group based open student models were studied and the increase in reflection and helpful interactions among teammates through their adoption was demonstrated.
Bull and Kay (2008) described a framework to apply open user models in adaptive learning environments and provided many in-depth examples. Open group modeling enables students to compare and understand their own state among their peers. Moreover, such group models have been used to support collaboration between learners among the same group, and to foster competition in a group of learners (Vassileva and Sun 2007). The authors investigated the role of social visualisations1 in online communities. They concluded that this kind of visualisation
increases social interaction among students, encourages positive competition, and provides stu- dents with the opportunity to build trust in others and in the group. Bull and Britland (2007) used their OLM implementation – called OLMlets – to investigate the facilitation problem for group collaboration and competition. The results showed that optionally releasing the models to peers increases the discussion among students and encourages them to start working sooner. The implementation of an Open Learner Model represents a possible solution to bridge the gap between the functionalities expected by learners and the capabilities offered by the system, in terms of interaction possibilities and presentation of relevant information in eLearning systems. It also allows to offer a fully customisable and adaptive interface to the learner’s model (Brusilovsky 2004) with respect to the users’ characteristics, preferences, knowledge, and tasks (Mazza and Dimitrova 2007).
Student-related data is collected in the student model, which is a component of adaptive systems that maintains an accurate representation of the user’s current state, enabling the sys- tem to perform adaptation based on the information stored in the model (Mitrovic and Martin 2007). The adaptation of the contents to the user’s knowledge and cognitive characteristics (Bull 2004) is a way to support the current learning needs of the learner. It is also generally accepted that it is a well-suited approach to increase the learner’s level engagement in the edu- cational experience (Zapata-Rivera and Greer 2004), thus allowing to offer a truly customised
1A social visualisation is a representation of the traces left by a user in the interaction with the platform
and others that enfatises the social purposes, such as information exchanges, cooperation, dialogs, reciprocal position in a network, common and different skills and achievements, etc. These kinds of visualisations can be used to enhance the awareness of one’s social environment or to express cues and patterns which are implicit in the underlying communication.
experience. Opening this internal model to user inspection could be useful for different reasons, and in particular for self-reflection (Dimitrova, Self, and Brna 2001).
In this view, the model is also an useful source of information that can be used to reinforce the user’s commitment to the online experience and to foster his/her self-reflection processes (Chen, Chou, Deng, and Chan 2007).
More recently some attention has been devoted to the aspect of social interaction supported by online platforms, and the relative representations provided by the systems have also been modified accordingly (Brusilovsky, Sosnovsky, and Shcherbinina 2004). The possibility to in- clude data from external sources could empower the profiling mechanism in having a model that also caters of social and affective characteristics of the learners (Vassileva and Sun 2007) and (Bull and Britland 2007).