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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

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

August 23, 2023

Lecture: Decision Theory

The learner gets acquainted with non-quantitative and quantitative approaches to ethics students can define and calculate optimal decisions using univariate distributions and utility/loss functions students can know the elements of decision networks: stochastic/action/utility nodes students can know and apply Bayes' rule (multivariate) students can construct a Naive Bayesian Network (NBN) model students can expand NBN to an NBN-based decision network students can calculate the value of perfect information

Website

HCAIM Consortium

February 1, 2021

Linear and logistic regression for social scientists

A course on the theory and application of one the most widely applied methods in the social sciences: regression analysis. Learn about what it is, how it works, when it’s (not) useful.

Video

Alex Lehr

2021

Tracking student skills real-time through a continuous-variable dynamic Bayesian network

The field of Knowledge Tracing is focused on predicting the success rate of a student for a given skill. Modern methods like Deep Knowledge Tracing provide accurate estimates given enough data, but beingbased on neural networks they struggle to explain how these estimates are formed. More classical methodslike dynamic Bayesian networks can do this, but they cannot give data on the accuracy of their estimatesand often struggle to incorporate new observations in real-time due to their high computational load.This paper presents a novel method, Performance Distribution Tracing (PDT), in which the distribution of the success rate is traced. It uses a dynamic Bayesian network with continuous random variablesas nodes. By tracing the success rate distribution, there is always data available on the accuracy of any success rate estimation. In addition, it makes it possible to combine data from similar/related skills tocome up with a more informed estimate of success rates. This makes it possible to accurately predictexercise success rates, even when an exercise requires a combination of different skills to solve. Andthrough the use of the right conjugate priors, all distributions are available in analytical form, allowing efficient online updates upon new observations. An application to an engineering physics course has shown that the PDT algorithm works well inreal-life settings. Students mainly appreciate the overview given of their learning process as well as the corresponding practice recommendations.

Document

Hildo Bijl

May 8, 2018

10-24: Data analysis (descriptive)- ASSIGNMENT 9: DISPLAYING UNIVARIATE DATA

In this assignment the following items will be explained regarding this assignment: In this assignment you will learn how to describe one variable using a frequency table, a bar chart and a pie chart using SPSS.

Document

Henk van der Kolk

May 8, 2018

10-09: Data analysis (descriptive) - micro lecture INTRODUCTION SPSS

In this micro lecture the following items will be explained regarding this lecture: introduction to spss

Website

Henk van der Kolk

May 8, 2018

10-07: Data analysis (descriptive)- micro lecture FREQUENCY TABLES

In this micro lecture the following items will be explained regarding this lecture: How to make frequency tables in spss

Website

Henk van der Kolk

May 8, 2018

10-12: Data analysis (descriptive)- micro lecture RECODING AND CREATING A VARIABLE

In this micro lecture the following items will be explained regarding this lecture: How to recode variables in spss

Website

Henk van der Kolk