Low-code development platforms (LCDPs) are transforming business practices by shifting the focus from traditional, code-intensive approaches to business-centered modeling. These platforms enable citizen developers-non-technical employees within organizations-to build and manage applications that address specific business needs. This democratization accelerates time-to-market and encourages agile, co-participatory development. However, the rise of citizen development also introduces challenges, such as risks to quality, security, and governance, due to limited technical expertise among some users. This paper investigates ways to enhance current low-code practices by integrating AI-based support for text-to-model generation and established business frameworks, such as the Business Model Canvas (BMC). Incorporating BMC into low-code platforms reinforces their core strengths-minimizing code dependency while grounding development in business models. This integration can offer a structured pathway for citizen developers to engage in meaningful learning while ensuring their projects align with organizational objectives. This approach positions low-code not only as a productivity tool aiming faster time to market, but as platforms for continuous learning and strategic alignment with business. The proposed integrations build on a novel feedback-inclusive approach, which received the innovative feedback nomination at the University of Leuven, Belgium1, and was informed by evidence-based learning experiences at the University of Twente, Netherlands.

Many IT organizations turn to agile software delivery approaches such as DevOps in order to reduce the number of IT projects that are running behind schedule and above budget. However, the DevOps paradigm calls for an increased set of capabilities that need to be built and aligned with their context in order to ensure superior team performance. This research aims to develop a context-capability coalignment profile for DevOps teams in public organizations. This profile and the corresponding design approach may serve as a model for other software production teams seeking to enhance their performance through improved coalignment. The resulting set of design principles places the traditional information systems theories of dynamic capabilities and contingency theory in a modern context. We adopt a longitudinal action design research approach centered around a DevOps team working in the IT department of a Dutch public organization. A mixed method design including scientific questionnaires, workshops, expert opinions and semi-structured interviews is employed to build and evaluate the profile. The resulting profile is characterized by technological complexity, a highly regulated environment, departmental interdependencies and high system relevance. The evaluation phase supports the validity of the artifact and suggests moderately improved coalignment of context and team capabilities after the research period, as well as a positive influence of coalignment on team performance. It is contended that software teams in public organizations can benefit from improved coalignment between context and DevOps capabilities by following the presented approach. We argue that it is important to create a profile which is internally consistent and views coalignment as a continuous process in order to maximize the positive effect on team performance.

Abstract Student feedback analysis is time-consuming and laborious work if it is handled manually. This study explores the use of a new deep learning-based method to design a more accurate automated system for analysing students’ feedback (called DTLP: deep learning and teaching process). The DTLP employs convolutional neural networks (CNNs), bidirectional LSTM (BiLSTM), and attention mechanism. To the best of our knowledge, a deep learning-based method using a unified feature set, which is representative of word embedding, sentiment knowledge, sentiment shifter rules, linguistic and statistical knowledge, has not been thoroughly studied with regard to sentiment analysis of student feedback. Furthermore, DTLP uses multiple strategies to overcome the following drawbacks: contextual polarity; sentence types; words with similar semantic context but opposite sentiment polarity; word coverage limit of an individual lexicon; and word sense variations. To evaluate the DTLP, we conducted an experiment on a large volume of students’ feedback. The results showed (i) DTLP outperforms the existing systems in the field, (ii) DTLP that learns from this unified feature set can acquire significantly higher performance than one that learns from a feature subset, (iii) the ensemble of sentiment shifter rules, word embedding, statistical, linguistic, and sentiment knowledge allows DTLP to obtain significant performance, and (iv) an attention mechanism into CNN-BiLSTM improves the performance of DTLP. In addition, the deployed method looks for potential causes behind student feedback.

Requirements analysis and modeling is a challenging task involving complex knowledge of the domain to be engineered, modeling notation, modelling knowledge, etc. When constructing architectural artefacts experts rely largely on the tacit knowledge that they have built based on previous experiences. Such implicit knowledge is difficult to teach to novices, and the cost of the gap between classroom knowledge and real business situations is thus reflected in further needs for post-graduate extensive trainings for novice and junior analysts. This research aims to explore the state-of-the art natural language processing techniques that can be adopted in the domain of requirements engineering to assist novices in their task of knowledge construction when learning requirements analysis and modeling. The outcome includes a method called Text-To-Model (TeToMo) that combines the state-of-the-art natural language processing approaches and techniques for identifying potential architecture elemen t candidates out of textual descriptions (business requirements). A subsequent prototype is implemented that can assist a knowledge construction process through (semi-) automatic generation and validation of Unified Modeling Lnaguage (UML) models. In addition, to the best of our knowledge, a method that integrates machine learning based method has not been thoroughly studied for solving requirements analysis and modeling problem. The results of this study suggest that integrating machine learning methods, word embedding, heuristic rules, statistical and linguistic knowledge can result in increased number of automated detection of model constructs and thus also better semantic quality of outcome models.

Abstract This paper reports on the design and validation of a capability measurement instrument for software delivery teams that make use of the DevOps approach. The instrument is based on the results of a systematic literature review and was developed and validated by involving a total of five domain experts and conducting a field study among six DevOps team members. To this end, we used qualitative and survey-based data collection methods from participatory action research as well as design science. The resulting instrument encompasses five dimensions, covering seventeen capabilities and thirty-eight associated practices. The practices are evaluated on five capability levels. The results of the validation process indicate clear agreement of the domain experts and team members with all aspects of the instrument. As a contribution to practice, this research offers a pragmatic tool for IS practitioners which provides insight into the status of their DevOps transformation and offers directions for improving DevOps team performance. Furthermore, this research contributes to the ongoing research stream on DevOps by providing novel insights into the nature of DevOps capabilities and their potential configurations.

Industries that are in transition to Industry 4.0 often face challenges in applying data-driven methods to improve performance. While ample methods are available in literature, knowledge on how to select and apply them is scarce. This study aims to address this gap reported on the design and implementation of data-driven models for predictive maintenance at TATA Steel, Shotton. The objective of the project is to predict the wearing behaviour of the components in the steel production line for maintenance activity decision support. To achieve the predictive maintenance goal, the approach applied can be summarized as follows: 1. business understanding and data collection, 2. literature review, 3. data preparation and exploration, 4. modelling and result analysis and 5. conclusion and recommendation. The data-driven methods that were analysed and compared are: Partial Least Squares Regression (PLSR), Artificial Neu- ral Network (ANN) and Random Forest(RF). After cleaning and analysing the production line data, predictive maintenance with the current available data in TATA Steel, Shotton is best feasible with PLSR. The study further concludes that, predictive maintenance is likely to be feasible in similar industries that are in transition to industry 4.0 and have growing volumes of production data with varying quality and detail. However, as illustrated in this case study, careful understanding of the industrial process, thorough modeling and cleaning of the data as well as careful method selection and tuning are required. Moreover, the resulting model needs to be packaged in a user friendly way to find its way to the job floor.

Over the past few years, academics have witnessed an increasing amount of attention being accorded to games as learning tools. According to several researchers, Serious Games (SGs) can assist learning by emerging as an alternate means of presenting instructions. Whilst SGs are increasingly gaining acceptance as a learning tool, their application in formal education remains rather limited, which underpins the importance about understanding what makes a game effective and how it must be used in classrooms. Given that teachers play a key role in shaping and responding to the intricate contextual factors influencing the manner in which games are experienced across educational settings, their opinions on what SGs can possibly accomplish in educational settings would inexorably impact the decisions relating to when, how, and for what purposes they would be incorporated in classrooms. Against this backdrop, this study administered a survey online and in hardcopy to ascertain teachers' perceptions on SGs and their effect on their contribution as a teacher. It also pinpointed the challenges and impediments of utilizing SGs in classrooms through teachers' perspective, which illuminated the attitudes teachers generally bring to games-based learning environments. According to the findings, teachers are typically open to using SGs in their classrooms. Overall, it can be inferred that the design/development of SGs aimed at formal education can benefit significantly by adopting game features and surmounting the hurdles addressed by teachers.

The long term prediction of maritime vessels' destinations and arrival times is essential for making an effective logistics planning. As ships are influenced by various factors over a long period of time, the solution cannot be achieved by analyzing sailing patterns of each entity separately. Instead, an approach is required, that can extract maritime patterns for the area in question and represent it in a form suitable for querying all possible routes any vessel in that region can take. To tackle this problem we use a genetic algorithm (GA) to cluster vessel position data obtained from the publicly available Automatic Identification System (AIS). The resulting clusters are treated as route waypoints (WP), and by connecting them we get nodes and edges of a directed graph depicting maritime patterns. Since standard clustering algorithms have difficulties in handling data with varying density, and genetic algorithms are slow when handling large data volumes, in this paper we investigate how to enhance the genetic algorithm to allow fast and accurate waypoint identification. We also include a quad tree structure to preprocess data and reduce the input for the GA. When the route graph is created, we add post processing to remove inconsistencies caused by noise in the AIS data. Finally, we validate the results produced by the GA by comparing resulting patterns with known inland water routes for two Dutch provinces.

The advent of global distribution of software development has made managing collaboration and coordination among developers more difficult due to various reasons including physical distance, differences in time, cultural differences etc. A nearly total absence of informal communication among developers makes coordinating work in a globally distributed setting more critical. The goal of this workshop is to provide an opportunity for researchers and industry practitioners to explore both the state-of the art in tools and methodologies for managing global software development (GSD).

The current highly dynamic business environment requires businesses to be agile. Business agility is the ability to swiftly and easily change businesses and business processes beyond the normal level of flexibility to effectively manage unpredictable external and internal changes. This study reports on a cross-industry analysis of change factors requiring agility and assesses agility gaps that companies are facing in four industry sectors in the Netherlands. A framework was constructed to measure the perceived gaps between the current level of business agility and the required level of business agility. The questionnaire and in-depth interviews held reveal that today's businesses perceive to lack the agility required to quickly respond to changes, whose speed and requirements are difficult to predict. The paper presents rankings of generic and sector-specific agility gaps. These show that although some generic change factors requiring agility exist, the change factors requiring agility that cause agility gaps differ across industry sectors. Among the factors that enable or hinder business agility, the existence of inflexible legacy systems is perceived to be a very important disabler in achieving more business agility. A number of basic principles and directions are discussed to transform Information Technology from barrier into key enabler for increased agility in organizations and business networks.