Developing pedagogic theory: the case of geometry proof teaching

This paper compares the teaching of proof in geometry at the lower secondary school level in the East (China, Japan) and in the West (UK). The aim is to seek to identify teaching strategies that might inform new pedagogic approaches for teaching deductive proof and proving. In the West, much theory focuses on examining the nature of classroom tasks. In the East, the heuristic nature of teaching and the theory of variation are useful as they focus on the dynamic role of the teacher. The paper suggests that the main need is for deeper thinking on the relationship between teachers’ instructional practices and the development of students’ mathematical reasoning

Curriculum Reform of C Language Programming and Cultivation of Computational Thinking

In the traditional teaching mode,students passively receive knowledge, in the way that hindered the development of students’ thinking. It limits training comprehensive analysis capabilities, innovation capability. The computational thinking is one of the basic objectives of teaching computer. This paper describes the methods of using computational thinking to analyze and solve problems, combined with C language programming with its characteristics, explained by an example in the theory and practice of teaching. After that, reform proposals put forward.  

The Application of Mobile Learning in College Experimental Teaching

First we analyzed the current forms of higher education and learning characteristics of the experimental courses, then we introduced mobile devices to the teaching process of experimental courses in colleges and universities. The introduction of mobile learning can meet the needs of higher education and to achieve the requirements of the reform. In this paper, we focuses on how to construct the learning platform in the integration of mobile learning and experimental courses. As well as the session framework for mobile learning activity design. Practice teaching proves that this method can better improve the efficiency of classroom teaching, and expand the depth and breadth of the students’ study. At the same time, it can also promote the improvement of students’ comprehensive ability

Learning to Rank for Active Learning via Multi-Task Bilevel Optimization

Active learning is a promising paradigm to reduce the labeling cost by strategically requesting labels to improve model performance. However, existing active learning methods often rely on expensive acquisition function to compute, extensive modeling retraining and multiple rounds of interaction with annotators. To address these limitations, we propose a novel approach for active learning, which aims to select batches of unlabeled instances through a learned surrogate model for data acquisition. A key challenge in this approach is developing an acquisition function that generalizes well, as the history of data, which forms part of the utility function's input, grows over time. Our novel algorithmic contribution is a bilevel multi-task bilevel optimization framework that predicts the relative utility -- measured by the validation accuracy -- of different training sets, and ensures the learned acquisition function generalizes effectively. For cases where validation accuracy is expensive to evaluate, we introduce efficient interpolation-based surrogate models to estimate the utility function, reducing the evaluation cost. We demonstrate the performance of our approach through extensive experiments on standard active classification benchmarks. By employing our learned utility function, we show significant improvements over traditional techniques, paving the way for more efficient and effective utility maximization in active learning applications

Based on MOOC+SPOC Teaching Reform and Practice of Computer Basic Course in University

The emergence of MOOC has attracted wide attention from the educational circles at home and abroad. It is both a challenge and an opportunity for the traditional higher education. With the teaching reform of the course of “College computing” is deepening, MOOC will be introduced into the traditional classroom and through the “MOOC ten SPOC” way to achieve the complementary advantages, which all the quality of teaching is of great significance. This paper takes the “University Computer Foundation” MOOC of Changchun University of Science and Technology as an example. Then this paper introduces the exploration and practice experience of the reform of computer course in University by the way of “MOOC+SPOC”. It uses the hierarchical MOOC teaching content which fuse MOOC with the traditional classroom teaching. It introduce the “MOOC ten SPOC” teaching practice process, to achieve the combination of online and offline, curricular and extra-curricular complementary Hybrid Teaching and which analyzes the students for the evaluation of the curriculum, summed up the “MOOC +SPOC” teaching reform practice experience

Improved Federated Learning for Handling Long-tail Words

Automatic speech recognition (ASR) machine learning models are deployed on client devices that include speech interfaces. ASR models can benefit from continuous learning and adaptation to large-scale changes, e.g., as new words are added to the vocabulary. While federated learning can be utilized to enable continuous learning for ASR models in a privacy preserving manner, the trained model can perform poorly on rarely occurring, long-tail words if the distribution of data used to train the model is skewed and does not adequately represent long-tail words. This disclosure describes federated learning techniques to improve ASR model quality when interpreting long-tail words given an imbalanced data distribution. Two different approaches - probabilistic sampling and client loss weighting - are described herein. In probabilistic sampling, the federated clients that include fewer long-tail words are less likely to be selected during training. In client loss weighting, incorrect predictions on long-tail words are more heavily penalized than for other words

Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning

The relational data model was designed to facilitate large-scale data management and analytics. We consider the problem of how to differentiate computations expressed relationally. We show experimentally that a relational engine running an auto-differentiated relational algorithm can easily scale to very large datasets, and is competitive with state-of-the-art, special-purpose systems for large-scale distributed machine learning.Comment: ICML 202