Providing Personalized Guidance in Arithmetic Problem Solving

Supervising a student's resolution of an arithmetic word problem is a cumbersome task. Di erent students may use di erent lines of reasoning to reach the nal solution, and the assistance provided should be consistent with the resolution path that the student has in mind. In addition, further learning gains can be achieved if the previous student's background is also considered in the process. In this paper, we outline a relatively simple method to adapt the hints given by an Intelligent Tutoring System to the line of reasoning that the student is currently following. We also outline possible extensions to build a model of the student's most relevant skills, by tracking user's actions

Exploring the future of mathematics teaching: Insight with ChatGPT

This study aims to provide a comprehensive overview of the future of mathematics teaching from the perspective of ChatGPT, an advanced language processing artificial intelligence (AI) developed by OpenAI. The results of the chat transcripts edited with ChatGPT suggest that the future of mathematics teaching will see the integration of technology and AI to provide personalized learning experiences, blended learning environments, and computational thinking, data literacy, and statistics. Problem-solving, critical thinking, and interdisciplinary connections will continue to be emphasized, and equity and inclusion will remain crucial. AI is expected to revolutionize mathematics education, but thoughtful implementation, ongoing professional development, and pedagogical considerations are essential. However, the future of teaching mathematics will continue to evolve. Therefore, teachers and lecturers need to keep abreast of the latest developments and adapt to them while remaining committed to providing quality teaching.Studi ini bertujuan untuk memberikan gambaran komprehensif tentang masa depan pengajaran matematika dari perspektif ChatGPT, Artificial Intelligence (AI) pemrosesan bahasa tingkat lanjut yang dikembangkan oleh OpenAI. Hasil transkrip obrolan yang diedit dengan ChatGPT menunjukkan bahwa masa depan pengajaran matematika akan melihat integrasi teknologi dan AI untuk memberikan pengalaman belajar yang dipersonalisasi, lingkungan pembelajaran campuran, dan pemikiran komputasi, literasi data, dan statistik. Pemecahan masalah, pemikiran kritis, dan koneksi interdisipliner akan terus ditekankan, dan kesetaraan dan inklusi akan tetap penting. AI diharapkan merevolusi pendidikan matematika, tetapi implementasi yang bijaksana, pengembangan profesional berkelanjutan, dan pertimbangan pedagogis sangat penting. Namun, masa depan pengajaran matematika akan terus berkembang. Oleh karena itu, guru dan dosen perlu mengikuti perkembangan terkini dan beradaptasi dengannya sambil tetap berkomitmen untuk memberikan pengajaran yang berkualitas

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing

An Analysis of Interactive Learning Environments for Arithmetic and Algebra Through an Integrative Perspective

International audienceThe analysis presented in this article tries to obtain a global view of the field of interactive learning environments (ILE) dedicated to arithmetic and algebra. As preliminaries, a brief overview of evaluation methods focusing on educational software is given and a short description of ten ILEs concerned by the study is provided as a kind of a state-of-the-art. Then the methodology of ILEs analysis developed in the TELMA project is explained consisting in the design and the refinement of an analysis grid and its use on the ten ILEs is mentioned. Next, a first level analysis of results leading to a compiled, analytic and synthetic view of the ILEs available and/or missing functionalities is given. A second level of the analysis is also proposed, with two concise representations of the ILEs, composed of graphical representations of the previous results, leading to a 3D map of ILEs dedicated to arithmetic and algebra. This map provides, as promised, a global view of the field and permits to define five sorts of ILEs according to two criteria: the first one is teacher-oriented and concerns usages enabled by the ILE; the second one is student-oriented and concerns control provided by the ILE to accomplish such usages

An Analysis of Interactive Learning Environments for Arithmetic and Algebra Through an Integrative Perspective

International audienceThe analysis presented in this article tries to obtain a global view of the field of interactive learning environments (ILE) dedicated to arithmetic and algebra. As preliminaries, a brief overview of evaluation methods focusing on educational software is given and a short description of ten ILEs concerned by the study is provided as a kind of a state-of-the-art. Then the methodology of ILEs analysis developed in the TELMA project is explained consisting in the design and the refinement of an analysis grid and its use on the ten ILEs is mentioned. Next, a first level analysis of results leading to a compiled, analytic and synthetic view of the ILEs available and/or missing functionalities is given. A second level of the analysis is also proposed, with two concise representations of the ILEs, composed of graphical representations of the previous results, leading to a 3D map of ILEs dedicated to arithmetic and algebra. This map provides, as promised, a global view of the field and permits to define five sorts of ILEs according to two criteria: the first one is teacher-oriented and concerns usages enabled by the ILE; the second one is student-oriented and concerns control provided by the ILE to accomplish such usages

Improving School Improvement

PREFACEIn opening this volume, you might be thinking:Is another book on school improvement really needed?Clearly our answer is yes. Our analyses of prevailing school improvement legislation, planning, and literature indicates fundamental deficiencies, especially with respect to enhancing equity of opportunity and closing the achievement gap.Here is what our work uniquely brings to policy and planning tables:(1) An expanded framework for school improvement – We highlight that moving from a two- to a three-component policy and practice framework is essential for closing the opportunity and achievement gaps. (That is, expanding from focusing primarily on instruction and management/government concerns by establishing a third primary component to improve how schools address barriers to learning and teaching.)(2) An emphasis on integrating a deep understanding of motivation – We underscore that concerns about engagement, management of behavior, school climate, equity of opportunity, and student outcomes require an up-to-date grasp of motivation and especially intrinsic motivation.(3) Clarification of the nature and scope of personalized teaching – We define personalization as the process of matching learner motivation and capabilities and stress that it is the learner's perception that determines whether the match is a good one.(4) A reframing of remediation and special education – We formulate these processes as personalized special assistance that is applied in and out of classrooms and practiced in a sequential and hierarchical manner.(5) A prototype for transforming student and learning supports – We provide a framework for a unified, comprehensive, and equitable system designed to address barriers to learning and teaching and re-engage disconnected students and families.(6) A reworking of the leadership structure for whole school improvement --We outline how the operational infrastructure can and must be realigned in keeping with a three component school improvement framework.(7) A systemic approach to enhancing school-community collaboration – We delineate a leadership role for schools in outreaching to communities in order to work on shared concerns through a formal collaborative operational infrastructure that enables weaving together resources to advance the work.(8) An expanded framework for school accountability – We reframe school accountability to ensure a balanced approach that accounts for a shift to a three component school improvement policy.(9) Guidance for substantive, scalable, and sustainable systemic changes –We frame mechanisms and discuss lessons learned related to facilitating fundamental systemic changes and replicating and sustaining them across a district.The frameworks and practices presented are based on our many years of work in schools and from efforts to enhance school-community collaboration. We incorporate insights from various theories and the large body of relevant research and from lessons learned and shared by many school leaders and staff who strive everyday to do their best for children.Our emphasis on new directions in no way is meant to demean current efforts. We know that the demands placed on those working in schools go well beyond what anyone should be asked to do. Given the current working conditions in many schools, our intent is to help make the hard work generate better results. To this end, we highlight new directions and systemic pathways for improving school outcomes.Some of what we propose is difficult to accomplish. Hopefully, the fact that there are schools, districts, and state agencies already trailblazing the way will engender a sense of hope and encouragement to those committed to innovation.It will be obvious that our work owes much to many. We are especially grateful to those who are pioneering major systemic changes across the country. These leaders and so many in the field have generously offered their insights and wisdom. And, of course, we are indebted to hundreds of scholars whose research and writing is a shared treasure. As always, we take this opportunity to thank Perry Nelson and the host of graduate and undergraduate students at UCLA who contribute so much to our work each day, and to the many young people and their families who continue to teach us all.Respectfully submitted for your consideration,Howard Adelman & Linda Taylo

A GRAPH-BASED APPROACH FOR ADAPTIVE SERIOUS GAMES

Traditional education systems are based on the one-size-fits-all approach, which lacks personalization, engagement, and flexibility necessary to meet the diverse needs and learning styles of students. This encouraged researchers to focus on exploring automated, personalized instructional systems to enhance students’ learning experiences. Motivated by this remark, this thesis proposes a personalized instructional system using a graph method to enhance a player’s learning process by preventing frustration and avoiding a monotonous experience. Our system uses a directional graph, called an action graph, for representing solutions to in-game problems based on possible player actions. Through our proposed algorithm, a serious game integrated with our system would both detect player errors and provide personalized assistance to direct a player in the direction of a correct solution. To verify system performance, this research presents comparison testing on a group of students engaging in the game both with and without AI. Students who played the AI-assisted game showed an average 20% decrease in time needed and an average 58% decrease in actions taken to complete the game

A literature review of personalized learning and the Cloud

In order to provide effective application of the Cloud in education it is essential to know how the learning should and could – if needed – be adapted. In this respect the concept of ‘personalising learning’ is frequently used. But what exactly is personalising learning. And how can it be implemented in using the cloud? The aim of WG3 i-Learner of the School on the Cloud network is to investigate this from the point of view of the learner, whereas WG2 i-Teacher looks on the role of the educators, and WG4 i-Future on the technology. The document has two parts: - The first part starts with an evaluation and synthesis of the definitions of personalized learning (Ch. 3), followed by an analysis of how this is implemented in learning style (e-learning vs. i-learning, m-learning and u-learning, Ch. 4) and learning approach (Ch. 5). To implement this an appropriate pedagogy (Ch. 6) is needed. - The second part is an attempt on how to implement this onto the learner of the future (Ch. 7), as well to the learning process and to the learning place. Recommendations are made in Ch. 8