ANALISIS BIBLIOMETRIK MATHEMATICS GAME-BASED LEARNING

Penelitian ini memaparkan tinjauan kritis mengenai Mathematics Game-Based Learning berdasarkan analisis bibliografi atas 200 artikel yang dipublikasikan dalam jurnal internasional berdasarkan database scopus selama kurun waktu 1980 – 2021. Tujuan penelitian ini untuk menjawab kekosongan dengan memberikan analisis bibliometrik ekstensif dari literatur yang berkaitan dengan istilah ini untuk menjawab pertanyaan-pertanyaan berikut: (1) Bagaimana artikel Mathematics Game-Based Learning diklasifikasikan?, (2) Bagaimana trend penelitian Mathematics Game-Based Learning? Topik penelitian apa yang menjadi subjek lebih banyak publikasi?, (3) Apa topik Mathematics Game-Based Learning masa depan yang memberikan kesempatan untuk penelitian lebih lanjut?. Analisis bibliometrik menggunakan lima langkah meliputi mendefinisikan istilah pencarian yang sesuai, hasil pencarian awal, penyempurnaan hasil pencarian, penyusunan data statistika awal, dan analisis data. Hasil penelitian memperoleh 200 makalah dengan hasil awal sebanyak 2107 kutipan (51.39 kutipan / tahun dan 10.54 kutipan/makalah). Pemurnian hasil menyisakan 60 artikel (penurunan 70%); data mengenai kutipan juga berubah, dengan 939 kutipan (menurun 55,44%), 22.90 kutipan/tahun (menurun 55,44%), dan 15.65 kutipan/makalah (meningkat 48,48%). Temuan ini menunjukkan bahwa jurnal Q1 dan Q2 tidak memberikan pengaruh yang signifikan terhadap kutipan dibandingkan jurnal lainnya. Penelitian ini menunjukkan informasi untuk masa yang akan datang dalam bidang Mathematics Game-Based Learning, serta merangkum dan mendukung temuan penting dari tinjauan tersebut.Secara keseluruhan, konsep Mathematics Game-Based Learning masih perlu ditingkatkan dalam penelitian-penelitian yang akan datang. Penelitian selanjutnya dapat mengambil topik tentang effect dan development

Measuring the noticing of an unexpected event in Magical Garden with a Teachable Agent using Eye-Tracking

Scientific views on what children are capable of have been revised through history again and again, usually when new methods of studying children’s capabilities are presented. What has often been concluded is that children are capable of more than what was previously thought. New technology has introduced a genre of educational games which utilize the captivating power of computer games which have shown a positive effect on learning and motivation. In this study, the educational game Magical Garden was used as a platform to train, teach, and test number sense. The pedagogical instrument Teachable Agent (TA) is a part of Magical Garden’s design which utilizes the protégé effect. A new method of measuring number sense, detecting an “unexpected event” by attending to it, is pro-posed and tested. The unexpected event was a tree elevator malfunction. The purpose of the unexpected event was to create a task where only the children who were attentive and knew which branch the elevator would go to would react to and detect the unexpected event. A model of detection of the unexpected event, looking back at the correct branch after the elevator passed the correct one, was proposed. Eye-tracking was used as the method of capturing detections of the unexpected event, as well as measuring the interaction between the children and the TA during the unexpected event. In this study, 42 preschoolers participated. The results show that children attend the TA significantly more when the TA was in charge of the decisions in the game. This indicates that preschoolers understand that the TA was in charge. The model of detection used in this study was not comprehensive. However, detecting an unexpected event could still be a promising method of measuring number sense. Therefore, future research could utilize this method to unveil more exciting capabilities of children with a more inclusive model of detection.Vetenskapliga synsätt på vad barn kan är kapabla till att klara av har reviderats om och om igen genom historien, oftast i samband med att nya metoder som undersöker barns förmågor uppkommit. Barn verkar gång på gång klara av mer än vad man tidigare hade trott. Lärspel utnyttjar datorspelens fängslande kraft, och har visat ha en positiv effekt för lärande och motivationen. I denna uppsats, kommer lärspelet Magical Garden användas som plattform för att träna, lära ut och testa förskolebarns taluppfattning. Det pedagogiska instrumentet Teachable Agent (TA) är en intrikat del av Magical Gardens design som försöker facilitera "protégé effect". I denna uppsats introduceras och testas en ny metod för att mäta taluppfattning, upptäcka en oväntad händelse genom att rikta uppmärksamhet mot händelsen. Den oväntade händelsen i spelet är att korghissen i ett träd åker fel. Den oväntade händelsen är utformad så att endast barn med tillräckligt god taluppfattning förstår att hissen åker till fel våning och kan uppmärksamma att hissen åker fel. Den modell som föreslogs var att barnen skulle uppmärksamma den korrekta våningen som hissen skulle stannat vid när de hade upptäckt att hissen åkte fel. En ögonrörelsekamera användes för att fånga upptäckterna och även för att mäta interaktionen mellan TA och barnen. Ett ögonrörelseexperiment utfördes på 42 förskolebarn på respektives förskola. Resultaten visade att barnen tittade i större utsträckning på TA när TAn styrde i spelet. En slutsats som kan dras från detta resultat är att förskolebarnen verkade förstå att det var TA som styrde när den styrde. Modellen för att upptäcka den oväntade händelsen var inte heltäckande. Men att upptäcka något oväntat kan fortfarande vara en lovande metod för att mäta taluppfattning. Därför borde framtida forskning använda denna metod för att avslöja fler förmågor hos barn och skapa en mer inkluderande modell för vad en upptäckt av något oväntat kan vara

"Teach AI How to Code": Using Large Language Models as Teachable Agents for Programming Education

This work investigates large language models (LLMs) as teachable agents for learning by teaching (LBT). LBT with teachable agents helps learners identify their knowledge gaps and discover new knowledge. However, teachable agents require expensive programming of subject-specific knowledge. While LLMs as teachable agents can reduce the cost, LLMs' over-competence as tutees discourages learners from teaching. We propose a prompting pipeline that restrains LLMs' competence and makes them initiate "why" and "how" questions for effective knowledge-building. We combined these techniques into TeachYou, an LBT environment for algorithm learning, and AlgoBo, an LLM-based tutee chatbot that can simulate misconceptions and unawareness prescribed in its knowledge state. Our technical evaluation confirmed that our prompting pipeline can effectively configure AlgoBo's problem-solving performance. Through a between-subject study with 40 algorithm novices, we also observed that AlgoBo's questions led to knowledge-dense conversations (effect size=0.73). Lastly, we discuss design implications, cost-efficiency, and personalization of LLM-based teachable agents

Diseño de Agentes Conversacionales Pedagógicos usando análisis de datos

Pedagogical Conversational Agents are systems or programs that represent a resource and a means of learning for students, making the teaching and learning process more enjoyable. The aim is to improve the teaching-learning process. Currently, there are many agents being implemented in multiple knowledge domains. In our previous work, a methodology for designing agents was published, the result of which was Agent Dr. Roland, the first conversational agent for Early Childhood Education. In this paper, we propose the use of Data Analytics techniques to improve the design of the agent. Two new techniques are applied: KDDIAE, application of (Knowledge Discovery in Databases) to the Data of the Interaction between Agents and Students – Estudiantes in Spanish, and BIDAE (use of Data Analytics to obtain information of agents and students). The use of KDDIAE and BIDAE proves the existence of a fruitful relationship between learning analytics and learning design. Some samples of rules related to learning analytics and design are the following: (Learning Analytics) Children who initially do not know how to solve the exercise, after receiving help, are able to understand  and solve it à (Learning Design) An agent for small children should be able to provide help. In addition, help should be entertaining and tailored to their characteristics because it is a resource that children actually use; or (Learning Analytics) Younger children use more voice interaction à (Learning Design) An agent interface for young children must incorporate voice commands. A complete list of rules related to learning analytics and design is provided for any researcher interested in PCA design. 72 children were able to use the new Dr. Roland after applying the learning analytics-design rules. They reported a 100 % satisfaction as they all enjoyed interacting with the agent.Los Agentes Conversacionales Pedagógicos son sistemas informáticos que facilitan la enseñanza a los estudiantes y un recurso de apoyo para los profesores haciendo el proceso de enseñanza más agradable. El objetivo es mejorar el proceso de enseñanza-aprendizaje. Actualmente, hay muchos agentes que se implementan en múltiples dominios de conocimiento. En nuestro trabajo previo se publicó una metodología para diseñar agentes. Con esta metodología se diseñó el agente Dr. Roland, el primer agente conversacional para Educación Infantil. En este artículo, se propone el uso de técnicas de análisis de datos para mejorar el diseño de Dr. Roland. Se implementan dos técnicas nuevas: KDDIAE, aplicación de KDD (Descubrimiento de Conocimiento en Bases de Datos) a los Datos de Interacción entre Agentes y Estudiantes, y BIDAE (uso de Análitica de Datos para obtener información de la interacción entre Agentes y Estudiantes). El uso de KDDIAE y BIDAE prueba la existencia de la fructífera relación que puede darse entre analítica de aprendizaje y diseño de aprendizaje. Los niños que inicialmente no saben cómo resolver un ejercicio, después de recibir ayuda son capaces de comprender el ejercicio y solucionarlo à (Diseño de Aprendizaje) Un agente para niños pequeños debería poder proporcionar ayuda. Además, la ayuda debería ser entretenida y adaptada a las características de los niños; o (Análitica de Aprendizaje) Los niños pequeños usan más la interacción por voz à (Diseño de Aprendizaje) Una interfaz de agente para niños pequeños debe incorporar la posibilidad de interactuar por voz. En este artículo se proporciona una lista completa que relaciona análitica y diseño de aprendizaje para cualquier investigador que pueda estar interesado en APC. 72 niños pudieron usar el agente mejorado Dr. Roland al implementar las reglas.  Reportaron un 100 % de satisfacción, ya que todos disfrutaron de la interacción con el agente

An Overview Of Learning Support Factors On Mathematic Games

 In this study, we examined the factors in game design that were used by developers to support the interests of mathematics learning. The aim is to overcome the lack of empirical evidence about the impact of factors in the game on learning outcomes, identify how the design of in-game activities affects learning, and develop an overview of general recommendations for designing mathematics education games. This study tries to illustrate the impact of game design factors in mathematics education games on the objectives and results of game-based learning

Developing Computational Thinking with Educational Technologies for Young Learners

This article aims to provide an overview of the opportunities for developing computational thinking in young learners. It includes a review of empirical studies on the educational technologies used to develop computational thinking in young learners, and analyses and descriptions of a selection of commercially available technologies for developing computational thinking in young learners. The challenges and implications of using these technologies are also discussed

ChatScratch: An AI-Augmented System Toward Autonomous Visual Programming Learning for Children Aged 6-12

As Computational Thinking (CT) continues to permeate younger age groups in K-12 education, established CT platforms such as Scratch face challenges in catering to these younger learners, particularly those in the elementary school (ages 6-12). Through formative investigation with Scratch experts, we uncover three key obstacles to children's autonomous Scratch learning: artist's block in project planning, bounded creativity in asset creation, and inadequate coding guidance during implementation. To address these barriers, we introduce ChatScratch, an AI-augmented system to facilitate autonomous programming learning for young children. ChatScratch employs structured interactive storyboards and visual cues to overcome artist's block, integrates digital drawing and advanced image generation technologies to elevate creativity, and leverages Scratch-specialized Large Language Models (LLMs) for professional coding guidance. Our study shows that, compared to Scratch, ChatScratch efficiently fosters autonomous programming learning, and contributes to the creation of high-quality, personally meaningful Scratch projects for children.Comment: 29 pages, 7 figures, accepted by CHI 202