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

Continuous Assessment in Agile Learning using Visualizations and Clustering of Activity Data to Analyze Student Behavior

abstract: Software engineering education today is a technologically advanced and rapidly evolving discipline. Being a discipline where students not only design but also build new technology, it is important that they receive a hands on learning experience in the form of project based courses. To maximize the learning benefit, students must conduct project-based learning activities in a consistent rhythm, or cadence. Project-based courses that are augmented with a system of frequent, formative feedback helps students constantly evaluate their progress and leads them away from a deadline driven approach to learning. One aspect of this research is focused on evaluating the use of a tool that tracks student activity as a means of providing frequent, formative feedback. This thesis measures the impact of the tool on student compliance to the learning process. A personalized dashboard with quasi real time visual reports and notifications are provided to undergraduate and graduate software engineering students. The impact of these visual reports on compliance is measured using the log traces of dashboard activity and a survey instrument given multiple times during the course. A second aspect of this research is the application of learning analytics to understand patterns of student compliance. This research employs unsupervised machine learning algorithms to identify unique patterns of student behavior observed in the context of a project-based course. Analyzing and labeling these unique patterns of behavior can help instructors understand typical student characteristics. Further, understanding these behavioral patterns can assist an instructor in making timely, targeted interventions. In this research, datasets comprising of student’s daily activity and graded scores from an under graduate software engineering course is utilized for the purpose of identifying unique patterns of student behavior.Dissertation/ThesisMasters Thesis Engineering 201

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

Science of Digital Libraries(SciDL)

Our purpose is to ensure that people and institutions better manage information through digital libraries (DLs). Thus we address a fundamental human and social need, which is particularly urgent in the modern Information (and Knowledge) Age. Our goal is to significantly advance both the theory and state-of-theart of DLs (and other advanced information systems) - thoroughly validating our approach using highly visible testbeds. Our research objective is to leverage our formal, theory-based approach to the problems of defining, understanding, modeling, building, personalizing, and evaluating DLs. We will construct models and tools based on that theory so organizations and individuals can easily create and maintain fully functional DLs, whose components can interoperate with corresponding components of related DLs. This research should be highly meritorious intellectually. We bring together a team of senior researchers with expertise in information retrieval, human-computer interaction, scenario-based design, personalization, and componentized system development and expect to make important contributions in each of those areas. Of crucial import, however, is that we will integrate our prior research and experience to achieve breakthrough advances in the field of DLs, regarding theory, methodology, systems, and evaluation. We will extend the 5S theory, which has identified five key dimensions or onstructs underlying effective DLs: Streams, Structures, Spaces, Scenarios, and Societies. We will use that theory to describe and develop metamodels, models, and systems, which can be tailored to disciplines and/or groups, as well as personalized. We will disseminate our findings as well as provide toolkits as open source software, encouraging wide use. We will validate our work using testbeds, ensuring broad impact. We will put powerful tools into the hands of digital librarians so they may easily plan and configure tailored systems, to support an extensible set of services, including publishing, discovery, searching, browsing, recommending, and access control, handling diverse types of collections, and varied genres and classes of digital objects. With these tools, end-users will for be able to design personal DLs. Testbeds are crucial to validate scientific theories and will be thoroughly integrated into SciDL research and evaluation. We will focus on two application domains, which together should allow comprehensive validation and increase the significance of SciDL's impact on scholarly communities. One is education (through CITIDEL); the other is libraries (through DLA and OCKHAM). CITIDEL deals with content from publishers (e.g, ACM Digital Library), corporate research efforts e.g., CiteSeer), volunteer initiatives (e.g., DBLP, based on the database and logic rogramming literature), CS departments (e.g., NCSTRL, mostly technical reports), educational initiatives (e.g., Computer Science Teaching Center), and universities (e.g., theses and dissertations). DLA is a unit of the Virginia Tech library that virtually publishes scholarly communication such as faculty-edited journals and rare and unique resources including image collections and finding aids from Special Collections. The OCKHAM initiative, calling for simplicity in the library world, emphasizes a three-part solution: lightweightprotocols, component-based development, and open reference models. It provides a framework to research the deployment of the SciDL approach in libraries. Thus our choice of testbeds also will nsure that our research will have additional benefit to and impact on the fields of computing and library and information science, supporting transformations in how we learn and deal with information

Links between the personalities, styles and performance in computer programming

There are repetitive patterns in strategies of manipulating source code. For example, modifying source code before acquiring knowledge of how a code works is a depth-first style and reading and understanding before modifying source code is a breadth-first style. To the extent we know there is no study on the influence of personality on them. The objective of this study is to understand the influence of personality on programming styles. We did a correlational study with 65 programmers at the University of Stuttgart. Academic achievement, programming experience, attitude towards programming and five personality factors were measured via self-assessed survey. The programming styles were asked in the survey or mined from the software repositories. Performance in programming was composed of bug-proneness of programmers which was mined from software repositories, the grades they got in a software project course and their estimate of their own programming ability. We did statistical analysis and found that Openness to Experience has a positive association with breadth-first style and Conscientiousness has a positive association with depth-first style. We also found that in addition to having more programming experience and better academic achievement, the styles of working depth-first and saving coarse-grained revisions improve performance in programming.Comment: 27 pages, 6 figure

TLAD 2010 Proceedings:8th international workshop on teaching, learning and assesment of databases (TLAD)

This is the eighth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2010), which once again is held as a workshop of BNCOD 2010 - the 27th International Information Systems Conference. TLAD 2010 is held on the 28th June at the beautiful Dudhope Castle at the Abertay University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.This year, the workshop includes an invited talk given by Richard Cooper (of the University of Glasgow) who will present a discussion and some results from the Database Disciplinary Commons which was held in the UK over the academic year. Due to the healthy number of high quality submissions this year, the workshop will also present seven peer reviewed papers, and six refereed poster papers. Of the seven presented papers, three will be presented as full papers and four as short papers. These papers and posters cover a number of themes, including: approaches to teaching databases, e.g. group centered and problem based learning; use of novel case studies, e.g. forensics and XML data; techniques and approaches for improving teaching and student learning processes; assessment techniques, e.g. peer review; methods for improving students abilities to develop database queries and develop E-R diagrams; and e-learning platforms for supporting teaching and learning

TLAD 2010 Proceedings:8th international workshop on teaching, learning and assesment of databases (TLAD)

This is the eighth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2010), which once again is held as a workshop of BNCOD 2010 - the 27th International Information Systems Conference. TLAD 2010 is held on the 28th June at the beautiful Dudhope Castle at the Abertay University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.This year, the workshop includes an invited talk given by Richard Cooper (of the University of Glasgow) who will present a discussion and some results from the Database Disciplinary Commons which was held in the UK over the academic year. Due to the healthy number of high quality submissions this year, the workshop will also present seven peer reviewed papers, and six refereed poster papers. Of the seven presented papers, three will be presented as full papers and four as short papers. These papers and posters cover a number of themes, including: approaches to teaching databases, e.g. group centered and problem based learning; use of novel case studies, e.g. forensics and XML data; techniques and approaches for improving teaching and student learning processes; assessment techniques, e.g. peer review; methods for improving students abilities to develop database queries and develop E-R diagrams; and e-learning platforms for supporting teaching and learning

Student Assessment in Cybersecurity Training Automated by Pattern Mining and Clustering

Hands-on cybersecurity training allows students and professionals to practice various tools and improve their technical skills. The training occurs in an interactive learning environment that enables completing sophisticated tasks in full-fledged operating systems, networks, and applications. During the training, the learning environment allows collecting data about trainees' interactions with the environment, such as their usage of command-line tools. These data contain patterns indicative of trainees' learning processes, and revealing them allows to assess the trainees and provide feedback to help them learn. However, automated analysis of these data is challenging. The training tasks feature complex problem-solving, and many different solution approaches are possible. Moreover, the trainees generate vast amounts of interaction data. This paper explores a dataset from 18 cybersecurity training sessions using data mining and machine learning techniques. We employed pattern mining and clustering to analyze 8834 commands collected from 113 trainees, revealing their typical behavior, mistakes, solution strategies, and difficult training stages. Pattern mining proved suitable in capturing timing information and tool usage frequency. Clustering underlined that many trainees often face the same issues, which can be addressed by targeted scaffolding. Our results show that data mining methods are suitable for analyzing cybersecurity training data. Educational researchers and practitioners can apply these methods in their contexts to assess trainees, support them, and improve the training design. Artifacts associated with this research are publicly available.Comment: Published in Springer Education and Information Technologies, see https://link.springer.com/article/10.1007/s10639-022-10954-

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future