The Swedish software industry is seeing explosive growth and Swedish colleges and universities play a crucial part in supplying industry professionals with relevant education. Studies show an existing gap between what software engineering (SE) education teaches students and what the software industry needs. This study looked specifically at what technologies Swedish SE education used in its syllabuses compared to what technologies were in demand by the Swedish software industry to determine if any knowledge gaps existed. Course syllabuses and job posts were collected and compared through text analysis, highlighting keywords associated with different technologies. The result showed that the Swedish SE education overall aligned with industry demands with some minor exceptions. Conclusions were that some improvements could be made to meet the demand of technologies such as C\#, TypeScript, Kubernetes, and Docker.

The gap between software engineering education and the software engineering industry is a prevalent factor for both the students and the companies recruiting them. The gap is specified as the lack of knowledge software engineering students obtain relative to what the industry requires. This gap increases the difficulty for the students whenmoving from education to industry. This thesis aims to provide insight for what role the capstone project played for the graduate students’ transition to industry by looking at it from a CDIO perspective. The subjects for this research were graduate students who now work in the software engineering industry and who realised their studies up to three years earlier. A total of 38 people took part in this research by answering a questionnaire. They provided their opinions on how they experienced their capstone project and how they now experience their work assignments. This research used metadata to categorically separate the respondents into groups to find outliers. The results show that 94% of the respondents got to perform three or more CDIO criteria in their capstone projects. The respondents also recognize that they are able to perform their industry assignments in terms of the CDIO criteria.

<p>Engineering graduates are called on by society to work with others to address wicked problems which incorporate a wide range of socio-technical considerations. One promising approach to more wholistically prepare students for the demands of engineering-related work and positively contributing as citizens is community-engaged learning. To help this pedagogy more closely meet its full potential, this study used the context of Engineers Without Borders USA (EWB-USA), as viewed through the lens of its alums in professional practice. It also explored individuals’ differentiated outcomes produced by the many types of variation inherent in the EWB-USA model. The goal of the project was to inform best practices for how community-engaged engineering programs can be implemented to support students’ professional preparation. This study took a QUAN QUAL explanatory sequential mixed-methods approach. The survey instrument (<em>n</em> = 268) led to non-parametric tests for group comparisons which were conducted on scores generated through exploratory factor analysis. Inductive thematic analysis was then used on the semi-structured interview transcripts (<em>n</em> = 29). EWB-USA was shown to support the transition between schooling and work through authentic experiential learning, which incorporated inherently-complex projects truly intended for implementation to meaningfully benefit end-users and engaging with a wide range of diverse stakeholders. It especially bolstered the development of competencies in project management, design and project processes, communication, diverse teaming, contextualization, addressing challenges and new situations, and functioning as a connected element of larger complex socio-technical systems. These gains were reflected in the alums’ perceived advantage in career outcomes, demonstrating their long-lasting transferability to professional practice. The results of this study also showed that while limited variations were found based on participant demographics, differences in personal experience within EWB-USA had a greater effect on outcomes. The differences found based on demographic groupings consisted of women reporting greater benefits to their confidence and sense of community. Impactful individual experience differences identified included length of time involved with EWB-USA, mentor engagement, leadership opportunities, repeating phases on different projects, seeing a project from start-to-finish, and number of trips taken to the community partner site. Across the competencies developed from the program, alums often reported perceiving greater benefits from their EWB-USA experiences once they had an opportunity to apply their learnings in professional practice.</p>

A multitude of data is being produced from the increase in instructional technology, e-learning resources, and online courses. This data could be used by educators to analyze and extract useful information which could be beneficial to both instructors and students. Educational Data Mining (EDM) extracts hidden information from data contained within the educational domain. In data mining, hybrid method is the combination of various machine learning techniques. Through this dissertation, the novel use of machine learning hybrid techniques was explored in EDM using three educational case studies. First, in consideration for the importance of students’ attention, on and off-task data to analyze the attention behavior of the students were collected. Two feature selection techniques, Principal Component Analysis and Linear Discriminant Analysis, were combined to improve the classification accuracies for classifying the students’ attention patterns. The relationship between attention and learning was also studied by calculating Pearson’s correlation coefficient and p-value. Our examination was then shifted towards academic performance as it is important to ensuring a quality education. Two different 2D- Convolutional Neural Network (CNN) models were concatenated and produced a single model to predict students’ academic performance in terms of pass and fail. Lastly, the importance of using machine learning in online learning to maintain academic integrity was considered. In this work, primarily a traditional machine learning algorithms were used to predict the cheaters in an online examination. 1D CNN architecture was then used to extract the features from our cheater dataset and the previously used machine learning model was applied on extracted features to detect the cheaters. Such type of hybrid model outperformed the original traditional machine learning model and CNN model when used alone in terms of classification accuracy. The three studies reflect the use of machine learning application in EDM. Classification accuracy is important in EDM because different educational decisions are made based on the results of our model. So, to increase the accuracies, a hybrid method was employed. Thus, through this dissertation it was successfully shown that hybrid models can be used in EDM to improve the classification accuracies.

The objective of this project is to examine the differences between Moroccan and American students with regards to the cultural influences that led them to pursue an engineering degree. Annually since 2015, a partnership between a university in Morocco and MSU allows senior engineering Moroccan students to study at MSU to obtain their graduate degree in aerospace or mechanical engineering. The roughly equal gender representation in most Moroccan cohorts prompted our research question: “How do students from Morocco and the United States describe the cultural reasons that factored into their choice to pursue an engineering degree?” This exploratory qualitative study is guided by the combined frameworks of Hofstede’s Cultural Dimension (HCD) and Expectancy-Value Theory (EVT). The influence of expectancy, family/social structure, and value are evaluated using EVT and cultural factors are evaluated through HCD. We conducted two phases of semi-structured interviews with senior and graduate Moroccan and American students. This study resulted in the modification of the EVT model to include the three constructs of Collectivism, Religion, and Power Distance Index. It also revealed how EVT’s task values manifest differently across cultures. Results indicate that cultural differences manifest primarily through the “Collectivist” mentality among Moroccans, explaining the gender participation difference between Moroccan and American engineering students.

This dissertation walks the balance between the western structures of academia and Indigenous ways of storytelling and knowing. Stories are how knowledge is shared and passed down in many Indigenous cultures. This study utilizes Indigenous Storywork methods, alongside western case study methodology, to explore how colonialism and the structures of public, non-Native higher education institutions and engineering programs impact the lived experiences of Indigenous STEM students. Using Tribal Critical Race Theory (TribalCrit), this study also connects individual student experiences through stories to systemic structures of universities and engineering programs in a way that honors and amplifies Indigenous ways of thinking and doing. The study was situated at a university in the eastern U.S. and had three primary forms of data: public documents such as university historical documents and program policies and structures, focus group discussions with a university Council of Elders from the Indigenous community, and individual interviews with Indigenous STEM students from the Lumbee and Coharie nations. The findings demonstrate the ways that the Indigenous STEM students at North Carolina State University hold community as a cultural value from their Tribal backgrounds that is paramount to their success at the university. The students utilize community to access knowledge and build power for themselves as well as for the whole university Indigenous community. NC State's Indigenous engineering students perceived the structures and policies of their engineering programs to be disconnected from community and relationality and thus did not utilize or connect to these structures as designed. This work also provides an example of a framework for engaging with university Indigenous communities to co-create meaningful and impactful research and demonstrates the differences in the experiences of Indigenous students in the eastern U.S. from those in the west, specifically in terms of their invisibility in the larger community, both on and off campus. / Doctor of Philosophy / This dissertation walks the balance between the western structures of academia and Indigenous ways of storytelling and knowing. Stories are how knowledge is shared and passed down in many Indigenous cultures. This study centers Indigenous methodologies and theories to explore how colonialism and the structures of public, non-Native, higher education institutions and engineering programs impact the lived experiences of Indigenous STEM students. This study also connects individual student experiences to the systemic structures of universities and engineering programs. The study focuses on a university in the eastern U.S. and used three forms of data: public documents such university historical documents and current policies, a group discussion with a Council of Elders from the Indigenous community, and individual interviews with Indigenous STEM students. The students were members of the Lumbee and Coharie nations. The findings highlight the way they hold community as a cultural value deeply tied to their Tribal backgrounds. This community is key to their success at the university and used community to access knowledge and build power for themselves as well as for the whole university Indigenous community. In particular, the Indigenous engineering students perceived the structures and policies of their engineering programs to be disconnected from community and relationships, and thus they did not use or connect to those structures in the intended ways. Instead, they went outside the system to gain the knowledge the needed. This work also provides a framework, grounded in Indigenous value of respect, reciprocity, responsibility, reverence, holism, interrelatedness, and synergy, for engaging with university Indigenous communities to co-create meaningful and impactful research and demonstrates the differences in the experiences of Indigenous students in the eastern U.S. from those in the west, specifically in terms of their invisibility in the larger community, both on and off campus.

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<p>  </p> <p>First-Year Engineering (FYE) programs are a critical part of engineering education, yet they are quite complex settings. Given the importance and complexity of FYE programs, research to better understand student learning and inform design and assessment in FYE programs is imperative. Therefore, this dissertation showcases various uses of data analytics and educational theory to support decision-making when designing and assessing FYE programs. Three case studies shape this dissertation work. Each study encompasses a variety of educational data sources, analytical methods, and decision-making tools to produce valuable findings for FYE classrooms. In addition, this dissertation also discusses the potential for incorporating data analytics into FYE programs. A more detailed description of the research methods, a summary of findings, and a list of resulting publications for each case study follows.</p> <p>The first case study investigated the relationship between two related Computational Thinking (CT) practices, data practices and computational problem-solving practices, in acquiring other CT competencies in a large FYE course setting. This study explored the following research questions: (1) What are the different student profiles that characterize their foundational CT practices at the beginning of the semester? and (2) Within these profiles, what are the progressions that students follow in the acquisition of advanced CT practices? To answer these questions, N-TARP Clustering, a novel machine learning algorithm, and sound statistical tools were used to analyze assessment data from the course at the learning objective level. Such a hybrid approach was needed due to the high-dimensionality and homogeneity characteristics of the assessment. It was found that early mastery of troubleshooting and debugging is linked to the successful acquisition of more complex CT competencies. This research was published in an article in the journal <em>IEEE Access</em>.</p> <p>The second case study examined self-regulation components associated with students' successful acquisition of CT skills using students' reflections and assessment data. This research was grounded in three subprocesses of the Self-Regulated Learning (SRL) theory: strategic planning, access to feedback, and self-evaluation. This study responded to the following research question: What is the relationship between SRL subprocesses: access to feedback, self-evaluation, strategic planning, and the acquisition of CT skills in an FYE course? Results from a structural equation model, which reflects the complexity and multidimensionality of the analysis, provided evidence of the relevance of the three subprocesses in the acquisition of CT skills and highlighted the importance of self-assessment as key to success in the acquisition of programming skills. Furthermore, self-assessment was found to effectively represent the task strategy and access to feedback from the students. This analysis led to the understanding that even though the three SRL subprocesses are relevant for the student's success, self-evaluation serves as a catalyst between strategic planning and access to feedback. A resulting article from this case study will be submitted to the <em>International Journal of Engineering Education</em> in the future.</p> <p>Lastly, the third study aimed to predict the students' learning outcomes using data from the Learning Management System (LMS) in an FYE course. The following research questions were explored in this case study: (1) What type of LMS objects contain information to explain students' grades in a FYE course? (2) Is the inclusion of a human operator during the data transformation process significant to the analysis of learning outcomes? Two different sections of a large FYE course were used, one serving as a training data set and the other one as a testing data set. Two logistic regression models were trained. The first model corresponded to a common approach for building a predictive model, using the data from the LMS directly. The second model considered the specifics of the course by transforming the data from aggregate user interaction to more granular categories related to the content of the class. A comparison was made between the predictive measures, e.g., precision, accuracy, recall, and F1 score for both models. The findings from the transformed data set indicate that students' engagement with the career exploration curriculum was the strongest predictor of students' final grades in the course. This is a fascinating finding because the amount of weight the career assignments contributed to the overall course grade was relatively low. This study will be presented at the 2022 American Society of Engineering Education (ASEE) national conference in Minneapolis, Minnesota.</p>

Today, many introductory computer science courses rely heavily on a specific programming language to convey fundamental programming concepts. For beginning students, the cognitive capacity required to operate with the syntactic forms of this language may overwhelm their ability to formulate a solution to a program. We recognize that the introductory computer science courses can be more effective if they convey fundamental concepts without requiring the students to focus on the syntax of a programming language. To achieve this, we propose a new teaching method based on the Design Recipe and Code Outlining (DRaCO) processes. Our new pedagogy capitalizes on the algorithmic intuitions of novice students and provides a tool for students to externalize their intuitions using techniques they are already familiar with, rather than with the syntax of a specific programming language. We validate the effectiveness of our new pedagogy by integrating it into an existing CS1 course at California Polytechnic State University, San Luis Obispo. We find that the our newly proposed pedagogy shows strong potential to improve students’ ability to program.

Cyber security education has evolved over the last decade to include new methods of teaching and technology to prepare students. Instructors in this field of study often deal with a subject matter that has rigid principles, but changing ways of applying those principles. This makes maintaining courses difficult. This case study explored the kind of teaching methods, technology, and means used to explain these concepts. This study shows that generally, cyber security courses require more time to keep up to date. It also evaluates one effort, the NxSecLab, on how it attempted to relieve the administrative issues in teaching these concepts. The proposed framework in this model looks at ways on how to ease the administrative burden in cyber security education by using a central engine to coordinate learning management with infrastructure-as-a-service resources.