An electronic record system in nursing education: evaluation and utilization

The main objective of the present work is to analyze the results of the utilization and evaluation of the LORETO Record System (LRS), providing improvement areas in the teaching-learning process and technology, in second year nursing students. A descriptive, prospective, cross sectional study using inferential statics has been carried out on all electronic records reported by 55 nursing students during clinical internships (April 1º-June 26º, 2013). Electronic record average rated 7.22 points (s=0.6; CV=0.083), with differences based on the clinical practice units (p<0,05). Three items assessed did not exceed the quality threshold set at 0.7 (p<0.05). Record Rate exceeds the quality threshold set at 80% for the overall sample, with differences based on the practice units. Only two clinical practice units rated above the minimum threshold (p <0.05). Record of care provision every 3 days did not reach the estimated quality threshold (p <0.05). There is a dichotomy between qualitative and quantitative results of LRS. Improvement areas in theoretical education have been identified. The LRS seems an appropriate learning and assessment tool, although the development of a new APP version and the application of principles of gamification should be explored.

Factors that determine the level of commitment of our teaching staff when the implementation of continuous evaluation system takes place

Comunicación presentada en CIDUI 2010, Congreso Internacional Docencia Universitaria e Innovación, Barcelona, 30 junio-2 julio 2010.

The Optimality of Decision Making During Motor Learning

In our daily lives, we often must predict how well we are going to perform in the future based on an evaluation of our current performance and an assessment of how much we will improve with practice. Such predictions can be used to decide whether to invest our time and energy in learning and, if we opt to invest, what rewards we may gain. This thesis investigated whether people are capable of tracking their own learning (i.e. current and future motor ability) and exploiting that information to make decisions related to task reward. In experiment one, participants performed a target aiming task under a visuomotor rotation such that they initially missed the target but gradually improved. After briefly practicing the task, they were asked to select rewards for hits and misses applied to subsequent performance in the task, where selecting a higher reward for hits came at a cost of receiving a lower reward for misses. We found that participants made decisions that were in the direction of optimal and therefore demonstrated knowledge of future task performance. In experiment two, participants learned a novel target aiming task in which they were rewarded for target hits. Every five trials, they could choose a target size which varied inversely with reward value. Although participants’ decisions deviated from optimal, a model suggested that they took into account both past performance, and predicted future performance, when making their decisions. Together, these experiments suggest that people are capable of tracking their own learning and using that information to make sensible decisions related to reward maximization.

Embracing english as a lingua franca : learning from portuguese users of english in higher education

Tese de doutoramento, Linguística (Linguística Aplicada), Universidade de Lisboa, Faculdade de Letras, 2016

Learning representations for Information Retrieval

La recherche d'informations s'intéresse, entre autres, à répondre à des questions comme: est-ce qu'un document est pertinent à une requête ? Est-ce que deux requêtes ou deux documents sont similaires ? Comment la similarité entre deux requêtes ou documents peut être utilisée pour améliorer l'estimation de la pertinence ? Pour donner réponse à ces questions, il est nécessaire d'associer chaque document et requête à des représentations interprétables par ordinateur. Une fois ces représentations estimées, la similarité peut correspondre, par exemple, à une distance ou une divergence qui opère dans l'espace de représentation. On admet généralement que la qualité d'une représentation a un impact direct sur l'erreur d'estimation par rapport à la vraie pertinence, jugée par un humain. Estimer de bonnes représentations des documents et des requêtes a longtemps été un problème central de la recherche d'informations. Le but de cette thèse est de proposer des nouvelles méthodes pour estimer les représentations des documents et des requêtes, la relation de pertinence entre eux et ainsi modestement avancer l'état de l'art du domaine. Nous présentons quatre articles publiés dans des conférences internationales et un article publié dans un forum d'évaluation. Les deux premiers articles concernent des méthodes qui créent l'espace de représentation selon une connaissance à priori sur les caractéristiques qui sont importantes pour la tâche à accomplir. Ceux-ci nous amènent à présenter un nouveau modèle de recherche d'informations qui diffère des modèles existants sur le plan théorique et de l'efficacité expérimentale. Les deux derniers articles marquent un changement fondamental dans l'approche de construction des représentations. Ils bénéficient notamment de l'intérêt de recherche dont les techniques d'apprentissage profond par réseaux de neurones, ou deep learning, ont fait récemment l'objet. Ces modèles d'apprentissage élicitent automatiquement les caractéristiques importantes pour la tâche demandée à partir d'une quantité importante de données. Nous nous intéressons à la modélisation des relations sémantiques entre documents et requêtes ainsi qu'entre deux ou plusieurs requêtes. Ces derniers articles marquent les premières applications de l'apprentissage de représentations par réseaux de neurones à la recherche d'informations. Les modèles proposés ont aussi produit une performance améliorée sur des collections de test standard. Nos travaux nous mènent à la conclusion générale suivante: la performance en recherche d'informations pourrait drastiquement être améliorée en se basant sur les approches d'apprentissage de représentations.

Learning representations for Information Retrieval

La recherche d'informations s'intéresse, entre autres, à répondre à des questions comme: est-ce qu'un document est pertinent à une requête ? Est-ce que deux requêtes ou deux documents sont similaires ? Comment la similarité entre deux requêtes ou documents peut être utilisée pour améliorer l'estimation de la pertinence ? Pour donner réponse à ces questions, il est nécessaire d'associer chaque document et requête à des représentations interprétables par ordinateur. Une fois ces représentations estimées, la similarité peut correspondre, par exemple, à une distance ou une divergence qui opère dans l'espace de représentation. On admet généralement que la qualité d'une représentation a un impact direct sur l'erreur d'estimation par rapport à la vraie pertinence, jugée par un humain. Estimer de bonnes représentations des documents et des requêtes a longtemps été un problème central de la recherche d'informations. Le but de cette thèse est de proposer des nouvelles méthodes pour estimer les représentations des documents et des requêtes, la relation de pertinence entre eux et ainsi modestement avancer l'état de l'art du domaine. Nous présentons quatre articles publiés dans des conférences internationales et un article publié dans un forum d'évaluation. Les deux premiers articles concernent des méthodes qui créent l'espace de représentation selon une connaissance à priori sur les caractéristiques qui sont importantes pour la tâche à accomplir. Ceux-ci nous amènent à présenter un nouveau modèle de recherche d'informations qui diffère des modèles existants sur le plan théorique et de l'efficacité expérimentale. Les deux derniers articles marquent un changement fondamental dans l'approche de construction des représentations. Ils bénéficient notamment de l'intérêt de recherche dont les techniques d'apprentissage profond par réseaux de neurones, ou deep learning, ont fait récemment l'objet. Ces modèles d'apprentissage élicitent automatiquement les caractéristiques importantes pour la tâche demandée à partir d'une quantité importante de données. Nous nous intéressons à la modélisation des relations sémantiques entre documents et requêtes ainsi qu'entre deux ou plusieurs requêtes. Ces derniers articles marquent les premières applications de l'apprentissage de représentations par réseaux de neurones à la recherche d'informations. Les modèles proposés ont aussi produit une performance améliorée sur des collections de test standard. Nos travaux nous mènent à la conclusion générale suivante: la performance en recherche d'informations pourrait drastiquement être améliorée en se basant sur les approches d'apprentissage de représentations.

Monitoring & evaluation of clinical services : meeting the JCAHO challenge.

"Department of Veterans Affairs"--Cover.

Partnering with Parents to Promote High School Graduation: An Evidence-based Program Evaluation

Thesis (Master's)--University of Washington, 2016-06

Toward Never-Ending Object Learning for Robots

Thesis (Ph.D.)--University of Washington, 2016-06

Teamwork in Health and Rehabilitation Sciences: Evaluation of a multiprofessional workshop

This paper outlines a multiprofessional education workshop piloted and subsequently conducted with a cohort of 81 graduate entry students of occupational therapy, physiotherapy, speech pathology and audiology. The rationale for, and format of, the workshop is outlined, followed by comparisons between students' knowledge about teamwork prior to and after the four-hour workshop. The workshop was based on a real case scenario of a child with Developmental Coordination Disorder (DCD). Students completed pre- and post-workshop questionnaires about their knowledge of DCD, teamwork and the roles of various professionals and parents; and a post-workshop questionnaire about their views regarding the utility of the workshop, its strengths, and learning outcomes. The evaluation indicated that the workshop was overwhelmingly successful from the students' perspective in: (1) enhancing their understanding about DCD and its multifaceted impact on school age children; (2) developing a deeper appreciation of the importance of teamwork itself; (3) refining their understanding of their own profession's role and (4) developing an appreciation of the role of other professions and parents in working with children with complex needs, and their families. Limitations of this study and directions for future research are discussed.

A longitudinal study of learning for a group of indigenous Australian university students: Dissonant conceptions and strategies

Conceptions of learning and strategies used by 15 indigenous students in three Australian universities were studied longitudinally over three years. Their academic achievements were good, but at a high cost in terms of time and effort. In spite of the fact that almost half of the students expressed higher-order (qualitative) conceptions of learning in the first year and more in the second and third years, all of the students reported using highly repetitive strategies to learn. That is, they did not vary their way of learning, reading or writing in the beginning of their studies and less than half of them did so at the end of the three years. It is argued that encountering variation in ways of learning is a prerequisite for the development of powerful ways of learning and studying.

Learning difficulties in numeracy in Australia

In this article, we provide an understanding of the term numeracy as it is used in Australia and a description of numeracy education in this country. In particular, we discuss the role of outcomes-based curriculum frameworks and outline the dominant teaching approaches. The focus is on students with learning difficulties and how they are identified and supported in schools. We create two vignettes based on real students with difficulties in numeracy, which highlight two of the most common problems. We report on the prevalence of learning difficulties in numeracy in Australia and describe some of the initiatives related to the assessment and enhancement of learning in numeracy that are being undertaken in various states in Australia. Finally, we identify some of the future challenges facing the Australian education system in this area. These challenges relate to teacher knowledge, mandated assessment, and the role of parents. We conclude the article by calling for the evaluation of current assessment and intervention initiatives as well as the development of a national program to support the goal of numeracy for all students.

Factors affecting student attitudes toward flexible online learning in management education

In response to recent technological advances and the trend toward flexible learning in education, the authors examined the factors affecting student satisfaction with flexible online learning. The authors identified 2 key student attributes of student satisfaction: (a) positive perceptions of technology in terms of ease of access and use of online flexible learning material and (b) autonomous and innovative learning styles. The authors derived measures of perceptions of technology from research on the Technology Acceptance Model and used locus of control and innovative attitude as indicators of an autonomous and innovative learning mode. First-year students undertaking an introductory management course completed surveys at the beginning (n = 248) and at the end (n = 256) of course work. The authors analyzed the data by using structural equation modeling. Results suggest that student satisfaction is influenced by positive perceptions toward technology and an autonomous learning mode.

Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping

The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in an unsupervised manner. It employs two stages of co-occurrence information extraction-semantic and relational-using a different algorithm for each stage. The algorithms used are statistical, but they employ nonlinear dynamics and machine learning. This article is an attempt to validate the output of Leximancer, using a set of evaluation criteria taken from content analysis that are appropriate for knowledge discovery tasks.

The rapid screen of concussion: An evaluation of the non-word repetition test for use in mTBI research

Primary objectives: (1) To investigate the Nonword Repetition test (NWR) as an index of sub-vocal rehearsal deficits after mild traumatic brain injury (mTBI); (2) to assess the reliability, validity and sensitivity of the NWR; and (3) to compare the NWR to more sensitive tests of verbal memory. Research design: An independent groups design. Methods and procedures: Study 1 administered the NWR to 46 mTBI and 61 uninjured controls with the Rapid Screen of Concussion (RSC). Study 2 compared mTBI, orthopaedic and uninjured participants on the NWR and the Hopkins Verbal Learning Test (HVLT-R). Main outcomes and results: The NWR did not improve the diagnostic accuracy of the RSC. However, it is reliable and indexes sub-vocal rehearsal speed. These findings provide evidence that although the current form of the NWR lacks sensitivity to the impact of mTBI, the development of a more sensitive test of sub-vocal rehearsal deficits following mTBI is warranted.