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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

9 juni 2026

Informatievaardigheden (e-learning cursus)

Met deze e-learning cursus ontwikkelen studenten een basisniveau in informatievaardigheden. De module is gericht op eerstejaars studenten die zich de basis van informatievaardigheden eigen willen maken. De module is opgedeeld in vijf stappen: oriënteren en specificeren, plannen en zoeken, kritisch beoordelen, organiseren en verwerken en publiceren en communiceren. Onderdeel van de cursus zijn een aantal APA-quizzen. In de bijlagen vind je naast de e-learning zelf ook het storyboard.

Website

Bibliotheek Hogeschool Arnhem Nijmegen

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

19 maart 2026

Testing

Main Concepts Technical / engineering testing: Checking whether the system or VR build works correctly, meets requirements, and behaves consistently. Research / participant testing: Testing with participants to verify that the experiment supports the hypothesis and produces valid data. Debugging: Systematically identifying, isolating, and fixing the cause of a technical problem. Learning Goals Distinguish between the two types of testing. Apply a simple technical testing protocol to a project. Systematically identify, document, and fix software or VR errors. Use the Testing Worksheet to maintain traceability and collaboration. Recognize testing as essential quality assurance for both research and development. Key Takeaways Technical testing validates the build before research testing validates the hypothesis. Debugging is guided observation — not random trial and error. Documenting your tests ensures accountability and repeatability. Continuous testing builds confidence and prevents surprises during research trials. A clear testing workflow saves time and makes collaboration smoother. Further Exploration Search for “Unity debugging and testing workflow.” Search for “How to create software testing protocols.” Search for “Difference between pilot study and technical testing.” Search for “Best practice bug tracking templates.”

Video

Lucia M Rabago Mayer, Jordi Weldink, Amanda Poulin

19 maart 2026

ShareXR : Challenge-Based Learning with XR (BMS Lab, University of Twente)

ShareXR introduces a challenge-based learning (CBL) approach for integrating research, design, and development in engineering education through extended reality (XR). Developed at the University of Twente’s BMS Lab, the model addresses the increasing need for multidisciplinary collaboration by teaching students to speak a shared language across research, design, and technical development. Even for those without an engineering, scientific or STEM background. This course offers a hands-on, multidisciplinary learning experience where students take real research ideas and transform them into innovation projects within a complete and validated open learning XR ecosystem. With reusable multimedia course content, the course enables students to understand interdisciplinary collaboration, while each module focuses on one of the four pillars: Research, Design, Development, and Project Management — guided by principles of balance, iteration, and collaboration. By working with different tools like one-page project canvases, students will learn how to translate research ideas into structured user stories, requirements, and project management tasks, while exploring how XR tools can act as a human-centered medium for prototyping, testing, and validating ideas without requiring coding skills.

App

Lucia M Rabago Mayer, Jordi Weldink, Amanda Poulin, Luca Frösler, Peter Slijkhuis, Mirjam Galetzka, Benno Pals, Jan-Willem van 't Klooster

3 maart 2026

Quick and dirty multilevel regression with SPSS syntax assignment

SPSS syntax assignment for teaching the basics of multilevel modeling. For when you need to teach or learn the basics but don't have a lot of time for it. Covers pooled OLS, fixed-effects and random effects (null model, intraclass-correlation, random intercept models with covariates at different levels, random slope models, and cross-level interactions). Uses European Social Survey data. Includes questions. Intended to be used in conjunction with a preceeding lecture.

Document

Alex Lehr

6 juni 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

1 juni 2025

Week 3: Creating and Selecting Options

Around this video, a lesson has been built with specific learning objectives, activities, and supporting materials for students. The next steps in the case-solving cycle are being explained: generating and selecting solutions. Students explore ideation techniques like brainwriting and mind mapping to develop creative, practical ideas. Ground rules help keep the process focused, collaborative, and open. To evaluate ideas, the SFA framework (Suitability, Feasibility, Acceptability) is used to shortlist the most promising options. A real-world example shows how these tools lead to well-founded decisions. Finally, students learn to apply a decision-making matrix to compare options using relevant criteria. This lesson helps students move from insights to action by developing structured, team-driven solutions.

Document

Hans van Oudheusden, Britta Stoelinga

10 maart 2025

EPIFFANI Project Results

These are the combined results for the EPIFFANI Project by Prof. Nicolas Mangin. This project was funded buy SURF's Stimuleringsregeling open en online onderwijs. The aim of the project was to build and test a set of four open source applications for R/integration into the LMS, which allowed teachers to easily automate formative testing through IT solutions. With these applications, teachers can easily and quickly create automate the processing of making tests with personalized feedback for students. The results have three elements: A full project report, which provides context for the creation and creation process A link to Youtube video demonstration of the tools, made for the University of Groningen's education festival 2022 A link to the Github page of project lead Nicolas Mangin. On this page, all the packages can be downloaded, and documentation and instruction are available All tools are licensed under a GPL-3 license, but as this license is not available, it has been noted as CC BY-SA here.

Document

Nicolas Mangin

20 juni 2024

Testing theory: an introduction

These lecture notes are a follow up on Adjustment theory. Adjustment theory deals with the optimal combination of redundant measurements together with the estimation of unknown parameters. There are two main reasons for performing redundant measurements. First, the wish to increase the accuracy of the results computed. Second, the requirement to be able to check for mistakes or errors. The present book addresses this second topic. Although one always will try one's best to avoid making mistakes, they can and will occasionally happen. It is therefore of importance to have ways of detecting and identifying such mistakes. Mistakes or errors can come in many different guises. They could be caused by mistakes made by the observer, or by the fact that defective instruments are used, or by wrong assumptions about the functional relations between the observables. When passed unnoticed, these errors will deteriorate the final results. The goal of this introductory course on testing theory is therefore to convey the necessary knowledge for testing the validity of both the measurements and the mathematical model. Typical questions that will be addressed are: 'How to check the validity of the mathematical model? How to search for certain mistakes or errors? How well can errors be traced? And how do undetected errors affect the final results?' The theory is worked out in detail for the important case of linear(ized) models. Both the parametric form (observation equations) and the implicit form (condition equations) of linear models are treated. As an additional aid in understanding the basic principles involved, a geometric interpretation is given throughout. Attention is also paid to the performance of the testing procedures. The closely related concept of reliability is introduced and diagnostic measures are given to determine the size of the minimal detectable biases. In this introductory text the methodology of testing is emphasized, although various examples are given to illustrate the theory. The methods discussed form the basis for geodetic quality control and they provide the ingredients for the formulation of guidelines for the reliable design of measurement set-ups.

Open (Access) Tekst Boek

Peter Teunissen