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Zoekresultaten (36)

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

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

15 december 2025

Systems Thinking Pressure Cooker

What – This pressure cooker is an introduction to systems thinking. This pressure cooker can be used to quickly familiarize a group of people with what the characteristics of a system are and have them experience various examples of using systems thinking to explore a topic or question. For whom – The format can be used (and adjusted) for a team of teachers that are not yet familiar with systems thinking, as well as a group of students (all levels; any group size). Why – Guided training in integration of systems thinking in a research project. How long – Approximately 2-2,5 hours in class and a 10 minutes pre-class video.

Presentatie

Programma Interdisciplinair Onderwijs

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

6 juni 2025

How To Write A Quantitative Research Paper

So, you need to write a ‘quantitative’ research paper, and you want to know: what is the correct way of writing such a paper? The answer is, unfortunately: it depends … For one, there is no universally accepted, ‘correct’ way of writing any paper in the social sciences in general. Moreover, even just within the so-called ‘quantitative’ research tradition specifically, there exist different viewpoints on what makes for a good research paper. To muddy things up even further, how to write a paper will also depend on the specific research question(s) that it addresses. Hence, even the same scientist might opt for varying approaches in different papers. Having said that, it is possible to identify some common rules and practices that are often followed, as well as a number of practices that should generally be avoided. Based on these common rules and practices, this document is meant to help you on your way with writing a ‘quantitative’ research paper by offering a number of tips, tricks and rules-of-thumb. None of these rules and practices are set in stone, and you (or your teacher/supervisor) may have excellent arguments to deviate from them. However, in general, following them will greatly increase the quality of your paper.

Document

Alex Lehr, Roderick Sluiter

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

23 augustus 2023

Lecture: Generalizability and Artificial General Intelligence (AGI)

Limitations of currrent AI approaches Definition of Artificial General Intelligence (AGI) Capabilities & core requirements of AGI How can we test for AGI? How far away is AGI and what are the benefits and risks? Update test

Website

HCAIM Consortium

26 juli 2022

E-Learning Module Urban Green Spaces

The University of Twente ITC together with Tropenbos Suriname developed this e-module on urban green spaces in the tropics in the context, and using the results, of their UTSN Twinning project on "Towards a green and more livable Paramaribo". With this e-module we aim to provide access to quality learning material for Surinamese and Dutch MSc students and other learners. This e- module is designed to be used in two ways, i) within a course, introducing students to the subject of urban green benefits and management. ii) as stand-alone short course. The module is entirely self-study and can be followed without an internet connection. Cite as: Schwarz. N. and Willemen L., 2021. E-learning module Urban Green Spaces, product of ‘Naar een groen en leefbaarder Paramaribo’ by Tropenbos Suriname and University of Twente-ITC. DOI:10.5281/zenodo.6405982

Website

Rob Lemmens

10 juni 2022

From Idea to Study — Research Questions, Hypotheses, and Variables

Main Concepts The pathway: Idea → Research Question → Hypothesis → Variables → Design Hypothesis: a testable prediction Variables: elements of your study that can change or vary Independent variable: the factor that you manipulate in your study to investigate its effects Dependent variable: the outcome in your study which might be influenced by the independent variable Control variable: the factor in your study that you keep constant Causation vs. correlation and how design affects claims Learning Goals Formulate a specific research question from a broad idea Write a testable hypothesis aligned with measurable outcomes Identify independent, dependent and control variables for a study Distinguish between experimental and observational designs Key Takeaways Clarity grows step by step: idea → question → hypothesis → variables Keep the IV manageable and the DV aligned to the hypothesis Control what you can; state what you can’t Avoid causal language when the design doesn’t support it Variables become the backbone for design, development, and testing Further Exploration Search: “operationalizing constructs in behavioral research” Search: “experimental vs. quasi-experimental vs. observational” Search: “writing testable hypotheses examples” Search: “construct validity and measurement reliability”

Video

Lucia M Rabago Mayer, Jordi Weldink, Amanda Poulin

13 juli 2021

Better Academic Research Writing: A Practical Guide

A pratical guide for better academic research writing, written by René Bekkers.

WoDocument

Rene Bekkers, Sjors Keijzer, Benthe Priester, Arjen Heijstek