This study examines the effect of analogies on conceptual understanding of machine learning (ML) loss functions, and the motivation to learn in first-year bachelor computer science students. For a set of 10 ML loss functions, analogies were generated and evaluated by 15 experts. 3 of these analogies were subsequently tested with 22 students. The results show no conclusive evidence for improvement in understanding and motivation to learn. The study outlines a general strategy for evaluation of analogies on student understanding and motivation. The study further provides 10 expert-rated analogies, 3 of which have been tested with students.

This research paper looks into the influence of domain specificity on the understanding and motivation of first-year computer science students learning different concepts in supervised machine learning. Two types of domains were chosen for the analogies, the general domain and the gaming domain, the latter being the more specific one. These were evaluated in two phases. First, experts rated the analogies based on different metrics. Then, a user study was carried out using A/B testing to measure knowledge gain and motivation when exposed to the analogies. Results from the user evaluation show no statistically significant differences in terms of understanding for domain-specific or general analogies. Motivation, similarly show little difference when comparing both domains. The findings suggest that if analogies are helpful when it comes to understanding a topic, as long as the learner knows the domain, they do not play a big role.

Machine Learning is becoming a standard part of Computer Science curriculums at universities. This paper aims to contribute to the education of Machine Learning in Computer Science, specifically through teaching concepts related to Gradient Descent (GD) through analogies. First, concepts related to Gradient Descent were collected through the use of academic textbooks, and analogies were created based on the definitions found. These analogies were then evaluated by experts, scoring the analogies on Target Concept Coverage, Mapping Strength, and Metaphoricity. The analogies that scored highest on a mean average were then used in an A/B survey distributed amongst Computer Science students that had not followed any Machine Learning course. One group was given the concept definitions, the other both the definitions and the analogies. The learning proficiency was measured, and no statistically significant result was found. In the end, this research explores the possibilities of creating analogies to explain machine learning concepts, and provides a modular framework for evaluating quality and measuring effectiveness of analogies.

Machine Learning education faces significant challenges due to the abstract and mathematically-complex nature of fundamental models, such as Multilayer Perceptrons (MLPs). This paper investigates the effectiveness of conceptual metaphors and analogies as pedagogical tools to improve novice learner's understanding of key MLP concepts. Using large language models, we generated a set of analogies for core MLP topics. These analogies were then evaluated by experts to assess their quality, followed by a user study with novice learners employing a between-subject A/B test comparing analogy-based explanations to formal definitions. Although the study found no statistically significant improvement in knowledge gain or engagement that could be attributed to analogy-based explanations, trends suggest potential benefits in learner confidence and motivation. The research contributes a curated set of expert-evaluated analogies for ML education and discusses methodology limitations and directions for future work. This study highlights both the promise and complexity of integrating analogy-based teaching approaches into ML education.

Unsupervised machine learning is a complex and abstract topic, posing challenges for student comprehension. Considering the considerable growth of relevance the topic of machine learning has seen in the past years, teaching it effectively has become ever-so important. Analogy-based teaching approaches offer a potential solution by mapping unfamiliar machine learning concepts to familiar real-world ideas. This paper investigates how analogies can improve the understanding of unsupervised learning, a rather relevant field within machine learning. Contributions include a collection of analogies for teaching unsupervised ML, an expert-based evaluation of these analogies’ quality, and a student-centred assessment of analogy-based teaching.
The findings from the expert evaluation show a consensus on the effectiveness of several analogies and highlight which analogies might be less effective. The findings from the student assessment suggest that the analogical explanations are more effective than 'generic' explanations and suggest that students have a higher satisfaction while learning through analogies.
We conclude that well-crafted analogies can enhance student understanding in unsupervised machine learning. The study’s insights can guide educators in integrating analogies to make unsupervised learning more accessible.

The rapid advancement of Large Language Models (LLMs) in recent years is not without concerns, such as a lack of privacy, environmental impact, and financial concerns. It might therefore be beneficial to use Small Language Models (SLMs) instead, which are more accessible to be run by individuals or organisations, thus resulting in more control over the model. This research investigates whether we can replace an LLM with an SLM inside an AI hint-generation system, and achieve comparable hint quality, by conducting an expert study to validate generated hints based on a set of criteria and by conducting a student experiment, investigating student satisfaction and trust in the system. The expert results show that the hints generated by the SLM-powered system are slightly less personalised to the situation, are noticeably more misleading and more often suggest the wrong approach. The student experiment shows similar results for these criteria, and shows a slight decrease in the overall perceived helpfulness of the hints, trust in the system and willingness to continue using the system. The most prevalent complaint for the SLM-powered system was its inconsistency in the hint quality, as it generated good and useful hints in some contexts, but also suggested wrong and unusable hints too often. Thus, while replacing the LLM with an SLM has potential, as it is capable of generating useful hints, current SLMs are still too inconsistent.

Generative AI has opened up new possibilities in computer science education. Large language models have made it possible for learners to get instantaneous and customised feedback on different programming concepts, as well as the ability to use natural language to implement these concepts. One such concept is program decomposition, an essential skill in software engineering. This work presents a novel method for teaching program decomposition, using a three-stage guided AI decomposition system. We analyse how this method affects a learner's program decomposition cognitive processes via a concurrent think-aloud protocol where a student decomposes three simple programming tasks. Furthermore, we measure how using the system changes a student's confidence in their decomposition skills. We find that participants do not display any significant change in confidence levels after using the system. We observe that the students display a significant improvement in performance during the course of the study. The participants also display a significant decrease in metacognitive confusion and a clear emergence of reflection based on previous errors. We conclude that the proposed method and the implemented system lead to a level of internalisation of decomposition skills in the students. We recommend that a study of change in decomposition skills is conducted over a longer time period to observe the full effects of the method.

Machine learning (ML) has become a vital skill across various disciplines, driving innovation and transforming industries. This growing demand emphasizes the need for effective teaching methods tailored to students with diverse academic and technical backgrounds. Teaching ML to non-majors presents significant challenges, as many students lack foundational knowledge in mathematics and programming. This study explores how university instructors addressing these challenges in bachelor’s and master’s level courses, based on semi-structured interviews. The analysis uncovered several key strategies used by instructors. Many emphasized connecting ML concepts to real-world applications, making the subject more relatable. Visualization tools were commonly employed to simplify abstract concepts and improve comprehension. Hands-on activities, including live coding and interactive assignments, were highlighted as effective methods to engage students and bridge theory with practice. However, instructors faced challenges as well such as accommodating diverse student backgrounds, correcting misconceptions, and designing assignments that balanced accessibility and depth. Although tracking student progress was not the focus of this study, some insights were provided. Some instructors mentioned using formative assessments, such as quizzes and project-based evaluations, to measure understanding. These findings highlight the importance of adaptable teaching methods and inclusive learning experiences in making ML education more accessible and effective for non-majors, while also providing actionable advice to improve the educational process and course design.

Machine learning (ML) is increasingly integrated across diverse academic disciplines, necessitating effective teaching strategies tailored to varied student backgrounds. This study investigates the influence of prior mathematical knowledge on the learning outcomes of ML topics among Computer Science (CS) and Aerospace Engineering (AE) students. Employing a mixed-methods approach, the research involved initial mathematical assessments, interactive tutorials on key ML topics (Bayes Rule, Perceptrons, ML Pipelines), and subsequent evaluations of ML comprehension.

The results reveal significant differences in performance between the two groups. CS students, with their integrated programming and mathematical preparation, consistently outperformed AE students, who demonstrated variability despite their strong quantitative foundations. Probability and linear algebra emerged as key contributors to ML learning, showing stronger correlations with outcomes than calculus. Qualitative analysis highlighted the need for tailored instructional approaches: AE students preferred application-driven and interactive learning, while CS students valued structured and technically detailed resources.

These findings underscore the importance of interdisciplinary teaching strategies that bridge gaps in programming and mathematical competencies. The study’s insights have implications for designing inclusive ML curricula, emphasizing real-world applications, adaptive learning technologies, and frameworks to support diverse learner needs. Future research should explore broader ML topics, larger participant groups, and long-term skill retention to further enhance ML education across disciplines.

Machine learning (ML) has become a critical skill across various disciplines, yet teaching it to students outside Computer Science and Engineering (CS) remains challenging due to differing academic backgrounds. This study investigates the differences in learning outcomes between Industrial Design (ID) and CS students when introduced to foundational ML topics, focusing on the influence of prior mathematical knowledge.

Through initial surveys on mathematical proficiency, structured ML tutorials, and final assessments on learning outcomes, the research examines correlations between mathematical proficiency and ML performance, faculty-specific challenges, and qualitative feedback on learning experiences. Results reveal that prior mathematics knowledge significantly impacts performance on mathematics-intensive topics such as Bayes' Rule, while its influence is minimal on less math-relevant topics like ML pipelines. Furthermore, ID students emphasized creative and interactive teaching methods, contrasting with the programming-oriented preferences of CS students.

These findings highlight the need for interdisciplinary instructional strategies that cater to diverse learner strengths. By uncovering faculty-specific patterns in ML learning, this study contributes to the design of more inclusive and effective educational practices, fostering a broader understanding and application of ML across disciplines.

With the growing need for machine learning knowledge for many different expertises and positions, comes a growing need for machine learning education for non-computer scientists. Teaching machine learning concepts to non-majors comes with the added challenge of dealing with different levels of prior mathematical knowledge. Existing research is inconclusive on the correlation between this prior knowledge and topic-specific machine learning knowledge gain. This paper evaluated this via an experiment conducted on Computer Science and Physics students without prior machine learning education. We find that there is no clear correlation between general math knowledge and knowledge gain. There is however a clear correlation of proficiency in probability and statistics, and algorithm heavy machine learning topics. The experiment also concluded that most students struggled most with these math-heavy topics, as well as understanding abstract systems such as perceptrons.

As Machine Learning (ML) continues to shape advancements in academia and industry, ensuring effective ML education is essential. This study examines the retention of four core ML concepts- Principal Component Analysis, Gradient Descent, Bayes’ Theorem, and Hierarchical Clustering- two years after students completed a university-level ML course. Using a survey-based methodology, it explores how prior mathematical knowledge, perceived difficulty, and confidence influence long-term retention. Results reveal a significant positive correlation between Calculus knowledge and Gradient Descent retention, with weaker correlations for Linear Algebra with PCA and Probability with Bayes’ Theorem. Perceived difficulty and confidence also shape retention outcomes. The findings emphasize the need for targeted mathematical refreshers in ML courses to strengthen foundational knowledge and improve retention. This research provides actionable insights for curriculum design, aiming to bridge mathematical gaps, enhance learning outcomes, and sustain student engagement with advanced ML concepts.

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 educational stages, including high school and secondary school. However, the reduced research focus on ML pedagogy results in a lack of standard teaching methods compared to other science-related subjects, which have established norms for topic introduction, teaching tools, and assessment methods. With inspiration from other fields, this research aims to look into using interactive visualizations in teaching ML topics, more specifically in teaching Gradient Descent and Principal Component Analysis (PCA). The research includes the development and evaluation of Jupyter Notebooks for introducing these visualizations to students. The targeted student population is composed of Computer Science and Engineering Bachelor students who have not yet followed any Machine Learning courses but have the necessary background knowledge, namely calculus, linear algebra, and statistics knowledge. The evaluation of this teaching method measures the knowledge gained and the motivation of the students, compared to a static version of the materials. The results of the evaluation have shown a significant effect of interactive visualizations on knowledge gained related to PCA. The evaluation has not identified any difference in knowledge gain for gradient descent and learning motivation for both topics. With these results, we contribute to the body of evidence-based teaching methods in Machine Learning and identify further research needed to generalize the effect of interactive visualizations as a teaching method for teaching ML basic concepts.

This study investigates the impact of scaffolded assignments on student learning, confidence, and the development of an empirical mindset in a Machine Learning (ML) course at TU Delft. Unlike traditional Computer Science subjects, ML requires an experimental approach, challenging students used to design-first methodologies. Through surveys of 25 students from the CSE2510 course, the study found that scaffolded assignments significantly enhance student confidence and perceived learning benefits, despite no positive correlation between the number of assignments completed and course grades. Qualitative feedback highlighted the value of scaffolded assignments in understanding the ML design process by providing structured guidance and enabling practice of specific sub-tasks. These findings suggest scaffolded learning is crucial in developing an experimental mindset and boosting student confidence in ML education. This study also proposes a second methodology for future research during an edition of the course to further explore this topic.

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.

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 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.

Motivation. DNA molecules mutate thousands of times every day. Some mutations are harmful to human cells, and may lead to the loss of function in important genes involved in DNA damage repair (DDR) mechanisms. Diseases such as tumors can exploit mutations in important, driver DDR genes to rapidly proliferate. Specific patterns of mutations (or signatures) are insightful indicators for the presence of DDR malfunctioning, which can be exploited to provide targeted treatment (e.g., by leveraging synthetic lethalities). Different methods have been developed to successfully extract relevant mutational signatures from the genomes of tumor patients. Most approaches are unsupervised and thus do not optimize toward distinguishing DDR deficiencies (DDRd). Supervised approaches achieve this, but rely on labeled in vitro data from tumor cell line genomes during training, due to the lack of DDRd ground truth for tumor patient genomes. Semi-supervised learning could bridge the gap and jointly exploit labeled cell line and unlabeled patient mutation profiles to generalize to patient tumors and provide more clinically relevant DDRd mutational signatures.
Results. We propose Pseudo-labeling Semi-Supervised NMF (PSS-NMF), a novel integrated signature extraction and label prediction method, which extends supervised non-negative matrix factorization (NMF) with the ability to incorporate unlabeled samples into the training via pseudo-labeling. Models learned using PSS-NMF were benchmarked on two different tasks, cancer type and DDRd prediction. PSS-NMF consistently improved prediction for patient tumors over the supervised NMF baseline for both tasks, learning signatures that better transferred to the patient tumor domain: the models achieved Macro F1 scores of 0.3842 and 0.1331 respectively for cancer type prediction, and 0.4928 vs 0.4704 for DDRd prediction. We further validated that PSS-NMF identified DDRd signatures were biologically relevant, by comparing them to known DDRd-related mutational signatures curated in COSMIC and investigating their exposures in patient tumor genomes.

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 knowledge gaps on the success of different teaching methods in ML education. The paper investigates whether traditional methods are effective in equipping future engineers with the necessary skills for tomorrow's challenges, amidst the rapid advancement of ML and its applications. The literature review explores the range of teaching methods in ML education and not only, emphasizing a shift towards technology-enhanced and active learning approaches when teaching ML. A survey of ML lecturers explores the landscape of ML education in Dutch universities. The study investigates teaching methodologies, tools, and challenges, providing valuable insights into the evolving practices of ML instruction. Findings indicate a predominant trend towards adopting a blended approach, with lectures, projects, and group work forming core instructional methods. Virtual environments, active learning strategies, and staying informed through community engagement are highlighted. Word frequency and thematic analyses reveal key themes, emphasizing student-centric learning, practical application, and the integration of diverse teaching methods. Additionally, an experimental comparison of two teaching methods, lecture and jigsaw, sheds light on their seemingly similar efficacy when applied to the domain of ML education. The research contributes to the optimization of ML education practices, offering comprehensive insights for educators and policymakers.