The Influence of Gender, Intellectual Ability, Academic Self-Concept, Self-Regulation, Learning Strategies, Popularity and Parent Involvement in Early Adolescence

The present study examined the predictive effects of gender, intellectual ability, self-concept, motivation, learning strategies, popularity and parent involvement on academic achievement. Hiearchical regression analysis were performed with six steps in which each variable was included, among a sample of 1398 high school students (mean age = 12.5; standard deviation = .67) of eight education centers from the province of Alicante (Spain). The results revealed significant predictive effects of all of the variables, explaining 59.1% of the total variance.

An exercises editor for an e-learning environment

One of the most important tenets of e-learning is that it bridges work and learning. A great e-learning experience brings learning into the work environment. This is a key point, the capacity to construct a work environment when the student can develop proper tasks to complete the learning process. This paper describes a work environment based on the development of two tools, an exercises editor and an exercises viewer. Both tools are able to manage color images where, because of the implementation of basic steganographic techniques, it is possible to add information, exercises, questions, and so on. The exercises editor allows to decide which information must be visible or remain hidden to the user, when the image is loaded in the exercises viewer. Therefore, it is possible to hide the solutions of the proposed tasks; this is very useful to complete a self-evaluation learning process. These tools constitute a learning architecture with the final objective that learners can apply and practice new concepts or skills.

An explanatory model of academic achievement based on aptitudes, goal orientations, self-concept and learning strategies

As a result of studies examining factors involved in the learning process, various structural models have been developed to explain the direct and indirect effects that occur between the variables in these models. The objective was to evaluate a structural model of cognitive and motivational variables predicting academic achievement, including general intelligence, academic self-concept, goal orientations, effort and learning strategies. The sample comprised of 341 Spanish students in the first year of compulsory secondary education. Different tests and questionnaires were used to evaluate each variable, and Structural Equation Modelling (SEM) was applied to contrast the relationships of the initial model. The model proposed had a satisfactory fit, and all the hypothesised relationships were significant. General intelligence was the variable most able to explain academic achievement. Also important was the direct influence of academic self-concept on achievement, goal orientations and effort, as well as the mediating ability of effort and learning strategies between academic goals and final achievement.

EmotiBlog: a finer-grained and more precise learning of subjectivity expression models

The exponential growth of the subjective information in the framework of the Web 2.0 has led to the need to create Natural Language Processing tools able to analyse and process such data for multiple practical applications. They require training on specifically annotated corpora, whose level of detail must be fine enough to capture the phenomena involved. This paper presents EmotiBlog – a fine-grained annotation scheme for subjectivity. We show the manner in which it is built and demonstrate the benefits it brings to the systems using it for training, through the experiments we carried out on opinion mining and emotion detection. We employ corpora of different textual genres –a set of annotated reported speech extracted from news articles, the set of news titles annotated with polarity and emotion from the SemEval 2007 (Task 14) and ISEAR, a corpus of real-life self-expressed emotion. We also show how the model built from the EmotiBlog annotations can be enhanced with external resources. The results demonstrate that EmotiBlog, through its structure and annotation paradigm, offers high quality training data for systems dealing both with opinion mining, as well as emotion detection.

Mentors’ implication in clinical learning and assessment of nursing students

Nursing Education in Europe is regulated by law from 2005. Clinical learning comprises at least 50% of the total degree program in nursing. It is necessary rely on professionals nurses involved in the learning process and skills development assessment. The level of implication in learning processes of these professional nurses is very important to ensure good results.

Goal Orientation Profiles and Self-Concept of Secondary School Students

The objective of this study is to identify possible combinations of multiple goals that lead to different goal orientation profiles and to determine whether there are significant group differences in self-concept dimensions. The Achievement Goals Tendencies Questionnaire (AGTQ) and the Self-Description Questionnaire-II (SDQ-II) were administered to a sample of 2,022 students of Compulsory Secondary education, ranging in age from 12 to 16 years (M = 13.81, SD = 1.35). Cluster analysis identified four profiles of motivational goals: a group of students with a generalized high motivation profile, a group of students with generalized low motivation profile, a group of students with a predominance of learning goals and achievement goals, and a last group of students with a predominance of achievement goals and social reinforcement goals. Results reveal statistically significant differences among the profiles obtained regarding self-concept dimensions.

Motivational profiles Spanish students of Compulsory Secondary Education: Differential analysis of academic self-attributions

Academic goals and academic self-attributions are relevant variables in school settings. The objective of this study is to identify whether there are combinations of multiple goals that lead to different motivational profiles and to determine whether there are significant differences between the groups obtained regarding causal attributions of success and failure (ability, effort, or external causes) in Mathematics and Language and Literature, and in overall academic performance. The Goal Achievement Tendencies Questionnaire (AGTQ) and the Sydney Attribution Scale (SAS) were administered to a sample of 2022 students of compulsory secondary education, ranging in age from 12 to 16 years (M = 13.81, SD = 1.35). Cluster analysis identified four motivational profiles: a group of students with a high generalized motivation profile, a group of students with low generalized motivation profile, a group of students with predominance of learning goals and achievement goals, and a final group of students with predominance of social reinforcement goals. Results revealed statistically significant differences between the profiles obtained in academic self-attributions.

Innovative education networking aimed at multimedia tools for geometrical optics learning

We present a purposeful initiative to open new grounds for teaching Geometrical Optics. It is based on the creation of an innovative education networking involving academic staff from three Spanish universities linked together around Optics. Nowadays, students demand online resources such as innovative multimedia tools for complementing the understanding of their studies. Geometrical Optics relies on basics of light phenomena like reflection and refraction and the use of simple optical elements such as mirrors, prisms, lenses, and fibers. The mathematical treatment is simple and the equations are not too complicated. But from our long time experience in teaching to undergraduate students, we realize that important concepts are missed by these students because they do not work ray tracing as they should do. Moreover, Geometrical Optics laboratory is crucial by providing many short Optics experiments and thus stimulating students interest in the study of such a topic. Multimedia applications help teachers to cover those student demands. In that sense, our educational networking shares and develops online materials based on 1) video-tutorials of laboratory experiences and of ray tracing exercises, 2) different online platforms for student self-examinations and 3) computer assisted geometrical optics exercises. That will result in interesting educational synergies and promote student autonomy for learning Optics.

Teaching-learning process of historical heritage: a Journey as a beginning, path and end

The implantation of new university degrees within the European Higher Education Area implies the need of innovative methodologies in teaching and learning to improve the skills and competencies of students and to answer the growing needs that society continuously demands to heritage management experts. The present work shows an application of the teaching methodology proposed during the international workshop entitled “I International Planning Preservation Workshop. Learning from Al Andalus”, which included the participation of the University of Alicante and Granada, Università Politecnico di Milano and Hunter College City University of New York; where we tried to dissolve traditional boundaries derived of interuniversity cooperation programs. The main objective of the workshop was to discuss and debate the role of urban Historical Centers within the Global Heritage by the integrated work through multidisciplinary teams and the creation of a permanent international working group between these universities to both teach and research. The methodology of this workshop was very participatory and considered the idea of a new learning process generated by "a journey experience." A trip from global to local (from the big city to the small village) but also a trip from the local (historical) part of a big city to the global dimension of contemporary historical villages identified by the students through a system of exhibition panels in affinity groups, specific projects proposed by lecturers and teachers or the generation of publications in various areas (texts, photographs, videos, etc.). So, the participation of the students in this multidisciplinary meeting has enhanced their capacity for self-criticism in several disciplines and has promoted their ability to perform learning and research strategies in an autonomous way. As a result, it has been established a permanent international work structure for the development of projects of the Historical City. This relationship has generated the publication of several books whose contents have reflected the conclusions developed in the workshop and several teaching proposals shared between those institutions. All these aspects have generated a new way of understanding the teaching process through a journey, in order to study the representative role of university in the historical heritage and to make students (from planning, heritage management, architecture, geography, sociology, history or engineering areas) be compromised on searching strategies for sustainable development in the Contemporary City.

Constrained self-organizing feature map to preserve feature extraction topology

In many classification problems, it is necessary to consider the specific location of an n-dimensional space from which features have been calculated. For example, considering the location of features extracted from specific areas of a two-dimensional space, as an image, could improve the understanding of a scene for a video surveillance system. In the same way, the same features extracted from different locations could mean different actions for a 3D HCI system. In this paper, we present a self-organizing feature map able to preserve the topology of locations of an n-dimensional space in which the vector of features have been extracted. The main contribution is to implicitly preserving the topology of the original space because considering the locations of the extracted features and their topology could ease the solution to certain problems. Specifically, the paper proposes the n-dimensional constrained self-organizing map preserving the input topology (nD-SOM-PINT). Features in adjacent areas of the n-dimensional space, used to extract the feature vectors, are explicitly in adjacent areas of the nD-SOM-PINT constraining the neural network structure and learning. As a study case, the neural network has been instantiate to represent and classify features as trajectories extracted from a sequence of images into a high level of semantic understanding. Experiments have been thoroughly carried out using the CAVIAR datasets (Corridor, Frontal and Inria) taken into account the global behaviour of an individual in order to validate the ability to preserve the topology of the two-dimensional space to obtain high-performance classification for trajectory classification in contrast of non-considering the location of features. Moreover, a brief example has been included to focus on validate the nD-SOM-PINT proposal in other domain than the individual trajectory. Results confirm the high accuracy of the nD-SOM-PINT outperforming previous methods aimed to classify the same datasets.

MOOCs, Open educational resources and social networking: bridging the gap between informal and formal learning

MOOCs and open educational resources (OER) provide a wealth of learning opportunities for people around the globe, many of whom have no access to formal higher education. OER are often difficult to locate and are accessed on their own without support from or dialogue with subject experts and peers. This paper looks at whether it is possible to develop effective learning communities around OER and whether these communities can emerge spontaneously and in a self-organised way without moderation. It examines the complex interplay between formal and informal learning, and examines whether MOOCs are the answer to providing effective interaction and dialogue for those wishing to study at university level for free on the Internet.

Model Probability in Self-organising Maps

Growing models have been widely used for clustering or topology learning. Traditionally these models work on stationary environments, grow incrementally and adapt their nodes to a given distribution based on global parameters. In this paper, we present an enhanced unsupervised self-organising network for the modelling of visual objects. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product.