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HBO/WO

MBO

July 21, 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

April 8, 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

June 6, 2025

Multilevel modeling for social scientists

The course builds on the knowledge and skills acquired in the previous methods courses. The course will introduce students to multivariate analyses of nested data using multilevel modeling techniques. The course focuses on the theory and application of fixed and, in particular, random effects models. Using data from the European Social Survey, the students will learn how to estimate and interpret such as models using Stata.

Document

Alex Lehr

September 1, 2023

Practical: Presenting statistical data

Understand and demonstrate the data representation for AI and ML modelling. Apply a range of plotting techniques to visualise data. Be able to check for fairness, handle outliers and missing data.

Document

HCAIM Consortium

August 23, 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.

Website

HCAIM Consortium

August 23, 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

Website

HCAIM Consortium

August 23, 2023

Tutorial: 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.

Website

HCAIM Consortium

August 23, 2023

Lecture: The Data Analysis Process

To be able to demonstrate knowledge of the data analysis process To understand differences between methodologies and standards

Website

HCAIM Consortium

September 15, 2022

Introduction to the eXtensible Markup Language: Exercises (lesonderdeel)

A tutorial on the

Spreadsheet

Peter Verhaar

February 20, 2021

Quantitative Methods and Statistics

This is an introductory textbook on Quantitative Methods and Statistics, developed and used at Utrecht University (Linguistics) for the humanities. This book offers an overview of and introduction into the most important quantitative methods and statistical techniques used in the humanities. The book provides a solid methodological foundation for quantitative research, and it introduces the most commonly used statistical techniques to describe data and to test hypotheses. This will also enable the reader to critically evaluate such quantitative research. This textbook is being used as a basic textbook in introductory courses in quantitative methods and statistics at Utrecht University. The book is also highly suitable for self-study at a basic level, for everybody who wishes to learn more about quantitative methods and statistics. The main text has been kept free of mathematical derivations and formulas. The explanations are rather conceptual, and rich in examples. Where necessary derivations and formulas are presented in separate sections. This book also contains instructions on how to “do” the statistical analyses and visualisations, using the three statistical packages SPSS, JASP, and R. These instructions too are in separate sections.

Open (Access) Text Book

Hugo Quené, Huub van den Bergh