Rosa M. Carro

The impact of learning styles on student grouping for collaborative learning: a case study

Learning style models constitute a valuable tool for improving individual learning by the use of adaptation techniques based on them. In this paper, we present how the benefit of considering learning styles with adaptation purposes, as part of the user model, can be extended to the context of collaborative learning as a key feature for group formation. We explore the effects that the combination of students with different learning styles in specific groups may have in the final results of the tasks accomplished by them collaboratively. With this aim, a case study with 166 students of computer science has been carried out, from which conclusions are drawn. We also describe how an existing web-based system can take advantage of learning style information in order to form more productive groups. Our ongoing work concerning the automatic extraction of grouping rules starting from data about previous interactions within the system is also outlined. Finally, we present our challenges, related to the continuous improvement of collaboration by the use and dynamic modification of automatic grouping rules.

Dynamic generation of adaptive Internet-based courses

In this paper we describe a new approach for developing adaptive Web based courses. These courses are defined by means of teaching tasks which correspond to basic knowledge units, and rules which describe how teaching tasks are divided into subtasks. Both tasks and rules are used at execution time to guide the students during their learning process by determining the set of achievable tasks to be presented to the student at every step. Adaptivity is implemented by presenting students with different HTML pages depending on their profile, their previous actions, and the active learning strategy. The HTML pages presented to the students are generated dynamically from general information about the type of media elements associated to each task and their layout. The whole approach is exemplified by means of a course on traffic signs.

Supporting the Development of Mobile Adaptive Learning Environments: A Case Study

In this paper, we describe a system to support the generation of adaptive mobile learning environments. In these environments, students and teachers can accomplish different types of individual and collaborative activities in different contexts. Activities are dynamically recommended to users depending on different criteria (user features, context, etc.), and workspaces to support the corresponding activity accomplishment are dynamically generated. In this article, we present the main characteristics of the mechanism that suggests the most suitable activities at each situation, the system in which this mechanism has been implemented, the authoring tool to facilitate the specification of context-based adaptive m-learning environments, and two environments generated following this approach will be presented. The outcomes of two case studies carried out with students of the first and second courses of ldquoComputer Engineeringrdquo at the ldquoUniversidad Autonoma de Madridrdquo are also presented.

A mechanism to support context-based adaptation in m-learning

In this paper we present a mechanism that supports the generation and management of adaptive mobile learning systems. Such systems are accessed by students and teachers for the accomplishment of diverse individual or collaborative learning activities. The main aim is for the systems to suggest the most suitable activities to be tackled by a given user in a specific context (location, idle time, devices). The basis of this mechanism, as well as an example of the context-based adaptation carried out for three different users in a specific scenario, are presented.

A Methodology for Developing Adaptive Educational-Game Environments

In this paper we present a methodology for describing adaptive educational-game environments and a model that supports the environment design process. These environments combine the advantages of educational games with those derived from the adaptation. The proposed methodology allows the specification of educational methods that can be used for the game environment generation. The educational goals, the activities that the users can perform, their organization and sequencing, along with the games to be played and the game stories are selected or dynamically generated taking into account the users features and behaviors.

Extracting Emotions from Texts in E-Learning Environments

Affective and emotional factors seem to affect student motivation and, in general, the outcome of the learning process. By detecting and managing the emotions underlying a learning activity it would be possible to contribute to improve the student motivation and performance. In this work we explore different possibilities aimed at automatically extracting emotions from texts. We present a case study in which twelve essays written by a fresher student along her first semester in college are analyzed. Those results support the idea of using non-intrusive emotion detection for providing feedback to students. An example of use in an existing context-based adaptive e-learning system is presented. Incorporating emotions to this type of systems broadens their possibilities, allowing dynamic recommendation of activities according to the student emotions at each time, as well as emotion-based content adaptation, among others.

Dynamic Generation of Adaptive Web-Based Collaborative Courses

In this paper we present the use of adaptation techniques to dynamically generate adaptive collaborative Web-based courses. These courses are generated at runtime by selecting, at every step and for each student, the most suitable collaborative tasks to be proposed, the time at which they are presented, the specific problems to be solved, the most suitable partners to cooperate with and the collaborative tools to support the group cooperation. This selection is based on the users’ personal features, preferences, knowledge and behavior while interacting with the course. The advantages of this approach and the peculiarities of combining individual adaptation with collaboration seamlessly are also presented.

Studying the impact of personality and group formation on learner performance

This paper presents a study being carried out at the Universidad Autonoma de Madrid to ascertain the influence of the way students are grouped to do collaborative work (regarding intelligence and personality parameters) on the results they get. Data about students personality are analysed along with information about group composition and student performance. The results of this analysis are expected to throw light about the impact of personal traits and group formation on learning. This information can be incorporated in collaborative systems as criteria for group formation, with the aim of favouring CSCL situations in which students are prone to get better results.

Inferring user personality in social networks: A case study in Facebook

In the context of adaptive intelligent systems, it is essential to build user models to be considered with adaptation purposes. Personality is an interesting user feature to be incorporated in user models; it may lead to know the user needs or preferences in different situations. In this direction, eliciting user personality is needed. This information should be obtained as unobtrusively as possible, yet without compromising the reliability of the model built. In this paper, we present a method for eliciting user personality by analyzing user interactions within the social network Facebook, with the goal of mining behavioral patterns. We have developed TP2010, a Facebook application based on the ZKPQ-50-cc questionnaire, to get information about both the user personality and his interactions within Facebook. We have built a classifier model starting from the analysis of a set of data from more than 11000 users. The results show that it is feasible to get information about the user personality by analyzing data from social network interactions.

A Rule-Based Formalism for Describing Collaborative Adaptive Courses

A significant number of collaborative and adaptive web-based systems have been created to support distance learning in the last years. However, there is a need of formalisms to support an extensible, flexible and maintainable specification of both collaboration and adaptation issues, especially when they are combined in an integrated environment. In this paper we present a rule-based formalism for the description of adaptive collaborative web-based courses, which are dynamically generated at runtime. The course elements, including the collaboration activities, are adapted to each student’s features, preferences, behaviors and achievements, at each time. The student’s actions while collaborating can also affect the later course adaptation. This formal representation provides some advantages regarding the desired features mentioned above.