1,721,008 research outputs found
BOARD GAME-BASED LEARNING: FROM COGNITIVE PROCESSES TO ASSESSMENT
Full text linkIl Board Game-Based Learning ha guadagnato sempre più attenzione
come approccio innovativo per promuovere l'impegno attivo e lo sviluppo
cognitivo olistico. Tuttavia, l'integrazione dei giochi da tavolo in una
pratica efficace è difficile, in parte a causa della mancanza di un quadro
didattico consolidato. Integrando la tassonomia di Anderson e Krathwohl
nella progettazione del bGBL, questo contributo si concentra sul modo in
cui specifiche meccaniche di gioco possono indirizzare le abilità di
pensiero di ordine superiore, allineando le dinamiche di gioco con quadri
didattici consolidati. Ciò consente un'implementazione efficace delle
strategie bGBL, delle valutazioni in-game e around-game e della
calibrazione in tempo reale del processo di apprendimento. Questi
risultati indicano il potenziale del bGBL per far progredire le pratiche
pedagogiche, supportando gli insegnanti nella creazione di ambienti
coinvolgenti e basati sull'evidenza che allineano gli obiettivi cognitivi con
una valutazione significativa.Board Game-Based Learning (bGBL) has gained increasing attention as an
innovative approach to foster active engagement and holistic cognitive
development. However, integrating board games into effective practice is
challenging, partly because of the lack of an established instructional
framework. By integrating Anderson & Krathwohl’s taxonomy into bGBL
design, this contribution focuses on how specific game mechanics can
target higher-order thinking skills, aligning gameplay dynamics with
established instructional frameworks. This allows effective
implementation of bGBL strategies, in-game and around-game
assessments, and real-time calibration of the learning process. These
findings indicate the potential of bGBL to advance pedagogical practices,
supporting teachers in creating immersive, evidence-based environments
that align cognitive objectives with meaningful evaluation
Using AI and Cognitive Taxonomies to map Learning Processes in Board Games
No full textBoard Game-Based Learning (bGBL) has gained increasing attention as an innovative approach to foster active engagement and holistic cognitive development. However, integrating board games into effective practice is challenging, partly because of the lack of an established instructional framework. The implementation of bGBL often relies on teachers’ personal initiative and familiarity with games, rather than on shared design practices. One of the main obstacles to implementing GBL lies in properly aligning learning goals with the actions that take place during gameplay, and the related learning processes. In this study, we develop a theoretical framework for aligning learning goals and the cognitive processes elicited by game mechanisms. We use this framework to train a GenAI assistant (GADbot) to assist bGBL instructional design, assessing its performance through human expert evaluation. Given the ever-increasing number of available board games and the constant innovation in game mechanics, this approach can revolutionize the field of bGBL, leveraging AI as an assistant to lower the entry barrier for teachers to choose the right game for their educational needs, thus providing the foundation to design meaningful learning experiences and advance active pedagogical practices
Board game-based learning: dai processi cognitivi alla valutazione
No full textBoard Game-Based Learning (bGBL) has gained increasing attention as an innovative approach to foster active engagement and holistic cognitive development. However, integrating board games into effective practice is challenging, partly because of the lack of an established instructional framework. By integrating Anderson & Krathwohl’s taxonomy into bGBL design, this contribution focuses on how specific game mechanics can target higher-order thinking skills, aligning gameplay dynamics with established instructional frameworks. This allows effective implementation of bGBL strategies, in-game and around-game assessments, and real-time calibration of the learning process. These findings indicate the potential of bGBL to advance pedagogical practices, supporting teachers in creating immersive, evidence-based environments that align cognitive objectives with meaningful evaluation
Towards AI-assisted Board Game-based Learning: Assessing LLMs in Game Personalisation
No full textBoard games are used in different educational settings to promote acquisition of disciplinary content, soft skills, foster engagement towards learning content, and sustain motivation. However, designing and conducting effective educational activities with board games requires instructional design skills, knowledge of games, as well as the ability to align the player's internal goals with the learning objectives. Board game-based learning (bGBL) design includes choosing appropriate games and personalising them to better fit with the educational setting and the students' individual needs. This complexity, coupled with a general lack of teacher familiarity with games andgame culture, is likely a reason for the relatively low use of board games in formal instructional settings such as schools. Artificial Intelligence (AI) has long been used in education, but the recent diffusion of user-friendly tools for large language models (LLM) opens a new range of possibilities to assist teachers and educators in instructional design: This includes the design and implementation of bGBL. In a preliminary study, we explored the ability of a well-known chatbot, ChatGPT, to select board games and suggest modifications to better align with the classroom context and personal student needs. However, this study was limited to a sample learning unit and was based on a posteriori evaluation by experts. In this contribution, we develop a new testing protocol to assess the reliability, effectiveness, and context sensitivity of several LLMs to adapt given board games to different classroom scenarios. The methodology features blind comparison of AI and human experts. The results suggest that general-purpose AI tools such as Copilot, Claude, and ChatGPT can provide quality and context-sensitive board game modification suggestions, to the point of slightly overperforming human experts in aligning personalisation suggestions to instructional goals. Thisstudy represents a first foray in the use of general Artificial Intelligence to assist the modding, or personalisation, of board game-based learning activities and, despite its limitations as a pilot experiment, might pave the way for successful integration of assistive technologies in game-based learning and facilitating its integration in the school curriculum
Board Game Based Learning: Strategies for Effective Learning
No full textBoard Game Based Learning (bGBL) is an educational method that utilizes board games to promote active and engaging learning, enhancing students' cognitive and social skills. However, currently the specific properties of bGBL with respect to the general methodology of Game-Based Learning (GBL) are not clearly defined. This is an issue as most instructional frameworks for GBL, usually centered on videogames, do not make full use of the potential of bGBL. This paper explores the key properties of bGBL as a subset of GBL: we define four characteristics, namely turn structure, personalization, transparency and shared experience, that define bGBL and provide opportunities to promote powerful and meaningful learning. The paper also discusses the importance of creating inclusive games that cater to different learning styles and motivations, making bGBL a versatile and accessible tool for teachers and students
Automating board-game based learning. A comprehensive study to assess reliability and accuracy of AI in game evaluation
No full textGame-Based Learning (GBL) and its subset, Board Game-Based Learning (bGBL), are dynamic pedagogical approaches leveraging the immersive power of games to enrich the learning experience. bGBL is distinguished by its tactile and social dimensions, fostering interactive exploration, collaboration, and strategic thinking; however, its adoption is limited due to lack of preparation by teachers and educators and of pedagogical and instructional frameworks in scientific literature. Artificial intelligence (AI) tools have the potential to automate or assist instructional design, but carry significant open questions, including bias, lack of context sensitivity, privacy issues, and limited evidence. This study investigates ChatGPT as a tool for selecting board games for educational purposes, testing its reliability, accuracy, and context-sensitivity through comparison with human experts evaluation. Results show high internal consistency, whereas correlation analyses reveal moderate to high agreement with expert ratings. Contextual factors are shown to influence rankings, emphasizing the need to better understand both bGBL expert decision-making processes and AI limitations. This research provides a novel approach to bGBL, provides empirical evidence of the benefits of integrating AI into instructional design, and highlights current challenges and limitations in both AI and bGBL theory, paving the way for more effective and personalized educational experiences
Students’ Coping and Study Strategies: Did Emergency Remote Teaching Support the Students During the Pandemic?
No full textSelf-initiated online communities of teachers as an expanded meso space
No full textThe pandemic-related educational emergency created the need to re-organise teaching and learning processes, as well as teacher professional development (TPD) opportunities. The forced nature of the transition to remote teaching during the pandemic somewhat shifted the micro or macro-level factors in the re-organisation of teaching and learning, consequently, the new opportunities contributed to a reconceptualisation of a space needed for TPD. This research is contextualised in a larger digital ethnographic and survey study of three different Italian teacher online communities. It reports on the results of the study and aims to further uncover relationships in re-organisation patterns while examining the role of professional online communities for TPD, reconceptualising them as Meso-level re-organising practices. The results of the current study indicate that among different macro and meso level supports, online communities and colleagues have contributed to re-organising processes, indicating the expansion of the meso level. This research has practical implications for TPD demonstrating how the relaxation of certain established frames of operation can mediate further opportunities to innovate. The findings can inform theory, policy, practice and further research to support the expansion of the meso space through the ‘dialogue’ between different organisational layers, creating re-organising opportunities for educational innovation and TPD
Didattica Non Lineare e Tecnologie in Educazione Fisica: Quali Prospettive per la Formazione degli Insegnanti?
No full textRe-organization of assessment during the educational emergency in primary and secondary teaching: an Italian case
Full text linkThe educational crisis caused by the pandemic created an unprecedented need
to reorganize teaching and learning processes, and the educational assessment
became one of the thorniest issues in this rapid change; assessment reorganization
entails layered complexities on micro, meso and macro levels. This
research is contextualized in a larger digital ethnographic study of three different
Italian teacher online communities, uncovering the experience from mixedmethods
research. Following this research, a survey instrument was developed
and launched. Current paper reports on the survey aiming to uncover the change
in assessment practices during the educational emergency while reflecting on
teachers’ beliefs on the assessment, the use of remote assessment methods
before and during the pandemic, and its re-organization. Findings suggest a
significant reorganization of assessment during the COVID-19 educational
emergency in all school orders. Through all school orders, teachers perceived a
reduction in the importance of assessment during the pandemic and,
consequently, used most assessment techniques significantly less than before.
However, different methods changed differently, with oral examinations
diminishing dramatically and increased use of closed-question quizzes
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