130,558 research outputs found
La dimensione territoriale dello sviluppo tra competitività e sostenibilità
No full textIl capitolo presenta una rassegna della letteratura che offre importanti spunti di riflessione sul ruolo strategico che sta assumendo la dimensione territoriale dello sviluppo nell’attuale contesto di competizione globale. Inoltre, evidenzia come i mutamenti dell’economia mondiale hanno modificato anche le fonti del vantaggio competitivo e intensificato la complessità delle relazioni che caratterizzano un territorio. Nella prospettiva delineata, i sistemi territoriali sono al centro della competitizione globale e si trovano a dover fronteggiare la sfida di uno sviluppo competitivo ma sostenibile
Competitività territoriale sostenibile e agroalimentare: l'approccio alla "qualità di sistema"
No full textIl capitolo, alla luce della centralità che stanno assumendo i sistemi territoriali nel contesto della globalizzazione, si interroga su quello che può essere il contributo del settore agro-alimentare ad una strategia di sviluppo competitivo sostenibile. Pertanto, il capitolo fornisce la chiave di lettura di tutto il volume, poiché introduce e spiega le variabili “peculiari”, che consentono di puntare ad una competitività “sostenibile” di un territorio. Inoltre, dopo aver analizzato lo scenario di riferimento e le possibili opzioni strategiche, propone come possibile strategia di sviluppo dell’intero comparto agroali-mentare un approccio orientato alla “qualità di sistema” e ne illustra le determinanti che ne consentono la realizzazione
Correction to: Implementation and Assessment Methodologies of Teachers’ Training Courses for STEM Activities (Technology, Knowledge and Learning, (2019), 24, 2, (247-268), 10.1007/s10758-018-9356-1)
No full textThe article listed above was initially published with incorrect copyright information. Upon publication of this Correction, the copyright of this article has been changed to “The Author(s)”. The original article has been corrected
Machine learning for modelling and identification of educational robotics activities
No full textEducational Robotics (ER) is a powerful tool to help students learn school subjects, robotics, and developing cognitive skills and soft skills. Assessing the learning outcomes of ER activities requires the identification of the model that underly the process. Machine learning can be useful to identify such models and to interpret data. This paper aims to present a system that could help integrating Educational Data Mining and Learning Analytics techniques into the open-ended learning environment that characterizes the constructionist approach of ER. Both supervised and unsupervised learning methods could be applied to extract meaningful information. Students' approaches to learning as well as a prediction of their final performance could inform teachers' decision and facilitate the implementation of effective ER activities in formal and non-formal education. First results show good premises for a future broader implementation, but more research is needed to face all the open issues
Purification and characterization of heparin from the Italian clam Callista chione
No full textAn unusual heparin (approximately 1.9 mg/g of dry tissue) was isolated from the marine italian bivalve mollusk Callista chione. Agarose gel electrophoresis showed a high content of the fast-moving heparin component (85 ± 7.6%) and 15 ± 1.3% of the slow-moving species. An average molecular mass of 10 950 was calculated by PAGE analysis. The anticoagulant properties were measured as APTT (97 ± 12.1 IU/ mg) and anti-Xa activity (52 ± 7.4 IU/mg). Structural analysis of clam heparin, performed by depolymerizing heparin samples with heparinase (EC 4.2.2.7) and then separating the resulting unsaturated oligosaccharides by SAX-HPLC, revealed the presence of low amounts of the trisulfated disaccharide [&UDelta; UA2S(1→ 4)-α-D-GlcN2S6S] and a significant increase of the disaccharides bearing nonsulfated iduronic and glucuronic acids, [→ 4)-α-L-IdoA(1→ 4)-α-D-GIcNAc6S(1→] and [→ 4)-α-L-IdoA(1→ 4)-α-D-GlcN2S6S(1→], and [→ 4)-β-D-GlcA(1→ 4)-α-D-GlcN2S6S(1→]. As a consequence, Callista chione heparin is a low-sulfated polysaccharide showing a specific decrease of the sulfatation in position 2 of the uronic acid units
Implementation and Assessment Methodologies of Teachers’ Training Courses for STEM Activities
No full textEducational Robotics is rapidly gaining attention as an effective methodology to develop skills and engage students preserving their peculiar style of learning. It is often tied together with two other methodologies, Coding and Tinkering, characterized by a similar hands-on approach. In order to fully exploit their inclusive features, teachers need to be prepared to introduce them into classroom. It is often noticed that in service teachers are not yet fully prepared to face this challenge. Many actions have been established to recover this situation, but a proper method for assessing whether these actions are successful or not is not yet developed. This paper presents a methodology for introducing in-service teachers to Educational Robotics, Coding and Tinkering and for assessing the outcomes. 184 in-service teachers were assessed and results analysed. Final considerations draw a picture of the situation amongst the sample chosen for the present study, observing that the intervention seemed to be successful in providing key notions and examples, and improving teachers’ self-confidence
Identification and Assessment of Educational Experiences: Utilizing Data Mining with Robotics
No full textThis article describes an example of data mining techniques applied to an open educational environment. These novel assessment methods in the educational robotics (ER) field provide empirical evidence of problem-solving styles behind the key tasks of proposed activities within real operative scenarios. A supervised, mixed machine learning (ML) approach was applied to data from seven Italian secondary schools (197 students), and four ML techniques [logistic regression (LR), support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF)] were explored to predict students' success
Analysis of Educational Robotics Activities Using a Machine Learning Approach
No full textThis paper presents the preliminary results of using machine learning techniques to analyze educational robotics activities. An experiment was conducted with 197 secondary school students in Italy: the authors updated Lego Mindstorms EV3 programming blocks to record log files with coding sequences students had designed in teams. The activities were part of a preliminary robotics exercise. We used four machine learning techniques—logistic regression, support-vector machine (SVM), K-nearest neighbors and random forests—to predict the students’ performance, comparing a supervised approach (using twelve indicators extracted from the log files as input for the algorithms) and a mixed approach (applying a k-means algorithm to calculate the machine learning features). The results showed that the mixed approach with SVM outperformed the other techniques, and that three predominant learning styles emerged from the data mining analysis
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