Argue to Learn:Accelerated Argumentation-Based Learning

Human agents can acquire knowledge and learn through argumentation. Inspired by this fact, we propose a novel argumentation-based machine learning technique that can be used for online incremental learning scenarios. Existing methods for online incremental learning problems typically do not generalize well from just a few learning instances. Our previous argumentation-based online incremental learning method outperformed state-of-the-art methods in terms of accuracy and learning speed. However, it was neither memory-efficient nor computationally efficient since the algorithm used the power set of the feature values for updating the model. In this paper, we propose an accelerated version of the algorithm, with polynomial instead of exponential complexity, while achieving higher learning accuracy. The proposed method is at least 200 times faster than the original argumentation-based learning method and is more memory-efficient

PENGARUH PEMBELAJARAN BERBASIS MATHEMATICAL ARGUMENTATION TERHADAP KEMAMPUAN BERPIKIR KRITIS DAN RASA PERCAYA DIRI SISWA KELAS IV SEKOLAH DASAR

Penelitian ini dilatarbelakangi oleh rendahnya kemampuan berpikir kritis dan rasa percaya diri siswa dalam pelajaran matematika yang diakibatkan dari penerapan pembelajaran konvensional. Salah satu model pembelajaran yang dapat diterapkan untuk meningkatkan kemampuan berpikir kritis dan rasa percaya diri siswa adalah pembelajaran berbasis Mathematical Argumentation. Tujuan penelitian ini untuk menganalisis pengaruh pembelajaran berbasis Mathematical Argumentation terhadap kemampuan berpikir kritis dan rasa percaya diri siswa. Penelitian ini menggunakan metode kuasi eksperimen dengan desain nonequivalent control groups. Penelitian dilakukan pada 58 siswa kelas IV di salah satu Sekolah Dasar di Kabupaten Kuningan. Instrumen yang digunakan terdiri dari instrumen tes kemampuan berpikir kritis dan angket skala sikap percaya diri. Analisis kuantitatif dilakukan terhadap rata-rata nilai pretes dan postes kemampuan berpikir kritis dengan menggunakan uji Mann-Whitney sedangkan rata-rata N-gain kemampuan berpikir kritis, skor pretes, skor postes, dan N-gain skala sikap percaya diri menggunakan Uji-t. Hasil penelitian menunjukkan bahwa pembelajaran berbasis Mathematical Argumentation dapat meningkatkan kemampuan berpikir kritis matematis. Hal ini karena pembelajaran berbasis Mathematical Argumentation mendukung siswa melakukan penalaran, eksplorasi contoh, mengidentifikasi data pendukung, dan menilai kebenaran dari suatu pernyataan matematika. Peningkatan rasa percaya diri siswa yang mendapatkan pembelajaran berbasis Mathematical Argumentation lebih baik dibandingkan dengan siswa yang mendapatkan pembelajaran konvensional. Hal ini karena dukungan interaksi kelompok dan motivasi belajar. Pembelajaran berbasis Mathematical Argumentation dapat menjadi alternatif model pembelajaran yang dapat digunakan di sekolah dasar karena dapat meningkatkan kemampuan berpikir kritis dan rasa percaya diri.;---This study is grounded by the low ability of critical thinking and self-confidence of students in the mathematics lessons caused by the implementation of conventional learning. A learning model that can be implemented to improve students' critical thinking and self-confidence is Mathematical Argumentation based learning. The purpose of this study is to analyze the influence of Mathematical Argumentation based learning on students' critical thinking ability and self-confidence. This study used quasi experimental method with nonequivalent control groups design. The study was conducted on 58 fourth-grade students in a primary school in Kuningan Regency. The instrument consisted of test and questionnaire of attitude scale. Quantitative analysis was performed on the mean of pretest and posttest score using Mann-Whitney test while the mean of N-gain critical thinking ability, mean of pretest, mean of posttest, and N-gain of self-confidence scores between the two sample groups using t-test. The results showed that Mathematical Argumentation based learning can improve students’ critical thinking ability. This is because Mathematical Argumentation based learning encourage students doing reasoning, exploring examples, identifying the supporting data, and assessing the validity of a mathematical statement. Self-confidence gain of students who obtained Mathematical Argumentation based learning is better than students who obtained conventional learning. It is due to the support of group interaction and motivation in learning. Mathematical Argumentation based learning can be an alternative model of instruction that is used in Primary School because it can improve the ability of critical thinking and self-confidence

Explain what you see:argumentation-based learning and robotic vision

In this thesis, we have introduced new techniques for the problems of open-ended learning, online incremental learning, and explainable learning. These methods have applications in the classification of tabular data, 3D object category recognition, and 3D object parts segmentation. We have utilized argumentation theory and probability theory to develop these methods. The first proposed open-ended online incremental learning approach is Argumentation-Based online incremental Learning (ABL). ABL works with tabular data and can learn with a small number of learning instances using an abstract argumentation framework and bipolar argumentation framework. It has a higher learning speed than state-of-the-art online incremental techniques. However, it has high computational complexity. We have addressed this problem by introducing Accelerated Argumentation-Based Learning (AABL). AABL uses only an abstract argumentation framework and uses two strategies to accelerate the learning process and reduce the complexity. The second proposed open-ended online incremental learning approach is the Local Hierarchical Dirichlet Process (Local-HDP). Local-HDP aims at addressing two problems of open-ended category recognition of 3D objects and segmenting 3D object parts. We have utilized Local-HDP for the task of object part segmentation in combination with AABL to achieve an interpretable model to explain why a certain 3D object belongs to a certain category. The explanations of this model tell a user that a certain object has specific object parts that look like a set of the typical parts of certain categories. Moreover, integrating AABL and Local-HDP leads to a model that can handle a high degree of occlusion

Online discussion compensates for suboptimal timing of supportive information presentation in a digitally supported learning environment

This study used a sequential set-up to investigate the consecutive effects of timing of supportive information presentation (information before vs. information during the learning task clusters) in interactive digital learning materials (IDLMs) and type of collaboration (personal discussion vs. online discussion) in computer-supported collaborative learning (CSCL) on student knowledge construction. Students (N = 87) were first randomly assigned to the two information presentation conditions to work individually on a case-based assignment in IDLM. Students who received information during learning task clusters tended to show better results on knowledge construction than those who received information only before each cluster. The students within the two separate information presentation conditions were then randomly assigned to pairs to discuss the outcomes of their assignments under either the personal discussion or online discussion condition in CSCL. When supportive information had been presented before each learning task cluster, online discussion led to better results than personal discussion. When supportive information had been presented during the learning task clusters, however, the online and personal discussion conditions had no differential effect on knowledge construction. Online discussion in CSCL appeared to compensate for suboptimal timing of presentation of supportive information before the learning task clusters in IDLM

Élaboration et étude d'une situation d'apprentissage médiatisée par ordinateur pour le développement de la compréhension de l'espace du débat

Sommaire des pré-actes : http://archive-edutice.ccsd.cnrs.fr/edutice-00000318La vidéo de l'intervention et les questions de la salle sont accessibles sur http://webcast.in2p3.fr/tematice/baker.ramLa recherche présentée dans cet exposé a été réalisée dans le cadre du projet « SCALE » (Intelligent Support for Collaborative Argumentation-Based Learning, ou Guidage Intelligent pour L'Apprentissage Coopératif fondé sur l'Argumentation et l'Internet : http://www.euroscale.net), financé par le 5ème Plan IST (Information Society's Technologies) de l'Union Européenne. L'objectif pédagogique du projet est de comprendre comment concevoir des situations d'apprentissage coopératif pouvant conduire les élèves à élargir et à approfondir leur compréhension d'un espace du débat (cf. Golder, 1996). Cet objectif se situe à l'intersection de deux autres, devenues classiques : « apprendre à argumenter » et « argumenter pour apprendre ». Il s'agit en effet d'amener les élèves du secondaire à élaborer des connaissances qui sont de nature argumentative, grâce à leur implication dans l'activité argumentative que constitue le débat

Schématisation argumentative et co-élaboration de connaissances: le cas des interactions médiatisées par ordinateur

International audienceDans le cadre du projet européen SCALE (IST-1999) (Internet-based intelligent tool to Support Collaborative Argumentation-based Learning in secondary schools, http://www.euroscale.net), nous avons élaboré et étudié des situations d'apprentissage coopératif médiatisé par ordinateur au travers Internet. Il a s'agit de proposer une variété d'activités argumentatives — lecture et production de textes argumentatifs, élaboration de textes et schémas argumentatifs, interactions (ou débats) langagières et/ou schématiques — dans le but d'amener les élèves à élargir et à approfondir leurs connaissances d'un espace du débat particulier. Par la notion de « l'espace du débat », nous entendons l'espace cognitifsémiotique partagé, co-construit par les élèves au cours de leurs interactions argumentatives, en référence à une question débattue (Baker, Quignard, Lund & Séjourné, 2003 ; Quignard, Baker, Lund & Séjourné, 2003). Ce travail a conduit à la production d'outils de recherche permettant une analyse fine des processus interactifs d'élaboration des connaissances en relation avec des activités argumentatives

ARGUMENTATION-BASED COMPUTER SUPPORTED COLLABORATIVE LEARNING (ABCSCL): THE ROLE OF INSTRUCTIONAL SUPPORTS

This paper investigates the role of instructional supports for argumentation-based computer supported collaborative learning (ABCSCL), a teaching approach that improves the quality of learning processes and outcomes. Relevant literature has been reviewed to identify the instructional supports in ABCSCL environments. A range of instructional supports in ABCSCL is proposed including scaffolding, scripting, and representational tools. Each of these instructional supports are discussed in detail. Furthermore, the extent to which and the way in which such instructional supports can be applied in ABCSCL environments are discussed. Finally, suggestions for future work and implications for the design of ABCSCL environments are provided.  Article visualizations

Explain What You See: Open-Ended Segmentation and Recognition of Occluded 3D Objects

Local-HDP (for Local Hierarchical Dirichlet Process) is a hierarchical Bayesian method that has recently been used for open-ended 3D object category recognition. This method has been proven to be efficient in real-time robotic applications. However, the method is not robust to a high degree of occlusion. We address this limitation in two steps. First, we propose a novel semantic 3D object-parts segmentation method that has the flexibility of Local-HDP. This method is shown to be suitable for open-ended scenarios where the number of 3D objects or object parts is not fixed and can grow over time. We show that the proposed method has a higher percentage of mean intersection over union, using a smaller number of learning instances. Second, we integrate this technique with a recently introduced argumentation-based online incremental learning method, thereby enabling the model to handle a high degree of occlusion. We show that the resulting model produces an explicit set of explanations for the 3D object category recognition task