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Zoekresultaten (38)

HBO/WO

MBO

21 juli 2026

Introduction to Stochastic Hydrology

Hydrologic systems are both complex and extremely heterogeneous in time and space. As hydrological models are simplified versions of reality, their predictions are by definition subject to uncertainty. The field of stochastic hydrology focuses on quantifying this uncertainty along with prediction. This book aims to present an overview of the field of stochastic hydrology at an introductory level. A wide range of topics and methods will be treated, while each topic and method is only treated at a basic level: descriptive statistics; probability and random variables; hydrological statistics and extremes; random functions; time series analysis; geostatistics; forward stochastic modeling; and state prediction and Kalman filtering. The book can be used for a 10-15-week course at third-year undergraduate or first-year graduate level.

Document

Marc F.P. Bierkens

8 april 2026

ACA-bot

ACA-bot is an engaging chatbot designed for Automated Conversational Assessment (ACA). Drawing inspiration from traditional oral exams, this text-based conversational agent transforms assessment into a dynamic learning experience. It supports both formative and summative evaluation by offering students personalised feedback and guidance at every step of their learning journey. The first ACA-bot prototype focuses on assessing students’ understanding of basic statistical concepts, but its flexible design allows for easy adaptation to a wide range of subjects and learning contexts. Through interactive dialogue, students can practise explaining ideas clearly under exam-like conditions, apply their knowledge to new and challenging questions, and refine their reasoning as they respond to follow-up prompts. They gain valuable insights into their own understanding, identify areas for improvement, and gradually build confidence for both oral and written assessments. For educators, ACA-bot offers a powerful way to scale oral examinations and enhance formative assessment without increasing workload. It automates parts of the assessment process, provides structured and actionable insights into student performance, and opens the door to more personal, interactive feedback at scale. By combining the strengths of conversational AI and educational assessment, ACA-bot turns evaluation into a collaborative, insightful process. It empowers students to think aloud, reason more effectively, and grow, while giving educators a smarter, more innovative way to understand and support learning. ACA-bot is een slimme en inspirerende chatbot die onderwijs en Artificial Intelligence samenbrengt. Geïnspireerd door het klassieke mondelinge examen tilt deze tekstgebaseerde gespreksagent Automated Conversational Assessment naar een nieuw niveau. Studenten krijgen via natuurlijke gesprekken directe, persoonlijke feedback en begeleiding, waardoor leren en toetsen samenvloeien in één boeiende ervaring. Het prototype van ACA-bot is ontwikkeld om kennis over statistiek te beoordelen, maar dankzij het flexibele ontwerp kan het systeem eenvoudig worden aangepast aan andere vakgebieden en toepassingen. Tijdens interactieve dialogen leren studenten helder uitleg geven, hun kennis toepassen op nieuwe vragen en hun redenering aanscherpen. Ze ontdekken waar hun begrip nog verdiept kan worden en groeien zichtbaar in zelfvertrouwen — waardevolle voorbereiding op zowel mondelinge als schriftelijke examens. Ook voor docenten biedt ACA-bot een slimme oplossing. Het maakt mondelinge toetsing schaalbaar, ondersteunt formatieve beoordeling via gesprek, en vermindert de werkdruk door delen van het beoordelingsproces te automatiseren. Bovendien geeft het gestructureerde inzichten in de voortgang en denkwijze van studenten, waardoor elke dialoog bijdraagt aan beter onderwijs. ACA-bot laat zien hoe Automated Conversational Assessment het leren persoonlijker, interactiever en effectiever maakt. Studenten denken hardop, verkennen hun eigen begrip, en verbeteren met vertrouwen — terwijl docenten op een innovatieve, datagedreven manier inzicht krijgen in hoe kennis werkelijk wordt begrepen.

App

Aki Härmä, Julie Bauer

13 mei 2025

Werboek inhoudsanalyse

Werkboek Inhoudsanalyse is een praktische handleiding om te leren hoe je op een betrouwbare en verantwoordelijke manier kunt analyseren hoe mensen met elkaar communiceren. Dit boek richt zich op het ontwerpen en uitvoeren van studies die gebruik maken van kwantitatieve inhoudsanalyse als onderzoeksmethode. Deze eerste editie is ontwikkeld in het kader van de opleiding Communicatie- en Informatiewetenschappen (CIW) van Tilburg University. Binnen één semester schrijven studenten een onderzoeksvoorstel en voeren ze het voorstel vervolgens ook uit. Deze groepsopdracht is volledig uitgewerkt in de bijlagen van het boek.

Open (Access) Tekst Boek

Emiel van Miltenburg

2 september 2023

Evaluating information & data: Assess original dataset

Assess the original dataset When finding quantitative information like a number, a table, or a whole dataset we want be able to judge its quality to help us select the right information. Exercise: Assess the original data set. Suggested. Related.

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Erasmus University Library

1 september 2023

Measuring academic impact: Journal level metrics

Journal level metrics Journal level metrics measure the influence of a journal, taking into account the number of citations received by articles published in the journal. Why can journal level metrics be important for you? JIF: Journal Impact Factor Calculation method Where to find the JIF Comparing journal based on the JIF. Video Journal Citation Reports 2021 CiteScore Calculation method Where to find CiteScore Comparing journal based on CiteScore Video: CiteScore metrics in Scopus SJR: SCImago Journal Rank Calculation method Where to find SJR Comparing journals based on SJR SNIP: Source Normalized Impact per Paper Calculation method Where to find SNIP Comparing journals based on SNIP Be aware of bogus journal metrics! Next to the Journal Impact Factor, CiteScore, SJR and SNIP you might encounter other journal level metrics, for example on the homepage of a journal or in an e-mail you receive from a publisher or editor. Related. Suggested. Support.

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Erasmus University Library

23 augustus 2023

Lecture: Understanding data

Learner acquires demonstrable knowledge of data forms used in AI. Learner can talk about data using the right terminology (e.g., features, variables, observations). Learner knows the different data types and knows how to transform them (e.g., nominal, ordinal). Learner knows the most common measures for data description (e.g., mean, median, standard deviation) and knows how to calculate them. Learner acquires demonstrable knowledge of how data are described via measures and visualisation. Learner knows the most common graphs and knows how to pick a visualisation, matching the given data.

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HCAIM Consortium

23 augustus 2023

Lecture: Exploratory Data Analysis

Naive Bayesian networks Bayesian networks Decision networks Students can define a multivariate joint distribution: multinomial and Gaussian Students can explain the difference between association versus causation Students can define observational, causal, and counterfactual inference Students can define fairness using observational and counterfactual reasoning

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HCAIM Consortium

23 augustus 2023

Interactive session: Risk & Risk mitigation

Recognize, analyze and discuss the necessity and the role of security testing in AI. Employ documents, such as the AI Act, to classify use cases into risk categories. Discuss in-depth and with common European vocabulary on AI risk and how can AI be designed safely and benevolently, in ways that will not expose humans to unacceptable risks of harm, even when risky behaviour may advance a system goal. Classify the obligations of providers and users in relation to AI risk management. Discuss the requirements for high-risk AI systems as per the AI Act. Elaborate and recognize specific risks and risk taxonomies, relevant throughout the different stages of the AI lifecycle.

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HCAIM Consortium

23 augustus 2023

Lecture: Risk & Risk mitigation

Learner understands the competing concepts of risk Learner understands how risk is perceived by a the modern society Learner understands approaches to risk evaluation Learner understand approaches to risk mitigation

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HCAIM Consortium

23 augustus 2023

Lecture: EU Human Rights Legislation

To rational and discuss the topics related to AI and human rights

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HCAIM Consortium