Recently published health reports from governments of western countries and the World Health Organization (WHO) provide insights on health benefits of being physically active. Performing physical activity of moderate intensity is, amongst others, associated with a smaller risk on symptoms of depression, cardiovascular disease and diabetes. Despite this information being widely available on the world wide web, one particular form of physical inactivity is still incorporated in the lives of many people: sedentary behaviour. An environment that is especially seductive for this type of physical inactivity is the workplace. Stress, depression, anxiety, and heart diseases are found to be work-related diseases, possibly as a result from sedentary behaviour. As a means of reflecting on physical activity, one could acquire an activity tracker such as FitBit or use one of the many available health monitoring mobile applications. However, it was found that activity trackers may not be very effective for stimulating a physically active lifestyle. In contrast, an emerging phenomenon may be able to do so: the intelligent personal assistant (IPA). In order to be a part of the solution for aforementioned problem, we aim to stimulate reflection on physical activity during work hours in order to incorporate physical activity into daily routines. Therefore, we carried out a user study with 16 participants in two countries. For carrying out this user study, we introduced Alexercise: a system consisting of multiple interacting components, used for collecting, processing and presenting physical activity data. Components in Alexercise exchange data in quasi-real time to provide workers with current representative physical activity data. As part of Alexercise, we developed a custom skill for Alexa to access physical activity data and present these by voice. In addition, we developed a cross-platform mobile application for tracking and reflecting on activity using the React Native framework. We investigated whether IPAs are more effective in stimulating reflection on physical activity, compared to activity trackers. In addition, we asked participants if they felt the need to take active breaks based on the experience of the user study. At last, we intended to compare results of participants who already incorporated physical activity in their lifestyles with participants who have not. In the development process, we encountered many challenges, such as the distribution of mobile applications and Echo Dots to participants in two countries, and facilitating real time tracking and reflecting on physical activity. Collected sensor data for Android smartphones was reported with infrequent intervals, and could therefore not be used to reliably assess physical activity. We investigated and elaborately explained how these challenges can be overcome for future work. In contrast to the challenges, participants reported that they perceived talking to Alexa as intuitive, and the majority of the participants reported that they feel the need to take active breaks based on their participation in the user study.

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 competences required to learn, and in what order should these be learned. This research paper focuses on realizing the answer to that question by combining past research papers on how to define competences, how to establish an order of competences, and how to establish competences through the help of questionnaires answered by academics within the machine learning sector teaching Computer Science students. The results from the academic analysis and the resulting data from the questionnaires are combined to organize a result, established through two different methods used by researchers. To conclude, the paper is reflected upon, and its merits, demerits, and trade-offs are summarized, presenting a final recommendation for any future work done on this topic.

Learning to program is not a easy task, as has become evident from the abundance of research papers concerning the subject. One of the learning barriers of learning a new programming language is understanding their error message, as coding errors have to be resolved before the programmer can run the code or add new functionality to the program. If the error message does not give the (kind of) information the programmer needs or uses terminology the programmer does not understand, it becomes a lot harder to fix the error it is reporting. This thesis aimed to enhance the understanding about why the error messages of the Python programming language fail to be understood by novice programmers. An experiment was performed where novice programmers constructed how they thought the error messages should look like when encountering errors often made by novice programmers. This thesis also introduces the Error Message Component Framework, a framework which can be used to determine the different components of information an error message contains and the structure of these components. This framework was used to compare the original error message to the custom error messages created by the participants of the experiment, to investigate if the original message matched the novice programmer's information expectations. This thesis concludes that even though some errors are often made by novice programmers, their resulting error messages may not targeted towards this group based on the terms used in these messages, the component of information these messages contains and the lack of precision of the information.

Background This research explores the role of visualization tools in enhancing collaborative learning in virtual reality environments. Methodology An experiment was conducted, involving two groups of participants, each tasked with collaboratively solving a complex maze in a virtual reality environment. Both groups experienced two conditions: the experimental condition, where participants had access to visualization tools such as visual cones and highlighting capabilities, and the controlled condition, where these tools were not available. The participants’ situational awareness were assessed using both the Situation Awareness Rating Technique questionnaire and the Situational Awareness Linked Indicators Adapted to Novel Tasks framework.Results The Situational Awareness Linked Indicators Adapted to Novel Tasks scores were higher for the experimental session for Group 2, while being lower for Group 1. However, the Situation Awareness Rating Technique scores were generally lowerin the experimental condition for both groups. Conclusion These results demonstrate the potential virtual reality has as a platform for collaborative learning and highlight the need for further research into the design of effective visualization tools and the measurement of situational awareness in virtual reality. Furthermore, this research opens up new possibilities into the design of virtual reality environments for collaborative learning and the potential of virtual reality as a tool for enhancing collaborative learning.

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 methods of machine learning. This paper investigates the existing instructional de- signs of ML by analysing various bachelor ML courses at different universities, the compare the results with the existing instructional designs at the TU Delft using the help of interviews with the education experts at TU Delft in order to draw conclusions regarding the differences and how to improve on the course at the TU Delft.

The online website Skill Circuits is a tool developed by teachers at the Delft University of Technology. Skill Circuits is an online learning tool that presents students with a node-link (i.e. a tree) structure where each node represents a skill, containing tasks that aim to teach the skill. The website aims to benefit teachers, by letting them think how all the 'skills' in their course relate and whether or not they have sufficient material to teach these skills. At the same time, the website aims to benefit students by giving them a learning path to follow, where each step is a small task, and each skill is visually connected to previous skills, showing how the learning material is connected. This thesis aimed to help new teachers understand the concept of these node-link structures --- called skill circuits --- and also expand the website with features to help them evaluate their skill circuits, both before and during the course. A documentation help page was developed, which explained the concept and intended usage of the website and was accompanied by a proof-of-concept skill circuit explaining the same topics. Tools to help evaluate a skill circuit were also developed, which included tools that could be used before the course was held, and tools that used analytics generated by students. One noteworthy tool took inspiration from the Constructive Alignment principle, where a teacher can label parts of their skill circuit with learning objectives to see if there is alignment with their course material. All of these features were evaluated with a small focus group consisting of teachers, which showed enthusiasm toward the proposed features.

Computational Thinking (CT) - the process of thinking like a programmer or computer scientist - is a skill that that has the potential to transform the way students learn at educational institutions in different domains and different grade levels. With the increasing integration of CT in classrooms, there is a growing need for CT assessment tools to evaluate the acquisition of CT skills. This research develops a framework for CT assessment that detects user micro-interactions in a university-level self-paced Python beginners course integrated into Jupyter notebooks. The users can improve their learning with the help of feedback via CT dashboards as part of this framework. A user evaluation study was conducted which showed that this framework can be used to improve the acquisition of CT skills via programming. The main contributions of this framework are the mapping between CT skills and user micro-interactions and development of the CT dashboards to help the user self-regulate their learning of programming. The framework developed can be easily integrated into any course that teaches Python programming using Jupyter notebooks and is yet to be extended to other programming courses.