14th Conference on DATA ANALYSIS METHODS for Software Systems

DAMSS-2023 is the 14th International Conference on Data Analysis Methods for Software Systems, held in Druskininkai, Lithuania. Every year at the same venue and time. The exception was in 2020, when the world was gripped by the Covid-19 pandemic and the movement of people was severely restricted. After a year’s break, the conference was back on track, and the next conference was successful in achieving its primary goal of lively scientific communication. The conference focuses on live interaction among participants. For better efficiency of communication among participants, most of the presentations are poster presentations. This format has proven to be highly effective. However, we have several oral sections, too. The history of the conference dates back to 2009 when 16 papers were presented. It began as a workshop and has evolved into a well-known conference. The idea of such a workshop originated at the Institute of Mathematics and Informatics, now the Institute of Data Science and Digital Technologies of Vilnius University. The Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea, which gained enthusiastic acceptance from both the Lithuanian and international scientific communities. This year’s conference features 84 presentations, with 137 registered participants from 11 countries. The conference serves as a gathering point for researchers from six Lithuanian universities, making it the main annual meeting for Lithuanian computer scientists. The primary aim of the conference is to showcase research conducted at Lithuanian and foreign universities in the fields of data science and software engineering. The annual organization of the conference facilitates the rapid exchange of new ideas within the scientific community. Seven IT companies supported the conference this year, indicating the relevance of the conference topics to the business sector. In addition, the conference is supported by the Lithuanian Research Council and the National Science and Technology Council (Taiwan, R. O. C.). The conference covers a wide range of topics, including Applied Mathematics, Artificial Intelligence, Big Data, Bioinformatics, Blockchain Technologies, Business Rules, Software Engineering, Cybersecurity, Data Science, Deep Learning, High-Performance Computing, Data Visualization, Machine Learning, Medical Informatics, Modelling Educational Data, Ontological Engineering, Optimization, Quantum Computing, Signal Processing. This book provides an overview of all presentations from the DAMSS-2023 conference

Steps to an Ecology of Networked Knowledge and Innovation: Enabling new forms of collaboration among sciences, engineering, arts, and design

SEAD network White Papers ReportThe final White Papers (posted at http://seadnetwork.wordpress.com/white-paper- abstracts/final-white-papers/) represent a spectrum of interests in advocating for transdisciplinarity among arts, sciences, and technologies. All authors submitted plans of action and identified stakeholders they perceived as instrumental in carrying out such plans. The individual efforts led to an international scope. One of the important characteristics of this collection is that the papers do not represent a collective aim toward an explicit initiative. Rather, they offer a broad array of views on barriers faced and prospective solutions. In summary, the collected White Papers and associated Meta- analyses began as an effort to take the pulse of the SEAD community as broadly as possible. The ideas they generated provide a fruitful basis for gauging trends and challenges in facilitating the growth of the network and implementing future SEAD initiatives.National Science Foundation Grant No.1142510. Additional funding was provided by the ATEC program at the University of Texas at Dallas and the Institute for Applied Creativity at Texas A&M University

Personal Analytics Explorations to Support Youth Learning

While personalized learning environments often include systems that automatically adapt to inferred learner needs, other forms of personalized learning exist. One form involves the use of personal analytics in which the learner obtains and analyzes data about himself/herself. More known in informatics communities, there is potential for use of personal analytics for design of instruction. This chapter provides two cases of personal analytics learning explorations to demonstrate their range and potential. One case is of a high school student examining how sleep influences her mood. The other case is of a sixth-grade class of students examining how deviations from typical walking behavior change distributional shape in plotted step data. Both cases show how social support and direct experience with data correction are intimately involved in how youth can learn through personal analytics activities

The Impact of Information Explicitness and Timing on Facilitating Online Learning: A Field Experiment

Online learning systems aim to support learners’ learning process by providing various kinds of information. However, scarce research has focused on examining whether such information support can indeed foster an active learning process and ultimately achieve enhanced learning outcome. This study draws upon active learning theory, which posits that effective information support should facilitate learners’ “generation” and “reflection” process. We examined two characteristics of information support to facilitate such an active learning process, information explicitness and presentation timing (during or after a learning task). A field experiment was conducted on an online learning platform. Our findings revealed that when provided during a task, less explicit information would improve learning outcomes by encouraging generation activities. Furthermore, for learners with a stronger knowledge base, more explicit information support provided after a task assisted in the reflection process, leading to improved learning outcomes. The mechanisms were revealed by using cursor tracking technology

Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

The Big Five:Addressing Recurrent Multimodal Learning Data Challenges

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback

Multimodal Challenge: Analytics Beyond User-computer Interaction Data

This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data

The student-produced electronic portfolio in craft education

The authors studied primary school students’ experiences of using an electronic portfolio in their craft education over four years. A stimulated recall interview was applied to collect user experiences and qualitative content analysis to analyse the collected data. The results indicate that the electronic portfolio was experienced as a multipurpose tool to support learning. It makes the learning process visible and in that way helps focus on and improves the quality of learning. © ISLS.Peer reviewe