Students may learn at different paces due to differences in prior programming experience (PPE), physical or mental health challenges, and economic or lifestyle barriers (e.g., employment or caregiving responsibilities). Additionally, increasing use of AI tools for homework can contribute to inaccurate self-assessment and poor preparation for supervised tests. Motivated by these challenges, we introduced bi-weekly low-stakes checkpoint quizzes in a large (~500-student) introductory CS course. Inspired by alternative grading paradigms such as mastery learning (ML), each quiz could be attempted multiple times without penalty, offering students frequent feedback and opportunities for iterative improvement. Our mixed-methods study investigated the impact of these quizzes on performance, self-assessment, stress levels, and overall experience, using performance and survey data (N=456). Results showed that though retake opportunities allowed students to improve quiz performance, frequent retake attempts were associated with lower final exam outcomes, suggesting continued struggle on novel problems. Despite limited performance benefits, survey data revealed strong affective outcomes based on overwhelmingly positive student sentiment: students reported high Likert-scale ratings for learning/engagement and stress reduction value (though subgroup differences by gender, PPE, English fluency, and retake frequency suggest room to improve equity outcomes), and the majority of open-ended responses described the quizzes as helpful for improving self-assessment, reducing stress, and supporting meaningful learning. Overall, our implementation allowed students to experience some benefits of ML while retaining enough structure to prevent procrastination, illustrating how ML?inspired assessment can be incorporated into courses without a full course redesign.

Peer Reviewed

Given natural language test case description for an Android application, existing testing approaches require developers to manually write scripts using tools such as Appium and Espresso to execute the corresponding test case. This process is labor-intensive and demands significant effort to maintain as UI interfaces evolve throughout development. In this work, we introduce LELANTE, a novel framework that utilizes large language models (LLMs) to automate test case execution without requiring pre-written scripts. LELANTE interprets natural language test case descriptions, iteratively generate action plans, and perform the actions directly on the Android screen using its GUI. LELANTE employs a screen refinement process to enhance LLM interpretability, constructs a structured prompt for LLMs, and implements an action generation mechanism based on chain-of-thought reasoning of LLMs. To further reduce computational cost and enhance scalability, LELANTE utilizes model distillation using a foundational LLM. In experiments across 390 test cases spanning 10 popular Android applications, LELANTE achieved a 73% test execution success rate. Our results demonstrate that LLMs can effectively bridge the gap between natural language test case description and automated execution, making mobile testing more scalable and adaptable.

In computer science education, cultivating soft skills alongside technical competencies is increasingly recognized as crucial for successful careers in industry and research. However, integrating soft skills training into curricula often remains a secondary consideration, separate from the primary program delivery or managed by a separate unit within the university. In this paper, we present three comprehensive curricula that intertwine soft skills within academic training for three distinct tiers of computer science education: undergraduate, master's, and professional levels. We identify common intrinsic motivations that we found most impactful for learner success within these programs, and present a detailed exploration of each program's curriculum and goals. By highlighting effective strategies and potential pitfalls, we offer valuable insights into harnessing these motivational drivers to enhance student engagement and learning. Furthermore, we outline emerging opportunities and challenges within the integrated curricula, inviting discussion on the broader implications for computer science education.

Recent work has confirmed that computing students experience the Imposter Phenomenon (IP) at higher rates than reported in other disciplines. However, no work has examined what aspects of the university computing experience might lead to a higher rate of IP experiences. We aim to illustrate the IP experiences students have, identify common sources of these experiences, and document the effects of these experiences and how students respond to them. We asked undergraduate students to share recent experiences that illustrate their experiences with the IP. We conducted an inductive thematic analysis on these open-ended responses, resulting in a set of inter-connected themes. A significant fraction of students related stories about making comparisons with peers or observing peer behaviour that made them question their abilities. Students also spoke about holding unrealistic expectations learned from their peers or imposed by the environment. These experiences may be particularly acute for minority-affiliated students who may come to feel they do not belong. Ultimately, these IP experiences can lead to a loss of motivation or a cycle of failure that leads students to leave computing. The central role social comparisons play in IP experiences suggests that it is particularly important to foster communities where opportunities for comparison are reduced and where realistic expectations are explicitly set.

Intrusion Detection Systems (IDSs) play an important part in securing computer networks from different malicious threats and attacks. Modern IDSs leverage machine learning algorithms for effective intrusion detection. However, network traffic flows contain a large number of redundant features in a high-dimensional feature space which decreases the performance of such data-driven approaches drastically. Existing feature reduction methods lack to effectively remove redundant features as well as to retain features with additional information (if there is any). In this paper, we propose a Redundancy based Hierarchical Clustering (RHC) method that groups redundant features in the same cluster based on mutual information. We use both feature selection and extraction for finding the final feature set. We conduct rigorous experiments on three benchmark security datasets and our results demonstrate that the proposed methods outperform the state-of-the-art methods in terms of accuracy, f-score and false positive rate. We show the superiority of our proposed methods in both binary class (normal vs attack) and multi-class classification.

Online learning is playing a significant role, especially during the COVID-19 pandemic. In this study, we perform an interview study through in-depth interviews with 22 high school students of a developing country (Bangladesh) to find out about their experience and practices with online learning during the pandemic. Our findings reveal several usage strategies, challenges of the conventional usage of online learning, workarounds students adopt to address those challenges. Through the adaptability lens, we find that many students are adapting to online learning despite being in favor of it.

Mental health crises among post-secondary Computer Science students are persistent and growing concerns as students are prone to high stress levels and feelings of anxiety and depression [1]. Although this issue is not unique to Computer Science, the prevalence of mental health issues in STEM [1] makes it extremely important for CS educators to find ways to support student well-being within their courses. One potential technique to help alleviate some of the negative feelings students face is for courses to incorporate mental wellness interventions that aim to improve students’ psychological well-being. This poster discusses an attempt at engaging students in such an intervention – weekly gratitude journaling – in an online CS1 course.

Motivation Despite the widespread belief that computing practitioners frequently experience the Imposter Phenomenon (IP), little formal work has measured the prevalence of IP in the computing community despite its negative effect on achievement.

In this paper, we present our experience with the use of breakout rooms in a second year undergraduate Software Design course at a large North American institution. Following the switch to remote instruction during the coronavirus pandemic, we revamped our in-person Software Design course to be delivered as a flipped online course, making extensive use of in-lecture exercises completed during breakout rooms. We report on the structure and logistics of this lecture design (for a large class of 300+ students). To gain insights into the impact of the use of breakout rooms on student experience, we conducted weekly student surveys asking for feedback on the lectures and specifically on the use of breakout rooms. Although many students had positive feelings regarding the use of breakout rooms, a significant percentage of students (an average of 47% of the survey responses each week) expressed negative feelings toward them. In an end-of-term survey, we specifically asked students about what they felt worked best for breakout rooms in terms of group size and pre-assigned versus randomized groups, and if there were any other areas that they felt needed improvement. Some of the patterns we observed were that most students liked smaller groups (2-5 people), preferred staying in the same group throughout the semester, and enjoyed the use of breakout rooms as long as others in their room were active participants. We share the details of these survey results as well as the tips and lessons that we learned through this experience.

Abstract Parameterized complexity is an exemplary approach that extracts and exploits the power of the hidden structures of input instances to solve hard problems. The tree-width ($tw$), path-width ($pathw$), branch-width ($bw$), clique-width ($cw$), rank-width ($rw$) and boolean-width ($boolw$) are some width measures of graphs that are used as parameters. Applications of these width parameters show that dynamic programming algorithms based on a path, tree or branch decomposition can be an alternative to other existing techniques for solving hard combinatorial problems on graphs. A large number of the linear- or polynomial-time fixed parameter tractability algorithms for problems on graphs start by computing a decomposition tree of the graph with a small width. The focus of this paper is to study the exact and random boolean-widths for special graphs, real-world graphs and random graphs, as well as to check their competency compared with several other existing width parameters. In our experiments, we use graphs from TreewidthLIB, which is a set of named graphs and random graphs generated by the Erdös–Rényi model. Until now, only very limited experimental work has been carried out to determine the exact and random boolean-widths of graphs. Moreover, there are no approximation algorithms for computing the near-optimal boolean-width of a given graph. The results of this paper demonstrate that the boolean-width can be used not only in theory but also in practice and is competitive with other width parameters for real graphs.

Computer science is a fast-growing field in today’s digitized age, and working in this industry often requires creativity and innovative thought. An issue within computer science education, however, is that large introductory programming courses often involve little opportunity for creative thinking within coursework. The undergraduate introductory programming course (CS1) is notorious for its poor student performance and retention rates across multiple institutions. Integrating opportunities for creative thinking may help combat this issue by adding a personal touch to course content, which could allow beginner CS students to better relate to the abstract world of programming. Research on the role of creativity in computer science education (CSE) is an interesting area with a lot of room for exploration due to the complexity of the phenomenon of creativity as well as the CSE research field being fairly new compared to some other education fields where this topic has been more closely explored. To contribute to this area of research, this article provides a literature review exploring the concept of creativity as relevant to computer science education and CS1 in particular. Based on the review of the literature, we conclude creativity is an essential component to computer science, and the type of creativity that computer science requires is in fact, a teachable skill through the use of various tools and strategies. These strategies include the integration of open-ended assignments, large collaborative projects, learning by teaching, multimedia projects, small creative computational exercises, game development projects, digitally produced art, robotics, digital story-telling, music manipulation, and project-based learning. Research on each of these strategies and their effects on student experiences within CS1 is discussed in this review. Last, six main components of creativity-enhancing activities are identified based on the studies about incorporating creativity into CS1. These components are as follows: Collaboration, Relevance, Autonomy, Ownership, Hands-On Learning, and Visual Feedback. The purpose of this article is to contribute to computer science educators’ understanding of how creativity is best understood in the context of computer science education and explore practical applications of creativity theory in CS1 classrooms. This is an important collection of information for restructuring aspects of future introductory programming courses in creative, innovative ways that benefit student learning.

Feature selection plays a vital role in the field of data mining and machine learning for analyzing high-dimensional data. A popular criteria for feature selection is Mutual Information (MI) as it can capture both the linear and non-linear relationship among different features and class variable. Existing MI based feature selection methods use different approximation techniques to capture the joint performance of features, their relationship with the classes and eliminate the redundant features. However, these approximations may fail to select the optimal set of features, especially when the feature dimension is high. Besides, due to the absence of an appropriate searching strategy, these MI based approximations may select unnecessary features. To address these issues, we propose a method namely Feature Selection based on Redundancy maximized Clusters (FSRC) that creates the clusters of redundant features and then selects a subset of representative features from each cluster. We also propose to use bias corrected normalized MI in this regard. Rigorous experiments performed on thirty benchmark datasets demonstrate that FSRC outperforms the existing state-of-the-art methods in most of the cases. Moreover, FSRC is applied to three gene expression datasets which are high-dimensional but small sample datasets. The result shows that FSRC can select the features (genes) that are not only discriminating but also biologically relevant.

Introductory Computer Science (CS1) is difficult for many (the course is notorious for its low student performance and retention rates), but some groups of students are especially disadvantaged such as female students (as CS is largely male-dominated), non-CS majors and students without prior programming experience. This poster discusses an attempt at engaging these disadvantaged subgroups through creative, open-ended exercises. Data was collected from a CS1 course with 284 students, where roughly half of the students completed exercises which included an open-ended aspect that allowed them to make their own decisions about their projects. The other half completed similar exercises but with a specifically defined, closed-ended checklist of requirements. The data analysis revealed several patterns that suggest the use of open-ended exercises can lead to higher levels of satisfaction and confidence among the different groups of traditionally disadvantaged students.

Online hateful speech detection and classification in social media for the various major languages other than English has drawn the attention of researchers recently. In this paper, we develop Machine Learning (ML) algorithms based model, as well as Gated Recurrent Unit (GRU), based deep neural network model for classifying users' comments on Facebook pages. We have collected, annotated 5,126 Bengali comments and classified them into six classes - Hate Speech, Communal Attack, Inciteful, Religious Hatred, Political Comments, and Religious Comments. The produced corpus is the first contribution to the field of hateful speech detection in the Bengali language for social media. Finally, we employ several machine learning algorithms, compare the performance, and attained 52.20% accuracy in Random Forest. The accuracy is improved in the case of GRU based model (70.10% accuracy) about 18%.

A pedagogical and technical HOW-TO for the git version control system, including administration, applications in class, for student collaboration, and assignment submission.

Hypertension is the single most significant risk factor for heart disease, stroke and kidney disease. The key causes of hypertension can be directly linked to the lifestyle of the patient, including age, family history, smoking, obesity etc. Our work consists of an interactive mobile application that acquires these lifestyle information and use several recommendation techniques to warn and guide the user towards well-being. So far, this is one of the earliest approaches in this domain for a developing country like Bangladesh.