The increasing demand for machine learning expertise calls for effective teaching methods for university-level courses. This research compares static versus interactive teaching methods in the context of machine learning, with the latter focusing on the student engaging more with the material. Specifically, this study investigates the impact of interactive visualisations on students' understanding of receiver operating characteristics (ROC) curve analysis, a critical concept in evaluating machine learning algorithms. Traditional static teaching methods often fall short of conveying complex ideas like ROC curves, which are pivotal in various fields, including medicine and psychology. This research also compares the efficacy of interactive versus static visualisations in enhancing student motivation. Twenty first-year computer science students from Delft University of Technology participated in the experiment and were randomly assigned to control (static visualisation) and experimental (interactive visualisation) groups. The results of the experiment were determined by analyzing the pre- and post-test scores, along with surveys measuring motivation. These indicate significant improvements in understanding for both groups, with a greater gain observed in the experimental group. This suggests that interactive visualisations may offer a superior approach to teaching complex machine learning concepts, but the experiment conducted in the study does not show a statistically significant difference between the static and interactive visualisations. The research also compares the student's motivation after completing an instructional material focused on the ROC, but the interactive visualisations did not provide better results. The study underscores the potential of interactive teaching tools to enhance educational outcomes in machine learning and highlights the need for further research into interactive methods for the teaching of machine learning.

Machine learning education often involves complex topics that can be challenging to teach engagingly, leading to difficulties in maintaining student focus and achieving optimal learning outcomes. This study aims to bridge the gap between machine learning-specific teaching techniques and those centred on student engagement by conducting a comprehensive analysis of related works and an empirical experiment. The related works section reveals differences between traditional and engagement-focused teaching methods. To address the knowledge gap regarding the impact of engagement-focused methods on learning outcomes, a controlled experiment was conducted, comparing a conventional 16-minute video lecture followed by practice questions against the same content divided into four shorter video segments, each followed by a subset of the practice questions. The results demonstrate that the experimental group achieved significantly higher average quiz scores and reported consistently higher satisfaction ratings, suggesting that even a simple engagement-boosting technique can substantially improve learning outcomes and student satisfaction in machine learning education. This study highlights the importance of prioritising student engagement as the field of machine learning continues to evolve.

The rapid advance of AI and ML asks for better and earlier education on these topics. However, research on teaching AI and ML topics is relatively underdeveloped. Especially applying the teaching method gamification has not yet been thoroughly tested. This research aims to explore the effectiveness of gamifying ML education in high schools. An experiment was designed based on previous research. This experiment revolves around a computer game where students learn to train a car to drive itself. Despite small sample size, results show increased motivation and low anxiety.

With the increasing influence of Machine Learning (ML) on our lives, the need for education on this topic is growing. A key component of education is assessment and improving this aspect could lead to better student learning performance. This study aimed to investigate the influence of different assessment methods on students' learning performance in k-means clustering. Two different assessment methods were used: a closed-book problem-based assignment and an open-book short answer exam. Participants were notified of their assessment method, after which they were instructed to watch a video lecture and take the assessment. Results show a significantly improved learning gain when using the open-book assessment, where learning gain was defined as the difference in score between the pre- and post-test. Between these two methods the open-book assessment is therefore favourable. However, future research is needed to develop a validated concept inventory for k-means clustering and identify other possible assessment techniques.

With the increasing development of artificial intelligence (AI), there is a more significant opportunity for humans and agents to collaborate in teamwork. In Human-Agent Teamwork (HAT) settings, collaboration requires communication, and the agent displaying emotion can impact how human teammates communicate and work together with the agent. This study investigated the impact of an explainable agent expressing emotion within explanations in a teamwork setting. We investigated how integrating an emotional component into an agent’s explanations influences trust in the agent, as well as humans’ perceptions of the agent’s anthropomorphism, animacy, likeability, and overall team performance when collaborating with the agent. With this goal, a pre-study was conducted using a focus-group meeting to investigate the relevant emotions to display in a simulated Search and Rescue (SAR) task and how these emotions can be incorporated into Explainable AI (XAI). Next, we conducted an in-between subject controlled experiment to study the effects of emotional components in explanations. The participants were divided into experimental and control groups, collaborating with agents that either displayed emotion or no emotion. The participants had to carry out a SAR task where they worked together with the agent to rescue victims. Our results confirmed that an agent displaying emotions increased perceived likeability, animacy, and anthropomorphism. Among these three, likeability and animacy are positively associated with trust. In contrast, an increase in anthropomorphism is associated with a decrease in trust. From the results, we could not conclude that team performance is directly affected by having emotion in the explanation. However, the results showed that emotion increases the messages sent from the human to the agent, and this increase in communication led to higher team performance.

Communication is one of the main challenges in Human-Agent Teams (HATs). An important aspect of communication in HATs is the use of explanation styles. This thesis examines the influence of an explainable agent adapting its explanation style to a supervising human team leader on team performance, trust, situation awareness, collaborative fluency, explanation satisfaction, understandability, and user-awareness. To perform a simulated Search And Rescue (SAR) task, a HAT is designed. With this design, a pre-study is then conducted using a questionnaire to discover the best-ranked explanation styles in the most important situations of the SAR task. Next, the user-study is carried out with 46 participants, using the HAT design and analysed data from the pre-study. There are two conditions: the agent adapting the explanation style to the human team leader and the baseline condition where the explanation style is randomised. The results show that the subjective measurements of trust, understandability, explanation satisfaction, and perceived user-awareness are significantly higher in the adaptive agent condition group. The same cannot be concluded for objective measurements such as team performance and situation awareness.

Objective: This thesis aided the 113 Suicide Prevention (113), the national suicide prevention center for The Netherlands, by investigating technical solutions for the helpline, implementing an e-learning prototype housing six suicidal personas within a conversational agent model, and evaluating and analyzing an experiment on its effect, which entailed interactions with one, two, and three simultaneous chats. Methods: The thesis conducted a participant observation with a total of seven triage-psychologists, organized three focus groups including triage-psychologists, managers, and training personnel with nearly forty participants, and administered an evaluation with thirty participants that included six triage-psychologist and twenty-four counselors regarding a prototype to assist in the training of 113's triage-psychologists. Prototype: The system specification provided a prototype with six personas where triage-psychologists can practice against one or many chatbots, or conversational agents, in different situations that pertain to training for 113. The conversational agents design was based upon the Rose of Leary interpersonal stance and the Beliefs, Desires, and Intentions (BDI) design paradigm. The system focused on how a conversational agent must react to triage-psychologists' inputs with respect to the subtleties in interpersonal communication and negotiation as it pertains to the 113 suicide helpline. Results: Evaluation results indicate that triage-psychologists found the learning environment motivational and the events in the environment as socially realistic. With the additional number of chats, counselors experienced an increase in three measurable areas: 1.) mental effort; 2.) situational awareness demand; and 3.) situational awareness supply; even so, counselors were positive about all learning aspects regarding the new software environment. Conclusion: This work identified the natural language processing, the BDI reasoning model plus natural language generation, and the usability and quality of the prototype as three areas of focus for 113 as they continue to improve their management of the helpline, its training, and research on suicide.

Making virtual characters seem sentient is the main goal of the study of believable virtual characters. Unbelievable behaviour, especially eye gaze behaviour, could make the whole virtual character seem less believable. In human-human social interactions eye gaze is used to interact non-verbally. People also apply Theory of Mind (ToM) to reason about other people's mental states. If a virtual character were to respond to a person's gazing behaviour with its own gazing behaviour, will it seem more believable and socially present to this person? Do people look at- and follow the gaze of such virtual characters more? Do people apply ToM to reason about the mental states of such virtual characters? This study employed a within-subject design (n=24) with two conditions that were given at the same moment. The conditions were represented by two virtual characters where one had gaze aware behaviour and the other did not. Using a think-aloud procedure during and questionnaires after the Virtual Reality experience the believability, social presence and the application of ToM were assessed. Gaze behaviour with respect to the virtual characters was assessed as well using the onboard eye-tracker of the Virtual Reality Head Mounted Display. The results suggest that virtual characters were more believable and socially present when they respond to people's gaze behaviour through their own gaze behaviour compared to a virtual character without such gaze behaviour. Results also showed that in some situations people looked more at the virtual character without gaze aware behaviour than at the virtual character with such behaviour. However, gaze following behaviour and whether or not people apply ToM were inconclusive. In conclusion, virtual characters that respond to people's gaze behaviour through their own gaze behaviour are perceived as more believable and more socially present than virtual characters without such gaze aware behaviour.