According to active learning, students should be responsible for their own learning. Automatic free-text scoring allows teachers to provide open-ended questions with their correct answers to a computer system, so when students answer the questions, they get immediate feedback (a score, a comment, or both). However, teachers are usually overloaded with many tasks, and they may not have time to create the questions with the correct answers. Therefore, in the 2012/2013 academic year, we asked a group of 124 Pre-Primary and Primary Education students to become the creators of the questions and their correct answers in groups in a free-text scoring system, so the questions use learners’ language, not teachers’ language. From them, 41 students (group of involved students, GIS) fulfilled all the requirements during the course. Our hypothesis was that GIS would be able to increase their academic performance and levels of engagement compared to the rest of the students. The results gathered provide statistic evidence to support that hypothesis. This study pretends to help teachers who want to increase the academic performance and levels of engagement of their students in courses that they may find boring and unrelated to the main topic of their degree, or not directly related with their main academic interests.

In this paper we address an open key issue during the development of web-based educational systems. In particular, we provide an educational-oriented approach for building personalised e-learning environments that focuses on putting the learners' needs in the centre of the development process. Our approach proposes user centred design methodologies involving interdisciplinary teams of software developers and domain experts. It is illustrated in an adaptive e-learning system, where a MOOC (Massive Open Online Course) was taken by nearly 400 learners. In particular, we report where user centred design methods can be applied along the e-learning life cycle to designing and evaluating personalisation support through recommendations in learning management systems.

A pedagogic conversational agent (PCA) can be defined as a computer system that interacts with the student in natural language assuming the role of the instructor, a student or a companion. It can have a personality and can generate different sentences according to the agent or the student mood. Empathy with the students' feelings seems to increase their motivation to study. However, the influence of the agent personality and role as well as the students' opinion is still unclear. Therefore, in this article, it is explored with the help of a field experiment, for the first time, how these factors can affect the interaction of children with PCAs, and their opinions according to an anonymous and voluntary opinion questionnaire and some personal interviews.

Willow is a free-text Adaptive Computer Assisted Assessment system, which supports natural language processing and user modeling. In this paper we discuss the benefits coming from extending Willow with recommendations. The approach combines human computer interaction methods to elicit the recommendations with data mining techniques to adjust their definition. Following a scenario-based approach, 12 recommendations were designed and delivered in a large scale evaluation with 377 learners. A statistically significant positive impact was found on indicators dealing with the engagement in the course, the learning effectiveness and efficiency, as well as the knowledge acquisition. We present the overall system functionality, the interaction among the different subsystems involved and some evaluation findings.

Willow is a Web-based application which automatically and adaptively assesses students' free-text answers written in Spanish and English. It is intended to help students review concepts outside of class, and provides an alternative assessment method. However, students tend to be greatly concerned by computer generated scores and, although they have been told that the system is not intended for summativepurposes and their teachers will not have access to that scores, they remain worried that the machine could make a mistake and this mistake could have a negative impact on their final course grade. Given that automatic scoring cannot assure 100% approximation to a human teacher's assessment, we have introduced self-assessment features in Willow. Thanks to the combination of automatic free-text scoring and self-assessment, students have more control over their evaluation and, whenever they do not agree with the automatic score assigned by the system, they can change it. This new feature has been tested by 22 students reviewing a course with Willow in the 2007-2008 academic year. We observed that the students did not abuse the self-assessment feature, only modifying the score assigned in 5% of answered questions.

In this paper, we present an automatic procedure to generate students’ knowledge conceptual models from their answers to an automatic free-text scoring system. The conceptual model is defined as a simplified representation of the concepts and relationships among them that each student keeps in his or her mind about an area of knowledge. It is considered that each area of knowledge comprises several topics and each topic several concepts. Each concept can be identified by a term that the students should use. A concept can belong to one topic or to several topics. The conceptual model is graphically displayed to the teachers as a conceptual map so that they can instantly see which concepts have already been assimilated and which ones should still be reviewed as they have been misunderstood.