Complex learning: the role of knowledge, intelligence, motivation and learning strategies

This work presents the main theories and models formulated with the purpose of offering a global overview on the acquisition of knowledge and skills involved in the initial development of expert competence. Setting from this background, we developed an empirical work whose main purpose is to define those factors in a complex learning situation such as chapter-sized in a knowledge-rich domain. The results obtained in a sample of Master students reveal that the several variables intervening, such as the qualitative organization of knowledge, intellectual ability, motivation, the deliberate use of strategies, and a rich learning environment, contribute in an independent way to provide an explanation for the acquired knowledge.

Profiles of emotional intelligence and learning strategies in a sample of Chilean students

In the last few years, one of the lines of research of great interest in the field of emotional intelligence (EI) has been the analysis of the role of emotions in the educational context and, in particular, their influence on learning strategies. The aims of this study are to identify the existence of different EI profiles and to determine possible statistically significant differences in learning strategies between the obtained profiles. The study involved 1253 Chilean school students from 14 to 18 years (M = 15.10, SD = 1.30), who completed the Trait Meta-Mood Scale-24 (TMMS-24) and the Inventory of Learning and Study Strategies—High School version (LASSI-HS). Cluster analysis identified four EI profiles: a group of adolescents with a high EI profile, a group with predominance of low emotional attention and high repair skills, a group with high scores on attention and low scores on clarity and repair, and a final group of adolescents with low EI. Also, students in groups with high overall scores in EI and low attention and high repair emotional obtained higher scores on the different learning strategies; however, the effect size analysis showed that these differences had no empirical relevance.

Language learning strategies

Fil: Jalo, Marcela Lilian. Universidad Nacional de La Plata. Facultad de Humanidades y Ciencias de la Educación; Argentina.

Improving learning in undergraduate control engineering courses using context-based learning models

Control Engineering is an essential part of university electrical engineering education. Normally, a control course requires considerable mathematical as well as engineering knowledge and is consequently regarded as a difficult course by many undergraduate students. From the academic point of view, how to help the students to improve their learning of the control engineering knowledge is therefore an important task which requires careful planning and innovative teaching methods. Traditionally, the didactic teaching approach has been used to teach the students the concepts needed to solve control problems. This approach is commonly adopted in many mathematics intensive courses; however it generally lacks reflection from the students to improve their learning. This paper addresses the practice of action learning and context-based learning models in teaching university control courses. This context-based approach has been practised in teaching several control engineering courses in a university with promising results, particularly in view of student learning performances.

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