Luis A. Casillas

A Network Analysis Method for Selecting Personalized Content in e-Learning Programs

Academic programs demand a constant updating. As the reader might agree, todays societies demand a new merge of knowledge elements. Current web-infrastructure is able to support new approaches in science development. On the one hand, due to current social, scientific and technological advances, academic programs in universities are clearly under pressure to be constantly restructured and renewed in order to provide valid and up-to-date knowledge to students. On the other hand, students obviously have different academic needs and backgrounds. So every person needs to follow a specific path in its training process. Our approach aims at proposing a well-ordered method for selecting personalized contents in e-Learning environments through the use of complex network analysis. Due to the similar nature and structure of knowledge and despite the diversity of knowledge types, the proposed method could be easily extrapolated to different academic programs and even account for some diversity in human profiles.

The Users' Avatars Nonverbal Interaction in Collaborative Virtual Environments for Learning

In a Collaborative Virtual Environment (CVE) for learning, an automatic analysis of collaborative interaction is helpful, either for a human or a virtual tutor, in a number of ways: to personalize or adapt the learning activity, to supervise the apprentices’ progress, to scaffold learners or to track the students’ involvement, among others. However, this monitoring task is a challenge that demands to understand and assess the interaction in a computational mode.

Constructing a Multi-agent System for Discovering the Meaning over Natural-Language Collaborative Conversations

On the one hand, natural language is the main communication media for humans. It has a complex construction, based on the diversity of meaning for words and expressions according to the context. On the other hand, computers are not prepared to handle this ambiguity. Our work aims at presenting a multi-agent approach for dealing with the problem of discovering the meaning of expressions written in Spanish, based on a flexible recovery system and Bayesian principles. At a first stage, agents are supposed to identify the role of the words composing a sentence. At a second stage, a second set of agents is ready to coordinate among them in order to assemble a meaning. Our research forms part and contributes to the analysis of collaborative conversations among participants in a web-based collaborative learning environment.

A Quantitative Treatment to Data from Computer-Supported Collaboration: An Ontological Approach

Collaborative activity performed over specific platforms, designed for such purpose, can provide a deep knowledge about the roles, intentions and effects regarding participants and their interaction among themselves and with the knowledge objects available. This study aims at proposing a structured process for gathering the semantics of the activity hidden behind the raw data collected in log files from CSCL platforms. The proposal is based on exploring the semantic elements (activity awareness) through a social networks analysis (SNA). The main focus of our work is to match different behavioral profiles detected in the collaborative activity from CSCL with the formal profiles identified inside a complex concept-network. This network defines an ontology that describes the behavior expected when collaborating in different scenarios and types of activity. When a certain activity sequence matches with a predefined pattern, the concepts related to the pattern are then bound to the real activity sequence.

Towards an Automated Model to Evaluate Collaboration through Non-Verbal Interaction in Collaborative Virtual Environments

Virtual environments represent a helpful resource for learning and training. In their multiuser modality, Collaborative Virtual Environments CVE support geographical distant people to experience collaborative learning and team training; a context in which the automatic monitor of collaboration can provide valuable and in time information, either for human instructors or intelligent tutor systems, about individual and group performance. CVE enable people to share a virtual space where they interact through a graphical representation, generating nonverbal behavior such as gaze-direction or deictic gestures, a potential means to understand collaboration. This paper presents an automated model and its inference mechanisms to evaluate collaboration in CVE based on the nonverbal activity of the participants. The model is a multi-layer analysis that includes: data filtering, fuzzy classification, and rule-based inference producing high-level assessment for group collaboration.

Mexican Spanish Affective Dictionary

In the study of Affective Computing, the lexicon-based approach represents a useful mechanism that consists on using rated words to understand their affective role in a conversation. One of the most used lists of affectively rated words is the Affective Norms for English Words (ANEW), which evaluates the dimensions of pleasure, arousal and dominance for the English language. This list has been translated for other languages such as German or Spanish with effective results; however, there is not an affective lexicon for Mexican Spanish, rated by Mexicans. Based on the ANEW methodology, but using the most frequently words in Mexican Spanish language, similar to emoticons figures for the evaluation and an ad hoc app to collect the data, a list with means and standard deviation for Mexican Spanish words was obtained. Results and main differences with the ANEW study are here discussed.

Scheme for the automatic generation of directions to locate objects in virtual environments

The automatic generation of direction in natural language, for the location of objects, is an ongoing research area heavily supported by the use of virtual environments (VEs). Important components of spatial language such as the selected reference object, along with specific features related to the situation of the scenario and the user, have to be properly combined in order to create a helpful direction to locate an object within a VE. In this paper we present a scheme, constructed upon literature review and specific empirical data, to link those different elements related to the location of objects, aimed to establish the suitable algorithms for the automatic generation of spatial language in VEs.

Towards the Construction of a Multi-agent Approach for Discovering the Meaning of Natural Language Collaborative Conversations

On the one hand, natural language is the main communication media for humans. It has a complex construction, based on the diversity of meaning for words and expressions according to the context. On the other hand, computers are not prepared to handle this ambiguity. The present work aims at presenting a multi-agent approach for dealing with the problem of discovering the meaning of expressions written in Spanish, based on a flexible recovery system and Bayesian principles. At a first stage, agents are supposed to identify the role of the words composing a sentence. At a second stage, a second set of agents is supposed to coordinate among them in order to assemble a meaning. Our research forms part and contributes to the analysis of collaborative conversations among participants in a web-based collaborative learning environment.