Large Language Models (LLMs) are increasingly integrated into CS education, with students using them to generate problem solutions. Although LLMs can produce correct solutions, do their approaches align with the diversity of approaches in student code? This work aims to propose an analytical workflow for investigating this topic. We proposed a workflow and evaluated it using 999 student Java code submissions from two questions in a CS1 dataset, along with matched zero-shot solutions generated by OpenAI GPT-4. Zero-shot prompting was chosen to mirror students' typical use of LLMs. Our preliminary results indicate that students consistently demonstrated greater diversity in problem-solving methods than the LLM, especially on more complex tasks. These findings reinforce concerns that heavy reliance on LLMs might reduce students' creativity when solving programming tasks. They also inspire design implications across several future directions, including cheating detection, the use of simulated students, and pedagogy refinement to promote more diverse approaches to students.

This innovative practice paper presents an LLM-supported technique to help instructors respond effectively to periodic students' reflections. Efficient communication between instructors and students is integral to supporting a productive learning environment. Recognizing the significance of understanding students' perceptions and challenges, we present the initial implementation of a system to help instructors analyze and respond to students' feedback promptly and effectively. This research is inspired by and extends prior works where instructors sent progress check emails to students, with some works finding that such communication increased students' motivation. To collect feedback, we administer regular student reflections throughout the semester that capture how students feel about the course and uncover the challenges they face. This regular feedback-gathering approach allows instructors to better track their students' progress and respond to comments throughout the semester to provide guidance. However, reading and responding to each reflection manually in the context of their overall learning experience can be time consuming. To address this challenge, we introduce an LLM-based automated approach that generates tailored, performance-contextualized responses to student reflections that can be used to guide first-contact interventions. The generated reflection responses (GRRs) address issues discussed in student reflections and provide advice, support, course information, and follow-up questions to the students. Additionally, they provide feedback to students based on their accomplishments and behavioral data within the learning management system (LMS), such as submission patterns. In this work, we discuss our method of generating responses based on students' reflections and their LMS behavior. We also present example scenarios of the proposed approach. Preliminary results indicate that this approach can help instructors facilitate positive educational interactions with students and that the participating students view the interventions favorably, fostering a constructive learning environment. This work provides an initial presentation of our large language model-based response generation method to motivate further investigation into AI-assisted student support mechanisms

One of SIGCSE's missions is to provide occasion for K-12 teachers and college/university professors to network, developing understanding of each other's roles as educators and generating ideas for collaborations for the sake of broadening participation in computing. We aim to promote opportunities for researchers and K-12 practitioners to work together in a BoF in order to participate in discussion regarding a)K-12 members' interests regarding CS education research; b) strategies for increasing opportunities for K-12 membersc) researcher/K12 collaborations d) ideas for future grants and/or collaborations The expected audience are the college/university faculty interested in K-12 education, as well as K-12 teachers or administrators interested in college/university CS education research. Regarding K-12/University collaborations, one might only think of the benefits to students. Yet, opportunities for learning and growth on the part of teachers and university faculty as work partners fosters motivation on both sides. This unexpected, valuable outcome has been documented in the literature as well [1,2,3]. The organizers of this BoF each have valued the learning from such collaborations and represent various roles as collaborative researchers from both sides.

Without a sense of belonging, students may become disheartened and give up when faced with new challenges. Moreover, with the sudden growth of remote learning due to COVID-19, it may be even more difficult for students to feel connected to the course and peers in isolation. Therefore, we propose a recommendation system to build connections between students while recommending solutions to challenges. This pilot system utilizes students’ reflections from previous semesters, asking about learning challenges and potential solutions. It then generates sentence embeddings and calculates cosine similarities between the challenges of current and prior students. The possible solutions given by previous students are then recommended to present students with similar challenges. Self-reflection encourages students to think deeply about their learning experiences and benefit both learners and instructors. This system has the potential to allow reflections also to help future learners. By demonstrating that previous students encountered and overcame similar challenges, we could help improve students’ sense of belonging. We then perform user studies to evaluate this system’s potential and find that participants rated 70% of the recommended solutions as useful. Our findings suggest an increase in students’ sense of membership and acceptance, and a decrease in the desire to withdraw.

This full paper discusses the implementation and results of a hybrid spatial skills intervention in multiple introductory computing courses. Spatial skills abilities have been shown to have high correlation with students' success in STEM related fields. Studies have shown that running spatial skills interventions have increased student success in their corresponding fields. Additional research has found that spatial skills interventions are more successful when run in person with hands on activities. These studies saw that when spatial skills interventions are conducted online, there is no significant gain in a student's spatial skills. We discuss the implementation and results of a hybrid spatial skills intervention where the learning material is presented asynchronously online, and exercises and worksheets are completed by hand. We conducted our intervention across three universities at the same time in multiple introductory programming courses and found that our hybrid model resulted in significant gains in both students' spatial skills and programming ability. Students who did not participate in the intervention had an average decrease of -1.5 points (-5%) from pre to post scores on the Purdue Spatial Visualisation Test: Rotations (PSVT:R), used to measure spatial skills, and an average increase of 1.1 points (12 %) from pre to post on the Second Computer Science 1 Exam: Revised (SCS1R), used to measure students programming abilities. Students who participated in the intervention had an average increase of 1.2 points (4%) on the PSVT:R and an average increase in score of 1.6 points (18%) on the SCS1R. Students who participated in the intervention had an average total increase on the PSVT:R of 2.7 points (9%) and an average total gain on the SCS1R of 0.4 points (6%) compared to students who did not participate in the intervention. These results show that the use of a hybrid spatial skills intervention is a viable option to administering a spatial skills intervention and can be replicable at scale with little to no extra work from instructors.

This full research paper studies the correlation of self-efficacy in computer science as well as learning and social skills with students' academic performance and their emotions in collaborative learning environments. Self-efficacy is an essential part of social cognitive theory and provides the foundation for analyzing human thoughts, motivations, and actions. Studies show that students' successful performance and accomplishment are directly affected by the level of self-efficacy. Therefore, analyzing self-efficacy in engineering education is important since it can impact the learning process in academic settings as well as provide a metric to track for improvement. Social cognitive theories also emphasize that students' interaction with each other affects their learning process and how they perform in educational settings. In previous work [5], we analyzed students' conversations in low-stake teams in an introductory programming course (CS1) and observed a strong positive correlation between students' positive emotions while interacting with each other with their performance in the course. In this study, we focus on the correlation of self-efficacy with learner's emotion and performance. We measure students' self-efficacy with a standard instrument called “Student Attitudes Toward STEM (S-STEM) Survey”. For this purpose, we asked the participants to self-report on a 5-point Likert-scaled survey including 20 questions. These 20 questions are grouped into 2 main categories of computer science and learning/social skills. Students' emotions were extracted from their speeches in teams by applying natural language processing (NLP) methods. The result of data analysis shows a statistically significant correlation between overall self-efficacy and performance in the course and positive emotions during the teamwork. We further investigate which category of self-efficacy questions most correlate with students' performance. The result shows self-efficacy in interpersonal skills and learning ability most impact students' performance.

We have established a research-practice partnership (RPP) to build a computer science (CS) and computational thinking (CT)-focused STEM ecosystem at two middle schools. Creating such an ecosystem to broaden student participation in computing through an RPP approach involves all stakeholders in the research process. Borrowing upon visual participatory research methods, we developed a graphic research instrument to engage teachers in the research process and elicit their perspectives on strategies for building the ecosystem. This experience report describes our research methodology across two distinct cases to demonstrate the utility of this drawing activity as an investigative and partnership development tool. The contribution is in offering a flexible approach to other university-based RPP teams that enables a synergistic partnership development tool and data collection instrument that can be tailored to a variety of RPP contexts, facilitating more productive and equitable ways of engaging stakeholders in the research process. We describe our project contexts and share results from the pilot study with practitioner-members of our RPP teams. We discuss two cases to highlight the contribution this approach made to the development of our partnerships.

This full research paper studies affective states in students' verbal conversations in an introductory Computer Science class (CS1) as they work in teams and discuss course content. Research on the cognitive process suggests that social constructs are an essential part of the learning process. This highlights the importance of teamwork in engineering education. Besides cognitive and social constructs, performance evaluation methods are key components of successful team experience. However, measuring students' individual performance in low-stake teams is a challenge since the main goal of these teams is social construction of knowledge rather than final artifact production. On the other hand, in low-stake teams the small contribution of teamwork to students' grade might cause students not to collaborate as expected. We study affective metrics of sentiment and subjectivity in collaborative conversations in low-stake teams to identify the correlation between students' affective states and their performance in CS1 course. The novelty of this research is its focus on students' verbal conversations in class and how to identify and operationalize affect as a metric that is related to individual performance. We record students' conversation during low-stake teamwork in multiple sessions throughout the semester. By applying Natural Language Processing (NLP) algorithms, sentiment classes and subjectivity scores are extracted from their speech. The result of this study shows a positive correlation between students' performance and their positive sentiment as well as the level of subjectivity in speech. The outcome of this research has the potential to serve as a performance predictor in earlier stages of the semester to provide timely feedback to students and enables instructors to make interventions that can lead to student success.

Full Paper. Research. We discuss possible ways to direct students to right level of introductory programming. While many schools offer college preparatory or advanced placement courses in computing, there is still, unfortunately, a large part of the "college-ready" population that has no opportunity to learn computing at all before they arrive. Regulation of CS education at the state/province or national level is still rare (but growing). Thus incoming students possess a wide range of skills and knowledge. When coupled with increasing enrollments, this diversity of experience can result in courses having large numbers of both absolute beginners and seasoned coders. Such courses are difficult to teach, intimidate novice students, and bore those with more experience. This can result in low engagement and retention.Unlike mathematics and language arts, introductory courses in CS vary widely from one institution to another in both conceptual material and programming language used. A standard point of entry to college mathematics is a calculus course, with some students instead starting earlier with pre-calculus or an algebra refresher, and others starting out in the second-term calculus course. There is rarely a concern about student skill being hidden by notational or other language differences, because the language of mathematics is close to universal. Similarly, freshman language arts courses in reading and/or writing assume a certain level of skill and maturity of comprehension and expressiveness in the target language; otherwise remedial courses are provided.We investigate placement of incoming first year students into appropriate introductory computer science courses at higher education institutions where there is more than one choice of first course. The goal is to determine the best way to decide which first course would be the most helpful for each student.

Spatial abilities have been shown to have high predictability in students' success in STEM related fields. Studies have also shown that there is a correlation between students' spatial skills and programming abilities, but it is unknown how well students' prior spatial abilities can predict students' introductory programming abilities at the end of the semester. During this study we used a multinomal logistic regression to create a predictive model to predict students' introductory programming abilities at the end of the semester. The highest model accuracy (64.6%) was obtained when accounting for students' prior programming abilities, prior spatial skills, socioeconomic status, and three factors regarding students' attitudes towards computing. It was also found that when looking at the predictability of each individual variable, students' prior spatial ability had the highest predictability (56.6% accuracy) when compared to all other variables.

This paper discusses the validation of a modified computer science attitudes instrument. Dorn and Tew's Computing Attitude Survey was modified by adding questions on gender issues and questions regarding students' perceptions of the utility of computing. Current trends indicate an increasing gap in the genders graduating with degrees in computer science. The new questions explore student attitudes and perceptions of women in computing while items related to computer science utility explore the importance of CS in students' lives and careers. These modifications necessitated the re-validation of the new revised instrument and comparison of the results obtained with in the original instrument by Dorn and Tew.

This paper discusses the results of replicating and extending a study performed by Cooper et al. examining the relationship between students' spatial skills and their success in learning to program. Whereas Cooper et al. worked with high school students participating in a summer program, we worked with college students taking an introductory computing course. Like Cooper et al.'s study, we saw a correlation between a student's spatial skills and their success in learning computing. More significantly, we saw that after applying an intervention to teach spatial skills, students demonstrated improved performance both on a standard spatial skills assessment as well as on a CS content instrument. We also saw a correlation between students' enjoyment in computing and improved performance both on a standard spatial skills assessment and on a CS content instrument, a result not observed by Cooper et al.

This project is a Research to Practice Partnership (RPP) between two middle schools and two universities. It focuses on investigating problems and on identifying solutions around increasing participation and interest in computer science (CS). We aim to do this by identifying, experimenting with, and fine-tuning methods to help students develop computational thinking (CT) skills. The research employs a STEM ecosystem model, which facilitates a support structure that aims to mitigate barriers and impact students as they progress in STEM areas. While this RPP is still a work in progress, we present data from the first year of our collaboration with one of the middle schools. While the research questions guiding this RPP are intended to be iterative and revised annually, year one data provides perspectives on (1) barriers to developing a STEM ecosystem that supports CS/CT for every student through integration into science, math, and language arts courses, (2) the factors or interventions needed for the development of a CS/CT focused ecosystem that supports everyone in the school, (3) the indicators of success for a CS/CT focused STEM ecosystem in a school, and (4) how the ecosystem prepares and engages all students for CS/CT work in high school. Year one data is discussed in terms of the STEM ecosystem framework and in how it will guide the next steps in this partnership. This project contributes to the understanding of how to prepare future generations for participation in a workforce where knowledge of the foundations of CS/CT is integral to success.

Data sharing is a common contribution to open science. The creation of open datasets can speed up research advancements by allowing researchers to focus efforts on developing and validating analytical techniques, rather than on obtaining data. Open datasets also allow researchers to benchmark new analytical approaches against a known standard, and increase the reproducibility of research. The field of higher education learning analytics could benefit from the creation of open, shared datasets on higher education students as these data do not currently exist in open and accessible formats. Here, we propose the use of synthetic data generators to create open access versions of student data. Synthetic datasets have an advantage over real data, as private student data is protected by federal laws. We compare the characteristics of the synthetic data to the original data and illustrate a model for how the synthetic data can be leveraged for developing and optimizing a common learning analytics algorithm.

This work in progress presents a model for first undergraduate software engineering course as a core course of the computer science curriculum. The course is designed to be offered in the fourth or fifth semesters (i.e., end of sophomore or beginning of the junior year) for students who have completed the introductory programming and data structures courses. In addition, they may have some basic knowledge of databases and web technologies. Moreover, in our curriculum, students at this level have not been exposed to any codebase of real-world application and particularly of large size (>10,000 lines) codebases yet. Based on such situations, the major focus of this course is on teaching the fundamentals of software engineering as a methodology of developing real-world software with an emphasis on: 1) software systems in the enterprise level, 2) basic modeling using functional, flow, and behavioral diagrams, and 3) team-based agile project development. The paper discusses our novel course configuration of the three emphasized elements above, where students work with open source software in this class as part of course activities and assignments to simulate working on an enterprise project and learn agile development Our observation as well as our industry partners indicated that students enjoy the open source challenges and demonstrate professional competency after this course. Our initial findings include a positive impact of open source and team work on our students.

We introduced a model of activity-based active learning class which has been practiced for a few years in [4]. While it may seem an easy task, designing an effective activity-based active learning can be quite challenging. Active learning adds new benefits to teaching including increased student involvement, social interaction, and hands-on learning, etc. [1]. However, in some situations, it may not provide an efficient learning environment. For example, we may assume that it is effective for educators to provide students with an environment where they can perform peer instruction and social learning. It could be further assumed that students will naturally find their way around the activities by following the instructions or peer instruction [2]. Such issues depend on the way activities are designed and executed. In this work, we discuss how the scaffolding of activities can help students stay engaged with the course without feeling lost or disconnected. Scaffolding refers to methods used to help students progress towards stronger understandings and eventually more independence in the learning process [3]. Our scaffolding methods smoothly transition students from lower to higher levels of challenge through an appropriate breakdown of course contents into activities of various types, proper sequencing of concepts and formative assessment questions. We believe that such proper breakdown is essential to keeping students engaged. We don't want students to feel bored (when challenge level is too low) or overwhelmed (when the challenge level is too high). Our findings indicate statistically significant differences in participation and engagement when using scaffolded activities in our introductory programming course (CS1).

We propose a working group to investigate methods of proper placement of university entrance-level students into introductory computer science courses. The main issues are the following. The ability to predict skill in the absence of prior experience The value of programming language neutrality in an assessment instrument Stigma and other perception issues associated with students' performance, especially among groups underrepresented in computer science The impact or potential impact on underrepresented populations (minorities, those with lower socioeconomic status) The outcomes/satisfaction/retention metrics in the major of the paced/tracked students compared to those in one-size-fits-all introductory classes

\beginabstract Cognitive tests have been long used as a measure of student knowledge, ability, and as a predictor for success in engineering and computer science. However, these tests are not without their own problems relating to priming, difficulty (resulting in test fatigue) and time on exam. This paper discusses efforts to modify Parker et al.'s Second CS1 aptitude test (SCS1) \citeParker16 to reduce the time spent on the exam, provide greater customization to match concepts taught across three universities, and reduce redundancy of test questions all while maintaining the instrument's reliability. This instrument was modified for use on an ongoing grant investigating whether spatial abilities impact the success of students in introductory CS courses. The instrument developed in this paper is a revised shortened version of Second Computer Science 1 (SCS1) aptitude test, designated as SCS1R. \endabstract

This paper describes and compares two categories of pedagogical design patterns that have emerged from CS education practice: lecture-based design patterns and active learning design patterns. Pedagogical design patterns provide faculty with combinations of generalized descriptions of problems and solutions that occur in teaching and learning. The benefit of forming design patterns is the codification of successful practice that can be reused in multiple scenarios and draw on the creativity of the instructor for defining the details relevant to the course and the students. Design patterns have been represented in many formats since Alexander’s initial design pattern model highlighting different aspects of what is important in each domain in which the patterns are created and used. This paper analyzes design patterns emerging from recent developments in lecture-based pedagogy and active learning in CS education. Traditional lectures in computer science, engineering, and other STEM disciplines are being reconsidered due to research that shows that students are less likely to learn while listening and more likely to learn while actively engaged. Design patterns that address problems and provide potential solutions to traditional lectures in computer science education have been published that provide solutions to engage students during the lecture. The pedagogy of flipped classrooms and active learning have recently been adopted by many faculty in Computer Science leading to emerging design patterns for active learning. We compare how previously published lecture-based patterns and our active learning patterns address similar problems with different solutions to engaging students. We show how an object-based structure for pedagogical design patterns can provide additional information about the problems and the solutions addressed by the patterns that are more easily indexed and combined.

This research to practice full paper provides preliminary evidence that integrating reflections is a significant feature to identify at-risk students early in a semester as verified and validated by a sequence-based learning analytics model. We've devised an active learning classroom model which incorporates reflection at multiple points in students' learning experience. This active learning model adopts Kolb's Learning model to provide a coherent and connected set of activities before, during, and after the class. Unlike periodic assessment through testing, reflections can provide nearly-real-time information about student's experiences in class. We extract sentiment feature vectors to capture students' affect from written reflections. These features typically aren't assessed on tests or during in-class activities. These features were extracted automatically using LIWC (Linguistic Inquiry and Word Count) is a tool for applied natural language processing) which is less cumbersome to implement than manually reading the written reflections. We find that using these sentiment feature vectors extracted from the reflections in our learning model increased accuracy while decreasing time-to-detect at-risk students significantly.

Reflection is a way to get quick opinions from users, clients, etc. Analysis of reflection often involves subjective review and interpretations. On the other hand, data analytics provides guidelines to collect and measure as well as analyze and reflect on data. In our case, learning analytics of students' reflections reveals information about learners, their learning experience, and all their related contexts. Eventually, learning analytics aim is to understand and optimize learning and the corresponding environments (in which learning occurs). It plays a critical role in evaluating students performance and making decisions on how to improve students' success and overall retention. In our study, we focus on applying learning analytics to a heterogeneous data set collected in the introductory programming course. This data set integrated self-assessment reflections along with the existing active learning group activities. By integrating self-assessment reflections, large amounts of valuable data can be gathered to facilitate continuous assessment of students' learning. Using activity-based active learning and peer-instruction, the effectiveness of the content interventions targeting students to understand the fundamental concepts of computer programming is also evaluated. For analysis purposes, we applied a time-based learning analytics model called sequence analysis to learn about the pattern of our at-risk students and use the learned model to predict at-risk students based on their reflections as well as performance in the course.

The use of Process-Oriented Guided Inquiry Learning model (POGIL) in introductory computer science courses has shown to be very useful in active learning delivery of fundamentals of computer science. Moreover, the aspect of organized teamwork in POGIL helps students develop professional skills to be ready to participate in team-based upper division CS courses such as software engineering or capstone courses. POGIL introduces a structured, yet flexible model for group activities. It also resolves the issue of member participations in activities since everyone needs to take a role and switch them in different activities. Time-management is also enhanced with POGIL. Though POGIL is a promising pedagogy, it also presents some unique challenge such as how to adopt the current activities to specific model of the classrooms. The implementation of courses can follow various approaches depending on factors like: class size, institutional culture, background of the students, the nature of facilities, and instructor preferences. In particular, faculty might need to invest significant amount of time to develop and/or work on modifying existing materials for specific courses. Therefore, instructors who choose to be POGIL facilitators has various decision choices. Detailed discussion about all these challenges, choices, options, and approaches are provided which can benefit faculty who are using and/or interested in adopting POGIL in CS courses. The discussions could be helpful to those who are only interested in group-based active learning teaching of CS courses.

Designing course activities in harmony with class assignments and tests while providing both adequate challenges and appropriate content progression is critical in introductory programming courses (CS1). Such fine-tuned practices help students build the right mindset to perform better and prevent potential discouragement due to disharmonious test challenges. In this study, we apply levels of the cognitive domain of Bloom's Taxonomy to determine the appropriate challenge level of test questions in CS1. Bloom's Taxonomy has been widely referenced by researchers as a benchmark for assessment of students' learning. Our proposed approach serves two purposes: 1) exposing students to a well-defined set of assessment tests to challenge them based on different levels of Bloom's Taxonomy; and 2) identifying the student's, difficulty areas to redesign and/or reorganize the class activities accordingly. For this purpose, a rubric is developed to classify questions based on cognitive domains of Bloom's Taxonomy. We applied the developed rubric to evaluate three semester tests and design a final exam. In designing the final test, we aim to challenge students' skills in a predetermined proportion and combination that maps onto the levels of Bloom's Taxonomy. After each test, students' problem areas were identified and related class activities were adjusted to address these weaknesses. Preliminary analysis of student grades shows the effectiveness of this method.

In industry, professionals often work with a variety of stakeholders and collaborators from multiple disciplines. This ability to work collaboratively can be as important to a project's success as their technical skills. Traditionally in STEM education, these collaborative skills are developed in a capstone course which mimics an industry experience. These experiences are invaluable in preparing students for the collaborative real-world nature of industry; however, these experiences can also be very stressful for students in dysfunctional teams with members who haven't developed necessary social, technical or teamwork skills. Although students may be exposed to some team-based activities in previous courses, it is not clear that this piecemeal exposure teaches students to work in teams effectively. Flipped classroom and active learning attempt to fill this gap by exposing students to peer learning earlier in the curriculum. However, these techniques are peppered throughout the curriculum and may not target all the skills necessary for teamwork. Design patterns in education formalize pedagogical approaches. But, applying design patterns without an intended progression or overarching goal may not lead students to successfully adopt these skills. Design patterns have the potential to scaffold students' development throughout the curriculum, but only if staged effectively and systematically. In this paper, we propose Spectrums and Dependency Graphs to ensure that students are prepared for each new design pattern as they experience it. Spectrums can plot design patterns along a continuum between introductory and capstone courses. Dependency graphs recursively specify patterns that prepare students for subsequent patterns. Each pattern will contain prerequisite skills or experiences that students have demonstrated in a previous pattern. In this way, students are systematically progressed from introductory to capstone courses. Through these two models, we attempt to get a better overview of the curriculum and create progressions through that curriculum that ensure students are prepared at each level, building on previous skills.

Course withdrawal and failure rates are known problems in introductory computer science programming courses (CS1). In turn, these problematic performance rates contribute to declines in retention rates between introductory programming courses and subsequent CS courses. In a bit of a twist, however, retention rates are also influenced by successful student performance. Some students frequently leave the computer science major due to unpleasant experiences and lack of satisfaction, despite earning good grades [1]. These competing retention factors create a paradox to provide a fun learning experience that makes students want to stay in the CS major while simultaneously emphasizing the rigor and discipline needed to advance in the CS major.

A key challenge for computer science educators worldwide is providing effective feedback and support to students, to ensure they are engaged with the course. This includes online feedback on discussion forums as well as feedback on programming assignments. Due to the significant problems of scale that need to be addressed, effective lecturer intervention is difficult, and at the same time the effect of intervention in online discussion forums is challenging to measure accurately. The same problem occurs when marking programming assignments, where detailed, in-depth feedback is often replaced with output from failed testcases, which the students sometimes proceed to address without giving thought to the quality of their overall solutions.

Software engineering courses face the challenge of covering all the stages of analysis, development, maintenance, and support while addressing practical issues such as dealing with large codebase. Free and open source software (FOSS) and more specifically humanitarian free and open source software (HFOSS) have been used by many educators to bring many add-ons to computer science education such as innovation and motivation. In addition, FOSS/HFOSS could give a better understanding of real world projects to students. In this work, we are looking at some activities developed for teaching upper division undergraduate and graduate software engineering courses using open source software projects and analyze the impacts of using this approach on students.

In this paper, an automated and customized brain tumor segmentation method is presented and validated against ground truth applying simulated T1-weighted magnetic resonance images in 25 subjects. A new intensity-based segmentation technique called histogram based gravitational optimization algorithm is developed to segment the brain image into discriminative sections (segments) with high accuracy. While the mathematical foundation of this algorithm is presented in details, the application of the proposed algorithm in the segmentation of single T1-weighted images (T1-w) modality of healthy and lesion MR images is also presented. The results show that the tumor lesion is segmented from the detected lesion slice with 89.6% accuracy.