Self-paced Curriculum Research Articles

The future of education: “Freedom” as the foundation of the curriculum

The Merdeka Curriculum is characterized by an innovative approach in the educational context that provides teachers and students with greater freedom to develop and manage learning processes that aim to increase students' creativity, initiative, and responsibility in facing dynamic changes in science. This article encourages further discussion and a deeper understanding of the curriculum to create a more effective and appropriate learning environment for students. This article also discusses the concept of freedom of learning and its relationship to thinking and innovation. The research method used is a literature study to collect and analyze information from previous studies. The findings and discussion relate to the determination of the 2020 independent curriculum, its differences from the previous curriculum, and its impact on teaching staff and students. One important outcome of the self-paced curriculum is the emphasis on developing cultural agility as an important skill for future professionals.

CLIP-VG: Self-Paced Curriculum Adapting of CLIP for Visual Grounding

CLIP-VG: Self-Paced Curriculum Adapting of CLIP for Visual Grounding

Analysis Of The Implementation Of Authentic Assessment In The Independent Learning Curriculum In Elementary Schools

Assessment or Assessment is a systematic activity whose process is continuous which is continuously used as a measurement tool for assessing student learning outcomes and as a basis for processing assessment data which is intended as a report on student learning progress ultimately based on certain criteria and considerations for educators. The readiness of symptom evaluation instruments for classroom teachers is an important job to assist in working on the nature of the school, in particular to provide an understanding of demonstrative evaluation, especially to action members, to be specific educators. This article discusses how authentic assessment activities for self-paced curriculum are implemented. The Seven Stages Model Theory of Program Planning and Development is used in service implementation, and qualitative analysis is used in service analysis. The method of reviewing articles and reference sources becomes a supporting instrument. The implementation of the independent curriculum, the introduction of diagnostic assessment, the manufacture of diagnostic assessment instruments, and the analysis of question items are the four topics discussed in the technical guidance material. The advantage that members gain after taking this special assistance is that they can build their insight into the demonstrative evaluations carried out in learning in the learning space; Learn the technical details of diagnostic assessment tests so that they can be used in daily learning, and at least the references are clear enough; Able to understand ongoing curriculum modifications and obtain teaching strategies based on the applicable curriculum.

Foregroundness-Aware Task Disentanglement and Self-Paced Curriculum Learning for Domain Adaptive Object Detection.

Unsupervised domain adaptive object detection (UDA-OD) is a challenging problem since it needs to locate and recognize objects while maintaining the generalization ability across domains. Most existing UDA-OD methods directly integrate the adaptive modules into the detectors. This integration procedure can significantly sacrifice the detection performances, though it enhances the generalization ability. To solve this problem, we propose an effective framework, named foregroundness-aware task disentanglement and self-paced curriculum adaptation (FA-TDCA), to disentangle the UDA-OD task into four independent subtasks of source detector pretraining, classification adaptation, location adaptation, and target detector training. The disentanglement can transfer the knowledge effectively while maintaining the detection performance of our model. In addition, we propose a new metric, i.e., foregroundness, and use it to evaluate the confidence of the location result. We use both foregroundness and classification confidence to assess the label quality of the proposals. For effective knowledge transfer across domains, we utilize a self-paced curriculum learning paradigm to train adaptors and gradually improve the quality of the pseudolabels associated with the target samples. Experiment results indicate that our method achieves state-of-the-art results on four cross-domain object detection tasks.

From Easy to Hard: Learning Language-Guided Curriculum for Visual Question Answering on Remote Sensing Data

Visual question answering (VQA) for remote sensing scene has great potential in intelligent human-computer interaction system. Although VQA in computer vision has been widely researched, VQA for remote sensing data (RSVQA) is still in its infancy. There are two characteristics that need to be specially considered for the RSVQA task. 1) No object annotations are available in RSVQA datasets, which makes it difficult for models to exploit informative region representation; 2) There are questions with clearly different difficulty levels for each image in the RSVQA task. Directly training a model with questions in a random order may confuse the model and limit the performance. To address these two problems, in this paper, a multi-level visual feature learning method is proposed to jointly extract language-guided holistic and regional image features. Besides, a self-paced curriculum learning (SPCL)-based VQA model is developed to train networks with samples in an easy-to-hard way. To be more specific, a language-guided SPCL method with a soft weighting strategy is explored in this work. The proposed model is evaluated on three public datasets, and extensive experimental results show that the proposed RSVQA framework can achieve promising performance.

A deep learning model to effectively capture mutation information in multivariate time series prediction

In real-world complex multivariate time series data, mutation phenomena can significantly affect variation rules of target series. Meanwhile, there is no specific learning mechanism for the current deep learning model to capture mutation information in time series prediction. To this end, we propose a new deep learning model to capture mutation information between data. To capture the impact of mutation information on target series, a new function mapping is designed in the attention mechanism of the encoder to process the fusion of historical hidden state and cell state information; and an LSTM with transformation mechanism is proposed in the encoder to process the input information flow and learn the mutation information. In addition, an adaptive self-paced curriculum learning mechanism is designed to obtain mutation information that may be ignored among mini-batch samples. Finally, we define an objective function for multivariate time series prediction, which can extract the influence of temporal correlation information and mutation information within the data on target series. Our model can achieve superior performance than all baseline methods on five real-world datasets in different fields.

Leveraging Prior-Knowledge for Weakly Supervised Object Detection Under a Collaborative Self-Paced Curriculum Learning Framework

Weakly supervised object detection is an interesting yet challenging research topic in computer vision community, which aims at learning object models to localize and detect the corresponding objects of interest only under the supervision of image-level annotation. For addressing this problem, this paper establishes a novel weakly supervised learning framework to leverage both the instance-level prior-knowledge and the image-level prior-knowledge based on a novel collaborative self-paced curriculum learning (C-SPCL) regime. Under the weak supervision, C-SPCL can leverage helpful prior-knowledge throughout the whole learning process and collaborate the instance-level confidence inference with the image-level confidence inference in a robust way. Comprehensive experiments on benchmark datasets demonstrate the superior capacity of the proposed C-SPCL regime and the proposed whole framework as compared with state-of-the-art methods along this research line.

Computer-Assisted Learning Applications in Health Educational Informatics: A Review.

Computer-assisted learning (CAL) as a health informatics application is a useful tool for medical students in the era of expansive knowledge bases and the increasing need for and the consumption of automated and interactive systems. As the scope and breadth of medical knowledge expand, the need for additional learning outside of lecture hours is becoming increasingly important. CAL can be an impactful adjunct to conventional methods that currently exist in the halls of learning. There is an increasing body of literature that suggests that CAL should be a commonplace and the recommended method of learning for medical students. Factors such as technical issues that hinder the performance of CAL are also evaluated. We conclude by encouraging the use of CAL by medical students as a highly beneficial method of learning that complements and enhances lectures and provides intuitive, interactive modulation of a self-paced curriculum based on the individual’s academic abilities.

Face detection of golden monkeys via regional color quantization and incremental self-paced curriculum learning

Animal detection plays a very vital role in wildlife protection and many other real life applications. In this paper, we focus on face detection of Golden monkeys who live in Qinling Mountains, Shaanxi province, China, and present a relatively complete face detection algorithm to detect these monkeys’ faces, which mainly includes three parts: the location of the monkeys’ bodies, the detection of th+e suspicious facial skin and the accurate detection of the true faces. Firstly, regional color quantization is proposed to quantize the HSV color space for the nature images with different sizes, and we can get the areas of the monkeys’ bodies according to the color distribution of the monkeys’ hair in the histogram of the quantized color space. Then the areas of suspicious facial skin can be extracted from these areas of the monkeys’ bodies. Further, we propose incremental self-paced curriculum learning (ISPCL) to detect the true monkeys’ faces accurately. In our method, regional color quantization can increase the color differences between the background and the monkeys’ hair, so that the segmented results have fewer background pixels. Besides, the basic idea of the incremental learning is introduced into the training process of SPCL, which is to simulate the process in which human learn something from easy samples to hard ones, this idea is able to improve the performances of face detection. The experimental results demonstrate that the proposed algorithm can locate the monkeys’ bodies in the images with different sizes, and can detect their faces effectively. This research lays a foundation for face recognition and behavior analysis of golden monkeys.

Self-Paced Curriculum Learning

Curriculum learning (CL) or self-paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex samples in training. The two methods share a similar conceptual learning paradigm, but differ in specific learning schemes. In CL, the curriculum is predetermined by prior knowledge, and remain fixed thereafter. Therefore, this type of method heavily relies on the quality of prior knowledge while ignoring feedback about the learner. In SPL, the curriculum is dynamically determined to adjust to the learning pace of the leaner. However, SPL is unable to deal with prior knowledge, rendering it prone to overfitting. In this paper, we discover the missing link between CL and SPL, and propose a unified framework named self-paced curriculum leaning (SPCL). SPCL is formulated as a concise optimization problem that takes into account both prior knowledge known before training and the learning progress during training. In comparison to human education, SPCL is analogous to "instructor-student-collaborative" learning mode, as opposed to "instructor-driven" in CL or "student-driven" in SPL. Empirically, we show that the advantage of SPCL on two tasks.

Web-based minimally invasive surgery training: competency assessment in PGY 1-2 surgical residents

Objectives Little published literature describes accurate evaluation and assessment of surgical residents' technical competencies. Work-hour limitations and the proposed changes in training duration challenge our ability to provide core technical competencies, particularly during PGY-1-2. We identified minimally invasive surgery as a particularly challenging competency training area, requiring significant allocation of resident and faculty time and resources, with inconsistent training results. This project evaluated a standardized competency training curriculum in minimally invasive surgery in comparison to existing training. Methods The Laparoscopy 101–a Resource for Resident Education minimally invasive surgery competency training curriculum consists of CD-ROM-based self-paced didactic self-instruction modules, skills laboratories, and web-based testing, evaluation, and reporting. Five didactic self-study modules and their corresponding web-based examinations were selected for this study. PGY 1-2 residents (11/group) were enrolled. PGY-1 residents received no formal minimally invasive surgery training in our program before participating, whereas PGY-2 residents were exposed to the minimally invasive surgery training program in place in our department. PGY-1 residents completed the training modules and web-based testing over 4 days. PGY-2 residents underwent testing before (pre-test) and after (post-test) completing identical training modules and testing over an identical time period. Test results were examined and compared in both groups. A user-satisfaction survey assessed resident opinions about the quality of the curriculum, use of the CD-ROM/web-based system, and trainee perceptions about performance. Results PGY-2 Pre-test scores were equivalent to PGY-1 scores after training (mean overall performance, 52.4% vs 65.5% p = ns). PGY-2 Post-test scores were significantly higher when compared with pre-test scores (mean overall performance; pre: 52.4% vs post: 85.3% p ≤ 0.001). Interestingly, post-training scores were significantly higher for PGY-2 than PGY-1 (mean overall performance PGY-1: 52.4% vs PGY-2: 65.5% p = ns). Competencies achieved persisted 6 months after initial training in both PGY 1-2 groups without additional training using this curriculum. Overall user satisfaction was positive in each category evaluated. Conclusions The Laparoscopy 101 minimally invasive surgery curriculum (1) p;rovides a structured self-paced curriculum for minimally invasive didactic training that is well accepted by trainees; (2) yields PGY-1 competencies equivalent to PGY-2 who receive training in our department's existing program; and (3) yields significant improvement in PGY-2 minimally invasive surgery competencies. Training effects persist to 6 months without reinforcement. Our observations of PGY-2 performance and competency suggest that this training would best be instituted during PGY-2.