The MOOC “AI and Imaging for Cancer” explores how artificial intelligence is revolutionizing medical imaging and precision medicine — from radiomics to pathomics — up to the clinical validation of models that bring innovation from the laboratory to the patient’s bedside.
The course is divided into 5 modules and aims to provide learners with an overview of the use of radiomics.
-
Introduction to AI
-
AI - Intro, definitions, turing test
-
Intelligent Systems - Expert, ML, DL
-
-
Radiomics
-
ARTIFICIAL INTELLIGENCE AND PRECISION MEDICINE
-
Radiomics - introduction
-
Clustering e classification models.
-
Interpretative use of AI
-
Usi non interpretativi AI
-
-
Deep learning and machine learning in medical imaging.
-
DL in medical imaging - explanation.
-
Example ML -> characterization of hepatic metastases in ComputedTomography (CT) images
-
Example ML -> characterization of response to chemotherapy of rectal cancer on MR and PET images
-
Example DL -> Segmentation of colorectal cancer on MR images
-
Example DL -> Segmentation of liver metastases on CT images
-
-
Pathomics
-
What pathomics is.
-
AI-Driven Pathomics for Predicting Chemotherapy Response in Metastatic Colorectal Cancer
-
-
Model assessment and clinical validation.
-
From bench to bedside
-
Steps to clinical validation
-
-
The student will be able to define artificial intelligence, explain the Turing test, and recognize the limitations of traditional definitions in the context of the historical evolution of intelligent systems.
-
The student will be able to compare expert systems, machine learning, and deep learning, discussing their features and areas of application in the context of radiology.
-
The student will be able to describe the radiomics workflow and identify the main oncological applications in the context of precision medicine.
-
The student will be able to explain how AI integrates clinical, radiomic, and genomic data to develop personalized predictive models in the context of precision oncology therapy.
-
The student will be able to apply clustering and classification techniques to distinguish pathological patterns in the context of medical image analysis and complex datasets.
-
The student will be able to distinguish between interpretative and non-interpretative applications of AI, assessing their advantages and limitations in the context of clinical practice and ethical implications.
-
The student will be able to describe the differences between ML and DL, explaining how they are applied to the processing and interpretation of medical images in the context of diagnostic imaging.
-
The student will be able to analyze ML applications for the characterization of hepatic metastases and the prediction of chemotherapy response in the context of oncological radiology.
-
The student will be able to evaluate DL applications for the automatic segmentation of colorectal tumors and hepatic metastases in the context of oncological treatment planning.
-
The student will be able to define pathomics, describe the extraction of features from digital histopathological images, and discuss their potential in the context of oncological diagnosis and prognosis.
-
The student will be able to analyze AI-based pathomics models for predicting chemotherapy response in the context of therapeutic personalization.
-
The student will be able to describe the steps of AI model validation, explain the concept of “from bench to bedside,” and discuss the challenges of clinical translation in the context of precision medicine.
At the end of each module the student will be able to try a self-evaluation test.
Interactive exercises and quizzes will be available to assess your learning progress and reinforce the knowledge you have acquired.
Basic knowledge of artificial intelligence and radiological techniques.
Valentina Giannini, Department of Oncology
Valentina Giannini, Department of Oncology
valentina.giannini@unito.it
edvancedeh@unito.it
We will see with simple examples on how to leverage a computational model of cancer to learn from experimental data by using Bayesian inference. By going through simple examples and step-by-step concepts we will build a machine learning algorithm from scratch.
The course is organized into 5 separate modules, each complete with an auto-evaluation test and teaching material, videos and complementary notes. The modules are:
-
Statement of the problem: why it is interesting to understand population dynamics in cancer
-
Galton-Watson branching processes for cell population models
-
Numerical simulations of a Galton Watson process in Python
-
A shallow and operative introduction to Bayesian inference
-
Putting it all together: inferring from cell population data
At the end of the learning process, students will be able to:
Knowledge and Understanding:
-
identify and describe elementary building blocks of machine learning through examples of a simple algorithm.
-
explain the role and relevance of computational models in ensuring the explainability of a process.
-
define Bayesian inference and stochastic branching processes, and explain how these can be integrated in a model.
Applying Knowledge and Understanding:
-
apply concepts to solve new problems by adapting examples from lectures and proposing small variations to presented solutions.
Making Judgements:
-
assess the level of transparency and interpretability of simple machine learning algorithms and justify the importance of explainable AI in scientific contexts.
Communication Skills:
-
present examples of simple algorithms and clearly describe their conceptual components in accessible language.
Learning Skills:
-
apply a step-by-step approach to break down and analyze the structure of a complex algorithm.
At the end of each module the student will be able to try a self-evaluation test, and to try the proposed additional exercises with solutions.
Within each module there will be submodules which are conceptually self-consistent and standing complete with additional resources, the slides used for learning and the written content in the form of a pdf booklet. Where possible there will be exercises either theoretical (for example a small calculation to perform) or computational (a script to write). Script used for teaching will also be available.
Basic calculus (integrals and derivatives in one or two variables), basic programming (suggested Python).
Alberto Puliafito
Per domande o problematiche tecniche relative al corso e alla piattaforma contattare:
edvancedeh@unito.it
Contatto del docente del MOOC:
alberto.puliafito@unito.it
This course explores the transformative role of Artificial Intelligence (AI) and robotic systems in modern surgical practice. Designed for healthcare students and professionals, it provides a multidisciplinary overview of surgical robotics, machine learning applications, and clinical decision support systems.
Through real-world case studies, and expert insights, learners will gain a critical understanding of how AI and robotics enhance precision, safety, and efficiency in surgical environments. Participants will also reflect on ethical, legal, and technical implications, and will be equipped to assess and apply innovative solutions in their professional contexts.
By the end of the course, learners will be able to analyze, evaluate, and simulate scenarios involving AI-augmented surgical interventions.
Module 1: Introduction to AI and Robotics in Healthcare
Goal: Provide foundational knowledge of AI and robotics as applied to the surgical field.
Subtopics:
- Overview of AI and Machine Learning in Medicine
- Key Terminologies and Concepts (ML, DL, Computer Vision, NLP)
- History and Evolution of Surgical Robotics
- Regulatory and Ethical Frameworks in AI and Robotics
Module 2: AI Technologies in Surgical Planning and Decision Support
Goal: Explore how AI enhances preoperative and intraoperative decision-making.
Subtopics:
- AI for Preoperative Diagnostics and Risk Assessment
- Predictive Modeling and Patient Stratification
- Surgical Navigation and Image-Guided Systems
- AI in Clinical Decision Support Systems (e.g., real-time alerts, complication prediction)
Module 3: Robotic Surgery Systems and Platforms
Goal: Understand the design, functionality, and applications of modern robotic surgical systems.
Subtopics:
- Overview of Robotic Platforms (e.g., da Vinci, Versius, Hugo)
- Haptics, Teleoperation, and Dexterity Enhancement
- Integration with Imaging and Navigation Tools
- Comparison of Robotic vs. Conventional Surgery
Module 4: Computer Vision and Augmented Reality in Surgery
Goal: Examine how visual computing technologies are transforming intraoperative practices
Subtopics:
- Image Segmentation and Organ Recognition
- Real-Time Surgical Video Analysis
- Augmented and Virtual Reality in the Operating Room
- AI-Driven Instrument Tracking and Scene Understanding
Module 5: Data, Learning, and Outcomes
Goal: Focus on how AI leverages surgical data for learning and continuous improvement.
Subtopics:
- Sources and Management of Surgical Data (EHRs, Video, IoT)
- Training AI with Surgical Videos: Annotation and Labeling
- Outcome Prediction and Performance Metrics
- Continuous Learning Systems and Adaptive AI
Module 6: Future Directions and Innovation in AI-Driven Surgery
Goal: Discuss trends, challenges, and emerging innovations in the field.
Subtopics:
- Autonomous Surgical Systems and AI Co-Pilots
- Human-AI Collaboration and Trust in the OR
- Emerging Trends: Nanorobotics, Smart Implants, and AI-Driven Biopsy
- Policy, Global Access, and the Future of Surgical Education
- Define key terminology related to artificial intelligence, machine learning, and robotic-assisted surgery
- Describe the core principles and functions of surgical robotic systems and AI-driven clinical decision support tools
- Explain how AI is integrated into surgical planning, navigation, and outcome prediction
- Interpret data from AI systems in simulated clinical scenarios.
- Apply basic principles of surgical robotics to identify appropriate use cases in different specialties
- Compare traditional and AI-assisted surgical workflows, identifying strengths and limitations of each
- Analyze real-world case studies where AI and robotics have impacted surgical outcomes
- Critically assess the ethical, legal, and clinical implications of adopting AI and robotics in surgery
- Evaluate the safety and reliability of AI tools in the context of patient care and surgical decision-making
- Design a hypothetical surgical scenario incorporating AI or robotic systems to address a specific clinical challenge
- Propose improvements or innovations in current AI-augmented surgical workflows
Passing the final quiz
No prerequisites
Sabrina DeCillis
Matteo Manfredi
Per domande o problematiche tecniche relative al corso e alla piattaforma contattare:
edvancedeh@unito.it
Contatto del docente del MOOC:
m.manfredi@unito.it
sabrinatitti.decillis@unito.it
This course offers a comprehensive overview to digital pathology and its integration with artificial intelligence tools, covering the full diagnostic pipeline from tissue preparation and immunohistochemistry to image analysis and clinical decision support. Participants will explore current technologies such as Whole Slide Imaging (WSI), antigen retrieval techniques, and multiplexed imaging platforms with more than 20 markers simultaneously, alongside the use of AI-based tools for automated quantification and diagnostic prediction. Through hands-on modules and case studies, the course highlights how digital workflows and machine learning are transforming pathology into a more precise, scalable, and data-driven discipline in both research and healthcare contexts.
The course is structured into six modules that provide a practical and up-to-date learning pathway on digital technologies applied to pathology, with a specific focus on the integration of digital pathology, immunohistochemistry, mass cytometry, and artificial intelligence for data analysis and interpretation in both diagnostic and research settings:
Module 1 – Tissue Preparation and Immunohistochemistry
Module 2 – Fundamentals of Digital Pathology
Module 3 – Multiplex Imaging and Mass Cytometry
Module 4 – Artificial Intelligence Tools and Image Analysis
Module 5 – Clinical Applications and Case Studies
Module 6 – Validation and Future Perspectives
Each module comprises multiple lessons, including demonstrative videos, in-depth reading materials, slides, and self-assessment quizzes. The course is designed to equip participants with practical skills that can be immediately applied in diagnostic, clinical, and research settings, with particular emphasis on the use of advanced digital tools and intelligent algorithms to support diagnostic processes.
Upon course completion, students will be able to:
- Describe and compare the workflows of traditional pathology and digital imaging pathology, highlighting their advantages, limitations, and differences in relation to traditional pathology. (Remember – Understand)
- Correctly set up histological and immunohistochemical preparation protocols, including fixation, embedding, sectioning, and staining, while assessing their impact on morphological and antigenic integrity. (Apply – Evaluate)
- Analyze and interpret the outcomes of antigen retrieval techniques (HIER and PIER), identifying critical variables such as pH, temperature, duration, and tissue type. (Analyze – Evaluate)
- Utilize digital tools for the management, visualization, and analysis of histological images, including software such as QuPath and VisioPharm, performing annotations, quantifications, and comparative analysis of tissue regions. (Apply – Analyze)
- Examine real clinical case studies and evaluate the integration of artificial intelligence tools in diagnostic support (e.g., tumor grading, biomarker identification), recognizing algorithmic biases and limitations. (Evaluate – Understand)
- Plan and design integrated workflows combining digital pathology, multiplex imaging, and AI-based tools, including clinical settings, with attention to reproducibility, validation, and regulatory compliance (e.g., CE, FDA). (Create – Evaluate – Apply)
- Critically discuss the role of emerging technologies in transforming diagnostic pathology, developing an informed and responsible perspective on the use of AI, automation, and digital infrastructures. (Evaluate – Create)
Passing a test (Quiz)
Basic knowledge of biology
Paola Cappello
Claudia Curcio
Per domande o problematiche tecniche relative al corso e alla piattaforma contattare:
edvancedeh@unito.it
Contatto del docente del MOOC:
paola.cappello@unito.it claudia.curcio@unito.it
The course provides both theoretical and practical foundations for the effective use of ChatGPT and other Large Language Models (LLMs) in scientific research, bioinformatics, and medical education.
Through thematic modules and hands-on activities, students will learn to:
-
understand how LLMs work;
-
design effective prompts;
-
use ChatGPT to generate, debug, and document code;
-
integrate LLMs into reproducible research workflows, from single-cell analysis to the creation of GitHub repositories.
The course is organized into five modules featuring video lectures, interactive examples, and three hands-on exercises.
Day 1 – Module 1: Understanding Large Language Models
What are LLMs and how do they work. Differences between general-purpose models (ChatGPT, Claude, Gemini) and code-oriented ones (CodeLlama, StarCoder). Reasoning limitations and potential applications in scientific and bioinformatics contexts.
Day 2 – Module 2: Prompt Engineering for Scientific Coding
How to structure effective prompts. The importance of context (OS, programming language, data format). Prompt examples for Bash, Python, and R in biomedical environments. Strategies to reduce hallucinations and obtain executable, reproducible code.
Day 3 – Module 3: Debugging with ChatGPT
Understanding different types of bugs (syntactic, logical, and silent). Using ChatGPT to identify and fix errors. Techniques to provide context (error traceback, print statements). Creating test datasets for code quality control.
Day 4 – Module 4: Advanced Use Cases and Documentation
Reverse engineering code with ChatGPT. Automated generation of README.md files and pipeline documentation. Using ChatGPT to explore libraries, tools, and packages in computational biology. Distinguishing between exploratory research (ChatGPT) and verified documentation (Google, PubMed).
Day 5 – Module 5: Practical Applications (Hands-on Exercises)
Exercise 1: Using ChatGPT to create a JupyterLab framework in Docker.
Day 6 – Module 5: Practical Applications (Hands-on Exercises)
Exercise 2: Supporting single-cell RNA-seq analysis in Drosophila with ChatGPT.
Day 7 – Module 5: Practical Applications (Hands-on Exercises)
Exercise 3: Creating an academic website (e.g., a research lab site) via GitHub Pages generated with ChatGPT.
At the end of the course, students will be able to:
- Describe how LLMs and ChatGPT work
- Design effective prompts for code generation
- Use ChatGPT to generate, debug, and document code
- Identify logical and syntactic errors through ChatGPT-assisted debugging
Multiple-choice quizzes at the end of each module.
Hands-on exercises in code generation and testing.
Guided analysis of real errors (debugging with ChatGPT).
Basic knowledge of Python or R.
Familiarity with bioinformatics analysis tools.
Basic understanding of Docker and GitHub is recommended but not required.
Alessandri’ Luca
Per domande o problematiche tecniche relative al corso e alla piattaforma contattare:
edvancedeh@unito.it
Contatto del docente del MOOC:
l.alessandri@unito.it
The course introduces the principles and practices of computational reproducibility applied to the life sciences. Through practical examples and guided activities, participants learn how to build reproducible environments, manage software and data versioning, and share their projects according to the FAIR principles (Findable, Accessible, Interoperable, Reusable).
Tools such as Docker, JupyterLab, and GitHub are used to develop traceable and shareable analysis pipelines, following an approach tailored to the daily practice of researchers.
The course is organized into five modules, each featuring video lectures, practical demonstrations, and interactive activities.
Day 1 – Module 1: Why Reproducibility Matters
Introduction to computational reproducibility; examples of non-replicability; differences between software versioning and environments. Concept of computational reproducibility and the impact of software versions on scientific results.
Day 2 – Module 2: Understanding and Using Docker
Differences between virtual machines and containers; concept of layers and images; manual construction of a Dockerfile and main commands (run, ps, exec, commit).
Day 3 – Module 3: JupyterLab in Docker
Creating an interactive analysis environment within a container; configuring R and Python in JupyterLab; managing shared volumes and saving modifications.
Day 4 – Module 4: Sharing and Publishing Research Outputs
Managing and publishing data on Zenodo and GEO; calculating MD5 checksums; using FTP/FileZilla for data transfer; principles of documentation and metadata annotation.
Day 5 – Module 5: Exercise 1 – Docker Commands
Recap of basic Docker commands.
Day 6 – Module 5: Exercise 2 – Building a Dockerfile
Explanation of Dockerfile structure and main instructions.
Day 7 – Module 5: Exercise 3 – Building a Dockerfile for Life Sciences
Introduction to applying Dockerfiles in the context of life sciences.
At the end of the course, students will be able to:
-
Describe the principles of computational reproducibility
-
Create and configure a reproducible analysis environment using Docker
-
Use JupyterLab within a container to integrate code and results
-
Publish data and metadata to open repositories such as Zenodo and GEO, in compliance with the FAIR principles.
Assessment quizzes at the end of each module.
-
Hands-on exercises on creating and using Docker containers.
-
Development of a Dockerfile for a bioinformatics analysis.
-
Uploading a dataset to Zenodo/GEO.
-
Managing and publishing a GitHub repository containing code and documentation.
-
Basic knowledge of bioinformatics or computational biology.
-
Basic familiarity with the command line (Linux/Mac/Windows).
Alessandri’ Luca
Per domande o problematiche tecniche relative al corso e alla piattaforma contattare:
edvancedeh@unito.it
Contatto del docente del MOOC:
l.alessandri@unito.it
In this MOOC, we’ll walk through the fundamental steps of single-cell RNA sequencing (scRNA-seq) analysis, using a set of tools specifically designed to ensure both computational and functional reproducibility.
We’ll start with a short introduction to scRNA-seq technologies, focusing on the strengths and limitations of the most widely used platforms. From there, we’ll move step by step through the data analysis workflow.
Along the way, I’ll share real-world examples so you can see firsthand how analyses are carried out in practice. A basic knowledge of R is helpful, but don’t worry, the functions we will use follow a consistent and intuitive structure.
This course is aimed at students, PhD candidates, and early-career researchers in the life sciences who want to make the leap from “I am scared to analyze data” to “I can do this!”.
I hope you will enjoy the journey and perhaps find these tools useful in your own research projects.
The course provides the foundations of data analysis generated using single-cell technologies.
The course is based on 7 modules and exercises:
-
Day 1 – Set up computational environment
-
Day 2 – Becoming acquainted the computational environment.
-
Day 3 – Module 1 Introduction to scRNA-seq, computational environment preparation and exercise
-
Day 4 - Module 2 QC for scRNA-seq data, genes annotation, cell filtering and exersise
-
Day 5 – Module 3 reducing the complexity of a scRNA-seq dataset and exercise
-
Day 6 – Module 4 clustering cells and exercise
-
Day 7 – Module 5 clusters specific gene marker detection sand exercise
-
Day 8 – Module 6 cell types annotation and exercise
-
Day 9 – Module 7 multiple datasets integration and exercise
At the end of the course, students will be able to
-
conduct research on matters relating to the genome focused on gene expression
-
independently analyze a single-cell RNA-seq experiment,
-
assess data quality,
-
understanding methods and statistics used to extract content from a dataset
-
gather, process and present quantitative data. Use the appropriate programs and methods for validating, organising and interpreting data
-
define the parameters required to generate well-resolved and homogeneous cell-type clusters.
Completing the requested exercises.
Listening the lessons, solving the requested exercises
Luca Alessandrì - abc computational reproducibility for life scientist
Luca Alessandrì - Efficient Utilization of ChatGPT for Code Generation in Medical and life Science Education
Raffaele Adolfo Calogero
Per domande o problematiche tecniche relative al corso e alla piattaforma contattare:
edvancedeh@unito.it
Contatto del docente del MOOC:
raffaele.calogero@unito.it
Questo corso propone un’introduzione alla storia della matematica in chiave transdisciplinare, attraverso l’esplorazione di archivi, fonti e strumenti digitali. Il corso analizza come le tecnologie digitali stiano trasformando l’accesso e la fruizione dei documenti storici (manoscritti, lettere, macchine matematiche, …) e delle opere antiche, rendendo la storia della matematica una disciplina sempre più aperta, inclusiva e integrata nel panorama della ricerca contemporanea.
Questo corso propone un’introduzione alla storia della matematica in chiave transdisciplinare, attraverso l’esplorazione di archivi, fonti e strumenti digitali. Il corso analizza come le tecnologie digitali stiano trasformando l’accesso e la fruizione dei documenti storici (manoscritti, lettere, macchine matematiche, …) e delle opere antiche, rendendo la storia della matematica una disciplina sempre più aperta, inclusiva e integrata nel panorama della ricerca contemporanea.
Il corso è organizzato in 6 moduli che presentano i principali strumenti digitali per la storia della matematica ed esplorano le potenzialità dell’adozione di una prospettiva transdisciplinare in questo ambito. I contenuti spaziano dalla digitalizzazione delle fonti e delle collezioni museali alla storia sociale e materiale della matematica, fino all’uso didattico delle fonti storiche e delle risorse digitali.
-
Modulo 1 – Opere matematiche e archivi digitali: accessibilità, conservazione e ricerca
-
Modulo 2 – Musei e collezioni matematiche virtuali
-
Modulo 3 – Visualizzare reti complesse di dati storici con strumenti digitali
-
Modulo 4 - Documenti, dati e memoria: il contributo della storia sociale della matematica
-
Modulo 5 – Oggetti che “contano”: fonti materiali per la storia della matematica
-
Modulo 6 – Storia della matematica per l’insegnamento: dalla ricerca alla pratica d’aula.
Al termine del corso, lo studente sarà in grado di:
Conoscenza e comprensione
- Identificare le principali risorse digitali per la storia della matematica, distinguendone finalità e contenuti.
- Riconoscere limiti e potenzialità dell’adozione di una prospettiva transdisciplinare e digitale nella storia della matematica
- Illustrare esperienze e casi di studio significativi tratti da progetti di ricerca internazionali
Capacità di applicare conoscenza e comprensione
- Utilizzare strumenti digitali per analizzare e visualizzare serie storiche e reti complesse di dati.
- Individuare e consultare in autonomia archivi e collezioni digitali su tematiche storico-matematiche specifiche
- Progettare attività didattiche che prevedano l’uso di fonti storiche digitali
- Analizzare e interpretare modelli storici e macchine matematiche come strumenti di rappresentazione del sapere scientifico
- Argomentare i vantaggi della digitalizzazione di fonti storiche, anche con esempi concreti
Autonomia di giudizio
- Valutare criticamente il ruolo degli strumenti digitali nella trasmissione e conservazione del sapere matematico.
- Elaborare una riflessione articolata sulle implicazioni epistemologiche e sociali dell’uso del digitale nella ricerca storica
- Discutere l’impatto della digitalizzazione sull’accessibilità e la diffusione del sapere storico-scientifico
Abilità comunicative
- Presentare in modo chiaro e fondato l’analisi di un documento o oggetto matematico, evidenziandone il significato storico e didattico
- Produrre materiali didattici (testi, slide, video, attività) che utilizzino fonti storiche e strumenti digitali
Capacità di apprendimento
- Selezionare e utilizzare in autonomia fonti e strumenti digitali per approfondire temi di storia della matematica
- Individuare connessioni tra la storia della matematica e altri ambiti disciplinari (arte, scienze, filosofia, tecnologia, …) attraverso esempi concreti
- Progettare attività formative originali che integrino risorse digitali e prospettive storiche nel contesto educativo.
Superamento di quiz
All’interno di ciascun modulo sono presenti diverse lezioni, composte da video, testi scritti, articoli scientifici, quiz con valutazione automatica e collegamenti esterni a risorse digitali.
Non ci sono prerequisiti
Erika Luciano
Elena Scalambro
Per domande o problematiche tecniche relative al corso e alla piattaforma contattare:
edvancedeh@unito.it
Contatto delle docenti del MOOC:
erika.luciano@unito.it
elena.scalambro@unito.it
Viviamo in un mondo di dati: comprenderli, interpretarli e comunicarli è una competenza fondamentale per ogni cittadino. Percentuali, grafici, medie e correlazioni sono ovunque: nei media, nei social, nei dibattiti pubblici. Ma sappiamo davvero cosa significano? E come possiamo usare i dati per costruire un'opinione solida, comunicare con chiarezza e non cadere in trappole retoriche? Questo corso introduce i concetti di base della statistica descrittiva e della comunicazione dei dati attraverso un linguaggio accessibile, esempi reali e strumenti digitali. Aperto a tutti, il corso affronta temi come indici di posizione e di variabilità, rappresentazioni grafiche, paradossi statistici, correlazione e causalità, con uno sguardo attento alle insidie della comunicazione visuale. Obiettivo primario è l’acquisizione di competenze trasversali utili per comprendere e comunicare i dati in modo critico e consapevole.
Il corso, articolato in 13 videolezioni, è organizzato in 4 moduli tematici per imparare a riconoscere dati corretti o fuorvianti, leggere grafici con spirito critico, usare strumenti digitali per rappresentarli e comunicare efficacemente i dati numerici che ci circondano.
-
Modulo A – Cosa sono i dati e la statistica? (2 video)
-
Modulo B – Comprendere i dati statistici (3 video)
-
Modulo C – Interpretare i dati (5 video)
-
Modulo D – Comunicare i dati (3 video)
Conoscenza e comprensione
-
Descrivere le principali tipologie di dati e fonti statistiche
-
Definire i concetti base della statistica descrittiva (media, varianza, moda...)
-
Identificare rappresentazioni grafiche corrette e fuorvianti
-
Esprimere il significato di correlazione e causalità
-
Spiegare l’importanza della comunicazione efficace dei dati
Capacità di applicare conoscenza e comprensione
-
Utilizzare strumenti grafici per rappresentare correttamente un insieme di dati
-
Calcolare media, mediana, deviazione standard in semplici dataset
-
Applicare criteri di lettura critica a grafici e infografiche
-
Produrre commenti (scritti e orali) relativi a tabelle e grafici
-
Costruire semplici infografiche digitali con strumenti dedicati
Autonomia di giudizio
-
Valutare l’attendibilità e la completezza di una rappresentazione statistica
-
Giudicare correttezza, accuratezza e trasparenza nell’uso dei dati nei media
-
Argomentare criticamente rispetto a un’affermazione basata su dati
-
Distinguere tra correlazione e causalità in diversi contesti
-
Selezionare informazioni rilevanti per sintetizzare un messaggio statistico.
Abilità comunicative
-
Interpretare e discutere grafici e tabelle in modo chiaro
-
Scrivere brevi testi informativi o descrittivi basati su dati
-
Spiegare in modo accessibile un concetto statistico a un pubblico non esperto
-
Utilizzare un linguaggio corretto e accessibile per presentare dati
-
Rappresentare graficamente dati in modo efficace, chiaro e coerente
Capacità di apprendimento
-
Riconoscere situazioni problematiche legate all’uso scorretto dei dati
-
Abbinare strumenti e risorse digitali a specifici obiettivi comunicativi
-
Giustificare le scelte fatte nella costruzione di un grafico o commento
-
Comparare diverse rappresentazioni di uno stesso insieme di dati
-
Pianificare attività di autoformazione e aggiornamento su dati e statistica
Superamento di quiz
All’interno di ciascun modulo sono presenti diverse lezioni, composte da video, testi e articoli scientifici di approfondimento, quiz con valutazione automatica e collegamenti esterni a risorse digitali.
Competenze elementari di matematica, acquisite durante il primo biennio della scuola secondaria di secondo grado.
Marina Marchisio Conte
Elena Scalambro
Mario Valenzano
edvancedeh@unito.it
marina.marchisio@unito.it
elena.scalambro@unito.it
mario.valenzano@unito.it
- Docente: Alice Barana
- Docente: Marina Marchisio Conte
- Docente: Matteo Sacchet
Questo MOOC introduce gli/le studenti/esse alle sfide dell'insegnamento e dell'apprendimento in un ambiente digitale. Esplora diversi quadri teorici per creare ambienti di apprendimento efficaci e inclusivi e coinvolgere gli studenti con varie metodologie, come il problem-solving, l'insegnamento adattivo, la gamification, la valutazione formativa e l'apprendimento cooperativo. Il MOOC offre spunti per progettare, realizzare e valutare attività didattiche digitali. I moduli analizzano esempi e casi studio che applicano i quadri teorici di riferimento.
Il corso è organizzato in 6 moduli che presentano le principali caratteristiche degli ambienti digitali di apprendimento ed esplorano i principi di progettazione di attività didattiche in un ambiente digitale.
- Modulo 1 - Cos’è un ambiente digitale di apprendimento?
- Modulo 2 - Quadri teorici per la progettazione di ambienti digitali
- Modulo 3 - Progettare attività innovative e inclusive in un ambiente digitale
- Modulo 4 - Metodologie e strategie didattiche in un ambiente digitale
- Modulo 5 - La valutazione in un ambiente digitale di apprendimento
- Modulo 6 - Open Educational Resources
Al termine del percorso di apprendimento, gli studenti e le studentesse saranno in grado di:
Conoscenza e comprensione
- Conoscere la definizione e le caratteristiche di un ambiente di apprendimento digitale come componente fondamentale nell’ambito formativo mediato dalle tecnologie.
- Conoscere i principali quadri teorici per la progettazione di attività didattiche in un ambiente digitale di apprendimento.
- Conoscere le principali metodologie e strategie didattiche che si possono usare in un ambiente digitale di apprendimento.
Applicare conoscenza e comprensione
- Applicare i modelli teorici per distinguere e scrivere obiettivi e risultati di apprendimento di un’attività formativa.
- Analizzare esempi di ambienti digitali di apprendimento utilizzando i quadri teorici studiati.
- Applicare le conoscenze acquisite per creare attività didattiche in ambienti digitali di apprendimento.
- Utilizzare le tecnologie digitali in ambito educativo per creare contenuti inclusivi e accessibili.
- Creare contenuti di valutazione efficaci che sfruttino appieno le potenzialità offerte dalle tecnologie digitali.
- Gestire e analizzare dati relativi all’apprendimento.
Autonomia di giudizio
- Analizzare un esempio di ambiente digitale di apprendimento per capire e valutare in modo critico le caratteristiche e le modalità di utilizzo efficaci.
- Discutere e valutare le implicazioni dell’uso della tecnologia in ambito educativo.
- Selezionare contenuti appropriati all’interno delle risorse didattiche aperte presenti in rete.
- Valutare l’inclusività e l’accessibilità di ambienti digitali di apprendimento.
Abilità comunicative
- Comunicare la struttura e le caratteristiche di un’attività didattica digitale in modo efficace.
- Argomentare punti di forza e criticità di un’attività didattica in un ambiente digitale.
Capacità di apprendimento
- Sviluppare attività didattiche digitali originali e innovative.
- Sviluppare autonomia nella ricerca di soluzioni didattiche innovative da proporre in un ambiente digitale.
- Rendere accessibili e comprensibili concetti didattici in un ambiente digitale.
Superamento di quiz
All’interno di ciascun modulo sono presenti diverse lezioni, composte da video, testi scritti, infografiche, quiz con valutazione automatica e attività interattive.
Non ci sono prerequisiti
Alice Barana
Marina Marchisio Conte
Matteo Sacchet
alice.barana@unito.it
marina.marchisio@unito.it
matteo.sacchet@unito.it