Abstract:There is evidence suggesting that providing adaptive assistance to collaborative interactions might be a good way of improving the effectiveness of collaborative activities. In this paper, we introduce the Collaborative Tutoring Research Lab (CTRL), a research-oriented framework for adaptive collaborative learning support that enables researchers to combine different types of adaptive support, particularly by using domain-specific models as input to domain-general components in order to create more complex tut… Show more
“…As [13] state in their review of collaboration support systems, "coaching systems help students engaged in computermediated collaboration by assessing the current state of student interaction, comparing the current state to a desired state, and then offering assistance to the students". For building effective coaching systems, we need well structured architectural frameworks such as the one proposed by the Collaborative Tutoring Research Lab (CTRL) [19]. Finally, systematic evaluation studies need to be performed in order to collect and analyze empirical data about the effective use of the proposed approaches for the on-the-fly design such coaching systems.…”
Various learning strategies have been widely used in the Computer-Supported Collaborative Learning (CSCL) script design practice. For this reason strategies have been documented in a designer friendly way as collaborative flow design patterns (CLFP). A CLFP defines the sequence of the tasks that the strategy dictates as well as other elements needed for the various tasks, such as the duration of a task, the use of a particular tool for a given task and so on. Designers often face the difficulty of mixing and matching strategies in order to create personalized scripts that are more appropriate to the learners' preferences, knowledge level, needs and the learning context in general. Designers need to make configurations of a "traditional" CLFP in order to balance a variety of organizational, administrative, instructional and technological components. This task is even more challenging when such configurations need to be made on-the-fly, i.e. during the learning process and in response to the learner' actions and history of interaction. The aim of this paper is to discuss how the learners' interaction data that is collected during the CSCL process and analysed using interaction analysis indicators can be used to make valuable alternatives of a CSCL script depending on learning conditions. The design challenge is to automate as much as possible this configuration process in order to help practitioners easily proceed in making these configurations easily and quickly.
“…As [13] state in their review of collaboration support systems, "coaching systems help students engaged in computermediated collaboration by assessing the current state of student interaction, comparing the current state to a desired state, and then offering assistance to the students". For building effective coaching systems, we need well structured architectural frameworks such as the one proposed by the Collaborative Tutoring Research Lab (CTRL) [19]. Finally, systematic evaluation studies need to be performed in order to collect and analyze empirical data about the effective use of the proposed approaches for the on-the-fly design such coaching systems.…”
Various learning strategies have been widely used in the Computer-Supported Collaborative Learning (CSCL) script design practice. For this reason strategies have been documented in a designer friendly way as collaborative flow design patterns (CLFP). A CLFP defines the sequence of the tasks that the strategy dictates as well as other elements needed for the various tasks, such as the duration of a task, the use of a particular tool for a given task and so on. Designers often face the difficulty of mixing and matching strategies in order to create personalized scripts that are more appropriate to the learners' preferences, knowledge level, needs and the learning context in general. Designers need to make configurations of a "traditional" CLFP in order to balance a variety of organizational, administrative, instructional and technological components. This task is even more challenging when such configurations need to be made on-the-fly, i.e. during the learning process and in response to the learner' actions and history of interaction. The aim of this paper is to discuss how the learners' interaction data that is collected during the CSCL process and analysed using interaction analysis indicators can be used to make valuable alternatives of a CSCL script depending on learning conditions. The design challenge is to automate as much as possible this configuration process in order to help practitioners easily proceed in making these configurations easily and quickly.
“…As noted by Walker et al (2009), wizard-of-Oz is impractical for large-scale research as it creates uncertainty as to whether different facilitators acting as wizards may have different effects.…”
The evaluation of interactive adaptive systems has long been acknowledged to be a complicated and demanding endeavour. Some promising approaches in the recent past have attempted tackling the problem of evaluating adaptivity by "decomposing" and evaluating it in a "piece-wise" manner. Separating the evaluation of different aspects can help to identify problems in the adaptation process. This paper presents a framework that can be used to guide the "layered" evaluation of adaptive systems, and a set of formative methods that have been tailored or specially developed for the evaluation of adaptivity. The proposed framework unifies previous approaches in the literature and has already been used, in various guises, in recent research work. The presented methods are related to the layers in the framework and the stages in the development lifecycle of interactive systems. The paper also discusses practical issues surrounding the employment of the above, and provides a brief overview of complementary and alternative approaches in the literature.
“…4, namely the Trial and Error type and the different Help-Seeking types H 1 , H 2 /H 4 and H 3 . System interventions can be grouped into means of individual user support (see, for instance, Koedinger and Aleven 2007) and means of collaboration support (see, for instance, Soller et al 2005or Walker et al 2009), yet based on individual users' model information. It should be noted that the example interventions proposed in this section are not being suggested as the best possible approaches in the respective cases (something that would definitely also depend on the didactic approach employed).…”
Section: Closing the Circle-potential System Interventionsmentioning
Monitoring and interpreting sequential learner activities has the potential to improve adaptivity and personalization within educational environments. We present an approach based on the modeling of learners' problem solving activity sequences, and on the use of the models in targeted, and ultimately automated clustering, resulting in the discovery of new, semantically meaningful information about the learners. The approach is applicable at different levels: to detect pre-defined, well-established problem solving styles, to identify problem solving styles by analyzing learner behaviour along known learning dimensions, and to semi-automatically discover learning dimensions and concrete problem solving patterns. This article describes the approach itself, demonstrates the feasibility of applying it on real-world data, and discusses aspects of the approach that can be adjusted for different learning contexts. Finally, we address the incorporation of the proposed approach in the adaptation cycle, from data acquisition to adaptive system interventions in the interaction process.
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