As machine learning (ML) becomes integral in more disciplines, introductory courses in the field are attracting increasingly diverse audiences. Design of these introductory ML courses needs to be theoretically sound, but also intuitive, engaging, and accessible to a range of students. Effective teaching of ML must go beyond teaching the theoretical or practical mechanics of algorithms. In this paper, we synthesize effective teaching strategies from 6 experienced ML instructors across 5 institutions to help students define appropriate ML problems, build intuition, develop reasoning skills, and apply models responsibly. We organize these strategies into eight thematic areas: preparing students for success, motivating learners through real-world relevance, integrating ethics and societal impact, avoiding common methodological pitfalls in model evaluation, guiding students on design decisions, adapting effective classroom practices, assessing student learning, and preparing for the future. Each section offers practical examples of classroom-tested activities (or references to existing resources), and in many cases, reflections on our experiences with the strategies. Our aim is for this paper to be a starting point for instructors aiming to improve learning in introductory ML courses. We hope this is a resource-rich guide for teaching ML to diverse learners, grounded in both pedagogy and practice.

As undergraduate computer science classes grow in size, institutions increasingly rely on asynchronous computer-based assessments. To investigate whether exam timing reveals evidence of cheating, we analyze 21,403 submissions from 51 asynchronous exams across two undergraduate courses in this retroactive study. We extend prior research on proctored multiday exams by introducing a comparison in student performance trends between two distinct assessment modes: on-site proctored and off-site unproctored. We find that performance declines throughout the exam window in both modes. We observe a weak negative correlation between start time and performance, with standardized scores decreasing by 0.14 points per hour (on-site proctored) and 0.61 points per hour (off-site unproctored). In addition, start-time distributions and student surveys reveal behavioral differences. On-site proctored exams follow a centered start-time distribution, likely influenced by a reserved lecture hour. In contrast, off-site unproctored exams show a left-tailed distribution, with most students starting later than intended. This pattern suggests that greater scheduling flexibility leads to later exam starts, potentially exacerbating performance declines due to academic procrastination.

Alternative grading (AG) is increasingly being implemented in higher education (Butler, 2025). However, instructor motivations and experiences with AG have yet to be fully understood (Hackerson et al., 2024). Existing research on AG has been inconsistent and fragmented, often focusing on individual course implementations without broader applicability and theoretical grounding. To move the field forward, it is essential to understand the perspectives of instructors who implement AG—how they adopt these practices, what motivates them, and what challenges they encounter. This study aims to provide a broader, more transferable understanding of AG by examining these experiences. By centering instructor voices, this work offers insights that may support others considering AG and help reorient future research toward generating empirical evidence, strong theoretical underpinnings, and broader adoption. Using a phenomenographic approach, this study explores the experiences of higher education instructors who have implemented AG through semi-structured interviews, a short survey, and course syllabus analysis. Participants use diverse AG methods, often mixing and adapting systems and practices to suit their course contexts. The results highlight a range of AG systems, motivations and perceived benefits and challenges. Their motivations were primarily driven by dissatisfaction with traditional grading and a desire to focus more on student learning, reflection, and inclusivity. The perceived benefits and challenges organized around five key areas: institutional climate, administrative buy-in, course logistics for instructors, instructor workload, and pedagogical implications for students. These findings offer a more comprehensive view of AG in practice and provide a foundation for making AG more accessible to other instructors. By synthesizing the perspectives of instructors across diverse contexts, this study contributes to a more coherent understanding of the structural and pedagogical conditions under which AG practices can be effectively implemented and sustained. Ultimately, these insights may inform institutional policies on grade submission, faculty development initiatives, and future empirical research that seeks to evaluate AG not only as a pedagogical innovation, but as a systemic intervention capable of reshaping assessment culture in higher education.

The popularity of autograding has grown due to increasing class sizes and the need to reduce grading load while ensuring quality. Autograding has conventionally been used for multiple choice and fill in the blank questions, or to check code correctness. In this work, we discuss the use of autograders at UBC and some non-conventional autograding implementations in our curricula. We reflect upon our autograder use in our courses and discuss the benefits, implications, and considerations of this pedagogical choice.

Delivery of affordable, secure, and scalable assessments is an essential component of large university courses, whether online or in-person. The transition to Computer-Based Testing (CBT) has a transformational effect on pedagogy. Modern CBT systems provide almost unlimited flexibility in the types of questions they can support for manual grading and autograding. In this BoF, faculty interested in learning about various components of CBT and how to implement it at their institution are invited to ask their questions and learn from others who have already done this. To facilitate these discussions, in this BOF we will break into four smaller groups to discuss CBT pedagogy, building and sharing question banks, technical or logistical considerations, and building buy-in from all levels of the institution.

Affordable, secure, and scalable assessment delivery is an essential component of large university courses, whether online or in-person. The switch to Computer-Based Testing (CBT) can have a surprising, and almost transformational effect on pedagogy. Modern CBT systems provide almost unlimited flexibility in the types of questions they can support, for both manual grading and autograding, and CBT has now been adopted at several universities and is under serious consideration at others. In this BoF, faculty interested in learning about CBT and how to implement it at their institution are invited to ask their questions. Faculty experienced with CBT are invited to share how CBT has changed their approach, pedagogy, and behavior and how to advocate for its adoption.

The rise of online assessments has motivated the development of randomized question banks, with randomization referring to the generation of different variants of a question. Although not all randomization efforts are equally effective in generating question isomorphs, the current classification of questions solely as randomized or not fails to address the varying degrees of randomization. To address this limitation in describing the diversity of randomization designs, we introduce a framework that outlines six distinct randomization levels. Additionally, we designed practical guides to assist educators in effectively using the framework, aligning with their pedagogical objectives. Through our application of this framework to classify around 200 questions from two courses, we further highlight the generalizability of the framework and reveal insights into the considerations and challenges associated with incorporating question randomization into computer science curricula.

PrairieLearn is an open source, extensible online assessment platform built on modern web technologies. In this workshop, we will focus on how PrairieLearn can be used to improve student learning in undergraduate computer science classes. However, the platform is also more than suitable for use as an assessment engine in a variety of courses including the humanities, social, physical, and life sciences. In the first part of the workshop, we will showcase multiple question styles that highlight PrairieLearn's abilities as an online platform, including deploying automatically and manually graded questions at scale in large classes. In the second part of the workshop, we will discuss the anatomy of a PrairieLearn question, create several custom questions, and design assessments in PrairieLearn. In the third part, we will share strategies on adopting PrairieLearn at your institution. In particular, how algorithmically generated questions can be used in support of alternative grading schemes such as Mastery- or Specifications-Grading. Finally, we will share how PrairieLearn can be extended to support other coding languages and paradigms with custom and external autograders. There will be plenty of opportunities for questions throughout the workshop, and we intend to leave plenty of time for additional 1:1 support and training. Attendees will be able to attend the session virtually and are recommended to bring a web-connected computing device. By the end of the session, attendees will know enough to run a whole class on PrairieLearn including designing questions appropriate for homework, labs, and tests.

The field of computer science has a problem of representation - many groups are not represented in our classroom at levels approaching their composition in society. Unfortunately, the representation issue is a larger societal issue and begins well before students enter our institutions. Though we acknowledge that building inclusive and equitable classroom environments cannot increase representation by itself, it can have an impact on retention and inclusion for members of marginalized communities.