This work-in-progress research-to-practice paper presents an intervention on integrating computational thinking modules into a software engineering course. The national consensus on the significance of computational thinking has prompted the expansion of related educational initiatives over the past decade. Since the definition of computational thinking by Wing in 2006, this concept has gained significant attention within the educational community. Particularly this surge of interest has led to extensive research into its conceptual foundations and subsequent integration into educational curricula since 2013. National initiatives have since emerged to incorporate computational thinking into the educational system. Furthermore, as artificial intelligence and computing systems become increasingly integrated into daily life, there is a growing demand from industries for a workforce and graduates adept at critical thinking and problem-solving. Aligned with this national movement, our study presents a two-year institutional initiative, aimed at integrating computational thinking into the software engineering program. The software engineering discipline extensively involves design thinking and problem-solving skills. However, we noticed that these higher-level skills are not imparted early in the program to teach students this method of thinking and approaching problems. To bridge this skill gap, we developed a set of computational thinking modules and integrated them into a gateway course in the software engineering program. Over two years, we implemented this intervention in an introductory-level course and evaluated its impact on students' computational thinking skills by analyzing their responses to a standard Computational Thinking Assessment survey. The results showed significant improvement in most components. These early findings underscore the effectiveness of integrating these computational thinking modules into the gateway courses, regardless of the specific course topic. A notable feature of these modules is their adaptability to diverse engineering courses, suggesting broader applicability across disciplines. Moving forward, our research aims to expand the integration of the computational thinking modules into various courses in other institutes across the nation and analyze their impact on student performance.

This research-to-practice paper describes the development of an innovative practice aimed at addressing the global surge in student stress and related mental health challenges, particularly in engineering education. Student well-being is foundational to academic success, with studies indicating that dropout rates for students with stress are more than 50% and mental health problems can range as high as 83% in engineering education. Stress and elevated levels of inattention are associated with decreased academic interest, decision-making, self-efficacy, study skills, and GPA. Research has shown that stress levels can be reduced by meditation techniques and they induce attention. In this research, a three-stage guided meditation called ChakraMarmaKosha Meditation II (CM-II) was developed to improve the attention level of students. It involves past emotional cleansing, reviewing the day, visualizing the future components, and is accompanied by flute music that is composed based on a chosen Raga. In this study, a data analysis pipeline was developed for detailed spectral analysis to measure variations in attention due to meditation. In an in-person EEG experiment, a study group of 15 college students underwent this CM-II meditation process in a lab setting. The EEG spectral analysis underscored an enhancement in attentional focus post-meditation, as evidenced by key shifts in spectral ratios such as the notable decrease in Theta to Beta Ratio (TBR) and the surge in Theta to Gamma Ratio (TGR). The observed results were validated using attention scores from tests on the TestMyBrain tool. This study underscores the potential benefits of the CM-II meditation technique in fostering enhanced attention, reduced stress, and consequently improved academic performance in engineering education. While the current study provides promising results with CM-II meditation in enhancing attention, cognition, and relaxation, it's essential to acknowledge the limitations, such as the confinement to a single lab setting and a small sample size. Future research could explore larger, more diverse cohorts and investigate the long-term benefits of CM-II, potentially incorporating it into regular academic curricula.

This research-to-practice study aims to develop an Artificial Intelligence (AI) MCQ generation system for engineering students, with a focus on adaptive learning, educational technology, and innovative assessment tools, to enhance personalized learning. Engineering education faces significant academic performance challenges, with first-year retention rates in STEM fields ranging between 27% to 46%, largely due to poor academic achievements. Multiple Choice Questions (MCQs) identify misconceptions, reinforce knowledge retention, and offer efficient assessment methods for engineering education. This interactive method improves attention and memory retention, reinforces knowledge, and improves comprehension. In this context, the emergence of Large Language Models (LLMs) such as GPT-4 has marked a significant advancement. Our literature review method employed a systematic approach, analyzing peer-reviewed articles, conference papers, and authoritative reports to uncover the trends and challenges in AI-driven quiz generation. The notable gap identified in our literature review is the lack of LLM-based adaptive quiz generation methods specifically for engineering education. Our methodology involved sourcing relevant structured datasets, data pre-processing, embedding generation, vector database storage, hybrid-search retrieval, LLM query results feed, prompt engineering, and context-based response. In this research, we adopted Vectara as a vector database tool for its automatic data ingestion capabilities and seamless integration with generative AI applications. Prompt engineering involves a dual-prompt approach, where the Contextual Question Prompt formulates questions based on user topics and chat history, while the Answer Question Prompt manages MCQ responses with explanations, ensuring relevant and contextually accurate interactions. Evaluation includes topic relevancy, answer relevancy, and a contextual relevancy score. Preliminary results indicate promising results for the generation of accurate and contextually appropriate questions with minimal hallucinations. The quiz generation system was deployed using Streamlit cloud-based architecture to showcase the functionality. Looking forward, we aim to expand the dataset to include more diverse engineering disciplines and to refine the retrieval algorithms to better handle complex diagrams and mathematical expressions commonly found in engineering texts.

Stress is considered one of the most prevalent concerns among individuals. Studies have shown that experiencing long-term stress can cause severe health issues such as cardiovascular diseases, hypertension, depression, etc. Preventative measures, such as early stress detection, can help individuals mitigate these health issues. When a person gets stressed, physiological values like blood volume pulse, temperature, and electrodermal activity signals get affected. Machine Learning techniques can be utilized to identify stress by analyzing these physiological signals. This paper presents a machine learning method for detecting stress levels of an individual using the publicly available dataset called "Wearable Stress and Affect Detection"(WESAD), which has physiological data collected from the wrist-worn and chest-worn sensors attached to 15 different subjects. We used physiological signals, including Blood Volume Pulse(BVP), Body Temperature(TEMP), and Electrodermal Activity(EDA) signals, collected from wrist-worn sensors to detect the state of the mind. For the implementation, we used different Machine Learning models, like Logistic Regression, Decision Tree, Random Forest, and Stacking Ensemble Learning technique. During the investigation, personalized models, utilizing individual subject data, and generalized models, amalgamating all subject data, were developed. Evaluation reveals accuracy values reaching up to 99% and 91% for individual subject data and combined data, respectively.

Traditional evaluation of students' learning primarily relies on assessing learning outcomes through either summative or formative assessment methods. In these approaches, the primary emphasis is on the students' learning outcome, rather than the learning process. However, assessing the learning process is as important since it allows providing timely feedback which can directly impact students' learning outcomes. One of the known approaches to getting information about the learning process is the use of formative reflection tools, which also help students in developing their meta-cognitive skills. One effective method for formative reflection is the Minute Paper technique which asks students two concise questions after each class session: what they have learned and what challenges they have encountered. While Minute Papers encourage brief responses, the analysis process can become time-consuming as the number of students and class sessions grows. To address this challenge, in this study, we propose a Large Language Model (LLM)-based reflection analysis tool designed to assess the challenging topics students encounter during each class session. This tool suggests additional learning modules for students to study based on the frequency of the challenging topics. To achieve this, the model utilizes a local repository of lecture materials to create query contexts, which are then input into the LLM as prompts. Students are given access to these recommended resources for further learning, and they are encouraged to provide feedback after completing these modules. These data-driven recommended learning resources serve as continuous content delivery channels to foster a deeper understanding of the subjects at hand.

The rapid advancements in artificial intelligence (AI) have been transforming various domains, including engineering education. The availability of AI-based content and easy access to information have made students more dependent on these technologies. With the rise of online courses, there is growing concern about student engagement, poor learning outcomes, and low retention rates in higher education. Engagement plays a critical role in student success and can be achieved through formative assessment, critical thinking, and reflective thinking strategies. Reflection plays a key role in developing critical thinking and meta-cognitive skills. According to the constructive alignment framework which is an outcome-driven approach, the teaching and assessment methods should be shaped around fulfilling the course learning outcomes. Although the idea of constructive alignment is not a new topic, the higher education sector has recently emphasized it at a large scale due to the diversity of new required skills for students to enter the 5th industrial revolution. In earlier work, we proposed an AI-based reflection analysis model that combines both aspects of learning outcome-based assessment and student engagement by applying ‘Minute Paper'. Minute paper is a formative assessment tool that helps the instructors identify the muddy points of the lesson and students' learning gaps as well as their learning outcomes by asking two questions at the end of each lecture (i.e., what they learned and what they didn't). In this work, we propose a more advanced version of the reflection analysis tool by applying transformer-based language models to analyze students' responses to the Minute Paper reflections with higher accuracy in the course context. For this purpose, we train the BERTopic model with the course syllabus and lecture material to get more accurate data in the context of the given courses. The proposed system aids instructors in future course development by adding additional resources for the muddy points, allowing for adjusting content delivery pace, and designing tests and assignments with appropriate challenge levels [1]. Adaptation of this system can enhance the learning experience for both students and teachers and can be extended beyond higher education.

One of the biggest concerns in the modern day especially in the educational domain centers on the student's mental health. High rates of anxiety and depression have especially brought the attention of researchers in engineering education to apply affective computing to help with students' academic performance. It is known that a person's emotional states cause physiological and physical changes in the body. Emotions may impact facial expression, tone of speech, blood pressure, pulse, etc. Since visual and auditory signals are two variables that can be measured without the need to attach any physical device to the individuals, they are most studied in this field. Speech in particular has been known as a means that transfers much information about the mental and emotional states of the person. Speech Emotion Recognition (SER) is a growing field that has been applied in several domains including engineering education. Recent advancements in AI, Natural Language Understanding (NLU), and Large Language Models (LLM) have significantly streamlined this line of research. In this work which is a continuation of our prior work, we propose a speech analysis model that extracts both the emotions and topics from verbal discussions in a computer science classroom to understand if the expressed emotions were mostly about the course related topics or not. The goal of this research is to develop a tool that helps educators gain insights into the students' emotional states in teamwork and also understand the context of their conversations. We further analyze if the expressed emotions in the verbal class discussions are mostly about the course content or other subjects outside class setting. To expand the emotion analysis module we added a new layer to our developed pipeline by passing the speech data into the ChatGPT API to generate summarized scripts and extract additional classes of emotion. The preliminary results from this study are promising, indicating the potential value of this research direction and its prospects for further development. Application of this model in the educational domain can greatly benefit both educators and students and allows the instructors to make necessary interventions needed to maximize students' positive experiences in team settings while considering their emotional states.

The increasing rate of attention deficit among students, attributed to social media, has far-reaching consequences on their academic performance. Inattention or lack of attention is a state of absent-mindedness or not paying enough attention to the details. A rich body of literature suggests that it is highly associated with underachievement in the academic context. In particular, students who have been clinically diagnosed with Attention Deficit Hyperactivity Disorder(ADHD), a neuro-developmental disorder characterized by inattention, hyperactivity, and impulsivity symptoms are more at risk of under-performance and retention, with some leaving school without a terminal degree. In addition to the academic domain, inattention generally impacts the quality of life and future occupations. Research suggests that ADHD is also attributed to societal and unemployment excess costs as well as productivity loss and healthcare expenses which are estimated to be over $14K per adult in the United States (US). Although more than eight million adults were identified with ADHD in the US by 2018, not all inattention cases are associated with ADHD. Inattention could be caused by several other factors such as stress or anxiety and its early detection and timely intervention is critical, especially in the academic domain. There are existing studies that analyze brain signals by Electroencephalogram (EEG) scans to identify individuals who have ADHD. In this study, we developed a Machine learning (ML) pipeline model that is trained on both ADHD and inattention data to determine if a person is having an attention problem. In the first phase of developing the model, we trained a few different classifiers on a 19-channel public EEG dataset of 60 ADHD and 61 non-ADHD participants. Data analysis showed K-Nearest Neighbor (KNN) classifier outperformed other classifiers with an accuracy of 89%. While many of the existing papers focus on ADHD data, in this work we expand our model to analyze the attention deficiency data as well. To train the model we used an attention dataset which is a collection of 34 recordings of 14-channel EEG that scan the attention states of five young adults. We further implemented Independent Component Analysis (ICA) to reduce noise and dimensions and tried different classifiers to improve the accuracy of the model. We achieved the highest accuracy of 98% with ensemble model classifiers using the improved ML pipeline. To evaluate the proposed model, we conducted a study to record the EEG of 15 young adults while taking a visual reasoning attention test for the duration of 8 minutes. We compared the model output (i.e. binary attention label) and their average test score. Findings showed consistent results between the proposed models' prediction and the visual attention test. this indicates there is a potential to use that test as an alternative to the EEG recording to assess students' attention levels in the academic domain. The goal of this research is not for any diagnosis purposes but merely as a tool to help in the early identification of inattention for timely help and interventions among students.

In this work in progress, we propose a model for analysis of students’ verbal conversation during teamwork to predict their academic performance based on expressed emotions. Our previous studies support the link between an individual’s attitude and emotional states during the cognitive process with their performance in the given context [1], [2]. Traditionally the learners’ affective states were assessed by having them fill out standard surveys. More recently the researchers have been using advanced methods to extract students’ emotions from their writings by using Natural Language Processing (NLP) models. These models are applied to data collected from different sources such as discussion forums, team chats, students’ reflective surveys, and journals. In this research, we take one step further by recording students audio in class as they converse about the course topic in low-stake teams and extract emotions from their conversations by NLP methods. The main contributions of the proposed model are 1) the audio transcription component 2) the multi-class emotion analysis unit and 3) the performance prediction model based on input data. SpeechBrain pre-trained models with transformer language models were applied for automated transcription of audio data and converting them to embedding vectors. NLP methods were applied for sentiment analysis. Next, we formed the feature set by combining the extracted emotions with students’ formative assessment grades during the semester to implement a prediction model. We further analyzed which features in the feature set have a higher impact on the students’ academic performance. The early result of this research is promising as we found high accuracy in the predicted scores of the students.

This work in progress proposes a method for automated analysis of students’ short reflections using NLP to get insights into their challenges and learning outcomes in the course. The importance of self-reflection in engineering education has been emphasized more recently as it improves the students’ learning experience and helps instructors to remove students’ learning gaps. Towards this goal, educational scholars have developed different types of reflection tools as well as analysis methods to get feedback on students’ learning outcomes. Reviewing narratives of the reflections is time taking specially in large class settings. One of the known approaches to get feedback from students are mini-reflections called ‘minute paper’ that asks students to answer two questions briefly about what they learned and what they didn’t learn in the class. Although these short surveys reduce the narrative load and help in the quicker review of the reflections still they require instructors to review them one by one. In this work, we apply clustering methods to the students’ reflection responses in a software engineering course to extract their challenge areas as well as learning outcomes from each session of the class. The result of the analysis is visualized in a dashboard that dynamically shows an overview of the challenging topics and learning outcomes based on their weight and frequency of their occurrence in students’ responses. This means the more students mentioned a certain topic as their challenge or learning outcomes, the topic acquires higher weight as a result. The application of this dashboard helps educators to get quick and real-time insight into students’ misconceptions in a formative style. It enables them to narrow students’ learning gap by discussing the challenging topics in the upcoming class sessions. We found this method to be very helpful in both improving students’ learning experience as well as creating an open channel for students to communicate their misunderstandings with confidence and feel being heard and supported.

This full research paper focuses on a natural language processing (NLP) driven approach to extract emotions from the speech in collaborative learning environments and analyze how they correlate with the learner's performance. Social competency is one of the base competencies that have been the target of many educational researchers in engineering and computing education during the past several years. Studies show that the low level of individual's performance is not just due to lack of intellectual or cognitive competencies but also lack of social skills impact performance in both educational and industrial domain. For this reason, Engineering Education is encapsulating social skills into the curriculum to prepare students for the fourth industrial revolution (i.e., Industry 4.0). Towards this goal in earlier work [1], we proposed a model to identify the correlation between the sentiments extracted from students' speech in teams and their performance. The results of polarity sentiment analysis showed a strong positive correlation between students' positive feelings in teams and their individual performance in the course. This study takes a further step and conducts multi-class emotion analysis on students' speech in teams. The process consists of two steps:1) extracting different classes of sentiment such as joy, anger, anxiety, etc., and identifying their correlation with students' performance using collaborative speech in an introductory programming course (CS1), 2) Aspect-Based Emotion Analysis (ABEA). The approach we adopt is the supervised machine learning method and rule-based models on speech datasets. After pre-processing the text, we identify multi classes of sentiments. Aspect extraction is accomplished through the Part of Speech (POS) tagging, and patterns are extracted from the identified aspects. Finally, we use the combination of emotion classes and aspect patterns as feature vectors to train the K-Nearest Neighbor (KNN) algorithm to predict students' performance.

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.

This work in progress research-to-practice paper proposes an architecture model for active learning design pattern retrieval based on the concept of conversational recommender systems (C-RS). In earlier work, we proposed an object-based pedagogical design pattern model to generate abstraction of multiple unique implementations of collaborative active learning practices. This model helped in formalizing the problem-solution pairs that the educators face in collaborative active learning such as students' engagement in teams and class activities, social skill improvement, and assessment issues. Based on this model multiple patterns in different categories were developed through a systematic pattern development cycle combined with deductive elicitation methods and workshops. In this study, we propose an architectural model for an interactive pattern recommendation system. We use NLP models to extract the context of the problem that each pattern addresses. The extracted aspects are combined with the dimensions of each pattern based on attributes of the developed pattern model. The proposed architectural model is based on the model, view controller (MVC) pattern. In this model, the user interacts with the system through the ‘view’ layer. The ‘model’ layer stores the pattern language in the form of a graph in which each node of the graph represents one pattern, and the edges denote the relationship between the patterns. The ‘controller’ handles text analysis by NLP algorithms based on user input. One of the main features of our object-based design pattern model is its emphasis on team attributes by which different dimensions of collaboration in engineering education are addressed. The implementation of the developed architectural model would enable the instructors to identify possible solutions to the pedagogical challenges they face in teaching engineering classes where teamwork is an integrated part of the learning process.

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.

The creative sketching partner (CSP) is a proof of concept intelligent interface to inspire designers while sketching in response to a specified design task. With this interactive system we are studying the effect of an AI model of visual and conceptual similarity for selecting the Al's sketch response as an inspiration to the current state of the user's sketch. Specifically, we are interested in the user's behavior and response to an AI partner when engaged in a design task. By developing deep learning models of the sketches from a large-scale dataset, the user can control the amount of visual and conceptual similarity of the AI response when requesting inspiration from the CSP. We conducted a study with 50 design students to examine the participants' interaction behavior and their self reports. The participants' behavior maps into clusters that are co-related with three types of design creativity: combinatorial, exploratory, and transformational. Our findings demonstrate that the tool can facilitate ideation and overcome design fixation. In addition, analysis suggests that inspiration related to conceptual similarity is more associated with transformational creativity and inspiration related to visual similarity occurs more frequently during the detailed stages of design and is more prevalent with combinatorial creativity.

This work-in-progress paper proposes a semiautomated method to analyze students' reflections. It is challenging to include reflection activities in computing classes because of the amount of time required from students to answer the reflection questions and the amount of effort required for instructors to review the students' responses. These challenges inspired us to adopt Digital Minute Paper (DMP) as a way to give students multiple, quick opportunities to stop and reflect on their experiences in class. In this way, students are given an opportunity to develop metacognitive skills and to potentially improve their performance in the class. In addition, we used these DMPs as formative feedback for the instructors to address students' problems in the class and to continuously improve the course design. Reading reflections is tedious, time-consuming, and does not scale to large classes. To extract insights from the DMPs, we created a semi-automated process for analyzing DMPs by applying natural language processing (NLP). Our process extracts unigrams and bigrams from the reflections and then visualizes related quotes from the reflections using a treemap visualization. We found that this semi-automatic analysis of the reflections is a good, low-effort way to capture student feedback in addition to helping students be more self-regulating learners.

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.

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.

Successful implementation of active learning depends on a wide range of practical tactics. In this work, we adopt pedagogical design patterns to bridge between theory and best practices of active learning. This offers practical solutions for known problems in implementing active learning in Computer Science Education (CSE). We believe these patterns would help instructors customize and apply the best practices in active learning CSE. The patterns can be applied iteratively and adaptively for designing course materials and activities to achieve desired goals, depending on the context of the course. Pedagogical design patterns can help educators share their teaching design ideas in a structured style as well as provide a framework for thinking about and comparing design decisions. Another contribution of this work will be the practical and theoretical distinctions between activity-based teams and project-based teams in CSE.

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.

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.