Influence of Interactive Visualizations on Students' Learning Experience in Machine Learning

With the fast integration of Machine Learning(ML) into several industries, the motivation to develop effective pedagogical strategies for teaching this complex and evolving field has become critical. Machine Learning, once mainly a topic in Computer Science Bachelor programs, is now widely integrated into various majors and introduced at earlier...

Navigating the Pedagogical Landscape: An Exploration of Machine Learning Teaching Methods

This study delves into machine learning (ML) education by conducting a comprehensive literature review, a targeted survey of ML lecturers in Dutch universities, and a comparative experiment. These methods aid in addressing the challenges of aligning teaching methods with the evolving nature of ML and the growing demands of the field, and fill in...

The impact of Goal-Oriented Visualization on Academic Performance: A Case study in Machine Learning

This research investigates the impact of goal-oriented visualization on machine learning knowl-edge acquisition, particularly exploring its poten-tial to address procrastination in academic settings. By examining participants with no prior machine learning experience, the study employs compara-tive and correlational measures to analyze quiz per-...

Long-Term Memory Retention of Educational Content: How Machine Learning concepts can be remembered for the rest of our careers with the right practice questions

To aid the teachings of machine learning (ML), the usage of elaborative interrogative practice questions (EIPQ) is proposed to increase the long-term memory retention of said teaching. Firstly, the existing expectations of students in the current educational landscape are analyzed by taking a look at the undergraduate course present in Delft...

A comparative analysis of coding approaches in machine learning among computer science students and non-computer science students

The increasing presence of Machine Learning in all fields of study requires an improvement in how it is taught. Previous research on this topic examined how to teach ML concepts and highlighted the importance of using technology and leveraging relevant pedagogical content knowledge. It did not compare the impact of previous programming knowledge...

Bridging the Knowledge Gap: Identifying Essential Machine Learning Concepts for Effective Progression in Follow-Up Courses

This research paper aims to investigate the adequacy of concepts taught during an introductory machine learning course in preparing students for subsequent courses and their professional careers. The study adopts a comprehensive approach, including a literature review, interviews with teaching staff of follow-up courses, and a survey...

Measuring students’ progress in Machine Learning: A case study of Decision Trees and Random Forests

Machine Learning (ML) is a rapidly growing field, therefore ensuring that students deeply understand such concepts is of key importance in order to certify that they are prepared for the challenges and opportunities of the future workforce. Despite this, literature on teaching ML and assessing students' understanding with regard to this field is...

Visualizing Complexity: Kernel Density Estimation in University Education: Investigating Misconceptions, Challenges, and the Role of Prior Knowledge in Comprehending KDE

This research investigates the improvement of Kernel Density Estimation (KDE) comprehension in a university context via visualization-enhanced teaching. The study tackles KDE misconceptions, the efficacy of visual aids, and the role of previous mathematical and machine learning knowledge. Using a mix of literature review, survey, and...

Discovering the misconceptions that influence learning of Machine Learning

The purpose of the study is to investigate and evaluate the most common misunderstandings that are associated with the subject of Machine Learning, and then to make a direct comparison between the findings of the study and the Machine Learning course that is currently being provided by the Delft University of Technology as part of their Bachelor...

A Guidline for Creating Assessments in Machine Learning Education

Even though machine learning field is growing rapidly, research on education of machine learning is scarce. In this paper a research about creating assessments in the machine learning’s context is presented. The aim of the research is to answer how to design assessments that reliably show progress on a module in machine learning. Learning...

Instructional Designs of Machine Learning: A research into the instructional designs used in introductory courses of machine learning in computer science bachelors

Instructional Design is a discipline and a science that has existed for decades. There has been research done into the most effective instructional designs for different study disciplines, the same cannot be said about machine learning. As ML is a relatively young discipline, no research has been done into instructional designs and teaching...

Competences in Machine Learning: The order of competences that students need to learn in ML

Machine learning is becoming more and more applied within business and academia alike. This has led researchers to look inwards and discuss whether the current way of teaching and learning machine learning is the right way. Within this train of thought, one must investigate the characteristics of teaching and learning. Namely, what are the...

CITYSENT: Sentiment analysis of the Netherlands and Flanders

The department of Urbanism at the TU Delft, our clients, research the sentiment in different places, times, ages or genders and compare them to each other. This report describes the purpose, design, implementation and accuracy of a web tool created to get insights into the sentiment people have, de- ducted from social media. The aim of our...