WIP: Enhancing Design Patterns Learning Through Gamification

This paper describes and compares two categories of pedagogical design patterns that have emerged from CS education practice: lecture-based design patterns and active learning design patterns. Pedagogical design patterns provide faculty with combinations of generalized descriptions of problems and solutions that occur in teaching and learning. The benefit of forming design patterns is the codification of successful practice that can be reused in multiple scenarios and draw on the creativity of the instructor for defining the details relevant to the course and the students. Design patterns have been represented in many formats since Alexander’s initial design pattern model highlighting different aspects of what is important in each domain in which the patterns are created and used. This paper analyzes design patterns emerging from recent developments in lecture-based pedagogy and active learning in CS education. Traditional lectures in computer science, engineering, and other STEM disciplines are being reconsidered due to research that shows that students are less likely to learn while listening and more likely to learn while actively engaged. Design patterns that address problems and provide potential solutions to traditional lectures in computer science education have been published that provide solutions to engage students during the lecture. The pedagogy of flipped classrooms and active learning have recently been adopted by many faculty in Computer Science leading to emerging design patterns for active learning. We compare how previously published lecture-based patterns and our active learning patterns address similar problems with different solutions to engaging students. We show how an object-based structure for pedagogical design patterns can provide additional information about the problems and the solutions addressed by the patterns that are more easily indexed and combined.

The present paper details the process we have carried out in order to articulate, assemble, and validate design values, principles and patterns for task-based technology-enhanced language learning. During the first phase, we identified an initial set of pedagogical principles based on language acquisition theories, computerassisted language learning literature, and pedagogical approaches to teaching second and foreign languages enhanced by the use of technology. Following a Delphi study, we refined the initial set of principles after running two rounds of consultations to a group of experts in order to achieve general consensus about the principles. After reaching consensus and assembling a list of sixteen principles, we focused on deriving values and epistemic principles from them. The next step in the process of unveiling a signature pedagogy (Shulman, 2005) for technology-enhanced language teaching involved eliciting concrete examples of practices from language teachers embodying the principles, and extract design patterns from these. One of the authors, who has a broad expertise as language educator in computer-assisted language learning and computer-mediated communication, offered several examples of practices and problems which derived into the six patterns we will present in this article. Finally, the six design patterns were linked back with the pedagogical principles.

The use of design patterns for creating technology-enhanced learning (TEL) environments has gained significant momentum in recent years. In the TEL community the concept of patterns was introduced and spread by various pattern initiatives and projects. Patterns are meanwhile a widely familiar concept for both researchers and practitioners. Essentially, a pattern gives a description of a reusable solution to a common design problem in some context. In the context of TEL and in pedagogy, patterns are typically used to describe effective teaching methods, situations or teaching/learning activities. Even with a significant number of e-learning and TEL design patterns available today, there is still only little evidence of actual usage and usability of that knowledge base in the TEL domain. The past focus has been on introducing patterns to the field and creating patterns from existing design and application experience. But any innovation needs to show its qualities and benefits to the users if it is intended to succeed; patterns without users working with them are, literally, useless artefacts. Therefore, in this paper, we aim to provide a thorough exploration of the user’s view on design patterns for TEL. We intend to investigate qualities and features of patterns and pattern collections which we consider essential for raising the usability of patterns. Thereby we consulted several sources on pattern qualities, requirements, life cycle and use cases as found in the original pattern work by Alexander as well as in more recent literature. On the other hand, we draw from own experience and research during several years of finding, writing and applying patterns in higher-education TEL settings. To enable continuing research and improvement based on findings, insights and lessons learned during TEL pattern application, we propose a cyclic model of the pattern application process; we investigate the application of design patterns along the five phases of Action Research: diagnosing, action planning, action taking, evaluating, and specifying learning. This cyclic phase arrangement seems particularly suited for pattern application, as we believe that the quality of patterns and the success of their application heavily rely on the user’s ability to integrate lessons learned and experiences drawn into future application cycles. In the core part of the paper we use the cyclic process of design pattern application for definition and analysis of critical factors of the user’s perspective for each phase as well as input and output elements of each phase. In a nutshell, the phases of the application cycle and their relation to pattern features from the user’s viewpoint are characterized as follows. The diagnosing phase includes understanding the application context, finding appropriate patterns and understanding the patterns. This is supported by building a pattern repository upon some clearly expressed value base or philosophy. Also, patterns need to be structured to guide users in finding and selecting appropriate patterns for their design problems. To be understandable, patterns need to convey their advice in an efficient, generative way. For action planning and action taking patterns should, particularly in the TEL domain, include details on required technology support, essential actions and critical success factors to be considered, as well as pitfalls known from previous applications. To be amenable to evaluation, patterns need to clearly express their intent and expected effects, and include evidence on their effectiveness; to collect evidence each pattern can, for instance, provide guidance on collecting empirical data. The pattern user also has a responsible role in pattern evolution through feeding back his/her insights and experience during pattern application. We should strive to exploit network effects by sharing advice, feedback, and experience through design patterns. In the paper, we ‘walk through’ the phases of the proposed application cycle based on an example case in the context of involving students in the evaluation of their learning at the end of a concrete course. We conclude that continuous information flow and cooperation between pattern designers and users are essential in equipping patterns with the features needed to facilitate their finding, selection, application, organization and evolution.

Despite the hype around machine learning (ML), many organizations are struggling to derive business value from ML capabilities. Design patterns have long been used in software engineering to enhance design effectiveness and to speed up the development process. The contribution of this paper is two-fold. First, it introduces solution patterns as an explicit way of representing generic and well-proven ML designs for commonly-known and recurring business analytics problems. Second, it reports on the feasibility, expressiveness, and usefulness of solution patterns for ML, in collaboration with an industry partner. It provides a prototype architecture for supporting the use of solution patterns in real world scenarios. It presents a proof-of-concept implementation of the architecture and illustrates its feasibility. Findings from the collaboration suggest that solution patterns can have a positive impact on ML design and development efforts.

Design patterns for learning are about articulating, testing and sharing the principles of problem solving in the educational context. In this way, multiple patterns are developed to solve common problems, described in various pattern language formats. Therefore, this work is about characterizing and establishing functional relationships between the components involved in the construction of a learning design pattern. The research was carried out in a Delphi study of double round individual non-contact between experts (n = 14), from different areas (educational technology, e-learning, distance education, higher education), selected through the coefficient of expert competence. The main result is a pattern language composed of attributes, components, and groups of processes. It is concluded that the language obtained allows externalizing knowledge from its consistent and shareable structure, which makes it suitable for use in different scenarios that require a formal pattern language and facilitates its implementation in online learning contexts.

Computer-Supported Collaborative Learning (CSCL) focuses on designing tools and strategies to enhance collaborative learning.However, design knowledge within CSCL is often embedded in empirical studies without explicit emphasis, limiting its systematic documentation and dissemination.We analyzed literature from the International Journal of Computer-Supported Collaborative Learning (ijCSCL) and identified five design patterns for online collaborative learning in higher education, highlighting components such as learning outcomes, task structures, technology affordances, and collaborative processes.This work seeks to elevate design knowledge in CSCL, encouraging the community to refine and share effective designs. FindingsFive distinct design patterns across three categories were identified from the analysis.Below, we report each design pattern by describing its representative learning outcomes, context, task sequence, and technologies. CE-oriented design patternsCE-oriented design patterns focus on CE tasks, aiming for deep knowledge exploration and co-construction.

In a computer-science lab course on software architectures and web technologies I tried to introduce student blogs as an opportunity to attain deeper insight into individual and team learning processes and to provide improved support and facilitation of learners during the online phases of the course. The research activities around this project were supported by the 'Technology Enhanced Learning' project funded by the University of Vienna (See http://elearn.pri.univie.ac.at/projects/tel/).

En el documento se abordan algunos principios de diseño instruccional vinculados con el constructivismo sociocultural y la cognición situada referidos al diseño de entornos de aprendizaje apoyados con tecnologías de la información y comunicación (TIC). Se analizan las potencialidades educativas de dichas TIC en su carácter de signos o instrumentos psicológicos en contraste con su empleo restringindo como herramientas técnicas. Se destacan algunas características de los entornos apoyados con las TIC que potencian el aprendizaje y se concluye remarcando la necesidad de realizar investigaciones sobre los mecanismos de influencia educativa y las practicas educativas reales que ocurren en diversas experiencias escolares y autoiniciadas mediadas por la tecnologia.

In massive MIMO systems, acquiring accurate channel state information (CSI) incurs significant overhead due to the need for extensive pilot signaling. This paper proposes a novel learning-based framework for pilot reduction and CSI reconstruction, inspired by image inpainting techniques. Pilot overhead is reduced through lightweight pilot pattern learning. At the receiver, a deep neural network reconstructs the full CSI matrix from partial observations. The pilot pattern design and CSI reconstruction can be jointly optimized in an end-to-end fashion. Unlike prior works that focus on feedback compression after full pilot transmission, our approach reduces pilot usage before transmission, enhancing efficiency while maintaining interpretability, which can enable hybrid signal processing and deep learning methods in practical systems.Simulation results on 3GPP channel models show consistent improvements of over 2 dB in normalized mean square error (NMSE) compared to baselines using uniform or random pilot patterns at only 25% pilot usage. Performance gains persist under low SNR conditions and higher compression ratios, making the approach promising for scalable 5G and beyond systems, such as intelligent CSI-RS configuration.