Teacher Student Learning Research Articles

Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation

We propose a novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information. To solve this unsupervised-learning problem, the teacher network adopts pose-dictionary-based modeling for regularization to estimate a physically plausible 3D pose. To handle the decomposition ambiguity in the teacher network, we propose a cycle-consistent architecture promoting a 3D rotation-invariant property to train the teacher network. To further improve the estimation accuracy, the student network adopts a novel graph convolution network for flexibility to directly estimate the 3D coordinates. Another cycle-consistent architecture promoting 3D rotation-equivariant property is adopted to exploit geometry consistency, together with knowledge distillation from the teacher network to improve the pose estimation performance. We conduct extensive experiments on Human3.6M and MPI-INF-3DHP. Our method reduces the 3D joint prediction error by 11.4% compared to state-of-the-art unsupervised methods and also outperforms many weakly-supervised methods that use side information on Human3.6M. Code will be available at https://github.com/sjtuxcx/ITES.

Enhancing Audio-Visual Association with Self-Supervised Curriculum Learning

The recent success of audio-visual representations learning can be largely attributed to their pervasive concurrency property, which can be used as a self-supervision signal and extract correlation information. While most recent works focus on capturing the shared associations between the audio and visual modalities, they rarely consider multiple audio and video pairs at once and pay little attention to exploiting the valuable information within each modality. To tackle this problem, we propose a novel audio-visual representation learning method dubbed self-supervised curriculum learning (SSCL) under the teacher-student learning manner. Specifically, taking advantage of contrastive learning, a two-stage scheme is exploited, which transfers the cross-modal information between teacher and student model as a phased process. The proposed SSCL approach regards the pervasive property of audiovisual concurrency as latent supervision and mutually distills the structure knowledge of visual to audio data. Notably, the SSCL method can learn discriminative audio and visual representations for various downstream applications. Extensive experiments conducted on both action video recognition and audio sound recognition tasks show the remarkably improved performance of the SSCL method compared with the state-of-the-art self-supervised audio-visual representation learning methods.

Enhanced Audio Tagging via Multi- to Single-Modal Teacher-Student Mutual Learning

Recognizing ongoing events based on acoustic clues has been a critical yet challenging problem that has attracted significant research attention in recent years. Joint audio-visual analysis can improve the event detection accuracy but may not always be feasible as under many circumstances only audio recordings are available in real-world scenarios. To solve the challenges, we present a novel visual-assisted teacher-student mutual learning framework for robust sound event detection from audio recordings. Our model adopts a multi-modal teacher network based on both acoustic and visual clues, and a single-modal student network based on acoustic clues only. Conventional teacher-student learning performs unsatisfactorily for knowledge transfer from a multi-modality network to a single-modality network. We thus present a mutual learning framework by introducing a single-modal transfer loss and a cross-modal transfer loss to collaboratively learn the audio-visual correlations between the two networks. Our proposed solution takes the advantages of joint audio-visual analysis in training while maximizing the feasibility of the model in use cases. Our extensive experiments on the DCASE17 and the DCASE18 sound event detection datasets show that our proposed method outperforms the state-of-the-art audio tagging approaches.

Normativity in language teacher learning: ELF and theEuropean Portfolio for Student Teachers of Languages(EPOSTL)

AbstractAs with any proposal for a change in pedagogy, the starting point for implementation is teacher education. Suggestions have been put forward for an approach to English language teaching (ELT) which takes into account the lingua franca function language can fulfill. Frameworks for how teachers might adopt a pedagogy of English as a lingua franca (ELF), however, are inconsistent with current policy guidelines for student teacher learning. This concerns most notably those directives provided in theEuropean Portfolio for Student Teachers of Languages(EPOSTL), which is used in a growing number of teacher formation programs. This paper examines the ‘can-do’ statements which the EPOSTL proposes should represent the required methodological competences of language teachers. Using discourse analytic methods, the article explores what the EPOSTL portrays that prospective teachers need to know about language and how to teach it. It is argued that the language education policy represented by the EPOSTL is based on a conceptual perspective that can only impede the development of a genuinely reflective approach to the education of English language teachers and the implementation of an ELF pedagogy.

The Computer-based Flipped Classroom Teaching Model is Integrated Into the Teaching Research of the Basic Principles of Marxism

Many schools now clearly ask for the establishment of a teacher-student learning community, with teachers as the leading role, but with students as the main body. The main mode of classroom promotion is to ask questions, first ask students, and finally let students ask themselves. The role of the teacher is to formulate the process and write an actionable guide. The process of answering questions is mainly based on group cooperation, and is supplemented by teacher’s instructions. This is the flipped classroom. The course of introduction to the basic principles of Marxism is a very abstract course. The teaching and research of the flipped classroom has been a hot topic in recent years, and whether it has significantly improved the learning efficiency and students’ understanding ability.

Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks.

Deep neural models, in recent years, have been successful in almost every field, even solving the most complex problem statements. However, these models are huge in size with millions (and even billions) of parameters, demanding heavy computation power and failing to be deployed on edge devices. Besides, the performance boost is highly dependent on redundant labeled data. To achieve faster speeds and to handle the problems caused by the lack of labeled data, knowledge distillation (KD) has been proposed to transfer information learned from one model to another. KD is often characterized by the so-called 'Student-Teacher' (S-T) learning framework and has been broadly applied in model compression and knowledge transfer. This paper is about KD and S-T learning, which are being actively studied in recent years. First, we aim to provide explanations of what KD is and how/why it works. Then, we provide a comprehensive survey on the recent progress of KD methods together with S-T frameworks typically used for vision tasks. In general, we investigate some fundamental questions that have been driving this research area and thoroughly generalize the research progress and technical details. Additionally, we systematically analyze the research status of KD in vision applications. Finally, we discuss the potentials and open challenges of existing methods and prospect the future directions of KD and S-T learning.

Practically Deploying Heavyweight Adaptive Bitrate Algorithms With Teacher-Student Learning

Major commercial client-side video players employ adaptive bitrate (ABR) algorithms to improve the user quality of experience (QoE). With the evolvement of ABR algorithms, increasingly complex methods such as neural networks have been adopted to pursue better performance. However, these complex methods are too heavyweight to be directly deployed in client devices with limited resources, such as mobile phones. Existing solutions suffer from a trade-off between algorithm performance and deployment overhead. To make the deployment of sophisticated ABR algorithms practical, we propose PiTree, a general, high-performance, and scalable framework that can faithfully convert sophisticated ABR algorithms into decision trees with teacher-student learning. In this way, network operators can train complex models offline and deploy converted lightweight decision trees online. We also present theoretical analysis on the conversion and provide two upper bounds of the prediction error during the conversion and the generalization loss after conversion. Evaluation on three representative ABR algorithms with both trace-driven emulation and real-world experiments demonstrates that PiTree could convert ABR algorithms into decision trees with <; 3% average performance degradation. Moreover, compared to original deployment solutions, PiTree could save considerable operating expenses for content providers.

The reform of initial teacher education in Wales: from vision to reality

ABSTRACT Over the last four years, initial teacher education in Wales has been fundamentally reformed. The stimulus for those reforms were concerns about the quality of current provision, but more importantly a recognition by the Welsh Government that if their wider reforms of curriculum and assessment were to succeed, then teachers themselves had a key role to play. Teachers would no longer be simply required to ‘deliver’ a curriculum defined by others; they themselves would need to become active agents in realising that curriculum in ways that were appropriate for the children and young people they taught. What is being required is a new vision of teacher professionalism in Wales. But what are the implications of these changes for initial teacher education? The Welsh Government has taken the view that they demand a fundamentally different conception of how initial teacher education is organised, with both universities and schools working closely together to support student teacher learning. This paper sets out the background to these recent reforms, and then examines the way in which three key dimensions of the new model are being implemented in practice. They are: co-construction and governance; integrating university and school perspectives; and re-visioning mentoring.

Integrating Knowledge Into End-to-End Speech Recognition From External Text-Only Data

Attention-based encoder-decoder (AED) models have achieved promising performance in speech recognition. However, because of the end-to-end training, an AED model is usually trained with speech-text paired data. It is challenging to incorporate external text-only data into AED models. Another issue of the AED model is that it does not use the right context of a text token while predicting the token. To alleviate the above two issues, we propose a unified method called LST (Learn Spelling from Teachers) to integrate knowledge into an AED model from the external text-only data and leverage the whole context in a sentence. The method is divided into two stages. First, in the representation stage, a language model is trained on the text. It can be seen as that the knowledge in the text is compressed into the LM. Then, at the transferring stage, the knowledge is transferred to the AED model via teacher-student learning. To further use the whole context of the text sentence, we propose an LM called causal cloze completer (COR), which estimates the probability of a token, given both the left context and the right context of it. Therefore, with LST training, the AED model can leverage the whole context in the sentence. Different from fusion based methods, which use LM during decoding, the proposed method does not increase any extra complexity at the inference stage. We conduct experiments on two scales of public Chinese datasets AISHELL-1 and AISHELL-2. The experimental results demonstrate the effectiveness of leveraging external text-only data and the whole context in a sentence with our proposed method, compared with baseline hybrid systems and AED model based systems.

Deep Model Intellectual Property Protection via Deep Watermarking

Despite the tremendous success, deep neural networks are exposed to serious IP infringement risks. Given a target deep model, if the attacker knows its full information, it can be easily stolen by fine-tuning. Even if only its output is accessible, a surrogate model can be trained through student-teacher learning by generating many input-output training pairs. Therefore, deep model IP protection is important and necessary. However, it is still seriously under-researched. In this work, we propose a new model watermarking framework for protecting deep networks trained for low-level computer vision or image processing tasks. Specifically, a special task-agnostic barrier is added after the target model, which embeds a unified and invisible watermark into its outputs. When the attacker trains one surrogate model by using the input-output pairs of the barrier target model, the hidden watermark will be learned and extracted afterwards. To enable watermarks from binary bits to high-resolution images, a deep invisible watermarking mechanism is designed. By jointly training the target model and watermark embedding, the extra barrier can even be absorbed into the target model. Through extensive experiments, we demonstrate the robustness of the proposed framework, which can resist attacks with different network structures and objective functions.

Learning Student Networks via Feature Embedding.

Deep convolutional neural networks have been widely used in numerous applications, but their demanding storage and computational resource requirements prevent their applications on mobile devices. Knowledge distillation aims to optimize a portable student network by taking the knowledge from a well-trained heavy teacher network. Traditional teacher-student-based methods used to rely on additional fully connected layers to bridge intermediate layers of teacher and student networks, which brings in a large number of auxiliary parameters. In contrast, this article aims to propagate information from teacher to student without introducing new variables that need to be optimized. We regard the teacher-student paradigm from a new perspective of feature embedding. By introducing the locality preserving loss, the student network is encouraged to generate the low-dimensional features that could inherit intrinsic properties of their corresponding high-dimensional features from the teacher network. The resulting portable network, thus, can naturally maintain the performance as that of the teacher network. Theoretical analysis is provided to justify the lower computation complexity of the proposed method. Experiments on benchmark data sets and well-trained networks suggest that the proposed algorithm is superior to state-of-the-art teacher-student learning methods in terms of computational and storage complexity.

Performance Analysis of Deep Neural Network based on Transfer Learning for Pet Classification

Deep learning frameworks have progressed beyond human recognition capabilities and, now it’s the perfect opportunity to optimize them for implementation on the embedded platforms. The present deep learning architectures support learning capabilities, but they lack flexibility for applying learned knowledge on the tasks in other unfamiliar domains. This work tries to fill this gap with the deep neural network-based solution for object detection in unrelated domains with a focus on the reduced footprint of the developed model. Knowledge distillation provides efficient and effective teacher-student learning for a variety of different visual recognition tasks. A lightweight student network can be easily trained under the guidance of the high-capacity teacher networks. The teacher-student architecture implementation on binary classes shows a 20% improvement in accuracy within the same training iterations using the transfer learning approach. The scalability of the student model is tested with binary, ternary and multiclass and their performance is compared on basis of inference speed. The results show that the inference speed does not depend on the number of classes. For similar recognition accuracy, the inference speed of about 50 frames per second or 20ms per image. Thus, this approach can be generalized as per the application requirement with minimal changes, provided the dataset format compatibility.

Role of information literacy in higher education during recent global pandemic: An overview

This paper discusses the concept information literacy, its role in higher education and the importance associated with information literacy in times of recent global health pandemic. Information is taking a new form each day and is continuously evolving with the changing world. People should become aware and make informed choices as a user of information. The use of information literacy helps an individual to recognize when information is required, how it can be located, evaluated and used productively. The entire education system around the globe faced the wrath of the lockdown period triggered due to the spread of Novel Corona virus (nCov-19). The intended purpose of this paper is to address the issues pertaining to information literacy in Higher Education Institutions (HEIs) during pandemic, to understand the role and usefulness of information literacy in times when there is a difficulty in moving ahead with the traditional student-teacher learning system.

Teachers’ And Student Teachers’ Perspectives On Reflective Practices In Teaching And Learning In Public Basic Schools In The Gomoa East District Of The Central Region Of Ghana

The purpose of the study was to explore teachers’ and student teachers’ perspective on reflective practices in teaching and learning in public basic schools in the Gomoa East District of the Central Region of Ghana. The study adopted sequential explanatory design. The study employed the theory of John Dewey (1932) as its theoretical framework. The study employed sequential explanatory design. The population was made up of teachers and student teachers in the Gomoa East District. The study used purposive and simple random sampling procedures to select student teachers and teachers respectively. Structured questionnaires and interview guides were used to gather data from one hundred and twenty nine (129) participants. Descriptive statistics such as simple frequency counts and percentages were used to analyse the quantitative data while content analysis was used to analyse the qualitative data. Finding from the study revealed that teachers understand the concept of reflective practices and its applications. Reflective practices enable teachers to reflect on their teaching experience. Reflective practices enable teachers to think about their knowledge in their subject area. Reflective practices help to improve teaching. Reflective practices provide direction on what could be done in future for professional growth and development. Reflective practices enable student teachers to engage in problem solving that helps to strengthen teaching ability. Findings from the study also indicate that there was inadequate in-service training for teacher and student teachers in the area of reflective practices. Strategies suggested to enhance reflective practice in teaching and learning were cordial relationship between teachers and student teachers, teachers and student teachers should encourage remedial teaching, effective collaboration in instructional activities between teachers and student teachers, teachers should provide adequate time for student teachers and supervise their performance, adequate in- service training for teacher and student teachers in the area of reflective practices and Regular support from headteachers, experience teachers and other educational stakeholders though mentorship and other teacher professional development programs.The study recommended that the Gomoa East District Education Directorate should organize intensive training in the area of reflective practices in teaching and learning for teachers and student teachers to enable them to gain adequate professional competencies to improve their teaching. Head teachers, experienced teachers and other educational stakeholders should readily offer support to ensure successful reflective practices in teaching and learning in schools. Finally, Ghanaian University Colleges of Education should offer teacher education programs or courses that teach pre-service teachers how to develop higher order thinking skills and how to critically reflect.

Learning to Teach in a Museum: Benefits of a Museum–University Partnership

ABSTRACT This article centers on the results of a two-year study of a museum–university partnership conducted with 81 secondary preservice student teachers enrolled in a large public university in the Midwestern United States. The focus is on preservice student teachers’ experiences working in a large urban museum which also houses a public middle school (grades 6–8), in partnership with the second largest urban district in the state. In this setting, preservice student teachers worked weekly with small groups of sixth-grade students in the museum as part of a required undergraduate content area literacy teacher education course. This study highlights ways this museum–university field experience served as an important clinical component for preservice student teacher learning. Working in a museum provided preservice student teachers with opportunities to experience and engage in teaching opportunities using integrated curriculum and inquiry-based teaching. Findings also showcase how this partnership provided preservice student teachers with learning opportunities, including studying and connecting concepts of storytelling related to the museum, recognizing the museum as a teacher, and learning from other museum personnel and educators.

Improvement of the student-teacher learning interface with selecting and designing a teaching method

This Research work explores the effective teaching method using a single case study design and produces a plenty of information about students requirements, teachers recommendations and some teaching methods and learning styles .The resulting analysis provides a complete teaching method that may develop teacher student learning interface as well as may satisfy both student’s requirements and the recommendations of teacher. The analysis also specifies the component essential to develop the relationship between teacher and students as well as the learning environment. In this analysis at first some literature review is done to know the aspects of teaching and learning methods and also different types of teaching and learning methods. From this it is easy to understand what should be the focus of learning and the strategy that can help students to empower themselves with their own thinking power .Then the survey is done among 250 students of Rajshahi University of Engineering and Technology to know the students requirements and teachers recommendation is also brought from the discussion with the teachers of this institute. After that according to the students requirements and teachers recommendation the candidates of teaching method is selected. And finally the Fuzzy Topsis Set Theory is applied to find out the effective teaching method which satisfies the student requirements. The characteristics and advantages are also mentioned which are able to alleviate the gaps that exist in the previous teaching methods.

Why Does Digital Learning Matter? Digital Competencies, Social Justice and Critical Pedagogy in Initial Teacher Education

Digital tools and spaces are becoming prevalent in schools across the world requiring the development of digital skillsets for student-teachers. Digital technology, in enabling education to extend beyond the space and time boundaries of the conventional classroom (Seifert, T., Sheppard, B. Wakeham, M., 2015) , brings the digital landscape into the classroom and firmly into the frame of reference for those preparing student-teachers to enter the profession. For Initial Teacher Education (ITE) programmes which foreground social justice, the digital (technology which is linked to the internet) goes far beyond a skillset or a discrete subject. Engaging with digital learning encompasses the 21st century context - both local and global - in which student-teachers and their future pupils are situated.&#x0D; Developing a critical pedagogic approach involves understanding the context in which one lives and enabling learners to challenge or change it (Freire, 1996) . For those working in ITE a postdigital lens provides a means to understand the context in which they are situated. Critical pedagogy enables student-teachers to understand that context and challenge the inequities which persist, preparing them not simply to navigate the digital landscape, but to engage with it critically. Reflecting on student-teacher learning this article explores the digital dimension, highlighting the importance of digital learning when engaging with critical pedagogy and social justice in ITE.

Teaching Practicum in Pre-Service Teacher Education

&lt;p&gt;This paper presents a review of the research literature on practicum. Since there have been changes in trends in Teaching Education since the 1960s that also influenced the shift of research topics on practicum, this paper first gives a short review of the research trends in general TE, changing from the teaching process-product paradigm to the investigation of teachers’ mental lives or cognition. Then the actual review will focus on the studies related to the teachers’ beliefs, perspectives, perceptions, understanding, and reasoning, and follows under these topics: student teachers learning, cooperating teachers, and school-university cooperation in the practicum. At the end of this paper, some future studies on teaching practicum are suggested. &lt;/p&gt;

Understandings of mentoring in school placement settings within the context of initial teacher education in Scotland: dimensions of collaboration and power

ABSTRACT This study critically addresses mentor and mentee understandings of mentoring primary education student teachers within existing operations of power in the context of Scottish Initial Teacher Education. Semi-structured interviews of mentors and student teachers were used to elicit relational understandings of the mentoring process within an instrumental, collective case study research design. Findings indicate that participants understood mentoring as a multifaceted process aimed at supporting the professional learning of student teachers. The article addresses the main mentoring relationship between the class teacher mentor and student teacher mentee. Analysis of their responses suggests an understanding of mentoring as involving both personal and professional dimensions. From these dimensions emerge the focus of this article: implicit collaboration and conceptions of power as a relational duality situated within a more Foucauldian ‘flux’ form. Critical discussion of findings extends understandings of the complexity of the mentoring process with reference to perceptions of collaboration, power and their co-existence. Conclusions focus on the need for a quality, consistent mentor education programme to promote an informed knowledge and understanding of the complex nature of mentoring in order to improve the quality and consistency of mentee learning experiences.

Learning Student Networks with Few Data

Recently, the teacher-student learning paradigm has drawn much attention in compressing neural networks on low-end edge devices, such as mobile phones and wearable watches. Current algorithms mainly assume the complete dataset for the teacher network is also available for the training of the student network. However, for real-world scenarios, users may only have access to part of training examples due to commercial profits or data privacy, and severe over-fitting issues would happen as a result. In this paper, we tackle the challenge of learning student networks with few data by investigating the ground-truth data-generating distribution underlying these few data. Taking Wasserstein distance as the measurement, we assume this ideal data distribution lies in a neighborhood of the discrete empirical distribution induced by the training examples. Thus we propose to safely optimize the worst-case cost within this neighborhood to boost the generalization. Furthermore, with theoretical analysis, we derive a novel and easy-to-implement loss for training the student network in an end-to-end fashion. Experimental results on benchmark datasets validate the effectiveness of our proposed method.