Towards a new proposal to evaluate the learning objects quality in learning strategies for education (QEES)

In Computer Science world several proposals have been developed for the assessment of the quality of the digital objects, based on the capabilities and facilities offered by current technologies and the available resources. Years ago researchers and specialists from both educational and technological areas have been committed to the development of strategies that improve the quality of education. At present, in the field of teaching-learning, another important aspect is the need to improve the manner of gaining knowledge and learning in education, which the use of learning strategies is a major advance in the teaching-learning process in institutions of higher education. This paper presents QEES, a proposal for evaluating the quality of the learning objects employed on learning strategies to support students during their education processes by using information extraction techniques and ontologies.

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.

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.

Development and psychometric testing of the clinical learning environment, supervision and nurse teacher evaluation scale (CLES+T): The Spanish version

Background: The Clinical Learning Environment, Supervision and Nurse Teacher scale is a reliable and valid instrument to evaluate the quality of the clinical learning process in international nursing education contexts. Objectives: This paper reports the development and psychometric testing of the Spanish version of the Clinical Learning Environment, Supervision and Nurse Teacher scale. Design: Cross-sectional validation study of the scale. Setting: 10 public and private hospitals in the Alicante area, and the Faculty of Health Sciences (University of Alicante, Spain). Participants: 370 student nurses on clinical placement (January 2011–March 2012). Methods: The Clinical Learning Environment, Supervision and Nurse Teacher scale was translated using the modified direct translation method. Statistical analyses were performed using PASW Statistics 18 and AMOS 18.0.0 software. A multivariate analysis was conducted in order to assess construct validity. Cronbach’s alpha coefficient was used to evaluate instrument reliability. Results: An exploratory factorial analysis identified the five dimensions from the original version, and explained 66.4% of the variance. Confirmatory factor analysis supported the factor structure of the Spanish version of the instrument. Cronbach’s alpha coefficient for the scale was .95, ranging from .80 to .97 for the subscales. Conclusion: This version of the Clinical Learning Environment, Supervision and Nurse Teacher scale instrument showed acceptable psychometric properties for use as an assessment scale in Spanish-speaking countries.

A "Know-How vs. Know-What" Approach in the Teaching-Learning of Competences in Physical Chemistry

The methodological approach a teacher uses in the competence teaching-learning process determines the way students learn. Knowledge can be acquired from a series of perspectives, mainly: “know-what” (concept), where facts and descriptions of (natural or social) phenomena are pursued; “know-how” (procedure), where methods and procedures for their application are described; and “know-why” (competence), where general principles and laws that explain both the facts and their applications are sought. As all the three cases are interconnected, the boundaries between them are not fully clear and their application uses shared elements. In any case, the depth of student’s acquired competences will be directly affected by the teaching-learning perspective, traditionally aiming to a “know-why” approach for full competence acquisition. In this work, we discuss a suitable teaching-learning methodology for evaluating whether a “know-how”, “know-what” or combined approach seems better for enhancing competence learning in students. We exemplify the method using a selection of formative activities from the Physical Chemistry area in the Grades of Chemistry and Chemical Engineering.

Computer Networks Virtualization with GNS3: Evaluating a solution to optimize resources and achieve a distance learning

Designing educational resources allow students to modify their learning process. In particular, on-line and downloadable educational resources have been successfully used in engineering education the last years [1]. Usually, these resources are free and accessible from web. In addition, they are designed and developed by lecturers and used by their students. But, they are rarely developed by students in order to be used by other students. In this work-in-progress, lecturers and students are working together to implement educational resources, which can be used by students to improve the learning process of computer networks subject in engineering studies. In particular, network topologies to model LAN (Local Area Network) and MAN (Metropolitan Area Network) are virtualized in order to simulate the behavior of the links and nodes when they are interconnected with different physical and logical design.

Record of undergraduates’ activities in virtual learning environments

Virtual learning environments (VLEs) have witnessed a high evolution, namely regarding their potentialities, the tools and the activities they provide. VLEs enable us to access large quantities of data resulting from both students and teachers’ activities developed in those environments. Monitoring undergraduates’ activities in VLEs is important as it allows us to showcase, in a structured way, a number of indicators which may be taken into account to understand the learning process more deeply and to propose improvements in the teaching and learning strategies as well as in the institution’s virtual environment. Although VLEs provide several data sectorial statistics, they do not provide knowledge regarding the institution’s evolution. Therefore, we consider the analysis of the activity logs in VLEs over a period of five years to be paramount. This paper focuses on the analysis of the activities developed by students in a virtual learning environment, from a sample of undergraduate students, approximately 7000 per year, over a period of five academic years, namely from 2009/2010 to 2013/2014. The main aims of this research work are to assess the evolution of activity logs in the virtual learning environment of a Portuguese public higher education institution, in order to fill possible gaps and to hold out the prospect of new forms of use of the environment. The results obtained from the data analysis show that overall, the number of accesses to the virtual learning environment increased over the five years under study. The most used tools were Resources, Messages and Assignments. The most frequent activities developed with these tools were respectively consulting information, sending messages and submitting assignments. The frequency of accesses to the virtual learning environment was characterized according to the number of accesses in the activity log. The data distribution was divided into five frequency categories named very low, low, moderate, high and very high, determined by the percentiles 20, 40, 60, 80 and 100, respectively. The study of activity logs of virtual learning environments is important not only because they provide real knowledge of the use that undergraduates make of these environments, but also because of the possibilities they create regarding the identification of a need for new pedagogical approaches or a reinforcement of previously consolidated approaches.

RESEARCHING SITUATED LEARNING AS THE DEVELOPMENT OF PRACTICE FOR COLLEGE ACCESS PRACTITIONERS: A CASE STUDY OF THE COLLEGE ASSISTANCE MIGRANT PROGRAM

Thesis (Ph.D.)--University of Washington, 2016-03

From a Vicious Circle of Anxiety to a Virtuous Circle of Learning: Experience of Teaching to a Heterogenous Clientele

A considerable body of literature suggests that significant psychological barrier and anxiety characterize the teaching and learning process in statistics. This study investigates the incidence of statistics anxiety, the extent to which it can be overcome and the factors that contribute to the process of overcoming it. Self-study and overall teaching quality, amongst others, significantly contributed to this outcome. This study identifies factors contributing to overall teaching quality. The teaching and learning process typified a highly effective communication mechanism based on an appropriate diagnosis of individual needs. This cumulative change resulted from circular causation. It is argued that given appropriate conditions the vicious circle of anxiety can be transformed into a virtuous circle of learning.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

Dynamics of on-line gradient descent learning for multilayer neural networks

We consider the problem of on-line gradient descent learning for general two-layer neural networks. An analytic solution is presented and used to investigate the role of the learning rate in controlling the evolution and convergence of the learning process.

Learning with noise and regularizers in multilayer neural networks

We study the effect of two types of noise, data noise and model noise, in an on-line gradient-descent learning scenario for general two-layer student network with an arbitrary number of hidden units. Training examples are randomly drawn input vectors labeled by a two-layer teacher network with an arbitrary number of hidden units. Data is then corrupted by Gaussian noise affecting either the output or the model itself. We examine the effect of both types of noise on the evolution of order parameters and the generalization error in various phases of the learning process.

Finite-size effects in on-line learning of multilayer neural networks

We complement recent advances in thermodynamic limit analyses of mean on-line gradient descent learning dynamics in multi-layer networks by calculating fluctuations possessed by finite dimensional systems. Fluctuations from the mean dynamics are largest at the onset of specialisation as student hidden unit weight vectors begin to imitate specific teacher vectors, increasing with the degree of symmetry of the initial conditions. In light of this, we include a term to stimulate asymmetry in the learning process, which typically also leads to a significant decrease in training time.

Learning with regularizers in multilayer neural networks

We study the effect of regularization in an on-line gradient-descent learning scenario for a general two-layer student network with an arbitrary number of hidden units. Training examples are randomly drawn input vectors labelled by a two-layer teacher network with an arbitrary number of hidden units which may be corrupted by Gaussian output noise. We examine the effect of weight decay regularization on the dynamical evolution of the order parameters and generalization error in various phases of the learning process, in both noiseless and noisy scenarios.

Globally optimal on-line learning rules for multi-layer neural networks

We present a method for determining the globally optimal on-line learning rule for a soft committee machine under a statistical mechanics framework. This rule maximizes the total reduction in generalization error over the whole learning process. A simple example demonstrates that the locally optimal rule, which maximizes the rate of decrease in generalization error, may perform poorly in comparison.