As machine learning becomes a standard part of computing and engineering curricula, teaching its core concepts effectively has become an important educational challenge. Regularization is one such concept: L1 and L2 penalties are widely used to prevent overfitting, with L1 producing sparse solutions and L2 shrinking weights more smoothly. It is commonly taught through two representational formats: an algebraic one, presenting the loss function and its penalty terms, and a geometric one, depicting constraint regions and their intersection with the loss contours. However, instructors choose between them without clear evidence on which better supports learning. This study asks whether teaching regularization algebraically or geometrically leads to different student performance, and in which kinds of understanding.

Two interactive notebooks, matched on learning objectives, length, and reading difficulty, were developed to teach the concept in each format and are released openly; they were compared in a between-subjects experiment with students who had completed an introductory machine learning course. Learning was measured with a post-test spanning conceptual understanding, problem-solving, and knowledge transfer, alongside a thematic analysis of students’ written explanations.

Both formats supported practical reasoning about regularization, but not identically: the algebraic group performed better overall, with its clearest advantage in conceptual understanding, no reliable difference on problem-solving, and an inconclusive result on transfer. The two groups largely shared the same core understanding but expressed it through different vocabularies: a penalty on the loss function versus a shrinking constraint region in weight space. Representational choice therefore appears to shape how students explain regularization more than whether they grasp it, suggesting that combining the two formats may best support learning.
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Machine learning is increasingly important in computer science education, but introductory concepts can be difficult because they combine mathematical notation, algorithmic reasoning, and conceptual understanding. Gradient descent is one such concept: students may reproduce the update rule while still struggling to explain the role of the loss function, gradient, learning rate, and repeated parameter updates.

This paper investigates whether multiple representations can support beginner understanding compared with a classic textbook-style explanation.

A small-scale exploratory experiment was conducted with students who had little or no prior machine learning experience. Participants completed a prerequisite pre-test, studied gradient descent using either a text-and-formula-based explanation or a multiple-representations explanation, completed a post-test, and answered an experience survey.

The multiple-representations condition showed higher post-test performance, especially on computation and application tasks, as well as higher confidence, clarity, usefulness, and engagement. Perceived cognitive load remained similar across conditions.

These findings suggest that aligned multiple representations can help beginners connect formal notation with concrete calculations and intuitive understanding, although the results should be interpreted cautiously because of the small sample size. ... Read more

Machine Learning (ML) is a rapidly growing field within Artificial Intelligence and one of the most prominent areas of technological study, which is particularly challenging for new learners as it requires a strong grasp of abstract algorithmic structures along with rigorous mathematical reasoning. Despite this, traditional instructional approaches often fail to support deep conceptual understanding, particularly for foundational models such as Decision Trees. This study examines whether integrating multiple instructional representations (including textual explanations, visualizations, analogies, videos, and interactive simulations) enhances student learning outcomes compared to traditional text-only materials.

To examine the lack of empirical evidence on multi-representational teaching for Decision Trees in ML education, a mixed-methods pilot experiment was employed with 10 participants was employed, comparing a multi-representation tutorial group to a text-only group. After a pre-test on mathematical and logical reasoning, participants completed a structured learning phase and a post-test measuring conceptual understanding, problem-solving, and transfer. Semi-structured interviews were also conducted to capture learner experiences.

Quantitative analysis included independent group comparisons using descriptive statistics, and inferential tests. Qualitative data were analyzed with inductive and deductive thematic analysis.

This study provides preliminary evidence that multi-representational instruction may improve problem-solving performance in Decision Tree learning. However, due to the small sample size and lack of statistical significance on two of three outcomes, these findings should be interpreted cautiously and require replication with larger samples.

The study contributes a structured evaluation framework for multi-representational ML education and provides evidence supporting the potential benefits of interactive instructional design in teaching Decision Trees. ... Read more

Machine Learning has become a prominent component of STEM curricula, yet there remains a lack of standardized pedagogical frameworks for teaching it. Principal Component Analysis represents a typical educational challenge, requiring students to simultaneously coordinate algebraic manipulation, geometric intuition, and algorithmic thinking. This study evaluates how different combinations of instructional representations affect undergraduate students’ conceptual understanding, problem-solving performance, and knowledge transfer ability. An experiment was conducted with 27 first-year Computer Science students at TU Delft. Participants were assigned to one of two experimental conditions: a traditional static representations group or a multimedia-enhanced representations group. Quantitative analysis revealed that the static group significantly outperformed the interactive group in total post-test scores and knowledge transfer. No statistically significant differences were observed in conceptual understanding or problem-solving performance. Qualitative thematic analysis indicated a disconnect between perceived and actual learning: while students preferred the interactive widgets for building geometric intuition, these features may have introduced extraneous cognitive load, provided a false sense of understanding, or students simply ran out of time because interactive exploration takes longer. ... Read more

Machine learning education presents unique challenges compared to traditional computer science courses: difficulties in actual implementation, differences in background knowledge, and quality of self-study resources. Language models have shown the potential to address these challenges and generate rich student-AI interaction data that may provide valuable insights for learning analytics. However, it remains unclear how such data can be systematically collected, analyzed, and presented to instructors in a meaningful way. To explore that, a case study was conducted in which bachelor students worked on a machine learning assignment using an AI supported programming system JELAI. The collected interactions illustrate how students use AI tools during work. To analyze these interactions, we developed a transformer-based classifier to categorize them into pedagogically relevant question types, and we also compared it with the prompt-based classifier on a classification task. In addition to that, we designed a learning analytics dashboard to visualize categorized interactions and evaluated it through a meeting focus on perceived usefulness. The results indicate that the automatic classification is feasible, but the accuracy is imperfect. The transformer-based classifier showed better performance in a challenging category, while other categories showed similar performance between models. The dashboard was perceived as useful, while also revealing areas for improvement in design and analysis. This thesis highlights the potential and challenge of using student-AI interactions for learning analytics and also motivates future large-scale studies. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more