Simulating microbial degradation of organic matter in a simple porous system using the 3-D diffusion-based model MOSAIC

This paper deals with the simulation of microbial degradation of organic matter in soil within the pore space at a microscopic scale. Pore space was analysed with micro-computed tomography and described using a sphere network coming from a geometrical modelling algorithm. The biological model was improved regarding previous work in order to include the transformation of dissolved organic compounds and diffusion processes. We tested our model using experimental results of a simple substrate decomposition experiment (fructose) within a simple medium (sand) in the presence of different bacterial strains. Separate incubations were carried out in microcosms using five different bacterial communities at two different water potentials of −10 and −100 cm of water. We calibrated the biological parameters by means of experimental data obtained at high water content, and we tested the model without changing any parameters at low water content. Same as for the experimental data, our simulation results showed that the decrease in water content caused a decrease of mineralization rate. The model was able to simulate the decrease of connectivity between substrate and microorganism due the decrease of water content

Building high-level features using large scale unsupervised learning

We consider the problem of building high-level, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images? To answer this, we train a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization on a large dataset of images (the model has 1 billion connections, the dataset has 10 million 200x200 pixel images downloaded from the Internet). We train this network using model parallelism and asynchronous SGD on a cluster with 1,000 machines (16,000 cores) for three days. Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not. Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation. We also find that the same network is sensitive to other high-level concepts such as cat faces and human bodies. Starting with these learned features, we trained our network to obtain 15.8% accuracy in recognizing 20,000 object categories from ImageNet, a leap of 70% relative improvement over the previous state-of-the-art

Situated Learning with Bebras Tasklets

A Bebras short task, a tasklet, is designed to provide a source for exploring a computational thinking concept: at the end of the contest it could be used as a starting point to delve deeper into a computing topic. In this paper we report an experience which aims at taking full advantage of the potential of Bebras tasklets. A math teacher asked her pupils to act as Bebras \u201ctrainers\u201d for younger mates. The pupils, in pairs, were assigned to design and prepare a tangible game inspired by a Bebras tasklet, devised for the younger pupils to practice. They also had to explain the game to the younger pupils, make them play and support them in solving it. In carrying out this assignment the pupils acting as trainers had to deeply explore the Bebras tasklet and face its computational thinking challenge, and also practiced soft skills as collaborating with peers towards a common goal, adapting language and communicative style to engage with younger mates, devising and designing a tangible object, and planning its creation. The experience proved that using Bebras tasklets as the social and cultural context for situated learning of computational thinking competencies is indeed quite productive

Informatica e pensiero computazionale : una proposta costruttivista per gli insegnanti

L\u2019articolo presenta una proposta formativa che ha per tema la didattica dell\u2019informatica con approccio socio-costruttivista. Tale proposta nasce dall\u2019esperienza sviluppata negli ultimi anni progettando e realizzando workshop nelle scuole, e si basa sull\u2019uso di strategie e strumenti costruttivisti per sviluppare il pensiero computazionale e far scoprire l\u2019informatica come disciplina scientifica. Illustriamo gli obiettivi formativi, i contenuti, la metodologia e le attivit\ue0 proposte e descriviamo gli esiti dello svolgimento di due momenti formativi realizzati secondo questa impostazione: un corso rivolto a studenti di laurea magistrale in informatica interessati all\u2019insegnamento e laboratori per insegnanti senza una specifica formazione informatic

Assessing How Pre-requisite Skills Affect Learning of Advanced Concepts

Students often struggle with advanced computing courses, and comparatively few studies have looked into the reasons for this. It seems that learners do not master the most basic concepts, or forget them between courses. If so, remedial practice could improve learning, but instructors rightly will not use scarce time for this without strong evidence. Based on personal observation, program tracing seems to be an important pre-requisite skill, but there is yet little research that provides evidence for this observation. To investigate this, our group will create theory-based assessments on how tracing knowledge affects learning of advanced topics, such as data structures, algorithms, and concurrency. This working group will identify relevant concepts in advanced courses, then conceptually analyze their pre-requisites and where an imagined student with some tracing difficulties would encounter barriers. The group will use this theory to create instructor-usable assessments for advanced topics that also identify issues caused by poor pre-requisite knowledge. These assessments may then be used at the start and end of advanced courses to evaluate to what extent students\u2019 difficulties with the advanced course originate from poor pre-requisite knowledge

La formazione degli insegnanti della classe 42/A – Informatica: l'esperienza dell'Università degli Studi di Milano

In Italia la formazione universitaria all'insegnamento nella scuola se- condaria ha una tradizione piuttosto recente: alle scuole di specializ- zazione attive negli anni 1999-2008 dovrebbero \u2013 secondo quanto pre- visto dal Decreto Ministeriale del 10 settembre 2010, n. 249 \u2013 sostituirsi lauree magistrali innestate sulla corrispondente formazione discipli- nare triennale; nel transitorio sono stati attivati corsi annuali riservati a laureati di secondo livello selezionati tramite esami (Tirocinio forma- tivo attivo, TFA) o titolari di un'esperienza professionale di insegna- mento di almeno 3 anni (Percorsi abilitanti speciali, PAS). Questo ca- pitolo descrive l'esperienza dell'Universit\ue0 degli Studi di Milano nel progettare e gestire i corsi 42/A TFA e PAS, nel triennio 2012-2015

How Challenging are Bebras Tasks? : An IRT Analysis Based on the Performance of Italian Students

This paper analyses the results of the 2014 edition of the Italian Bebras/Kangourou contest, exploiting the Item Response Theory statistical methodology in order to infer the difficulty of each of the proposed tasks starting from the scores attained by the participants. Such kind of analysis, enabling the organizers of the contest to check whether or not the difficulty perceived by pupils was substantially different from that estimated by those who proposed the tasks, is important as a feedback in order to gain knowledge to be used both in ranking participants and in organizing future editions of the contest. We show how the proposed analysis essentially highlights that the 63% of tasks was perceived at the same level of difficulty estimated by those who proposed them, but a 37% of tasks were either easier or more difficult than expected