University of Girona (SPAIN)
About this paper:
Appears in: EDULEARN14 Proceedings
Publication year: 2014
Pages: 6567-6575
ISBN: 978-84-617-0557-3
ISSN: 2340-1117
Conference name: 6th International Conference on Education and New Learning Technologies
Dates: 7-9 July, 2014
Location: Barcelona, Spain
In the scientific community is widely accepted that self-esteem and self-image directly affect to learning and academic achievement. Most of the investigations carried out show that improving self-esteem improves academic outcomes. Thus, through these researches we can conclude that the increase in self-esteem fosters better results in the learning process.

Our working hypothesis proposes to take advantage of a new approach based in the concept of reverse causality, and in which way can it be highly amplified using ICT adapted procedures. That is, how a better academic performance increases self-esteem and, as it is expected, a positive feedback to learning activity is obtained. However, other factors are clearly influential in the improvement of the academic results. The closed related to self-esteem are motivation, anxiety and level of introversion.

The main hypothesis is based on how the use of ICT decreases levels of anxiety as well as pupil’s avoidance. Such hypothesis takes advantage of group pressure elimination throughout working individually. As a consequence, effects of competitive climate are also reduced.
The student checks his individual progress without comparing his results to their peers. As a consequence, two positive and complementary effects can be detected: the increases and strengthens of self-image, and that self-esteem in no longer under the negative influence of other schoolmates best results.

The learning process has been designed as a state machine that includes:
a) Factors that are directly related to academic content, such as the difficulty level of the exercises or their importance in the curriculum.
b) Factors directly related to the student's work such as: the achieved level in key concepts, number of re- attempts at resolution, time resolution, the number of solved exercises, tutorials consulted and so forth.

The challenge we face is to obtain enough quantifiable data to evaluate anxiety and motivation. A significant parameter is the percentage of students who give up before completing their studies, and at what period of year it occurs. Also have special relevance the ratings obtained from partial evaluations as well as the number of queries in tutorials, which have increased significantly since the very moment that new technologies were implemented.

The state machine that guides the student has been programmed with individual settings for each of them. The ultimate goal was to increase motivation and reduce anxiety. The procedure adjusts the time setting according to dedication, avoiding the undesirable effects of stringent deadlines which can create anxiety. The state machine also controls the level of the exercises, the curriculum of the course, and individual capabilities.
In order to apply individual processes through new technologies, factors that affect or contribute to the increase in self-esteem or motivation must be parameterized.

For example, self-esteem depends on a number of factors f1, f2, ... fn, that is: SE = SE(f1, f2, ... fn). In general we have determined that these factors depend on the time variable, usually through quadratic models:
f1 = f(atx1+bt x2+ ... + c), f2 = f(dt x1+et x2+... +f), f3 = f(g x1+ht x2+... +i), etc.

Looking for the best conditions for each individual student entails that the SE function has to be maximized. In summary, a function associated to each learning parameters of interest (self-esteem, motivation, or anxiety) has to be implemented and optimized.
State machine, self-esteem, motivation, ICT.