The Impact of Software Team Project Measurements on Students' Performance in Software Engineering Education

It is essential to the software engineering instructors to monitor the students' performance in their course projects. Detecting key measures of software engineering project helps to get a better assessment for students' performance, resolve difficulties of low expectation-team's, and consequently improves the overall learning outcomes. Several studies attempted to present the important measures of software project but they only captured the early phases of the whole project time period. This paper introduces a hybrid approach of classification and feature selection techniques, which aims to comprehensively cover all phases of software development through investigating all product and process measures of software project. Experiments were conducted using five classifiers and two feature selection techniques. The results show the significant process and product measures for the software engineering team projects, which primarily improves the students' performance assessment. The performance prediction of our proposed assessment model outperforms prediction of the previous models. Keywords: Assessment, Classification, Feature selection, Software engineering education, Software team DOI: 10.7176/JEP/11-31-02 Publication date: November 30th 2020

Female Student Participation in Software Engineering Projects: Opportunities to Model Project Evaluation and to Improve Early Prediction of Teamwork Failure

Software engineering project is the preferred mean to measure competency and practical skills among learners in IT-fields. This study investigates the opportunities to model software engineering project final evaluation and improve early prediction of academic software engineering project failure by considering female student participation as a teamwork member, regardless of being a teamwork leader or a teamwork regular member. Four distinct arrangements of software engineering development teamwork are advised and studied. Those arrangements range from female-less participation teamwork to female-dominated participation teamwork. Machine learning techniques are leveraged to build prediction models. Teams are evaluated from two distinct perspectives. First, software products submitted at the end of each project life cycle milestone, namely product perspective. Second, the degree of obeying the good practices of software engineering project development, namely process perspective. Results reveal significant differences due to female student participation. Arrangement of female-less participation attains the worst modeling and prediction performance compared to the other arrangements of female student participation. Keywords: Gender diversity, E-learning, Software engineering, Project failure, Machine learning DOI: 10.7176/JEP/11-35-06 Publication date: December 31st 2020

Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects

Software engineering is one of the most significant areas, which extensively used in educational and industrial fields. Software engineering education plays an essential role in keeping students up to date with software technologies, products, and processes that are commonly applied in the software industry. The software development project is one of the most important parts of the software engineering course, because it covers the practical side of the course. This type of project helps strengthening students' skills to collaborate in a team spirit to work on software projects. Software project involves the composition of software product and process parts. Software product part represents software deliverables at each phase of Software Development Life Cycle (SDLC) while software process part captures team activities and behaviors during SDLC. The low-expectation teams face challenges during different stages of software project. Consequently, predicting performance of such teams is one of the most important tasks for learning process in software engineering education. The early prediction of performance for low-expectation teams would help instructors to address difficulties and challenges related to such teams at earliest possible phases of software project to avoid project failure. Several studies attempted to early predict the performance for low-expectation teams at different phases of SDLC. This study introduces swarm intelligence -based model which essentially aims to improve the prediction performance for low-expectation teams at earliest possible phases of SDLC by implementing Particle Swarm Optimization-K Nearest Neighbours (PSO-KNN), and it attempts to reduce the number of selected software product and process features to reach higher accuracy with identifying less than 40 relevant features. Experiments were conducted on the Software Engineering Team Assessment and Prediction (SETAP) project dataset. The proposed model was compared with the related studies and the state-of-the-art Machine Learning (ML) classifiers: Sequential Minimal Optimization (SMO), Simple Linear Regression (SLR), Naïve Bayes (NB), Multilayer Perceptron (MLP), standard KNN, and J48. The proposed model provides superior results compared to the traditional ML classifiers and state-of-the-art studies in the investigated phases of software product and process development

Founder Success in Norwegian Startups: A Machine Learning Approach : A study on the use of machine learning and personality traits to predict startup performance from a pre-seed perspective

This thesis aims to investigate founder characteristics in the Norwegian startup ecosystem and if machine learning can help venture capital firm identity successful founders at a startup’s earliest stages, when information is greatly limited. The authors collected and refined data from multiple sources, resulting in a unique dataset of 1918 tech-driven, scalable startups and 2700 unique founders. Especially outstanding in the dataset is the inclusion of personality traits estimated though the use of artificial intelligence. Four supervised machine learning models were employed to classify the founders into two created success categories, low success, and high success. The two tree-based methods, Extreme Gradient Boosting and Random Forest performed best considering the evaluation metrics, resulting in a classification accuracy of over 62%, while Logistic Regression and K-Nearest Neighbours did not follow far behind. The thesis finds significant evidence that the Number of Founders of a company and the personality trait Conscientiousness are strong predictors of success in the Norwegian startup landscape. Both of our findings showcase a positive correlation with startup performance, meaning entrepreneurs who inherits high Conscientiousness and are part of founding teams are more likely to succeed as entrepreneurs in Norway. The research has two use cases. One, to narrow the research gap on founders in Norwegian startups, and two, motivate venture capital firms in Norway to adapt and implement machine learning models to help with decision-making, despite the challenges of limited data. The authors encourage others to continue research on this area, such as investigating the validity of personality traits obtained through artificial intelligence and broadening and expanding the research to other companies in Norway and other Scandinavian countries. The thesis recognizes the potential ethical considerations that arise when collecting public data on private individuals. The weaknesses of this research are also discussed, which include the chosen data structure and biases in the data.nhhma

Automated detection of reflection in texts. A machine learning based approach

Promoting reflective thinking is an important educational goal. A common educational practice is to provide opportunities for learners to express their reflective thoughts in writing. The analysis of such text with regard to reflection is mainly a manual task that employs the principles of content analysis. Considering the amount of text produced by online learning systems, tools that automatically analyse text with regard to reflection would greatly benefit research and practice. Previous research has explored the potential of dictionary-based approaches that automatically map keywords to categories associated with reflection. Other automated methods use manually constructed rules to gauge insight from text. Machine learning has shown potential for classifying text with regard to reflection-related constructs. However, not much is known of whether machine learning can be used to reliably analyse text with regard to the categories of reflective writing models. This thesis investigates the reliability of machine learning algorithms to detect reflective thinking in text. In particular, it studies whether text segments from student writings can be analysed automatically to detect the presence (or absence) of reflective writing model categories. A synthesis of the models of reflective writing is performed to determine the categories frequently used to analyse reflective writing. For each of these categories, several machine learning algorithms are evaluated with regard to their ability to reliably detect reflective writing categories. The evaluation finds that many of the categories can be predicted reliably. The automated method, however, does not achieve the same level of reliability as humans do

Development of a Multivariate Poisson Hidden Markov Model for Application in Educational Data Mining

Managers and policymakers in higher education institutions try to improve graduation rates and decrease halt rates. To achieve this goal, it is important to understand academic and demographic factors that correlate with academic performance. Many studies in the field of education analytics have identified student grade point averages (GPA) as an important indicator and predictor of final academic outcomes (graduating or halting their studies). While semester-to-semester fluctuations in GPA are considered normal, significant changes in academic performance may warrant more thorough investigation and consideration, particularly with regard to final academic outcomes. However, it is challenging to represent complex academic trajectories over an academic career. To overcome this challenge, in this dissertation two different Hidden Markov Models (HMMs) are developed to provide a standard and intuitive classification over students\u27 academic-performance levels. This leads to a compact representation of academic-performance trajectories. Next, the relationship between different academic-performance trajectories and their correspondence to final academic success are explored. Based on student transcript data from the University of Central Florida, the proposed HMMs are trained using sequences of students\u27 course grades for each semester to estimate the students\u27 academic-performance levels. Through the HMMs, the analysis follows the expected finding that higher academic performance levels correlate with lower halt rates. However, in this dissertation, we identify many scenarios in which both improving or worsening academic-performance trajectories actually correlate to higher graduation rates. This counter-intuitive finding is made possible through the two proposed HMMs