Samara Ruiz

Helping Teachers to Track Student Evolution in a B-Learning Environment

SigMa is an adaptable feedback generation tool for the three kinds of actors involved in a blended-learning process: teachers, students, and the e-learning platform. This paper focuses on the teacher tool and defines its desirable functionalities and main components. The teacher tool gathers data provided by the e-learning platform’s diagnostic capabilities and analyses it. The results are shown to teachers by means of a Visualisation Interface that informs them about course development and suggests possible changes and improvements. A Teacher Profile is used to adapt the feedback to match teachers’ strategies and preferences.

Visualizing Students’ Performance in the Classroom: Towards Effective F2F Interaction Modelling

Students are supposed to work and learn when they are in the classroom, but how are they learning? Answering this question is not so easy, but perhaps knowing the learning activities of the student and following his or her learning progress can be good supporting mechanisms. The PresenceClick environment is intended to let teachers and students nimbly capture what happens in class to provide them, in return, with information about students’ behavior during class. The system involves two platforms −a web system and a mobile application− that working together allow recording of students’ interactions and provide updated graphical visualizations about their behavior.

Focusing on Personal Organization to Enhance Overall E-learning

This paper describes the inclusion of a recommendation system in the MAGADI [1] blended-learning environment. It seeks to help students with their overall study process by recommending both which courses to study for and the contents to be developed on each

Predicting students’ outcomes from emotional response in the classroom and attendance

ABSTRACT A positive emotional state of students has proved to be essential for favouring student learning, so this paper explores the possibility of obtaining student feedback about the emotions they feel in class in order to discover emotion patterns that anticipate learning failures. From previous studies about emotions relating to learning processes, we compiled a Twelve Emotions in Academia Model, which is composed of six positive and six negative emotions. Using this model as a base we built the EmotionsModule in the PresenceClick system, allowing students to identify their emotions and follow their evolution by means of visualizations. Instructors can also view an anonymized version of these data to increase their knowledge about the emotional state of the group and propose new learning strategies to improve the group’s overall state. Information about attendance in face-to-face sessions has been also considered due to the fact that it is positively related to students’ progress. Over the course of two academic years, we carried out an experiment in a single subject in the Computer Science degree in which students’ emotional data and attendance were collected through PresenceClick. Then, we analyzed the compiled data through a correlation study and a principal component analysis whose results demonstrate the consistency of the data, allowing the prediction models to be fed each academic year. Once the correctness and stability of data were verified, data mining techniques were applied and two models based on probability tables and decision trees were obtained. These models enable instructors and students to detect problems early and avoid failure.