Technical and Didactical Scenarios of Student-centered Teaching and Learning

In this paper we envision didactical concepts for university education based on self-responsible and project-based learning and outline principles of adequate technical support. We use the scenario technique describing how a fictive student named Anna organizes her studies of informatics at a fictive university from the first days of her studies to make a career for herself.(DIPF/Orig.)

Self-regulated learning in computer programming : strategies students adopted during an assignment

The SimProgramming teaching approach has the goal to help students overcome their learning difficulties in the transition from entry-level to advanced computer programming and prepare them for real-world labour environments, adopting learning strategies. It immerses learners in a businesslike learning environment, where students develop a problem-based learning activity with a specific set of tasks, one of which is filling weekly individual forms. We conducted thematic analysis of 401 weekly forms, to identify the students’ strategies for self-regulation of learning during assignment. The students are adopting different strategies in each phase of the approach. The early phases are devoted to organization and planning, later phases focus on applying theoretical knowledge and hands-on programming. Based on the results, we recommend the development of educational practices to help students conduct self-reflection of their performance during tasks.

Self-regulated learning in higher education : strategies adopted by computer programming students

Trabalho apresentado em PAEE/ALE’2016, 8th International Symposium on Project Approaches in Engineering Education (PAEE) and 14th Active Learning in Engineering Education Workshop (ALE)

An ESL/EFL Computer Assisted Language Instruction (CALIS) Micro-Teaching Lesson on Coherence Through Sentences When Writing Compositions in the Foreign Language Classroom

[Sin resumen]

Investigating the effects of cognitive apprenticeship-based instructional coaching on science teaching efficacy beliefs

The overall purpose of this collected papers dissertation was to examine the utility of a cognitive apprenticeship-based instructional coaching (CAIC) model for improving the science teaching efficacy beliefs (STEB) of preservice and inservice elementary teachers. Many of these teachers perceive science as a difficult subject and feel inadequately prepared to teach it. However, teacher efficacy beliefs have been noted as the strongest indicator of teacher quality, the variable most highly correlated with student achievement outcomes. The literature is scarce on strong, evidence-based theoretical models for improving STEB.^ This dissertation is comprised of two studies. STUDY #1 was a sequential explanatory mixed-methods study investigating the impact of a reformed CAIC elementary science methods course on the STEB of 26 preservice teachers. Data were collected using the Science Teaching Efficacy Belief Instrument (STEBI-B) and from six post-course interviews. A statistically significant increase in STEB was observed in the quantitative strand. The qualitative data suggested that the preservice teachers perceived all of the CAIC methods as influential, but the significance of each method depended on their unique needs and abilities. ^ STUDY #2 was a participatory action research case study exploring the utility of a CAIC professional development program for improving the STEB of five Bahamian inservice teachers and their competency in implementing an inquiry-based curriculum. Data were collected from pre- and post-interviews and two focus group interviews. Overall, the inservice teachers perceived the intervention as highly effective. The scaffolding and coaching were the CAIC methods portrayed as most influential in developing their STEB, highlighting the importance of interpersonal relationship aspects in successful instructional coaching programs. The teachers also described the CAIC approach as integral in supporting their learning to implement the new inquiry-based curriculum. ^ The overall findings hold important implications for science education reform, including its potential to influence how preservice teacher training and inservice teacher professional development in science are perceived and implemented. Additionally, given the noteworthy results obtained over the relatively short durations, CAIC interventions may also provide an effective means of achieving improvements in preservice and inservice teachers’ STEB more expeditiously than traditional approaches.^

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support Vector Machines (SVMs) are widely used classifiers for detecting physiological patterns in Human-Computer Interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the application of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables, and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

Computer and laboratory simulation in the teaching of neonatal nursing: innovation and impact on learning

Objectives: to evaluate the cognitive learning of nursing students in neonatal clinical evaluation from a blended course with the use of computer and laboratory simulation; to compare the cognitive learning of students in a control and experimental group testing the laboratory simulation; and to assess the extracurricular blended course offered on the clinical assessment of preterm infants, according to the students. Method: a quasi-experimental study with 14 Portuguese students, containing pretest, midterm test and post-test. The technologies offered in the course were serious game e-Baby, instructional software of semiology and semiotechnique, and laboratory simulation. Data collection tools developed for this study were used for the course evaluation and characterization of the students. Nonparametric statistics were used: Mann-Whitney and Wilcoxon. Results: the use of validated digital technologies and laboratory simulation demonstrated a statistically significant difference (p = 0.001) in the learning of the participants. The course was evaluated as very satisfactory for them. The laboratory simulation alone did not represent a significant difference in the learning. Conclusions: the cognitive learning of participants increased significantly. The use of technology can be partly responsible for the course success, showing it to be an important teaching tool for innovation and motivation of learning in healthcare.

Healthcare Computer Reasoning Addressing Chronically Ill Societies Using IoT: Deep Learning AI to the Rescue of Home-Based Healthcare

The authors present a proposal to develop intelligent assisted living environments for home based healthcare. These environments unite the chronical patient clinical history sematic representation with the ability of monitoring the living conditions and events recurring to a fully managed Semantic Web of Things (SWoT). Several levels of acquired knowledge and the case based reasoning that is possible by knowledge representation of the health-disease history and acquisition of the scientific evidence will deliver, through various voice based natural interfaces, the adequate support systems for disease auto management but prominently by activating the less differentiated caregiver for any specific need. With these capabilities at hand, home based healthcare providing becomes a viable possibility reducing the institutionalization needs. The resulting integrated healthcare framework will provide significant savings while improving the generality of health and satisfaction indicators.

Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.

Iis--integrated Interactome System: A Web-based Platform For The Annotation, Analysis And Visualization Of Protein-metabolite-gene-drug Interactions By Integrating A Variety Of Data Sources And Tools.

High-throughput screening of physical, genetic and chemical-genetic interactions brings important perspectives in the Systems Biology field, as the analysis of these interactions provides new insights into protein/gene function, cellular metabolic variations and the validation of therapeutic targets and drug design. However, such analysis depends on a pipeline connecting different tools that can automatically integrate data from diverse sources and result in a more comprehensive dataset that can be properly interpreted. We describe here the Integrated Interactome System (IIS), an integrative platform with a web-based interface for the annotation, analysis and visualization of the interaction profiles of proteins/genes, metabolites and drugs of interest. IIS works in four connected modules: (i) Submission module, which receives raw data derived from Sanger sequencing (e.g. two-hybrid system); (ii) Search module, which enables the user to search for the processed reads to be assembled into contigs/singlets, or for lists of proteins/genes, metabolites and drugs of interest, and add them to the project; (iii) Annotation module, which assigns annotations from several databases for the contigs/singlets or lists of proteins/genes, generating tables with automatic annotation that can be manually curated; and (iv) Interactome module, which maps the contigs/singlets or the uploaded lists to entries in our integrated database, building networks that gather novel identified interactions, protein and metabolite expression/concentration levels, subcellular localization and computed topological metrics, GO biological processes and KEGG pathways enrichment. This module generates a XGMML file that can be imported into Cytoscape or be visualized directly on the web. We have developed IIS by the integration of diverse databases following the need of appropriate tools for a systematic analysis of physical, genetic and chemical-genetic interactions. IIS was validated with yeast two-hybrid, proteomics and metabolomics datasets, but it is also extendable to other datasets. IIS is freely available online at: http://www.lge.ibi.unicamp.br/lnbio/IIS/.

A Brazilian Original Pedagogical Approach To The Teaching Of Neurology.

The two-arm Clinical Decisions/Diagnostic Workshop (CD/DW) approach to undergraduate medical education has been successfully used in Brazil. Present the CD/DW approach to the teaching of stroke, with the results of its pre-experimental application and of a comparative study with the traditional lecture-case discussion approach. Application of two questionnaires (opinion and Knowledge-Attitudes-Perceptions-KAP) to investigate the non-inferiority of the CD/DW approach. The method was well accepted by teachers and students alike, the main drawback being the necessarily long time for its completion by the students, a feature that may better cater for different educational needs. The comparative test showed the CD/DW approach to lead to slightly higher cognitive acquisition as opposed to the traditional method, clearly showing its non-inferiority status. The CD/DW approach seems to be another option for teaching neurology in undergraduate medical education, with the bonus of respecting each learner`s time.

Neural networks and statistical analysis for classification of corneal videokeratography maps based on Zernike coefficients: a quantitative comparison

PURPOSE: The main goal of this study was to develop and compare two different techniques for classification of specific types of corneal shapes when Zernike coefficients are used as inputs. A feed-forward artificial Neural Network (NN) and discriminant analysis (DA) techniques were used. METHODS: The inputs both for the NN and DA were the first 15 standard Zernike coefficients for 80 previously classified corneal elevation data files from an Eyesys System 2000 Videokeratograph (VK), installed at the Departamento de Oftalmologia of the Escola Paulista de Medicina, São Paulo. The NN had 5 output neurons which were associated with 5 typical corneal shapes: keratoconus, with-the-rule astigmatism, against-the-rule astigmatism, "regular" or "normal" shape and post-PRK. RESULTS: The NN and DA responses were statistically analyzed in terms of precision ([true positive+true negative]/total number of cases). Mean overall results for all cases for the NN and DA techniques were, respectively, 94% and 84.8%. CONCLUSION: Although we used a relatively small database, results obtained in the present study indicate that Zernike polynomials as descriptors of corneal shape may be a reliable parameter as input data for diagnostic automation of VK maps, using either NN or DA.

Gender identification of five genera of stingless bees (Apidae, Meliponini) based on wing morphology

Currently, the identification of pollinators is a critical necessity of conservation programs. After it was found that features extracted from patterns of wing venation are sufficient to discriminate among insect species, various studies have focused on this structure. We examined wing venation patterns of males and workers of five stingless bee species in order to determine if there are differences between sexes and if these differences are greater within than between species. Geometric morphometric analyses were made of the forewings of males and workers of Nannotrigona testaceicornis, Melipona quadrifasciata, Frieseomelitta varia, and Scaptotrigona aff. depilis and Plebeia remota. The patterns of males and workers from the same species were more similar than the patterns of individuals of the same sex from different species, and the patterns of both males and workers, when analyzed alone, were sufficiently different to distinguish among these five species. This demonstrates that we can use this kind of analysis for the identification of stingless bee species and that the sex of the individual does not impede identification. Computer-assisted morphometric analysis of bee wing images can be a useful tool for biodiversity studies and conservation programs.